Molecular diagnostics : the key driver in personalized cancer medicine 9789814241441, 9789814241458

Table of contents :
Content: Developing Molecular Diagnostics for Cancer, H Winther & J T JorgensenThe Estrogen & Progesterone Receptor - Setting the Scene for Pharmacodiagnostics, K L CheungThe HercepTest (R) - From Bench to Bedside, J T Jorgensen & H WintherPharmacodiagnostics and Translational Cancer Medicine, F G Rojo TodoPharmacodiagnostic Testing - Immunohistology, A S-Y Leong & T Y-M LeongIn Situ Hybridisation for Pharmacodiagnostic Testing, J Bartlett & F M CampbellPharmacodiagnostics Test - Microarray, A GlasDNA Biomarkers in the Diagnosis and Management of Cancer, R R Serizawa & P GuldbergValidating the Analytical Power of a Pharmacodiagnostic Test, C TaylorDrug and Pharmacodiagnostic Co-Development Statistical Considerations, R SimonClinical Validation of Biomarkers in Cancer, S J Mandrekar & D J SargentPharmacogenomic Markers for Cancer Therapy, S MarshSafety and Efficacy Biomarkers in Oncology Drug Label and Clinical Usage: A Regulatory Perspective, A Rahman & F KalushImage Analysis Systems for Cancer Diagnostics, K BloomPersonalizing Treatment Selection for Breast Cancer, L PusztaiPharmacodiagnostics and Personalized Medicine in Cancer, J T Jorgensen

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MOLECULAR

DIAGNOSTICS The Key Driver in Personalized Cancer Medicine

Copyright © 2010 by Pan Stanford Publishing Pte. Ltd.

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1/26/10 2:37:04 PM

The Key Driver in Personalized Cancer Medicine

MOLECULAR

DIAGNOSTICS Editors

Jan Trøst Jørgensen Dx-Rx Institute, Denmark

Henrik Winther Dako, Denmark

Copyright © 2010 by Pan Stanford Publishing Pte. Ltd.

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1/26/10 2:37:07 PM

Published by Pan Stanford Publishing Pte. Ltd. Penthouse Level, Suntec Tower 3 8 Temasek Boulevard Singapore 038988 Email: [email protected] Web: www.panstanford.com

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

MOLECULAR DIAGNOSTICS The Key Driver in Personalized Cancer Medicine Copyright © 2010 by Pan Stanford Publishing Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.

ISBN 978-981-4241-44-1 (Hardcover) ISBN 978-981-4241-45-8 (eBook)

Printed in Singapore. Copyright © 2010 by Pan Stanford Publishing Pte. Ltd.

INTRODUCTION

It is now 10 years ago since Robert Langreth and Michael Waldholz announced the “New Era of Personalized Medicine” in the Wall Street Journal. This article introduced the concept of personalized medicine to a broader audience, but in fact, the first step towards a more individualized drug therapy was taken already several decades before this manifesto. With the discovery of the estrogen receptor in the 1960’s and the introduction of the anti-estrogen tamoxifen in the 1970’s the road was prepared for a more individualized treatment of cancer patients – in this case patients with breast cancer. The assay for estrogen receptor (ER) status became an important stratification factor for anti-estrogen treatment. In the 1990’s another targeted drug was introduced which was also aimed at a selected group of cancer patients: women whose breast cancer tumors over-expressed the human epidermal growth factor receptor 2 (HER2). The drug was the monoclonal antibody trastuzumab (Herceptin®, Genentech, CA, USA), which was specifically targeted towards the HER2 protein of the tumor cells. Also, for trastuzumab a pharmacodiagnostic test played an important role in relation to treatment stratification. This was the immunohistochemical assay for the HER2 protein (HercepTest™, Dako, Glostrup, Denmark). Since the turn of the century other targeted cancer drugs have been introduced which are guided by a pharmacodiagnostic test or companion diagnostics in order to identify the patients who are most likely to respond to treatment. The development of pharmacodiagnostic tests are, however, not restricted to new drugs only. As our knowledge about the cancer pathophysiology at the molecular level increases and the mechanisms of action of the drugs are explained, it has also become possible to develop pharmacodiagnostic tests for drugs that are already used in the clinic. One recent example is the predictive fluorescence

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in situ hybridization (FISH) assay for anthracycline treatment of patients with primary breast cancer (TOP2A FISH pharmDx™, Dako Glostrup, Denmark). In fact, the cover of this book is embellished with a picture of breast cancer cells showing amplification of the TOP2A gene assessed by this assay. The advancement within molecular diagnostics, especially genomics, is a very important driver of this development. In a recent report from the US; “Priorities for Personalized Medicine”, published by the President’s Council of Advisors on Science and Technology (PCAST), it is stated that molecular diagnostics is identified as a key drivers of personalized medicine. In this respect pharmacodiagnostics or companion diagnostics are not only important tools used in the development of new targeted anticancer drugs, but they are just as important with respect to improvement of the quality of patient care. The PCAST report points at the potential of personalized medicine in relation to two important trends - the increasing costs of healthcare and the decreasing rate of new medical products being developed. For the quality of patient care the use of diagnostics will enable the treating physician to distinguish in advance those patients who will benefit from a given treatment and those who are likely to suffer the adverse effects only. The use of pharmacodiagnostics or companion diagnostics will lead to a more rational treatment of cancer that could also result in cost savings for the healthcare system. Moreover, with respect to drug development, diagnostics can be used to stratify patients according to their likelihood of response to the drug under development. Such approach could result in reduced size, duration, and costs of clinical trials, thus facilitating the development of more new drugs. Drug-diagnostic co-development will be the future development model in stratified and personalized cancer medicine. We will see more and more drugs and diagnostics being developed in parallel in order to increase the effectiveness and safety of new anticancer drugs, and it is our hope that this book will contribute to this development and be an inspiration to those who are engaged in this important work.

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Introduction

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Working with this book has been a pleasure and has convinced us even more that molecular diagnostics will be a decisive factor with respect to further improvement of cancer treatment. We would like to express our gratitude to all the authors who have contributed so positively to this book. Jan Trøst Jørgensen and Henrik Winther Copenhagen, November 2009

Copyright © 2010 by Pan Stanford Publishing Pte. Ltd.

CONTENTS

Introduction Jan Trøst Jørgensen and Henrik Winther

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Developing Molecular Diagnostics in Cancer Henrik Winther and Jan Trøst Jørgensen

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The Estrogen and Progesterone Receptors — Setting the Scene for Pharmacodiagnostics Kwok-Leung Cheung

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The Development of the HercepTest™ — From Bench to Bedside Jan Trøst Jørgensen and Henrik Winther

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Predictive Biomarkers and Translational Cancer Medicine Anne-Sofie Schrohl, Pernille Bræmer Hertel, Maj-Britt Jensen and Nils Brünner

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Pharmacodiagnostic Testing — Immunohistology Anthony S.-Y. Leong and Trishe Y.-M. Leong

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In Situ Hybridisation for Pharmacodiagnostic Testing John M.S. Bartlett and Fiona M. Campbell

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MammaPrint® Translating Research into a Diagnostic Test Annuska M. Glas, Leonie Delahaye and Oscar Krijgsman

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DNA Biomarkers in the Diagnosis and Management of Cancer Reza Rafiolsadat Serizawa and Per Guldberg

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Validating the Analytical Power and Parameters of an Immunohistochemical Test Søren Nielsen

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Drug and Pharmacodiagnostic Co-Development: Statistical Considerations Richard Simon

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Clinical Validation of Biomarkers in Cancer Sumithra J. Mandrekar and Daniel J. Sargent

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Pharmacogenomic Markers for Cancer Therapy Sharon Marsh

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Safety and Effective Biomakers in Oncology — A Regulatory Drug and Device Perspective Atiqur Rahman and Francis Kalush

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Personalizing Treatment Selection for Breast Cancer Lajos Pusztai and Chit Cheng Yeoh

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Pharmacodiagnostics and Personalized Medicine in Cancer Jan Trøst Jørgensen

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Copyright © 2010 by Pan Stanford Publishing Pte. Ltd.

CHAPTER 1 DEVELOPING MOLECULAR DIAGNOSTICS IN CANCER

Henrik Winther ImmunoHistology R&D Business Area, Dako A/S Produktionsvej 42, DK-2600 Glostrup, Denmark E-mail: [email protected] Jan Trøst Jørgensen Dx-Rx Institute Baunevaenget 76 DK-3480 Fredensborg, Denmark E-mail: [email protected] The development of molecular diagnostics in cancer includes careful considerations around the molecular endpoint, the assay specificity, the assay sensitivity, robustness and standardisation. Within the present chapter, the molecular endpoint is a protein and the diagnostic assay includes immunohistochemistry (IHC) as the main technology. However, even if a protein is the molecular endpoint the activation state of the protein has to be decided upon before designing the test assay. Activated proteins may be a stronger predictive endpoint as compared to non-activated proteins. To differentiate between proteins and activation stage it is extremely important to ensure the optimal specificity of the assay. In respect of an IHC assay, the specificity is very much dependent on the specificity of the primary antibody, which can be assured by several technical tools. In contrast, the IHC assay sensitivity is more dependent on pre-analytical issues as well as the analytical visualisation process. New visualisation techniques will in a short time enable “single molecule detection” which will supersede all previous IHC sensitivity levels and revolutionise IHC to become a strong quantitative molecular diagnostic technique. The pre-analytical parameter of tissue fixation is still a challenge when it comes to standardisation of the IHC assay, but the future inclusion of detecting

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housekeeping proteins (internal tissue controls) will indeed increase the standardisation and robustness levels and thereby optimise the IHC assay quality and patient care.

1. Introduction When discussing the development of molecular diagnostics, the obvious starting point would be a clear definition of biomarkers and their indicator classification – e.g. classification into diagnostics, prognostics and predictive biomarkers. A biomarker is to be considered a substance used as an indicator of a biologic state. It is a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention1. Within this chapter, the biomarker substances will be dealt with as antibodies. In literature, there is a confusion of terms with regard to the indicator classification of biomarkers and hence we will briefly take you through our definition of a diagnostic-, prognostic- and predictive biomarker (Figure 1). A diagnostic biomarker will be considered to be an antibody that is presently used in a routine setting in routine diagnostic pathology laboratories. The antibody is used as a confirmatory test and is therefore an add-on to the pathologist in confirming the diagnosis initially based on the patient clinical history and tissue biopsy morphological (macroscopic and microscopic) observations. Classification and subtyping of tumours, e.g. lymphomas, is typically performed by using a diagnostic biomarker2. The majority of diagnostic biomarkers are classified as IVD class I products when considered from a regulatory point of view. IVD class I products can be defined as “confirmatory products”. A prognostic biomarker will be characterised as an antibody being able to provide additional information to the pathologist – e.g. it is not just a confirmatory test, but a test that will guide the pathologist towards a therapeutic rational. In its strict sense a prognostic biomarker could be

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Diagnostics

Predictive (new Therapy) Antibody Development Prognostics

Predictive (estab. Therapy)

Figure 1. Biomarkers (antibodies) can be developed as diagnostics, prognostics or predictive markers (Companion Diagnostics) in selecting patients for a specific therapy. Sometimes a biomarker starts out as a diagnostic, but later turns into a prognostic or even a companion diagnostic. Predictive biomarkers can be developed both for new therapies and for drugs already used. From a development point of view, diagnostics are of low risk whereas prognostics are difficult to get implemented and companion diagnostics are of higher risk – especially with regard to new drug development, which include the risk of drug-failure.

considered as an indicator of the course of the tumour and by knowing the course one will also be guided towards the therapeutic rationale. From the regulatory perspective, prognostic biomarkers are typically classified as IVD class II products – i.e. products that add extra information to the pathologist3. An excellent example of a prognostic marker is the Estrogen Receptor (ERalpha). Testing for ERalpha in a patient with breast cancer will provide information on the cause of the disease and further help in deciding whether or not to treat the patient with a selective estrogen receptor modulator (SERM) or an aromatase inhibitor. A predictive biomarker will be defined as a companion diagnostic (also known as a pharmacodiagnostic) that is used to select patients for a specific treatment/drug. In comparison to a prognostic biomarker, which

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is pointing towards a broader portfolio of relevant therapeutics, the predictive biomarker is strictly linked to one specific therapeutic invention. One of the best known examples of a predictive biomarker is HER-2/neu overexpression, which is closely linked to the treatment outcome with HER2-inhibitors such as the monoclonal antibody trastuzumab (Herceptin®, Genentech) or the tyrosine kinase inhibitor lapatinib (Tykerb®, GlaxoSmithKline)4. Predictive biomarkers fall under the highest regulatory classification category, i.e. IVD class III. The development of molecular diagnostics typically refers to the development of prognostic or predictive biomarkers as described above. Molecular diagnostics are hence molecular patient profiling assays or rather assays that identify patient specific targets that interact with the anticancer drug5. 2. The Biological Aspect of Predictive Biomarkers When developing a molecular diagnostic, the initial selection of the appropriate molecular endpoint is of major importance. Within cancer development and progression, several key signalling pathways come into play and it is therefore very often a difficult task to pinpoint the driving molecular target in tumour development and progression. Understanding the biology of the pathway and choosing the right pathway level (gene, protein, activated protein) is a prerequisite to the design of high quality molecular diagnostics6. When developing new anticancer drugs within the pharmaceutical industry, there has been and still is a tendency to focus on the tissue slide-based detection of protein overexpression as the appropriate molecular endpoint. The preferred technology is applying immunohistochemistry (IHC) on formalin fixed and paraffin embedded (FFPE) tissues sections, allowing for both morphological evaluation of the tissue and a semi-quantitative assessment of the biomarker status. Detection of gene amplifications, through the in situ hybridisation technology, is a method which is increasing in propagation; however at present it is still exceeded by IHC assays looking for protein overexpression.

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The detection of gene amplification or protein overexpression has been argued to include a potential confounding aspect as these detections do not necessarily reflect the functional activity of the target protein. As an example, the HER-2 transmembrane tyrosine kinase receptor that has been linked to the response to therapy with the anti-HER-2-humanised monoclonal antibody trastuzumab, in patients with breast cancer, is typically assessed by HER-2/neu protein overexpression assays and fluorescence in situ hybridisation assays designed to detect HER-2 gene amplification7. If HER-2 is truly important in the pathology of breast cancer, the receptor must be activated to exert its effects. A common feature of signal transduction through membrane-bound receptor tyrosine kinases is autophosphorylation of the receptor. Autophosphorylation of HER-2, therefore, may be used as a surrogate for active signalling. Antibodies have been developed to detect phosphorylated (activated) HER-2 by IHC, but the role of activated HER-2 as a predictor to trastuzumab therapy is currently unknown8. Apart from phosphorylation, the functional activity of the target protein can also include other post-translational modifications such as acetylation, methylation and ubiquitination. 3. Biomarker Validation In the process of developing molecular diagnostics, “quality” is given pride of place. The important parameters in designing a high quality molecular diagnostic assay include assay specificity (analytical and clinical), assay sensitivity (analytical and clinical), assay robustness and assay standardisation. General issues such as specificity, sensitivity, robustness and standardisation apply to all diagnostic techniques; however in the present chapter the topics will be illustrated through the development of an IHC pharmacodiagnostic assay. 3.1. Assay specificity Assay specificity will, in cases of immunohistochemical assays, rely on the specificity of the given antibody. The antibody specificity should be

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considered from both a “sequence” and “modification” point of view. Sequence-specificity is typically related to the specific recognition of a specific amino acid sequence in a protein, whereas “modification” specificity is describing the ability of an antibody to recognise a specific post-translational modification of a protein. Sequence-specificity is very much dependent on the nature of the antibody. Antibodies used in today’s companion diagnostic kit are of either monoclonal or polyclonal nature. As polyclonal antibodies by definition recognise several different epitopes on the same target molecule, there would be a higher risk of having cross reactivity to one or more similar epitopes on different (non-target) molecules. Consequently, polyclonal antibodies are typically affinity isolated to make sure that only the actual target molecule is recognised by the antibody, whereas the immunoglobulin fraction of the polyclonal serum recognising non-target molecules is removed. Whenever the specific recognition of one target molecule is ensured, the actual binding of the polyclonal antibody to several different epitopes on the one target molecule will be beneficial to the assay, as pre-analytically tissue modifications will not allow for masking of all different recognisable epitopes. However, seen from a manufacturing point of view, the supply of polyclonal antibodies without batch-to-batch variation can be a very challenging task. Monoclonal antibodies by definition recognise only one epitope on the target molecule, and the more unique the epitope is for that specific target molecule the more specific is the antibody. Mouse monoclonal antibodies have been manufactured since the invention of the technique back in 1975 by Köhler & Milstein9. In recent years, rabbit monoclonal antibodies are becoming more and more popular as they are claimed to be associated with higher affinity antibodies (compared to mouse monoclonal antibodies) and able to recognise many epitopes that are non-immunogenic in mice10. Clear evidence is still lacking, but since the isolation of the rabbit hybridoma fusion partner cell line back in 1995 by Knight11,12, there has been an amazing increase in the numbers of rabbit monoclonal antibodies developed. Whether the antibody is polyclonal or monoclonal, the most challenging aspect to slide-based diagnostic companies is to ensure that

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the antibodies are applicable in IHC on formalin fixed and paraffin embedded (FFPE) tissues. Many companies are capable of generating specific monoclonal antibodies, but only a few are capable of generating IHC FFPE applicable monoclonal antibodies with minimal lot-to-lot variation. During the antigen design stage, i.e. the stage in which you define and design the antigen/epitope you want the antibody to recognise in the FFPE tissue, it is extremely important to analyse the epitope carefully. Very often the epitope will not appear as a linear epitope in the FFPE tissue but rather as a conformational epitope13 and hence it is of major importance to design the antigen (used for immunisations) due to these restrictions/demands. Designing the antigen would therefore include analyses of the numbers of amino acids known to be cross-linked during fixation as well as analyses of the similarity between the target epitope and other epitopes of non-relevant proteins. The latter analysis is typically performed by using techniques such as aniline substitution for mapping the epitope (Figure 2). Other techniques for mapping the epitope have been developed using the expanding field of molecular tools – genetransfer to bacteria expressing the epitopes on their bacterial surface14.

Figure 2. Epitope mapping of two different antibodies. Amino acids (within the epitope) essential for antibody binding are indicated in orange, whereas antibodies contributing to antibody binding are marked in bold. The upper antibody is very specific in regard to phosphorylated (activated) S6-protein at the S240 position (pS). The lower antibody display strong cross-reactivity to the back-bone (non-phosphorylated) S6-protein at several positions (S240, S244, T240, A240 and A244). In addition, the lower antibody strongly cross-reacts to other phosphorylated amini acids (pT240 and pT244).

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Another very important issue to consider when it comes to the design of antigens is to elaborate on the immunogen, e.g. should one use a protein, a protein fragment, peptides, etc. Other typically used tools for securing sequence specificity of the antibody include ELISA, Western Blotting, peptide antigen absorption tests and IHC testing on relevant normal and pathological tissues as well as IHC testing on “pseudotissue”, e.g. transiently transfected eukaryotic cells forced to express the target molecule (Figure 3).

H1

H2

H3

H4

DAK-EGFR DAK-EGFR Wild type Wild type

DAK-H1 PY1197

Figure 3. IHC performance on formalin fixed and paraffin embedded transiently transfected cell lines expressing HER1 (H1), HER2 (H2), HER3 (H3) and HER4 (H4). Upper row of cells are stained with an anti-HER1 (EGFR) antibody. Cell lines in the lower row have been activated and subsequently stained with an activation state specific anti-HER1-pY (activated EGFR) antibody.

Xenografts, e.g. implantation of human cancer cells into immunodeficient mice and subsequent solid tumour growth used in conjunction with Western Blotting are also a very strong tool to verify target specificity. During the development of monoclonal antibodies applicable in FFPE-tissues one should consider testing the plasma from early bleedings before establishment of the hybridoma – in an IHC setting on FFPE tissues (Figure 4).

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Developing Molecular Diagnostics in Cancer The use of screening systems

• Priming

•

ELISA

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Western Blotting

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IHC: •• Pseudotissue (transiently (transiently

• Immunizations • Test of plasma

3-4 month

Development of monoclonal antibodies

• Selection of mice

transfectedeucaryotic eucaryoticcells) cells) transfected

• Fusion

Several Re-cloning Re-cloning/tests • Several /tests

• Final tests • Seed clone bank • Antibody production

3-4 month

• Screening

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• Tissue Tissue sections sections

•

Additional Quality Criterias: • Peptide antigen absorption

• CIP • Epitope mapping • Drug Inhibition model

Figure 4. Illustration of the steps included in monoclonal antibody development (left), the timeline (middle) and the use of different screening tools (right). During development of monoclonal antibodies the IHC screening tool is used at three different levels – test of plasma, screening of fusions (hybridomas) and in the final clone selection. ELISA screening is used at the same levels, whereas Western Blotting is applied on the final clone selected. Testing for additional quality criteria includes absorption studies, tests for phospho-specificity by inducing de-phosphorylation using calf intestinal phosphatase (CIP), epitope mapping and drug inhibition models.

Apart from sequence specificity it is also important to assure the “modification” specificity when developing IHC molecular diagnostics. Very useful tools include phosphatase treatment of tested tissues to assure phospho-specificity of the antibody, the application of nonstimulated versus stimulated cell-lines in order to induce signal changes and thereby verify modification specificities such as acetylation, phosphorylation, cleavage etc. Drug inhibition models combined with Western Blotting are very effective in showing modulation in response to treatment. Blocking a signalling pathway by treating cell lines with a drug inhibitor will hence reveal no activation of proteins upstream of the blocking point (Figure 5).

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Figure 5. Western blotting using primary antibodies Anti-AKT-pS-473 and Anti-S6pS-240 on LNCaP cell extracts. Non (DMSO) and + (Wortmannin) treated. Following treatment cells were harvested in a 9 M Urea buffer containing Na3VO4 and 10 micrograms of each extract were applied per lane in standard SDS-PAGE gel.

3.2. Assay sensitivity After having secured the assay specificity, validation of the assay sensitivity is needed. Several pre-analytical and analytical parameters influence the sensitivity of a biomarker. In the case of antibodies and immunohistochemistry, the processes around antibody affinity, tissue sampling (representative tissue sample), tissue fixation, effects of tissue processing and visualisation of the biomarker influence the sensitivity level of the assay15. The affinity of an antibody is technically defined by the binding constant which is an adjustable and controllable parameter. When it comes to the pre-analytical steps around tissue handling and thereby the preservation of cells and tissues in as reproducible and lifelike manner as possible it is much more difficult to standardise the processes between laboratories because these processes are not all yet automated, but rather done by hand. Fixatives stabilise the cells and tissue to protect them from the rigours of subsequent processing and staining techniques. In performing their protective role, fixatives denature proteins by coagulation, by forming additive compounds or by a combination of the two16. The dilemma of fixation has always been that it is necessary to introduce some artefact in order to have a protective effect; by definition, fixatives alter the original chemical composition of the tissues involved. Different fixation times will introduce different degrees of protein protection and thereby

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different accessibility of the antibody and hence a variation in assay sensitivity. By far the largest proportion of samples used for immunostaining is embedded in paraffin, and a number of fixatives have been formulated with this in mind. The most popular fixative contains formalin diluted in a neutral salt to maintain tonicity and buffered to maintain pH. These fixatives are well tolerated by tissues and have a good penetration17. Conformational changes resulting from the reaction of formaldehyde with amino acids adjacent to the epitope can sometimes destroy the epitopes or alter them to reduce reactivity with the antibody and thereby reduce sensitivity of the assay18. These conformational changes can occur in a number of ways. The most common alterations occur chemically by fixation, or physically by heat during paraffin embedding. Many epitopes are sensitive to heat, and thus overheating of tissues during embedding or overheating of sections during drying can induce detrimental effects on immunostaining. It is essential not to overheat at any stage of processing if immunostaining is to be optimally sensitive. Recently, the IHC quality organisations UKNequas, US CAP and NordiQC had to repeat this sensitivity issue in regard to routine pathology laboratories testing for IHC HER2 overexpression in breast cancer tissues19. Overheating during the IHC procedure would apply too low a sensitivity level to the assay which would increase the risk of having false negative results. Although some proteins are not well demonstrated after fixation in formaldehyde-based fixatives, many can be demonstrated after the use of appropriate pretreatment methods, such as proteolytic enzyme digestion and/or protein retrieval – collectively known as antigen retrieval20. The principle of antigen retrieval relies on the application of heat for varying lengths of time to the FFPE tissue sections in an aqueous medium. Antigen retrieval has made a significant contribution in refining the immunohistochemical technique, increasing the sensitivity of the technique as many biomarkers, previously believed to be lost to the process of FFPE, can now be routinely demonstrated. The benefits are especially obvious with such important diagnostic/prognostic/predictive markers as the estrogen and progesterone receptors, Ki-67 and HER2/neu. The greater sensitivity in their demonstration gained through

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antigen retrieval may, however, require the reevaluation of the staining results and their clinical interpretation18. One of the important goals in immunohistochemistry is to achieve greater sensitivity with detection systems using the shortest possible incubation time. However, multi-step detection systems have several drawbacks, such as complex time-consuming protocols, difficulties with standardisation, suboptimal detection of hard to detect antigens, endogenous biotin activity, etc. Recently, new detection systems have been introduced using natural or synthetic polymer carriers that are coupled to linker antibodies21. This approach increases the number of available enzymes or ligands binding at the antigenic site, thus increasing their reactivity with the chromogen. Because these systems avoid the use of (strept)avidin and biotin, nonspecific staining as a result of endogenous biotin is eliminated. Ultimately, this gives the user higher sensitivity and antibody efficiency as well as better signal-to-noise ratios. Very recent studies imply that the sensitivity of IHC detection systems can be made so high as to make it possible to identify single molecules within the cell. The numbers of molecules can be made either absolute or relative to a housekeeping protein. Applying this approach in IHC will be a strong driving force within image analysis as the detection of single molecules will make the stainings very easily countable by use of software algorithms. The digitalisation of the IHC staining results will convert the technique from a qualitative/semi-quantitative technique to a full-blown quantitative technique. This conversion will revitalise the IHC solution as a strong diagnostic, prognostic and predictive tool – just as competitive as many of the new molecular qualitative techniques. When adding the benefit of also having the possibility of a morphological tissue evaluation performed on the IHC slide, it is believed that single molecule detection IHC will be one of the most preferred diagnostic tools in the future also. 3.3. Assay standardisation and robustness In conjunction with the analytical and clinical testing of assay specificity and sensitivity, the assay protocol standardisation will also be optimised. Several variable laboratory IHC procedures exist (Figure 6). The majority

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Developing Molecular Diagnostics in Cancer

Tissue size Fixative

Microwave oven

Section cutting - thickness - storage

Tissue handling

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High pH

Epitope retrieval

Citrate Pressure cooker

Temperature

Fixation time

PT-module

Proteolytic digestion

Polyclonal antibody

Primary antibody Manual

Automated

Interpretation

ACIS “SMD”

Automated/manual staining procedures

Clone I Clone II

Clone III

Staining pattern

Intensity

Visualization Visualisation system system

Scoring system

AP

CSAII Number of positive cells

HRP

Flex/Flex+

Advance

Figure 6. The IHC procedure consists of several different entities beginning with tissue handling (upper left corner) continuing on to the choice of retrieval method of the target protein (epitope retrieval). During the analytical steps one has to decide on which antibody to use, manual or automated staining run and which visualisation system to apply. Finally the scoring system and interpretation guidelines have to be defined. Standardisation is the key word to obtain robust and reliable results. ACIS: Automated Cellular Imaging System; SMD: Single Molecule Detection; PT: Pre Treatment.

of steps included in the process going from biopsy sampling to slide interpretation are controlled and standardised by automated solutions. The automated steps include tissue embedding, tissue sectioning, antigen retrieval, IHC staining and interpretation through image analysis. However, some procedures are still not automated but manual and hence at risk of being carried out in a more subjective manner than through the objective instrumentation. One of the non-standardised procedures includes the initial tissue handling. Issues like type of fixative, fixation time, fixation temperature, tissue size, tissue slide thickness and tissue slide storage are all very important parameters affecting the final test result, but in all cases these parameters are only controlled by guidelines and hence not locked in standards. As tissue handling constitutes the

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initial step (input) of the IHC procedure and since this procedure is not strictly standardised there is a risk that the final output will reflect this non-standardisation in input. The stringent requirements for reproducibility and quality in combination with high output demands and requests for reduced costs of labor and material led to the automation of IHC22. The types of automated IHC stainers on the market today vary in several aspects, including the technology for the processing of slides, the slide capacity per run and in the flexibility of reagents (“open” versus “closed” systems). However, no matter which automated IHC system one applies, it will allow better standardisation and inter-laboratory comparisons. In order to secure a standardised and robust diagnostic assay it is important during development to consider carefully which kind of controls to include in the assay. The control procedure cannot only focus on/be sensitive to one of the parameters constituting the whole process of pre-analytical and analytical steps. The two most non-standardised steps important for the IHC outcome include tissue fixation and baking of cut slides23. In regard to tissue fixation it is especially the under-fixation which is impacting on the final results, as the lack of formalin-fixation will lead to a later alcohol-fixation of the tissues during the pre-analytical steps24. Alcohol fixation is a non cross-linking fixative which is in contradistinction to the cross-linking fixation performed by formalin25. Diagnostic assays developed for cross-linked (formalin-fixed) tissue will lose their specificity and sensitivity when applied on non cross-linked (alcohol-fixed). Over-fixation of tissue is not considered to be as serious a problem, as the antigen retrieval should almost “zero fill” the tissue. In respect to the baking of tissue slides, 60 degrees Celsius is considered to be the upper limit for many epitopes23; however lots of laboratories use higher temperatures and hence the ideal control will unveil this type of protocol error. A true valid positive IHC quality control must be an internal tissue control (housekeeping protein) – also known by the name “built-in” control26. This control is ideal because it evaluates the whole preanalytical (from tissue sampling to storage – see Figure 7) and analytical (from cutting to IHC-result – see Figure 7) phases in one control and hence eliminates the impact of pre-analytical and analytical variables

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between specimens and controls. Built-in controls contain the target antigen not only in the tissue elements under scrutiny, e.g. tumors, but also in adjacent normal tissue elements. Built-in controls have the additional advantage that no separate positive control sections are required. A true valid positive IHC quality control is used to validate the staining run. External positive controls, e.g. cell lines or small multi-tissue arrays mounted on the same slide as the clinical tissue will still not incorporate the pre-analytical part that has been imposed on the clinical tissue. Pre-analytical

Analytical

Grossing?

Tissue sample

Fixation

Embedding Cutting Grossing Storage

Baking

Quenching Epitope-retrieval

IHC-result

De-paraf. Re-hydrate

Figure 7. The molecular diagnostic test contains pre-analytical and analytical actions. The post-analytical evaluation is not depicted here.

According to regulatory requirements when developing and launching diagnostic (prognostic and predictive) assays, several specifications relating to the robustness of the assay have to be clarified during the development stage3. These specifications will not be dealt with in greater detail in this chapter; it should only be mentioned that they include issues such as repeatability, reproducibility and stability. Repeatability is also known as intra-run precision in which the biomarker analyte (antibody) used in one staining run must perform similarly in a number of tissue sections coming from the same patient. Reproducibility is also known as inter-run precision and in this case the biomarker analyte has to perform identically in a number of independent tests (several staining runs) applying tissue sections from the same patient. Stability testing includes several aspects to be analysed. Real-time stability data of the assay is the

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ultimate goal. However, the assay may be released based on data coming from accelerated stability studies. The real-time stability data will later confirm (or even prolong the stability of the assay) the data generated through the accelerated stability studies. Additional stability criteria to be tested during development include stability during transportation, in-use stability and on-board stability. All data arising from studies on repeatability, reproducibility and stability will reach a conclusion on the robustness and thereby the shelf life of the final assay product3. 4. Development of Companion Diagnostics (Pharmacodiagnostics) As earlier mentioned, companion diagnostics are assays that identify patient-specific targets that interact with the anticancer drug. Within immunohistochemical companion diagnostics, the assays are developed according to the specifications mentioned above, e.g. specificity (analytical and clinical), sensitivity (analytical and clinical), standardisation and robustness. Selection of the appropriate molecular endpoint understanding the biology of the signalling pathway and choosing the right pathway level (gene, protein, activated protein) is extremely important when developing a companion diagnostic. Selecting the appropriate pathway level can with advantage be performed in a collaborative fashion between the drug and diagnostic companies. Ideally, the drug development and the companion diagnostic biomarker assay development should run in parallel as illustrated in Figure 8. This parallel development model has also been suggested by the US Food and Drug Administration (FDA) in their concept paper about drug-diagnostic co-development27. Initial feasibility studies will allow for a focus around the specificity of the assay ensuring an unequivocal identification of the chosen molecular endpoint by the biomarker analyte when used in the optimal protocol. Protocol optimisation will be another important activity during the feasibility period The next step will include the development of a prototype companion diagnostic assay, typically in a kit format. The prototype assay contains all the reagents, except positive and negative control materials, needed to perform the actual test and it is developed to

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Developing Molecular Diagnostics in Cancer

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Example

Pre-Clinical Research Feasibility Studies

Pharmaceutical Clinical Trial Phase I Phase II Phase III Phase IV Prototype Assay dev. Validated Assay*

*Analytical Validation – PhII Clinical Validation – PhIII

Approved Product

No significant change in assay performance characteristics

Figure 8. The parallel development model of drug (upper box) and companion diagnostic.

be used in Phase II clinical trials. The aim of a prototype assay is to secure a standardised test procedure by using a standardised assay, and as such the assay is labelled “Investigation Use Only”, as the performance characteristics of this product have not yet been established. Following the prototype assay applied in Phase II clinical trials, the validation of the companion diagnostic assay will take place in a continuum of two phases. In the first phase, analytical validation will be established during the phase II clinical trial. Clinical data from the phase II clinical trial will be used to establish the cut-off value for the assay and to give an indication of the predictive value. The analytically validated assay will therefore contain all the reagents, including positive and negative control materials and pre-diluted antibody, needed to perform the assay for the detection of the molecular endpoint in formalin fixed and paraffin embedded tissue samples. The second phase of validation includes the analytically validated assay (kit) together with the clinical data from the phase II clinical trial, which in combination will be used to stratify patients to the different treatment groups during the phase III clinical trial. By analysing the clinical outcome data from the phase III clinical trial and correlating the measurable target with the treatment outcome by applying a powerful statistical analysis, the final predictive value of the companion diagnostic can be established28. Stage four of the companion

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18

diagnostic biomarker assay development can be commenced and should be successfully concluded by the final regulatory approval. This approval process will be coordinated by the authorities so that both the drug and the diagnostics will receive the marked authorisation simultaneously. The final companion diagnostic assay, now clinically validated, contains all the reagents, including positive and negative control materials and pre-diluted antibody, needed to perform the assay for the detection of the molecular endpoint in formalin fixed and paraffin embedded, is manufactured under GMP, is approved by regulatory authorities and labelled “In Vitro Diagnostic” and contains interpretation guidelines. These interpretation guidelines are finally established during validation of the companion diagnostic and will constitute a written recommendation to assist the user of the companion diagnostic in interpreting the assay’s results, including staining pattern and scoring, definition of positive and negative results, an image gallery of different staining patterns and scores and negative and positive controls validated in the outcome of the clinical study. In the same way as the development of the therapeutic drug and companion diagnostic has been a coordinated effort, the market launch should be performed simultaneously. It needs to be so, due to the fact that the diagnostic must be used to select the patients for treatment with new drug. Acknowledgments We thank Dr. David Dabbs and Professor Lars-Inge Larsson for valuable comments and suggestions when reading through the manuscript. References 1.

2. 3.

Dalmasso EA. Planning for success in biomarker discovery: Appropriate proteomics platform and careful study design can improve positive results. Genetic Engineering & Biotechnology News 2008; 28(12): 28. Dabbs D, ed. Diagnostic Immunohistochemistry (2nd edition ed.). Churchill Livingstone, Philadelphia, 2006: 65. US Food and Drug Administration. Guidance for Industry – Guidance for Submission of Immunohistochemistry Application to FDA, 1998.

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Developing Molecular Diagnostics in Cancer 4.

5. 6.

7.

8.

9. 10.

11. 12.

13. 14. 15.

16. 17. 18.

19.

19

Jørgensen JT, Nielsen KV, Ejlertsen B. Pharmacodiagnostics and targeted therapies – A rational approach for individualizing medical anti-cancer therapy in breast cancer. Oncologist 2007; 12: 397. Sikora K. Personalized medicine for cancer: From molecular signature to therapeutic choice. Adv Cancer Res 2007; 96: 345. Bunn PA Jr, Dziadziuszko R, Vallea-Garcia M, et al. Biological markers for nonsmall cell lung cancer patient selection for epidermal growth factor receptor tyrosine kinase inhibitor therapy. Clin Cancer Res 2006; 12(12): 3652. Ross JS, Fletcher JA, Bloom KJ, Linette GP, Stec J, Symmans WF, Pusztai L, Hortobagyi GN. Targeted therapy in breast cancer. Molecular and Cellular Proteomics 2004; 3.4: 379. DiGiovanni MP, Chu P, Davison TL, Howe CL, Carter D, Claus EB, Stern DF. Active signaling by HER2/neu in a subpopulation of HER-2/neu-overexpressing ductal carcinoma in situ: Clinicopathological correlates. Cancer Res. 2002; 62, 6667. Köhler G, Milstein C. Continuous cultures of fused cells secreting antibody of predefined specificity. Nature 1975; 256: 495. Lanning D, Zhu X, Zhai S-K, Knight KL. Development of the antibody repertoire in rabbit: Gut associated lymphoid tissue, microbes, and selection. Immunological Reviews 2000; 175: 214. Knight KL. Immortalized rabbit hybridoma fusion partner. United States Patent 5,675,063, 1997. Spieker-Polet H, Sethupathi P, Yam PC, Knight KL. Rabbit monoclonal antibodies: Generating a fusion partner to produce rabbit-rabbit hybridomas. Proc Natl Acad Sci U S A. 1995; 26; 92(20): 9348. Goldsby R, Kindt TJ, Osborne BA, Kuby J. Antigens (Chapter 3). Immunology (5th edition ed.). New York: W. H. Freeman and Company. 2003: 57. Knorpp T, Templin MF. On display on a bug: A systematic approach to characterize antibodies. Nature Methods 2008; 5(12): 1003. Shi S-R, Guo J, Cote RJ, et al. Sensitivity and detection efficiency of a novel two-step detection system (PowerVision) for immunohistochemistry. Appl Immunohistochem Mol Morphol 1999; 7: 201. Larsson L-I, ed. Immunohistochemistry: Theory and Practice. Boca Raton, FL: CRC Press; 1988: 41. Leong AS-Y, Gilham PN. The effects of progressive formaldehyde fixation on the preservation of tissue antigens. Pathology 1989; 21: 266. Shi S-R, Cote RJ, Chaiwun B, et al. Standardization of immunohistochemistry based on antigen retrieval technique for routine formalin-fixed tissue sections. Appl Immunohistochem 1998; 6: 89. Lundgaard B, Winther H, Moller K. Excessive section drying of breast cancer tissue prior to deparaffinization and antigen retrieval causes a loss in HER2 immunoreactivity. UKNEQAS Immnunocytochemistry Journal 2008; 6(3): Run 76.

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20. Shi S-R, Cote RJ, Taylor CR. Antigen retrieval techniques: Current perspectives. J Histochem Cytochem 2001; 49: 931. 21. Sabattini E, Bisgaard K, Ascani S, et al. The EnVision+ system: A new immunohistochemical method for diagnostics and research. Critical comparison with APAAP, ChemMate, CSA, LABC and SABC techniques. Journal of Clinical Pathology 1998; 51: 506. 22. Bogen SA. Apparatus and method for immunohistochemical staining. United States Patent 5073504, 1988. 23. Williams JH, Mepham BL, Wright DH. Tissue preparation for immunohistochemistry. The Journal of Clinical Pathology 1997; 50: 422. 24. Marzo AMD, Fedor HH, Gage WR, Rubin MA. Inadequate formalin fixation decreases reliability of p27Kip1 immunochemical staining: Probing optimal fixation time using high-density tissue microarrays. Human Pathology 2002; 33: 756. 25. Grizzle WE. The use of fixative in diagnostic pathology. J Histotechnol 2001; 24: 151. 26. Fetsch PA, Abati A. Overview of the Clinical Immunohistochemistry Laboratory: Regulations and Troubleshooting Guidelines. In: Immunocytochemical Methods and Protocols (2nd edition). Javois LC ed. Humana Press, SpringerLink, 1999: 405. 27. US Food and Drug Administration. Drug Diagnostic Co-development Concept Paper, 2005. 28. Prebula R. The Ever-Evolving Role of “Companion Diagnostics”. Update 2008; 5: 1.

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CHAPTER 2 THE ESTROGEN AND PROGESTERONE RECEPTORS — SETTING THE SCENE FOR PHARMACODIAGNOSTICS

Kwok-Leung Cheung Professorial Unit of Surgery Nottingham University Hospitals City Hospital Campus Hucknall Road Nottingham NG5 1PB, UK E-mail: [email protected] Following the discovery of estrogen and progesterone receptors (ER/PR), the direct role of estrogen on breast cancer growth and its relation to ER were recognized by late 1960s. Immunohistochemistry is now the standard measurement technique although gene expression assay may have potential. The first reported case of endocrine therapy was in form of oophorectomy in 1895. With the anti-estrogen tamoxifen reaching the clinic in 1973, it became possible to “personalize” cancer treatment. Meta-analyses of worldwide randomized trials have shown significant reduction in recurrence (~50%) and breast cancer mortality (~40%) in patients with ERpositive cancers treated with adjuvant tamoxifen. No effects were seen with ER-poor tumors. Recent trials have demonstrated superiority of the third-generation aromatase inhibitors over tamoxifen, in efficacy and tolerability. There is data suggesting that an ER-positive tumor may be more responsive to tamoxifen if it is PR-positive and HER2negative. The reverse may be true with response to an aromatase inhibitor. Furthermore, patients with genetically defective metabolism (due to CYP 2D6) may have adverse clinical outcome when treated with tamoxifen. Yet current data does not appear to be sufficient to change clinical practice, but biological modulation (including identification of further biomarkers) and pharmacogenomics are promising and require further studies.

21

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1. The Historical Perspective of Estrogen Receptor and Progesterone Receptor Experiments done in 1950s and 1960s led to the discovery of an “estrogen binding component” which specifically took up estrogen in the uterus of immature rats. Jensen later called this component estrogen receptor (ER)1,2. By late 1960s the direct role of estrogen on breast cancer growth and its relation to ER was described3,4. Clinical correlation with hormone sensitivity followed in 1970s5. Estrogen receptor is a 65-kDa estrogen binding protein, with two activating functions (AF1 and AF2) which could be activated by estradiol stimulating the transcription of target genes (Figure 1). This leads to proliferation of tumor cells. The receptor can also be activated by growth factors (e.g. epidermal growth factor), intracellular mediators such as cyclic AMP and phorbol esters acting via protein kinases6. Another ER (ER-β) was discovered later in 1990s, which may be related to acquired hormone resistance7. The “original” ER has since been known as ER-α, and it is the most widely studied hormone receptor.

OestrogenX

Growth factor inhib itors

X X AF2

SERD

AF2

X AF1

Tyrosine Tyrosine kinase etc etc AF AI OFS SERM SERD

OFS

SERMs

Co- activator Coactivator/ Co-- repressor Co

Growth factor receptor

AIs

AF1

Oestrogen Receptor

Activating function Aromatase inhibitor Ova rian function suppression Selective oestrogen receptor modulato r Selective oestrogen receptor dow n-re gulator

Fig. 1. Estrogen receptor and endocrine treatment modalities.

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The Estrogen and Progesterone Receptors

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Progesterone receptor (PR) was discovered in the same period as ER. The PR gene produces two protein isoforms PR-A and PR-B via distinct pathways8. These two isoforms are identical except for an additional 165 amino acids present in PR-B. Historically PR was thought to be simply a surrogate for ER expression. Increasing evidence has now suggested that PR-B expression may indicate better clinical outcome, and the lack of its expression may be related to underlying growth factor signaling or promoter methylation9,10. Work is underway to further delineate the roles of these two isoforms. 2. The Different Types of Estrogen Receptor and Progesterone Receptor Assays There has been significant improvement on the conduct and quality of ER and PR assays. Historically frozen samples were required for such assays which can now be performed using paraffin embedded tumor samples. Qualitative (ER or PR positive or negative) assessment was emphasized until it was subsequently recognized that quantitative (the degree of level of positivity within ER or PR positive groups) assessment is vital in predicting therapeutic response. The importance of quality control has also been recognized early and improvement in the assay techniques has enhanced concordance between central and local laboratories, and between tumor tissue obtained from surgical specimens and needle core biopsies. Historically ER and PR assays were carried out using an enzyme immunoassay of fresh frozen tissue, which has now been replaced by an immunohistochemical (IHC) assay. Studies have shown excellent concordance and that IHC appears to be superior11,12. It also has the advantages of allowing ER and PR to be assayed on paraffin embedded samples (e.g. archival of tumor tissue) and on smaller tumor blocks. Common primary antibodies used nowadays are 1D5 (Dako) and 636 (Dako) for ER and PR respectively. It is a semi-quantitative assay, the report of which is based on a scoring system taking into account of the proportion (or percentage) of cells with staining and intensity of staining. In fact the importance of accurate quantitative assessment of ER and PR levels was recognized in the late 1970s/early 1980s, as exemplified in

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one of the pivotal adjuvant trials carried out by the National Surgical Adjuvant Breast and Bowel Project (NSABP), when ER and PR contents were measured by dextran charcoal method (in terms of femtomoles per milligram of cytosol protein (fmol))13,14. Common scoring systems currently being used include Allred and Histochemical (H) scores (Table 1)15,16. Table 1. Scoring systems for hormone receptors. Histochemical (H) score Intensity of staining None Mild Moderate Marked

Intensity score 0 1 2 3

Percentage of cells (%) A B C D

H score = (0 x A) + (1 x B) + (2 x C) + (3 x D) i.e. range = 0–300 Percentage of staining = B + C + D Allred score (AS) Intensity of staining

Intensity score (X)

None Mild Moderate Marked

0 1 2 3

Score for proportion of positive cells (Y) 0 (Nil) 1 2 3 4 5 (100%)

AS = X + Y i.e. range = 0–8

With standardization of techniques, it is now possible to improve concordance between central and local laboratories17. Furthermore, excellent concordance (approaching 100% using IHC) between needle core biopsies and surgical specimens has been demonstrated by our institution (N = 338) and others (e.g. Boston: N = 56) when hormone receptors were measured18,19. This is important in current clinical practice as often ER and PR status obtaining from diagnostic needle core

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biopsies are required for management decision e.g. neoadjuvant setting, and in advanced disease when surgery for the primary cancer is not normally performed. Recently the quantitative reverse-transcriptase polymerase chain reaction (RT-PCR) by Oncotype DX20 assessing gene expression from RNA extracted from paraffin embedded tumor tissue, has also been employed to measure ER17. In this case-control series of 776 breast cancer patients from Eastern Cooperative Oncology Group (ECOG) study RT-PCR in whole sections was performed using this 21-gene assay. The concordances between IHC and RT-PCR measured in central laboratories for ER and PR were 91% and 90% respectively. 3. The First “Personalized Treatment” of Cancer (Estrogen Receptor Positive and Tamoxifen) The concept of hormone dependence of breast cancer can be seen in the first report of oophorectomy as treatment of advanced breast cancer over a century ago21. On June 15th 1895, George Beatson, a surgeon from Glasgow, took the ovaries and fallopian tubes out from a 33-year old lady with recurrent breast cancer which was inoperable, because “it was clear to him that the changes that take place in the mammary gland in the process of lactation are almost identical, up to a certain point, with what takes place in a cancerous mamma”. This resulted in a complete response at eight months – “…all vestiges of her previous cancerous disease had disappeared”. He carried out oophorectomy on another lady with a similar problem a few months later, without success. This was well before the discovery of ER and in today’s terminology; it is highly likely that his first patient had an ER positive cancer while the tumor in the second one was ER negative. The first non-steroidal anti-estrogen, ethanmoxytriphenol (MER25) was described by Lerner et al. in 195822. The compound never reached the clinic due to its toxicity. ICI 46,474 (now known as tamoxifen) was discovered in 1962 by Arthur Walpole at the Imperial Chemical Industries (ICI) Pharmaceuticals Division23-25. It was initially developed as a post-coital contraceptive in rats but was subsequently found to stimulate ovulation in humans suggesting that the pharmacology and

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physiology are different between rats and humans! Mary Cole and her colleagues at Christie Hospital in Manchester then demonstrated efficacy of ICI 46,474 in the treatment of postmenopausal advanced breast cancer26. Nolvadex, the original ICI brand of tamoxifen, was approved for clinical use to treat breast cancer in the UK in 1973, and similar approval by the Food and Drug Administration in the United States was obtained on 30th December 1977. Thereafter for over three decades, tamoxifen has improved survival in numerous women with breast cancer, both pre- and post-menopausal, in both early and advanced disease. Early use of tamoxifen as primary therapy (e.g. advanced disease, and in patients who are unfit or refuse surgery for primary disease) has been reviewed showing its effectiveness in ER positive breast cancer27,28. One of the earliest prospective adjuvant randomized trials carried out by NSABP, involving 1,891 women recruited from 1977, showed benefit of addition of tamoxifen to L-phenylalanine mustard and 5-fluorouracil and that the benefit was related to ER levels of the tumor14,29. The Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) has carried out quinquennial meta-analyses since 1985 of randomized trials of adjuvant therapies from the whole world (Oxford Overview). At the review undertaken in 2000, data from over 70 tamoxifen related trials randomizing over 80,000 patients worldwide has shown marked reduction in recurrence and breast cancer mortality with ER positive and unknown disease (Figure 2)30. The latest Oxford Overview in 2006 showed over 50% of reduction of recurrence and around 40% reduction of breast cancer mortality at 10 years in women taking five years of adjuvant tamoxifen for ER positive early breast cancer (Unpublished). The curves showing these benefits continue to diverge indicating a carryover effect after stopping tamoxifen. With the EBCTCG overview, it has been clearly shown that patients with ER poor tumors do not derive benefits from tamoxifen. There was no demonstrable difference in terms of recurrence and breast cancer mortality rates, irrespective of whether they had tamoxifen or not.

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Fig. 2. About five years of tamoxifen versus none in estrogen receptor positive (or unknown) disease in 10,386 women (20% estrogen receptor unknown): 15-year probabilities of recurrence and of breast cancer mortality.

Distinct benefit is also seen with ovarian function suppression or ablation. In a recent meta-analysis of randomized trials involving 9,022 premenopausal women with ER/PR positive (92% being ER positive) breast cancer recruited between 1987–2001 (median follow-up = 7.3 years), adjuvant use of a luteinizing hormone releasing hormone (LHRH) agonist (versus none) has been shown to significantly reduce recurrence (by 12.7%) and deaths after recurrence (by 15.1%) when used alone or in combination with other therapies31. 4. The Different Endocrine Treatment Modalities in Breast Cancer, Especially Aromatase Inhibitors Simplistically the ER needs to be activated through binding with circulating estradiol, following which ER-stimulated tumor growth occurs via various growth factor related pathways. Endocrine treatment modalities broadly operate by acting as an anti-estrogen interfering with the binding between estradiol and ER, or by depriving circulating estradiol (Figure 1).

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Tamoxifen is the prototype anti-estrogen though it possesses estrogen antagonistic (on breast) and agonistic (on endometrium and bone) properties. It can be classified as one of the selective ER modulators (SERMs). Raloxifene, another SERM, was found to reduce the risk of breast cancer when given in postmenopausal women with an intention to reduce osteoporosis32. Its role as a potential preventative agent is being investigated. Fulvestrant, a selective ER down-regulator (SERD), working via a novel mode of action, does not have any known estrogen agonistic properties. Tamoxifen works in both pre- and post-menopausal women with efficacy shown in both early and advanced disease while fulvestrant at present has only been approved for use in postmenopausal advanced breast cancer, following progression on a prior endocrine agent33-36. While this section will focus the discussion mainly on aromatase inhibitors, it is worth mentioning that apart from anti-estrogens and methods to achieve estrogen deprivation (as with an LHRH agonist and aromatase inhibitors in pre- and post-menopausal women respectively), there is evidence showing effectiveness of other endocrine treatment modalities. They include progestogens (e.g. megestrol acetate), high dose estrogens (e.g. ethinylestradiol) and discontinuation of an existing endocrine agent aiming to achieve a “withdrawal” response27. Aromatase inhibitors produce a state of estrogen deprivation due to inhibition of the aromatase enzyme system which controls the conversion of androgens to estradiol. This process occurs in the adrenal cortex and also peripherally such as fatty tissue. This class of drugs is ineffective on their own in premenopausal women owing to the high levels of estradiol produced from the ovaries. Peripheral suppression of estradiol production via this mechanism can potentially stimulate further surge of estradiol from the ovaries through negative feedback. Historically the first generation aromatase inhibitor aminoglutethimide is non-selective on the adrenal cortex and patients on treatment need to receive corticosteroid supplement and they tend to experience more side effects. Third generation aromatase inhibitors are highly selective with very good side effect profiles. They include non-steroidal (anastrozole and letrozole) and steroidal (exemestane) categories due to differences in their biochemical structures (Figure 3). These two classes of aromatase

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The Estrogen and Progesterone Receptors

NonNon-steroidal

Steroidal N

Anastrozole

CH3 O

Exemestane N

N

CH3

NC

CN O

H3C

CH3 H3C

CH3

CH2

N

Letrozole

N N

NC

CN

Fig. 3. Biochemical structures of third generation aromatase inhibitors.

inhibitors do not appear to have cross resistance and clinical data is available showing further endocrine response when a member from one group is used after progression on a prior agent from the other group37,38. To date these third generation aromatase inhibitors have been shown in randomized clinical trials to have superior efficacy over tamoxifen, in advanced39-43, as summarized in Table 227, neoadjuvant44, and adjuvant settings, for postmenopausal women with hormone receptor positive breast cancer. The latter includes using them upfront following surgery for early breast cancer (anastrozole and letrozole)45-47, switching after 2–3 years of tamoxifen (anastrozole and exemestane)48-50, or as extended adjuvant endocrine therapy after five years of tamoxifen (anastrozole and letrozole)51-53 (Table 3). As an adjuvant systemic therapy, although improved disease free survival (primary end point in all the adjuvant trials) and time to distant metastases were consistently shown across these trials and they are reasonably good surrogates for survival in these

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Table 2. Summary of results of randomized trials on aromatase inhibitors versus tamoxifen as first line endocrine therapy for advanced breast cancer in postmenopausal women. ANA TAM (European study)

ANA TAM (North American study)

ANA TAM (Combined study)

ANA TAM (Spanish study)

LET TAM (International study)

EXE TAM (European study)

340

328

171

182

511

510

121

117

453

455

31

32

Objective response (CR+PR)

33%

33%

21%

17%

29%

27%

34%

27%

30%*

20%*

42%

16%

Clinical benefit (CR+PR+SD ≥ 24 weeks)

56%

56%

59%

46%

57%

52%

49%*

38%*

58%

31%

Median TTP (months)

8.2

8.3

11.1*

5.6*

7.1

8.5

9.4*

6.0*

8.9

5.2

10.6*

5.3*

Dosage: Anastrozole (ANA) 1 mg od, Letrozole (LET) 2.5 mg od, Exemestane (EXE) 25 mg od, Tamoxifen (TAM) 20 mg od CR = Complete response PR = Partial response SD = Stable disease TTP = Time to progression * Statistically significant difference

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K.-L. Cheung

N

Table 3. Summary of results of randomized trials on aromatase inhibitors versus tamoxifen as adjuvant endocrine therapy for early breast cancer in postmenopausal women. ANA (ATAC)

Context

TAM

LET TAM (BIG 1-98)

TAM»EXE (IES)

Upfront

Upfront

Median follow-up (months)

100

N

TAM

TAM»ANA TAM (ABCSG, ARNO 95)

TAM»LET (MA 17)

Switch

Switch

Extended adjuvant

Extended adjuvant

51

55.7

28

30

62.3

2618 2598 (HR+ only)

2463 2459 (HR+ only)

2352 2372 (HR+/unknown)

1618 (HR+ only)

DFS (‘HR’)

0.85

0.82

0.76

0.6

0.58

0.62

OS (‘HR’)

ns

ns

ns

ns

ns (whole group) 0.61 (in node positive subgroup)

ns

Dosage: ‘HR’

Anastrozole (ANA) 1 mg od, Letrozole (LET) 2.5 mg od, Exemestane (EXE) 25 mg od, Tamoxifen (TAM) 20 mg od Hazard ratio (statistically significant in favor of aromatase inhibitor, unless stated as ‘ns’ (non-significant))

» ATAC BIG IES ARNO Switch

Sequence Arimidex, Tamoxifen, Alone or in Combination Breast International Group Intergroup Exemestane Study Group Arimidex Nolvadex Following 2-3 years of TAM

DFS OS ABCSG Extended adjuvant HR+

1606

2593 (HR+ only)

TAM

2594

TAM»ANA (ABCSG)

387 (HR+ only)

TAM

469

The Estrogen and Progesterone Receptors

Trials

Disease free survival Overall survival Austrian Breast and Colorectal Cancer Study Following 5 years of TAM Hormone receptor positive

31

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patients with relatively good prognosis i.e. postmenopausal women with ER positive tumors, overall survival benefit has not yet been clearly demonstrated. On the other hand there is a statistically significantly lower risk of some potentially life threatening conditions such as cerebrovascular and venous thromboembolic events as well as endometrial carcinoma, when compared with tamoxifen. The main problems of aromatase inhibitors are osteoporosis (e.g. fractures), musculoskeletal symptoms including joint pain, and vaginal dryness, most of which relate to the effect of estrogen deprivation, while tamoxifen has estrogen agonistic properties as described earlier. Monitoring of bone mineral density is therefore important when adjuvant aromatase inhibitors are used. Given the significant reduction in contra lateral breast cancers as shown in all these randomized trials when adjuvant aromatase inhibitors were compared with tamoxifen, their role is currently being investigated in the preventative setting. The International Breast Cancer Intervention Study (IBIS) II is underway comparing five years of anastrozole with placebo in postmenopausal women at risk of breast cancer. 5. The Relation between Estrogen Receptor/Progesterone Receptor Status and the Clinical Outcome of Different Endocrine Treatment Modalities in Breast Cancer The relationship between ER and PR has been looked at even in the late 1970s and early 1980s. The overall concordance of ER and PR was 70.2%, as demonstrated in an NSABP study of 1,887 patients with primary breast cancer13. Their relationship in relation to age is summarized in Table 4. Essentially, most patients (around 75% overall and more common in older patients) had tumors which were ER and/or PR positive. Specifically the ER negative but PR positive subgroup only formed a minority (12.8%). There is data based on adjuvant and neoadjuvant studies, including the Arimidex, Tamoxifen Alone and in Combination (ATAC) trial, suggesting that the efficacy of aromatase inhibitors might be comparable to that of tamoxifen when the tumor is both ER and PR positive, and especially when it is also HER2 negative54,55. However the data is based

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Table 4. Concordance between estrogen and progesterone receptors according to age. Receptor status

All patients (N = 1887)

(N = 790)

(N = 1097)

ER−, PR−

26.7%

30.9%

23.7%

ER−, PR+

12.8%

17.6%

9.4%

ER+, PR−

16.9%

13.0%

19.8%

ER+, PR+

43.5%

38.5%

47.1%

ER PR − +

< 50 years

≥ 50 years

Estrogen Receptor Progesterone Receptor 0–9 fmol ≥ 10 fmol

on retrospective analysis of sub-groups. Furthermore, no such phenomenon has been seen in the BIG1-98 study which compares letrozole with tamoxifen as adjuvant endocrine therapy46. On the other hand, in the most recent Oxford Overview where large datasets were available for meta-analysis, PR status has not been shown to be of any significant value in adjuvant endocrine therapy using five years of tamoxifen (unpublished). Data is not yet mature for aromatase inhibitors at this overview. Among the ER poor disease, tamoxifen has not been shown to reduce recurrence up to 10 years, and this phenomenon is observed in the ER poor and PR positive population. For patients with ER positive disease, there is no difference in the absolute reduction of recurrence at 10 years, whether the tumors are also PR positive (13.4%) or PR poor (15.6%). Only limited data is available for the pure anti-estrogen fulvestrant. While fulvestrant (given as 250 mg every four-weekly) has not been shown to be superior to tamoxifen as a first line endocrine therapy for advanced disease, retrospective analysis of the data in the same randomized trial has demonstrated a higher objective response rate for fulvestrant when compared with tamoxifen, if the tumors were both ER and PR positive56.

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It would therefore appear that ER status remains the most important predictor of endocrine response. While PR status may contribute, its effect may have been diluted in the ER positive disease. For patients with ER negative but PR positive tumors who form a minority group, where PR status may be of more clinical importance, large dataset is not available. Moreover, while the use of ER, PR and HER2 appear to have the potential in selecting the endocrine therapy of choice (e.g. tamoxifen versus aromatase inhibitors), currently available data does not substantiate a change in clinical practice yet. 6. Future Predictive Endocrine Biomarkers in Breast Cancer In modern clinical practice, it is becoming a standard for reporting ER, PR and HER2 at diagnosis. There are also recent interests on the pathological features and clinical behavior of the triple negative (ER, PR and HER2 all negative) breast cancer. Among all three, as described above, ER remains the most powerful predictive endocrine biomarker to date. The use of PR at present is limited except when the rare situation of ER negative/PR positive combination is encountered and when endocrine therapy may be a clinically appropriate option. While most HER2 positive tumors tend to be ER negative anyway, HER2 overexpression on its own appears to be associated with a lack of hormone sensitivity. Pilot work from our institution has shown correlation of HER2 positivity with shortened progression free survival (18 versus 21 months, p = 0.003) in patients with ER positive breast cancer receiving primary endocrine therapy (unpublished). A huge number of factors other than ER have been studied as potential biomarkers of hormone sensitivity or resistance. The other ER ie ER-β may be associated with hormone resistance, and so is the cell cycle regulatory protein cyclin D1 within the ER positive population57-59. Cross talks between the ER and growth factors mediated pathways are evident by observation of clinical efficacy of the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib in patients with ER positive advanced breast cancer who had prior treatment with tamoxifen60. In the same pilot work described above from our institution, over-expression of Ki-67, a measurement of proliferation, in

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ER positive tumors was associated with shorten progression free survival (17.5 versus 23 months, p = 0.042) when patients were treated with primary endocrine therapy (Unpublished). Similarly increased efficacy has been seen when the HER2 monoclonal antibody trastuzumab is added to anastrozole in advanced disease61. While biomarkers to predict response to endocrine therapy are available, though none is yet comparable in terms of power, to ER, biological modulation may be possible to select different endocrine agents among patients who have hormone sensitive tumors. The degree of hormone receptor positivity may play a role. In the neoadjuvant setting, patients with relatively weakly ER positive tumors, and/or HER2 positive tumors appeared to have better efficacy results with letrozole when compared to tamoxifen55. A study of around 2,000 elderly patients at our institution shows that most of them have strongly ER positive (H-score > 200) tumors62, and this may explain the lack of efficacy differences in an observational study when tamoxifen and anastrozole were compared as primary endocrine therapy for patients who refused or were unfit for surgery63. Low ER and/or PR positivity also suggests higher benefit with chemotherapy64. Similarly, ER positive and PR negative tumors may be more sensitive to treatment by an aromatase inhibitor when compared to tamoxifen, though criticisms exist as mentioned previously. The possible impact of PR on response to the pure anti-estrogen fulvestrant has already been discussed above56. While conclusions from all retrospective and sub-group analyses need to be applied with caution, the data is interesting and suggests a similar concept of possible biological modulation for selecting appropriate endocrine agents. Within the ER positive hormone sensitive population, tumors which are PR negative and HER2 positive may be more responsive to aromatase inhibitors than to tamoxifen, but the data is not mature yet to adopt this approach in clinical practice. In addition to refining biomarkers to predict hormone sensitivity, both overall and specific to different endocrine agents, pharmacogenomics has recently become important with a potential to “personalize” endocrine therapy for breast cancer. Tamoxifen is metabolized by the cytochrome P450 (CYP) 2D6 enzyme, into the potent anti-estrogen, endoxifen (Figure 4)65.

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Fig. 4. Metabolic pathway of tamoxifen.

Paroxetine, a selective serotonin re-uptake inhibitor, which is commonly used to treat hot flushes, can inhibit the action of CYP 2D6, hence reducing the level of endoxifen. This in turn potentially leads to reduced effectiveness of tamoxifen, and there is data suggesting a worsening clinical outcome. Genetic variations of CYP 2D6 have now been identified. The plasma level of endoxifen changes with the number of mutant alleles including the *4 allele, of the gene encoding CYP 2D6. Patients who are homozygous for the wild type (wt/wt) have the highest level while those with *4/*4 genotype have the lowest level of endoxifen. The latter group has also been found to have worse clinical outcome in terms of recurrence and survival66,67. As described earlier, anastrozole has been shown to produce additional benefit in terms of disease free survival when compared to tamoxifen in the ATAC trial. Such additional benefit is of similar magnitude to the difference due to genotype for CYP 2D6, suggesting that selection of patients for individual endocrine therapy can be potentially based on information from pharmacogenomics.

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References 1. O’Malley B. Thirty years of steroid hormone action: personal recollections of an investigator. Steroids 1995;60:490-8. 2. Jensen E, Greene G, Closs L, DeSombre E, Nadji M. Receptors reconsidered: a 20year perspective. Recent Progress in Hormone Research 1982;38:1-40. 3. Sanders S. The in vitro uptake of oestradiol in biopsies from 25 breast cancer patients. Acta Pathologica et Microbiologica Scandinavica 1968;74:301-2. 4. Johansson H, Terenius L, Thoren L. The binding of estradiol-17beta to human breast cancers and other tissues in vitro. Cancer Research 1970;30:692-8. 5. Jensen E, Block G, Smith S, Kyser K, DeSombre E. Estrogen receptors and breast cancer response to adrenalectomy. NCI Monographs 1971;34:55-67. 6. Howell T. New endocrine agents. Cancer Treat Rev 1997;23:S49-57. 7. Speirs V, Malone C, Walton D, Kerin M, Atkin S. Increased expression of estrogen receptor ß mRNA in tamoxifen-resistant breast cancer patients. Cancer Research 1999;59:5421-5424. 8. Wen D, You-Feng X, Mais D, Goldman M, McDonnell D. The A and B isoforms of the human progesterone receptor operate through distinct signaling pathways within target cells. Molecular Cellular Biology 1994;14:8356-64. 9. McCormack O, Harrison M, Kerin M, McCann A. Role of the Progesterone Receptor (PR) and the PR Isoforms in Breast Cancer. Critical Reviews in Oncogenetics 2007;13:283-302. 10. McCormack O, Chung W, Fitzpatrick P, Cooke F, Flynn B, Harrison M, et al. Progesterone receptor B promoter hypermethylation in sporadic breast cancer. Breast Cancer Research and Treatment 2007. 11. Robertson J, Bates K, Pearson D, Blamey R, Nicholson R. Comparison of two oestrogen receptor assays in the prediction of the clinical course of patients with advanced breast cancer. British Journal of Cancer 1992;65:727-30. 12. Saccani Jotti G, Johnston S, Salter J, Detre S, Dowsett M. Comparison of new immunohistochemical assay for oestrogen receptor in paraffin wax embedded breast carcinoma tissue with quantitative enzyme immunoassay. Journal of Clinical Pathology 1994;47:900-5. 13. Fisher B, Wickerham DL, Brown A, Redmond C, et al. Breast cancer estrogen and progesterone receptor values: their distribution, degree of concordance, and relation to number of positive axillary nodes. Journal of Clinical Oncology 1983;1:108-17. 14. Fisher B, Redmond C, Brown A, Wickerham DL, Wolmark N, Allegro J, et al. Influence of tumor estrogen and progesterone receptor levels on the response to tamoxifen and chemotherapy in primary breast cancer. Journal of Clinical Oncology 1983;1:93-107. 15. Howell A, Barnes D, Harland R, et al. Steroid-hormone receptors in survival after first relapse in breast cancer. Lancet 1984;1:588-591.

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16. Harvey J, Clark G, Osborne C, et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. Journal of Clinical Oncology 1999;115:44-58. 17. Badve SS, Baehner FL, Gray RP, Childs BH, Maddala T, Liu ML, et al. Estrogenand progesterone-receptor status in ECOG 2197: comparison of immunohistochemistry by local and central laboratories and quantitative reverse transcription polymerase chain reaction by central laboratory. Journal of Clinical Oncology 2008;26:2473-81. 18. Hodi Z, Chakrabarti J, Lee A, Ronan J, Elston C, Cheung K, et al. The reliability of assessment of oestrogen receptor expression on needle core biopsies of invasive carcinomas of the breast. Journal of Clinical Pathology 2007;60:299-302. 19. Jacobs T, Siziopikou K, Prioleau J, Raza S, Baum J, Hayes D, et al. Do prognostic marker studies on core needle biopsy specimens of breast carcinoma accurately reflect the marker status of the tumor? Modern Pathology 1998;11:259-63. 20. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine 2004;351:2817-26. 21. Beatson G. On the treatment of inoperable cases of carcinoma of the mamma: suggestion for a new method of treatment, with illustrative cases. Lancet 1896;2:104-107. 22. Lerner LJ, Holthaus JF, Thompson CR. A nonsteroidal estrogen antagnoist 1-(p-2diethylaminoethoxyphenyl)-1-phenyl-2-p-methoxyphenyl-ethanol. Endocrinology 1958;63:295-318. 23. Harper MJK, Walpole AL. Contrasting endocrine activities of cis and trans isomers in a series of substituted triphenylethylenes. Nature (London) 1966;212:87. 24. Harper MJK, Walpole AL. A new derivative of triphenylethylene: effect onn implantation and mode of action in rats. Journal of Reproduction and Fertility 1967;13:101-19. 25. Harper MJK, Walpole AL. Mode of action of ICI 46,474 in preventing implantation in rats. Journal of Endocrinology 1967;37:83-92. 26. Cole M, Jones C, Todd I. A new antioestrogenic agent in late breast cancer. An early appraisal of ICI 46,474. British Journal of Cancer 1971;25:270-275. 27. Cheung K. Endocrine therapy for breast cancer - An overview. Breast 2007;16: 327-43. 28. Cheung K, Howell A, Robertson J. Preoperative endocrine therapy for breast cancer. Endocrine-related Cancer 2000;7:131-141. 29. Fisher B, Redmond C, Brown A, et al. Treatment of primary breast cancer with chemotherapy and tamoxifen. New England Journal of Medicine 1981;305:1. 30. Early Breast Cancer Trialists’ Collaborative Group. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 2005;365:1687-717.

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31. Cuzick J, Ambroisine L, Davidson N, Jakesz R, Kaufmann M, Regan M, et al. Use of luteinising-hormone-releasing hormone agonists as adjuvant treatment in premenopausal patients with hormone-receptor-positive breast cancer: a metaanalysis of individual patient data from randomised adjuvant trials. Lancet 2007;369:1711-23. 32. Cummings S, Eckert S, Krueger K, et al. The effect of raloxifene on risk of breast cancer in postmenopausal women: results from the MORE randomized trial. JAMA 1999;281:2189-2197. 33. Cheung K, Robertson J. Fulvestrant. Expert Opinion in Investigational Drugs 2002;11:303-8. 34. Howell A, Robertson J, Quaresma Albano J, Aschermannova A, Mauriac L, Kleeberg U, et al. Fulvestrant, formerly ICI 182,780, is as effective as anastrozole in postmenopausal women with advanced breast cancer progressing after prior endocrine treatment. Journal of Clinical Oncology 2002;20:3396-3403. 35. Osborne C, Pippen J, Jones S, Parker L, Ellis M, Come S, et al. Double-blind, randomized trial comparing the efficacy and tolerability of fulvestrant versus anastrozole in postmenopausal women with advanced breast cancer progressing on prior endocrine therapy: results of a North American trial. Journal of Clinical Oncology 2002;20:3386-3395. 36. Robertson J, Osborne C, Howell A, Jones S, Mauriac L, Ellis M, et al. Fulvestrant versus anastrozole for the treatment of advanced breast carcinoma in postmenopausal women: a prospective combined analysis of two multicenter trials. Cancer 2003;98:229-238. 37. Lonning P, Bajetta E, Murray R, Tubiana-Hulin M, Eisenberg P, Mickiewicz E, et al. Activity of exemestane in metastatic breast cancer after failure of nonsteroidal aromatase inhibitors: a phase II trial. Journal of Clinical Oncology 2000;18: 2234-2244. 38. Bertelli G, Garrone O, Merlano M, Occelli M, Bertolotti L, Castiglione F, et al. Sequential treatment with exemestane and non-steroidal aromatase inhibitors in advanced breast cancer. Oncology 2005;69:471-7. 39. Bonneterre J, Thurlimann D, Robertson Jea. Anastrozole versus tamoxifen as firstline therapy for advanced breast cancer in 668 postmenopausal women: results of the Tamoxifen or Arimidex randomized group efficacy and tolerability study. Journal of Clinical Oncology 2000;18:3748-3757. 40. Nabholtz J, Buzdar A, Pollak M, et al. Arimidex Study Group: Anastrozole is superior to tamoxifen as first-line therapy for advanced breast cancer in postmenopausal women: results of a North American multicenter randomized trial. Journal of Clinical Oncology 2000;18:3758-3767. 41. Milla-Santos A, Milla L, Portella J, Rallo L, Pons M, Rodes E, et al. Anastrozole versus tamoxifen as first-line therapy in postmenopausal patients with hormonedependent advanced breast cancer: a prospective, randomized, phase III study. American Journal of Clinical Oncology 2003;26:317-322.

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42. Mouridsen H, Gershanovich M, Sun Y, Perez-Carrion R, Boni C, Monnier A, et al. Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group. Journal of Clinical Oncology 2001;19:2596-2606. 43. Paridaens R, Dirix L, Lohrisch C, Beex L, Nooij M, Cameron D, et al. European Organisation for the Research and Treatment of Cancer (EORTC) - Investigational Drug Branch for Breast Cancer (IDBBC). Mature results of a randomized phase II multicenter study of exemestane versus tamoxifen as first-line hormone therapy for postmenopausal women with metastatic breast cancer. Annals of Oncology 2003;14:1391-1398. 44. Eirmann W, Paepke S, Appfelstaedt J, Llombart-Cussac A, Eremin J, Vinholes J, et al. Letrozole Neo-Adjuvant Breast Cancer Study Group: Preoperative treatment of postmenopausal breast cancer patients with letrozole: a randomized double-blind multicenter study. Ann Oncol 2001;12:1527-1532. 45. Arimidex T, Alone or in Combination (ATAC) Trialists’ Group, Forbes J, Cuzick J, Buzdar A, Howell A, Tobias J, et al. Effect of anastrozole and tamoxifen as adjuvant treatment for early-stage breast cancer: 100-month analysis of the ATAC trial. Lancet Oncology 2008;9:45-53. 46. Breast International Group (BIG) 1-98 Collaborative Group, Thürlimann B, Keshaviah A, Coates A, Mouridsen H, Mauriac L, et al. A comparison of letrozole and tamoxifen in postmenopausal women with early breast cancer. New England Journal of Medicine 2005;353:2747-57. 47. Coates A, Keshaviah A, Thurlimann B, Mouridsen H, Mauriac L, Forbes J, et al. Five years of letrozole compared with tamoxifen as initial adjuvant therapy for postmenopausal women with endocrine-responsive early breast cancer: update of study BIG 1-98. Journal of Clinical Oncology 2007;25:486-92. 48. Coombes R, Hall E, Gibson L, et al. A randomized trial of exemestane after two to three years of tamoxifen therapy in postmenopausal women with primary breast cancer. New England Journal of Medicine 2004;350:1081-1092. 49. Jakesz R, Jonat W, Gnant M, Mittlboeck M, Greil R, Tausch C, et al. Switching of postmenopausal women with endocrine-responsive early breast cancer to anastrozole after 2 years’ adjuvant tamoxifen: combined results of ABCSG trial 8 and ARNO 95 trial. Lancet 2005;366:455-62. 50. Coombes R, Kilburn L, Snowdon C, Paridaens R, Coleman R, Jones S, et al. Survival and safety of exemestane versus tamoxifen after 2-3 years’ tamoxifen treatment (Intergroup Exemestane Study): a randomised controlled trial. Lancet 2007;369:559-70. 51. Goss P, Ingle J, Martino S, et al. A randomized trial of letrozole in postmenopausal women after five years of tamoxifen therapy for early-stage breast cancer. New England Journal of Medicine 2003;349:1793-1802.

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52. Jakesz R, Greil R, Gnant M, Schmid M, Kwasny W, Kubista E, et al. Extended adjuvant therapy with anastrozole among postmenopausal breast cancer patients: results from the randomized Austrian Breast and Colorectal Cancer Study Group Trial 6a. Journal of National Cancer Institute 2007;99:1845-53. 53. Goss P, Ingle J, Martino S, Robert N, Muss H, Piccart M, et al. Randomized trial of letrozole following tamoxifen as extended adjuvant therapy in receptor-positive breast cancer: updated findings from NCIC CTG MA-17. Journal of National Cancer Institute 2005;97:1262-71. 54. Dowsett M. Analysis of time to recurrence in the ATAC (arimidex, tamoxifen, alone or in combination) trial according to estrogen and progesterone receptor status. Breast Cancer Research and Treatment 2003;82:S7. 55. Ellis MJ, Coop A, Singh B, Mauriac L, Llombert-Cussac A, Janicke F, et al. Letrozole is more effective neoadjuvant endocrine therapy than tamoxifen for ErbB-1- and/or ErbB-2-positive, estrogen receptor-positive primary breast cancer: evidence from a phase III randomized trial.[see comment]. Journal of Clinical Oncology 2001;19(18):3808-16. 56. Howell A, Robertson J, Abram P, Lichinitser M, Elledge R, Bajetta E, et al. Comparison of fulvestrant versus tamoxifen for the treatment of advanced breast cancer in postmenopausal women previously untreated with endocrine therapy: a multinational, double-blind, randomized trial. Journal of Clinical Oncology 2004 22:1605-13. 57. Hui R, Ball J, Macmillan R, et al. EMSI gene expression in primary breast cancer: relationship cyclin D1 and oestrogen receptor expression and patient survival. Oncogene 1998;17:1053-1059. 58. Hui R, Cornish A, McClelland R, et al. Cyclin DI and estrogen receptor messenger RNA levels are positively correlated in primary breast cancer. Clinical cancer research: an official journal of the American Association for Cancer Research 1996;2:923-928. 59. Kenny F, Hui R, Musgrove E, et al. Overexpression of cyclin D1 messenger RNA predicts for poor prognosis in estrogen receptor-positive breast cancer. Clinical cancer research: an official journal of the American Association for Cancer Research 1999;5:2069-2076. 60. Robertson J, Gutteridge E, Cheung K, Owers R, Koehler M, Hamilton L, et al. Gefitinib (‘Iressa’, ZD1839) is active in acquired tamoxifen-resistant oestrogen receptor-positive and oestrogen receptor-negative breast cancer: results from a Phase II study. Journal of Clinical Oncology Program and Proceedings of ASCO Annual Meeting 2003. 61. Mackey J, et al. Trastuzumab prolongs progression-free survival in hormonedependent and HER2-positive metastatic breast cancer. Breast Cancer Research and Treatment 2006;100 (Suppl):S5-6.

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62. Cheung K, Wong A, Parker H, Li V, Winterbottom L, Morgan D, et al. Pathological features of primary breast cancer in the elderly based on needle core biopsies – A large series from a single centre. Critical Reviews in Oncology-Hematology 2008. 63. Ying M, Agrawal A, Winterbottom L, Morgan D, Ellis I, Cheung K. Anastrozole versus tamoxifen as primary endocrine therapy for early operable primary breast cancer in the elderly. Journal of Clinical Oncology ASCO Annual Meeting Proceedings Part I 2007;25:18S:11025. 64. Viale G, Regan M, Maiorano E, Mastropasqua M, Golouh R, Perin T, et al. Chemoendocrine compared with endocrine adjuvant therapies for node-negative breast cancer: predictive value of centrally reviewed expression of estrogen and progesterone receptors--International Breast Cancer Study Group. Journal of Clinical Oncology 2008;26:1404-10. 65. Stearns V, Johnson M, Rae J, Morocho A, Novielli A, Bhargava P, et al. Active tamoxifen metabolite plasma concentrations after coadministration of tamoxifen and the selective serotonin reuptake inhibitor paroxetine. Journal of National Cancer Institute 2003;95:1758-64. 66. Punglia R, Burstein H, Winer E, Weeks J. Pharmacogenomic variation of CYP2D6 and the choice of optimal adjuvant endocrine therapy for postmenopausal breast cancer: a modeling analysis. Journal of National Cancer Institute 2008;100:642-8. 67. Goetz M, Knox S, Suman V, Rae J, Safgren S, Ames M, et al. The impact of cytochrome P450 2D6 metabolism in women receiving adjuvant tamoxifen. Breast Cancer Research and Treatment 2007;101:113-21.

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CHAPTER 3 THE DEVELOPMENT OF THE HERCEPTEST™ — FROM BENCH TO BEDSIDE Jan Trøst Jørgensen Dx-Rx Institute Baunevaenget 76 DK-3480 Fredensborg, Denmark E-mail: [email protected] Henrik Winther ImmunoHistology R&D Business Area, Dako A/S Produktionsvej 42, DK-2600 Glostrup, Denmark E-mail: [email protected] The development of the humanized monoclonal antibody trastuzumab (Herceptin®) has become a model for successful development in targeted cancer therapy. Throughout all phases of the clinical development program, a clinical trials assay (CTA) was used for immunohistochemical detection of HER2 overexpression in the tumor specimens to identify patients with metastatic breast cancer who were eligible for treatment with trastuzumab. While the clinical phase III study was conducted with trastuzumab, a new simplified IHC assay (HercepTest™) was developed by Dako. Studies were performed in order to demonstrate that the HercepTest™ assay was concordant with the CTA and could be utilized as an aid in the assessment of patients for whom trastuzumab treatment should be considered. The final concordance study (n = 548) performed on a stratified sampled specimen, with a relative high number of 2+ cases, showed an agreement between the HercepTest™ and the CTA of 79% (95% CI: 76%-82%). The HercepTest™ was approved by FDA in September 1998 simultaneously with trastuzumab in a novel coordinated process between the Center for Drug Evaluation and Research and the Center for Devices and Radiological Health. The parallel drug-diagnostic

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development model used for trastuzumab by Genentech has become the standard for today’s development of targeted cancer drugs.

1. Introduction Trastuzumab (Herceptin®, Genentech), a humanized monoclonal antibody directed against the extracellular domain of the HER2 (ErbB2) protein, received the US Food and Drug Administration (FDA) approval in September 1998 for the treatment of patients with metastatic breast cancer whose tumors overexpress the HER2 protein (1). A technically complex assay, the clinical trial assay (CTA), was used throughout all phases of the trastuzumab clinical development program. This assay was based on an immunohistochemical (IHC) detection of HER2 overexpression in the breast tumor specimens and used to identify patients who were eligible for treatment with trastuzumab in the different clinical trials organized by Genentech. During phase III of the clinical development of trastuzumab a new simplified IHC assay, the HercepTest™, was developed by Dako, and studies were performed to demonstrate that this assay was concordant with the CTA. The HercepTest™ assay was approved by FDA in September 1998 simultaneously with trastuzumab (2). The background for and the development of the assay has only been described to a limited extent previously, and the purpose of this review is to summarize the development of the HercepTest™. 2. Background for the Development of the HercepTest™ The discovery of the HER2 gene and its association with breast cancer must be regarded as one of the major findings within cancer research in the 20th century (3, 4, 5). Dennis Slamon and colleagues showed that tumors from 30% of women with breast cancer had an amplification of the HER2 gene and that this was associated with a poor prognosis with respect to both overall survival and time to relapse (5). The HER2 gene is located on chromosome 17 where it codes for the HER2 receptor protein, which is expressed at the cell surface. An amplification of the HER2 gene will often lead to an overexpression of the receptor protein. The

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number of HER2 receptors in normal human breast epithelial cells usually range from 20,000 to 50,000, but in cases of overexpression the number may increase up to two million per cell (6). The use of an IHC assay to identify the patients with overexpression of the HER2 receptor turned out to be crucial for the development of trastuzumab. Without the use of such an assay, the clinical development would have been very difficult due to fact that the efficacy of trastuzumab in an unselected patient population would have been very low (7). The clinical trials that have subsequently been performed with trastuzumab in patients with HER2 overexpression or HER2 amplification have shown that it is an effective drug in the treatment of breast cancer (8, 9, 10). Especially in primary breast cancer, in combination with chemotherapy, different clinical trials have shown that trastuzumab reduces the three-year risk of recurrence by about 50% (11). The use of a companion diagnostic/pharmacodiagnostic assay for selection of an appropriate patient population has gradually become an integrated part of the development of new targeted anticancer drugs. The coordinated FDA approval of trastuzumab and HercepTest™ has often been described as an example of the successful use of the parallel drugdiagnostic co-development model. Since the approval of trastuzumab and HercepTest™ several other HER2 assays, both IHC, fluorescent in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) have obtained FDA approval. These assays are listed in Table 1. However, so Table 1. HER2 assays that have obtained FDA approval. Type of Assay IHC

Assay HercepTest™ (Dako) PathWay™ (Ventana)

FISH

HER2 FISH pharmDx™ (Dako) INFORM® HER2/neu Probe (Ventana) PathVysion® HER2 DNA Probe Kit (Abbott Diagnostics)

CISH

SPOT-Light HER2 CISH™ Kit (Invitrogen)

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far only a limited number of other assays, beside HER2/HER2, have been developed and approved having a predictive claim for selection of patients for treatment with other targeted anticancer drugs. Since HER2 overexpression occurs only in a subset of patients with breast cancer, preselection with a pharmacodiagnostic assay were used throughout the clinical development of trastuzumab in order to identify patients eligible for treatment (7). Because trastuzumab was developed to target the HER2 protein on the tumor cell membrane, IHC was initially considered the method of choice. For this purpose Genentech developed the CTA, which was a relatively technically complex assay, consisting of two separate immunohistochemical procedures. The CTA was used in a single centralized laboratory, which tested more than 7000 cases during the clinical trials with trastuzumab, allowing precise definition of the study population. However, the complexity of this assay precluded its accurate and practical use by the general pathology community, and in order to simplify it the HercepTest™ was developed by Dako and subsequently shown to be adequately concordant with the CTA (12). The CTA assays utilized two different mouse monoclonal antibodies, the parent murine antibody to trastuzumab, clone 4D5 and anti HER2, clone CB11. The majority of specimens from the clinical trials with trastuzumab were stained using the 4D5 antibody only. However, approximately 25% were stained using both 4D5 and CB11, where as only a minor group of specimens (1%) were stained using the CB11 alone. The CTA consisted of more than 35 steps, requiring four or more tissue sections and the assay was not considered robust for being developed for a broader use. During the trastuzumab clinical trial program a scoring system adapted after Slamon et al. was implemented (13). Table 2 and Figure 1 give a short description of the scoring system. Patients with mamma carcinoma tumor having a score of 2+ and 3+ respectively were considered positive and were eligible for entering into the clinical trials with trastuzumab.

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Table 2. Scoring system for HER2 Protein Staining Pattern. Note, that the recent issued ASCO/CAP guideline for HER2 testing has increased the cut-off value for membrane staining from 10 to 30% (14, 15). IHC HER2 Scores Score

HER2 Protein Overexpression Assessment

Staining Pattern

Negative

No staining is observed, or membrane staining is observed in 10% of tumor cells. The cells exhibit incomplete membrane staining.

2+

Weakly Positive (Equivocal)

A weak to moderate complete membrane staining is observed in >10% of tumor cells.

3+

Strongly Positive

A strong complete membrane staining is observed in >10% of tumor cells.

0

Score 0 ~ Negative

Score 1+ ~ Negative

Score 2+ ~ Weakly Positive (Equivocal)

Score 3+ ~ Strongly Positive

Fig. 1. Mamma carcinoma specimens with different degrees of HER2 overexpression stained with HercepTest™.

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3. Assay Development 3.1. Establishment of control cell lines Human breast cancer cell lines SK-BR-3, MDA-175, and MDA-231 were used as cell line controls for immunohistochemistry analysis to allow semi-quantitative assessment of levels of HER2 overexpression as an integrated parts of the CTA as well as of the HercepTest™. HER2 receptor density of these cell lines were determined and normal or low HER2 expression was represented by the MDA-231 cell line. The number of HER2 receptors present on MDA-231 cells corresponds to approximately 20,000 receptors per cell, which is similar to that found on normal human breast epithelial cells (6). The absence of detectable staining of this cell line is shown in Figure 2a and it is assigned an IHC score of 0. When FISH analysis was performed on the MDA-231 cell line the expected normal gene range for a non-amplified cell was demonstrated, which corresponds to a ratio of the HER2 gene copy number to centromere 17 (CEN17) less or equal to 2. The MDA-175 cell line is a 1+ or moderate HER2 overexpressing cell line standard and is observed to express ~92,000 HER2 receptors per cell, which is 4-5 times higher than that measured on normal breast epithelial cells or the MDA231 cell line. The number of HER2 gene copies measured by FISH in the MDA175 cell line is 3.0. When this value was corrected for CEN17, a ratio of 1.3 was obtained. The typical IHC staining pattern for this cell line is shown in Figure 2b. High HER2 protein expression is represented by the SK-BR-3 cell line and exhibited at least 100-fold higher HER2 levels than that observed with MDA-231 cells and was assigned a 3+ IHC score (Figure 2c). The FISH analysis of SK-BR-3 cells showed a HER2 gene amplification with a HER2/CEN17 ration higher than 2. Further, Table 3 provides a summary of Scatchard- and FISH analysis of breast cancer cell lines used as performance control standards.

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(a)

(b)

(c) Fig. 2. Breast cancer control cell lines for HER2 protein overexpression. (a) MDA-231 stained with HercepTest™. No staining of the membrane is observed, (b) MDA-175 stained with HercepTest™. A faint perceptible staining of the membrane is observed. The cells are only stained in part of their membrane, and (c) SK-BR-3 stained with HercepTest™. A strong staining of the entire membrane is observed.

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Table 3. Summary of Scatchard and FISH analyses of breast cancer cell lines used as performance control standards. Cell Line

IHC Score

Receptors/Cell

HER2 Gene Copies/Cell

HER2/Chrom 17 Copies/Cell

MDA-231

0

21,600 ± 6,700

2.4 ± 0.2

1.1 ± 0.1

MDA-175

1+

92,400 ± 12,000

3.0 ± 0.4

1.3 ± 0.1

SK-BR-3

3+

2,390,000 ± 130,000

15.3 ± 3.9

4.5 ± 1.1

3.2. Pilot concordance study During the initial development of the HercepTest™ IHC assay the selection of the optimal primary antibody, epitope retrieval and staining procedure took place. Two different primary antibodies were assessed as prototypes, and in order to enhance and simplify the performance of the assay a two-step immunohistochemical staining procedure using the Envision™ visualization systems (Dako) was incorporated into each prototype kit. One kit contained a mouse monoclonal antibody to a linear peptide corresponding to the cytoplasmic domain of HER2, clone CB11, and the other contained the affinity purified, rabbit polyclonal antibody to a similar HER2 peptide, for simplicity already at this stage designated HercepTest™. A previous internal study at Dako had shown the Dako polyclonal antibody was superior with regard to immunohistochemical specificity and sensitivity to either of the two mouse monoclonal antibodies to HER2; clone 4D5 or clone CB11. To determine whether the CB11 or HercepTest™ performed similarly to the CTA, a pilot study was conducted. For this study 197 specimens, obtained from a single clinical site, were screened for HER2 overexpression using the CTA. From this screened cohort, 103 specimens were selected for the comparisons with CB11 and the HercepTest™ assay. Out of these 103 specimens, 51 were determined HER2 positive by the CTA, and 52 were HER2 negative. These 52

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negative specimens were selected randomly for the pool of CTA negative specimens. A perfect match between CB11 and the CTA on the IHC scores (0, 1+, 2+, 3+) was achieved in 74 of the 103 specimens (72%). If the data was reduced to a binary concordance analysis of HER2-positive or HER2-negative, the overall agreement of the CB11 kit to the CTA was 90% with a 95% confidence interval of 83-94%. For the discrepant specimens, 8 (8%) of them were rated positive by the CTA and negative by the CB11 kit. Conversely, 2 (2%) of the specimens were rated negative by the CTA and positive by the CB11 kit. The comparison of HercepTest™ to the CTA showed a similar level of concordance. A perfect match was observed in 71% of the cases tested. The overall binary concordance was calculated as 89% with a 95% confidence interval of 82-94%. The number of discrepant specimens was similar to that observed with the CB11 kit, specifically 8 (8%) of the samples were rated positive by the CTA and negative by HercepTest™ kit. Conversely, 3 (3%) of the specimens were rated negative by the CTA and positive by HercepTest™. Although the rate of false negative readings was slightly higher than the rate of false positive readings either by either the CB11 kit or HercepTest™ compared to the CTA, both discordant populations were relatively small. Because cross-reactive staining was occasionally observed with the CB11 kit, the Dako polyclonal affinity isolated antibody was selected for further development of the HercepTest™ assay. 3.3. FISH/IHC concordance To confirm the biological relevance of HER2 protein expression based on IHC staining intensity with HercepTest™ in comparison to HER2 gene amplification, determined by FISH, a study was conducted using both techniques on 37 breast tumor specimens. The results of this study are shown in Figure 3. IHC staining was reported using the same 0 to 3+ scale as utilized in the trastuzumab clinical development program. Two different cut-off values were used for the FISH analysis and these values are shown as horizontal lines on the graph. One cut-off value for HER2

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gene amplification was based on an earlier feasibility study conducted using normal breast tissue. A value of 1.3 represents the observed mean value of 1.1 (n = 36) for normal tissue plus three standard deviations of this value. Moreover, 1.3 is also the value measured for the 1+ standard cell line MDA-175. When analyzed in this manner, as shown in the inset of Figure 3, the level of agreement between the HercepTest™ and HER2 FISH is 89%. This two-probe FISH system (PathVysion® HER2 DNA Probe Kit, Abbott Diagnostics) used in the concordance study was later approved by the FDA. With respect to the approval of this assay patients were classified as HER2 amplified if the HER2/CEN17 ratio was ≥ 2.0. A cut-off value of 2.0 is also shown in Figure 3. The calculated concordance (see inset to Figure 3) with HercepTest™ staining using this value is 86%. In a separate study with a cohort of 192 specimens comparing clone 4D5 alone based IHC to FISH a concordance of 83% was obtained (data not shown). FISH 1.3

IHC-NEG

18

1

IHC-POS

3

15

FISH Score

FISH 2.0

IHC-NEG

19

0

IHC-POS

5

13

10

Concordance 86 % 89 %

1 0

1+

2+

3+

IHC Score Fig. 3. Concordance analyses between HER2 IHC (HercepTest™) and HER2 FISH (n = 37). For the HER2 FISH assay two cut-off values of 1.3 and 2.0, respectively, were used.

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3.4. Concordance study comparing HercepTest™ to the clinical trials assay As previously described the CTA was a combination of two individual IHC procedures, which made it a challenging standard to duplicate. In order to determine prospectively an “acceptable” level of concordance between the CTA and the HercepTest™ assay, the results of two different studies were taken into consideration. Firstly, that the result of the concordance between the two IHC assays that constitute the CTA was 89%. In this context, it is worth mentioning that the cohort in this concordance study was selected among the patients screened for the trastuzumab clinical trials, thus representing the distribution of positive and negative specimens in a metastatic breast cancer population. However, the mix of HER2 positive to negative specimens in the final concordance study between the CTA and the HercepTest™ assay was significantly different from the patient population in the trastuzumab clinical trials, as seen in Figure 4. Secondly, the overall concordance in the pilot studies was similarly 89%. Taking these results into consideration together with the realization that the tumor blocks would be obtained from multiple sites, it was predetermined that a concordance level above 75% would be an adequate level of agreement between the HercepTest™ and the CTA. Assuming that a concordance level less than or equal to 75% reflects that the assays do not show an adequate level of agreement, a total sample size of 600 would be sufficient to provide 90% power to detect a level that is 5% higher than the unacceptable concordance level. In other words, if the actual concordance rate is 80%, this trial will have 90% power to reach the 5% significance level. To demonstrate concordance between the CTA and HercepTest™, 1198 breast cancer specimens were collected from the Cooperative Breast Cancer Tissue Resource following approval of the study by the National Cancer Institute. Out of these 1189 specimens 274 were determined HER2 positive by the CTA, i.e. their staining intensity score was 2+ or 3+, whereas 915 were HER2 negative, i.e. 0 or 1+ staining intensity score. All the 274 HER2 positive specimens and a random sample of 274 HER2 negative specimens were selected for the

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Patients (%)

60 Herceptin Clinical Trial N = 1000 Concordance Trial N = 548

50 40 30 20 10 0 0

1+

2+

3+

IHC Score Fig. 4. Trastuzumab clinical trial population compared to concordance study population.

concordance study between the CTA and the HercepTest™ assay. The IHC scores for these 548 specimens compared to the specimens from the patient population of the trastuzumab clinical trial are shown in Figure 4. When comparing the distribution of the two study populations, the proportion of equivocal cases (2+) is somewhat higher in the concordance study. A high proportion of equivocal cases in a concordance study will always make it more difficult to achieve a perfect agreement between two assays under any circumstances. The result of the concordance study between the CTA and HercepTest™ assay is depicted in Table 4 (12). The overall concordance between the two assays was 69%. When looking at the individual IHC scores, the poorest agreement between the two assays was obtained for the 2+ group, which was also expected. When the data from Table 4 was reduced to a binary concordance analysis of HER2 positive and negative, as shown in Table 5, the concordance increased to 79%, which was significantly higher than the pre-specified “unacceptable” level for the study (p = 0.0153, one-sided test of proportion, no continuity correction). The two-sided 95% confidence interval is 76% to 82% (12).

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Table 4. A 3 x 3 concordance matrix for HercepTest™ and Clinical Trial Assay (n = 548). Clinical Trial Assay HercepTest™

0-1

2+

3+

TOTAL

0-1

3+

215 53 6

50 57 36

8 16 107

273 126 149

TOTAL

274

143

131

548

2+

Table 5. A 2 x 2 concordance matrix (positive/negative) for HercepTest™ and Clinical Trial Assay (n = 548). Clinical Trial Assay HercepTest™

Positive

Negative

TOTAL

Positive

216

59

275

Negative

58

215

273

TOTAL

274

274

548

4. Regulatory Approval Process With respect to the regulatory approval process a pre-investigational device exemptions (pre-IDE) meeting took place in the summer of 1997 and the protocol for the concordance study was presented and discussed and subsequently updated based on the recommendations from the FDA. In May 1998 Genentech and Dako almost simultaneously filed a biologic license application (BLA) for trastuzumab and a pre-market approval (PMA) for HercepTest™, respectively. The approval of both drug and the diagnostic assay came on September 25th the same year following a positive opinion at an Oncologic Drugs Advisory Committee Meeting and a Device Panel Meeting organized by the Center for Drug Evaluation and Research (CBER) and the Center for Devices and Radiological Health (CDRH), respectively. The intended use granted for HercepTest™

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by the FDA had the following wording: “The device is a semiquantitative immunohistochemical assay to determine HER2 overexpression in breast cancer tissues routinely processed for histological evaluation. The HercepTest™ is indicated as an aid in the assessment of patients for whom Herceptin® (trastuzumab) treatment is being considered (see Herceptin® package insert)” (12). The development of trastuzumab is often held up as a model for successful development in targeted drug cancer therapy. When Genentech developed trastuzumab they realized early on that it was important to develop a diagnostic test that could select the patients who might response to the drug. A statistical sample size calculations based on data from one of the clinical trial that was used to grant the approval for the use of trastuzumab in metastatic breast cancer has subsequently substantiated that this was a very important decision (8, 16). The parallel drug-diagnostic development model has later been adapted by FDA, when they issued a concept paper on drug-diagnostic co-development in 2005 (17). Today it is impossible to talk about targeted anticancer therapy without integrating a companion diagnostic/pharmacodiagnostic assay that can identify the patients that are likely to respond to the drug in question, and the HercepTest™ paved the way for this approach (7, 18). Today the different HER2 tests are not only used to identify patients for treatment with trastuzumab, but also with lapatinib (Tykerb®, GlaxoSmithKline), a novel small molecule tyrosine kinase inhibitor that targets both HER2 and EGFR, which is used for the treatment of metastatic breast cancer (18). 5. Implementation in the Marked Place After the approval of and the release of HercepTest™ for sale a training program for the implementation of the assay in the pathology laboratories and the use of the scoring system was implemented. A booklet containing photomicrographs of stained mammary carcinoma specimens were likewise distributed among pathologists. The HercepTest™ Kit included control slides to be enclosed to ensure valid staining runs. At the release of the HercepTest™ adherence to the detailed staining procedure and the heat induced epitope retrieval procedure was a

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challenge for many laboratories. In Great Britain testing was coordinated, and for the first several years only three laboratories participated in the testing, all using the HercepTest™. Alignment between the three laboratories ensured a very high level of agreement among these laboratories (19). In many countries ring studies were initiated to ensure optimal implementation of the HER2 testing in order to avoid false positive and false negative cases. Also quality assurance programs were initiated to align HER2 testing. Apart from challenges related to the technical part of the HercepTest™ procedure also the scoring has been seen as a challenge for many pathologists. Especially borderline cases caused problems mainly for pathologists scoring fewer cases on monthly basis. Mammary carcinoma specimens having a score of 2+ and 1+, respectively, have been found to have gene amplification in 24% and 7% of the cases tested (20). Several studies have documented differences in the scoring between laboratories testing a small number of mammary carcinoma specimens for HER2 overexpression compared to a larger number. In 2005 the American Society of Clinical Oncology (ASCO) and the College of American Pathologist (CAP) initiated discussion among a selected group of pathologists in order to establish more detailed guidelines related to HER2 testing using immunohistochemistry as well as FISH. These guidelines also included a description of proficiency-testing to be implemented in laboratories offering HER2 testing. These guidelines were published early 2007 in both the Journal of Oncology (14) and Archives of Pathology and Laboratory Medicine (15), and subsequently both CAP and ASCO organized several educational lectures. Although the published guidelines differ from the package inserts for the FDA approved IHC and FISH assays, especially with respect to the scorings of the tissue slides, it is the expectation that together with the ongoing education they will ensure a higher quality of the HER2 testing in the future. Also in Europe proficiency-testing for HER2 has been implemented by organizations like the Nordic immunohistochemical Quality Control (NordiQC) and the United Kingdom National External Quality Assessment Service (UK NEQAS) (21, 22). From a recent proficiency-testing conducted by UK NEQAS it was concluded that rigorous quality control procedures for HER2 testing are needed, and that

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a validation of results from less experienced laboratories by a centralized reference laboratory is desirable (23). 6. Conclusion The HER2 IHC assay played a very crucial role in the development of trastuzumab. At a very early stage of the development Genentech decided to develop a companion diagnostic/pharmacodiagnostic assay that could identify the patients who were likely to respond to the trastuzumab. If such an assay had not existed, it is very likely that trastuzumab would have been discarded in the clinical development phase because of lack of efficacy in an unselected patient population, and today we would have been without a very important anticancer drug for the treatment of breast cancer. Acknowledgments We would like to thank Dr. Jon Askaa, Genentech Inc. for his invaluable help in relation to the preparation of the manuscript. Further, we would like to thank Dako Denmark A/S for the permission to use the microscopic images of tissue and cell lines stained with the HercepTest™. References 1. Product approval information: letter to Genentech dated September 25, 1998, for Trastuzumab (Herceptin®). (http://www.fda.gov/Cder/foi/appletter/1998/trasgen092598l.pdf). Accessed July 2008. 2. Product approval information: letter to Dako dated September 25, 1998, for the DAKO HercepTest™. (http://www.fda.gov/cdrh/pdf/p980018.pdf). Accessed July 2008. 3. Bargmann CI, Hung MC, Weinberg RA. The neu oncogene encodes an epidermal growth factor receptor-related protein. Nature1986; 319: 226-230. 4. Yamamoto T, Ikawa S, Akiyama T, Semba K, Nomura N, Miyajima N, et al. Similarity of protein encoded by the human c-erb-B-2 gene to epidermal growth factor receptor. Nature 1986; 319: 230-235.

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5. Slamon DJ, Clark GM, Wong SG, Levin WJ, Ullrich A, McGuire A. Human breast cancer: correlation of relapse and survival with amplification of the HER-2/neu oncogene. Science 1987; 235: 177-182. 6. Slamon D, Pegram M. Rationale for Trastuzumab (Herceptin) in adjuvant breast cancer trials. Semin Oncol 2001; 28 (Suppl 3): 13-19. 7. Hortobagyi GN. Opportunities and challenges in the development of targeted therapies. Semin Oncol 2004; 31 (Suppl 3): 21-27. 8. Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V, Bajamonde A et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001; 344: 783-792. 9. Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE, Davidson NE, Tan-Chiu, E, Martino S, Soonmyung P, Kaufmann PA et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positve breast cancer. N Engl J Med 2005; 353: 1673-84. 10. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, Goldhirsch A, Untch M, Smith I, Gianni L, Baselga J, Bell R, Jackisch C et al. Trastuzumab after adjuvant chemotherapy in HER2-positve breast cancer. N Engl J Med 2005; 353: 1659-72. 11. Baselga J, Perez EA, Pienkowski T, Bell R. Adjuvant trastuzumab: a milestone in the treatment of HER-2-positive early breast cancer. Oncologist 2006; 11 (Suppl 1): 4-12. 12. HercepTest™, Package Insert, Dako. (http://www.dakousa.com/prod_downloadpackageinsert.pdf?objectid=114972002). Accessed July 2008. 13. Slamon DJ, Godolphin W, Jones LA, Holt JA, Wong SG, Keith DE, Levin WJ, Stuart SG, Udove J, Ullrich A, et al. Studies of the HER-2/neu proto-oncogene in human breast and ovarian cancer. Science 1989; 244: 707-712. 14. Wolff AC, Hammond ME, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, Dowsett M, Fitzgibbons PL, Hanna WM et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2007; 25: 118-145. 15. Wolff AC, Hammond ME, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, Dowsett M, Fitzgibbons PL, Hanna WM et al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer. Arch Pathol Lab Med 2007; 131(1): 18. 16. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 2004; 10: 6759-6763. 17. Drug-Diagnostic Co-Development Concept Paper. Draft. Food and Drug Administration (FDA) April 2005. (http://www.fda.gov/cder/genomics/pharmacoconceptfn.pdf). Accessed August 2008.

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18. Jørgensen JT, Nielsen KV, Ejlertsen B. Pharmacodiagnostics and targeted therapies – A rational approach for individualizing medical anti-cancer therapy in breast cancer. Oncologist 2007; 12: 397-405. 19. Ellis IO, Dowsett M, Bartlett J, Walker R, Cooke T, Gullick W, Gusterson B, Mallon E, Lee PB. Recommendations for HER2 testing in the UK. J Clin Pathol 2000; 53: 890-2. 20. Dybdal N, Leiberman G, Anderson S, McCune B, Bajamonde A, Cohen RL, Mass RD, Sanders C, Press MF. Determination of HER2 gene amplification by fluorescence in situ hybridization and concordance with the clinical trials immunohistochemical assay in women with metastatic breast cancer evaluated for treatment with trastuzumab. Breast Cancer Res Treat 2005; 93: 3-11. 21. Nordic immunohistochemical Quality Control (http://www.nordiqc.org). Accessed October 2008. 22. United Kingdom National External Quality Assessment Service. (http://www.ukneqas.org.uk/content/Pageserver.asp). Accessed October 2008. 23. Dowsett M, Hanna WM, Kockx M, Penault-Llorca F, Rüschoff J, Gutjahr T, Habben K, van de Vijver MJ. Standardization on HER2 testing: Results of an international proficiency-testing ring study. Mod Pathol 2007; 20: 584-591.

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CHAPTER 4 PREDICTIVE BIOMARKERS AND TRANSLATIONAL CANCER MEDICINE

Anne-Sofie Schrohl, Pernille Bræmer Hertel, Maj-Britt Jensen and Nils Brünner Section of Pathobiology, Department of Veterinary Disease Biology Faculty of Life Sciences, University of Copenhagen and Danish Breast Cancer Cooperative Group, Copenhagen, Denmark E-mail: [email protected] Developing biomarkers for use in the clinical setting is a long and strenuous process. Several preclinical and clinical studies are needed to prove the value of a predictive marker for clinical decision-making. Overall, the evaluation must prove the utility and the reliability of the candidate biomarker. This chapter will describe the theory behind development of predictive biomarkers and in addition give an example on predictive biomarker development based on our experience with Tissue Inhibitor of Metalloproteinases. The chapter will cover 10 of the most important issues to be considered when developing and validating predictive biomarkers in cancer (1) describe the clinical need; (2) specify a research hypothesis to guide the research; (3) test the hypothesis in preclinical models; (4) select or develop an analytical platform; (5) identify available clinical samples and perform a retrospective study of the potential biomarker; (6) refine the assay and perform an additional retrospective study if necessary; (6a) if necessary modify the analytical platform, study the preanalytical variables; (6b) perform an additional retrospective study employing the improved assay; (7) develop the assay to standards compatible with daily clinical routine; (8) perform a prospective study with the biomarker as a secondary aim; (9) design a clinical trial including the biomarker; (10) implement the biomarker in daily routine diagnostics.

61

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1. Introduction “Translational cancer research transforms scientific discoveries arising from laboratory, clinical, or population-based studies into clinical applications to reduce cancer incidence, morbidity, and mortality.” -National Cancer Institute As stated above translational research relates to the transfer of new discoveries and knowledge into clinically useful applications. Often, this type of research is described as “bench-to-bedside” emphasizing the process of transforming basic knowledge into applications that improve treatment and outcome for patients. Discovery, development and clinical implementation of most predictive biomarkers include a large number of “bench-to bedside” steps and these will be described in the present chapter which will also illustrate that translational research requires a multidisciplinary team including research scientists, medical doctors and statisticians who should all be included from the beginning. 2. Rationale A key point in translational research is the coupling of research activities with a well-described clinical need. Thus, from the beginning until the findings are translated into a useful clinical application the clinical need or question must guide the decisions made during the process. At present, there appears to be two major options for improving medical treatment of cancer patients. One option is to develop new therapeutic approaches. For example, new types of chemotherapeutics can be developed with mechanisms of action different from former drugs, better pharmacokinetic and toxicological features, targeting new molecules and pathways or introducing new strategies such as immunotherapy. An alternative path, however, is to introduce predictive biomarkers to be used for selecting patients for specific therapies thereby optimizing treatment effects in subpopulations of patients. It is anticipated that approximately 50% of cancer patients receiving systemic chemotherapy do not benefit from this treatment but still they may suffer from the side effects related to the treatment. By applying predictive biomarkers, patients who have a high or low likelihood of

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obtaining benefit from the treatment can be identified and those having a low or even non-existing chance of disease improvement by the treatment should be spared the burden of an ineffective treatment and instead be offered an alternative one. Thus, with validated predictive markers available the physician is capable of directing the most efficient treatment to the individual patients. Moreover, this will reduce the cost of ineffective therapies. Often, new predictive markers are developed for selection of cancer patients for new targeted treatment modalities, but predictive markers could also be used to transform classical chemotherapeutics into targeted drugs. Based on the available literature it is anticipated that the patient benefit arising from the use of predictive biomarkers will be at least of the same magnitude as the benefit related to the introduction of a new class of drugs or a new drug combination. The following describe the standard features and considerations in the discovery, development and clinical implementation of predictive biomarkers. 3. From Discovery to Clinical Utility — Theory Developing biomarkers for use in the clinical setting is a long and strenuous process. Several preclinical and clinical studies are needed to prove the value of a predictive marker for clinical decision-making. Overall, the evaluation must prove the utility and the reliability of the candidate biomarker. The “References” part at the back of the present chapter lists some of the published guidelines on the evaluation of tumor markers for clinical use1-8. However, in general, the area is characterized by lack of widely accepted common guidelines and, in addition, substantial complexity is related to the process of developing and validating a new predictive tumor marker. A step-wise description of the elements in predictive tumor marker validation is described below. Many steps in the development process are linked with other steps and the listing below is not necessarily exhaustive for all candidate predictive biomarkers whereas it might be too detailed for others. Still, it emphasizes some of the crucial aspects which have to be considered in the development and validation process.

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1. Describe the clinical need

2. Specify a research hypothesis to guide the research

3. Test the hypothesis in preclinical models

4. Select or develop an analytical platform

5. Identify available clinical samples and perform a retrospective study of the potential biomarker

6. Refine the assay and perform an additional retrospective study if necessary

6a. If necessary modify the analytical platform, study the preanalytical variables

6b. Perform an additional retrospective study employing the improved assay

7. Develop the assay to standards compatible with daily clinical routine

8. Perform a prospective study with the biomarker as a secondary aim

9. Design a clinical trial including the biomarker

10. Implement the biomarker in daily routine diagnostics Flow-chart outlining the steps in development and validation of a predictive biomarker.

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Besides identifying the necessary steps in the process, the order of the steps in validating different predictive biomarkers might vary depending on any preliminary information about the candidate biomarker and availability of biomarker assays. For example, preliminary studies of the association between a marker and a clinical outcome depend on the availability of a reliable assay, preferably one suitable for later clinical use. At the same time, the development of such an assay will only be profitable after an association between the potential biomarker and the clinical outcome has been demonstrated. Similarly, identification of a cut-point for discrimination between marker high and marker low patients often requires several studies, which, at the same time, need to employ exploratory cut-points to evaluate the usefulness of a potential marker. 3.1. Describe the clinical need As described earlier in this chapter, a key point in translational research is the coupling of research activities with a defined clinical need or problem. Therefore, developing a predictive biomarker starts with the recognition that prediction of treatment response needs to be improved. Generally, however, this has to be thoroughly considered since problems or questions being relevant today may not be relevant after some time. Also, the clinical problem must be further described, for example by specifying the type of therapy in question and the target. 3.2. Specify a research hypothesis to guide the research Based on the clinical question a hypothesis on the association between the biomarker and the clinical scenario must be defined. This hypothesis should be the focus throughout the development and validation of the predictive marker. 3.3. Test the hypothesis in preclinical models The hypothesis must be tested in preclinical models. Often, this includes studying the potential biomarker (e.g. a protein) in cell lines. Here, the

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role of the protein or other molecules in various processes can be studied. Moreover, in vitro systems can be manipulated in order to observe how the characteristics and the behavior of a cell changes in response to this. For example, the molecule of interest can be added or over-expressed, it can be neutralized or down-regulated and the effects of such alterations on cell growth etc. can be studied. The consequences of combining such expression changes with stress such as the stress induced by chemotherapy can also be examined. Preclinical studies also include investigations of the potential biomarker in animal models. Often, such in vivo studies are performed in mice, which may be genetically manipulated, in various ways. 3.4. Select or develop an analytical platform If preclinical studies support the initial hypothesis further investigations of the biomarker candidate in clinical samples are warranted. However, to do this a reliable assay must be available. In some cases such an assay is commercially available whereas with other markers an assay must be developed. This step is crucial as the results of these studies will form the basis of further clinical evaluation of the marker candidate. Applying an unreliable assay will lead to invalid conclusions and waste of time and clinical samples. Thus, the assay used must fulfill strict requirements with respect to analytical performance. Ideally, even at this early stage the assay used should be compatible with clinical implementation and suitable for daily use in a routine laboratory. Moreover, the specimen type must be considered. Early clinical studies should be performed in the same specimen type as the one intended for future clinical use and in this regard compatibility with clinical routine diagnostics must also be kept in mind. For example, the use of assays that require fresh frozen tissue is limited by a lack of excess tissue and, often, a need for extensive preanalytical sample preparation. On the other hand, blood-based samples or formalin-fixed paraffin-embedded tissue will be readily available for most cancer patients. The performance of the assay must always be scrutinized and checked with the chosen specimen type. This includes evaluation of sensitivity with specification of the assay working range, specificity,

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linearity, recovery, analytical accuracy, and reproducibility. Depending on the nature of the assay, specific additional tests might be required to fully characterize the assay; for example, presence of various antibodies in human serum or plasma may interfere with immunological assays. 3.5. Identify available clinical samples and perform a retrospective study of the potential biomarker After the chosen or newly developed assay has been validated with acceptable results the first clinical study can be performed. Usually, this study will be retrospective in nature and will include a previously collected set of patient samples with follow-up information. The quality of the samples analyzed at this stage is important. In order to ensure the validity of the conclusions drawn from these early studies the samples analyzed should be collected in a controlled manner, the patients must be representative of the group in which the biomarker will be applied, and it should be known how the samples have been handled prior to analyzing the candidate biomarker, e.g. whether the samples have undergone cycles of freezing and thawing and how they have been stored. Most often, the quality of the samples will be high if collected as part of a controlled prospective clinical study as such samples and follow-up data are collected with adherence to standard operating procedures (SOPs), subgroups are comparable, and previous use of the samples is registered. Depending on the prior knowledge about the biomarker beforehand, the purpose of the first clinical study may vary. If nothing is known about the presence and distribution of the marker, this will be the information sought in the first study. However, for many potential biomarkers preliminary information is available with regard to the presence of the biomarker and the first clinical study can then aim at generating more information, for example data on the association between the marker and clinical outcome. Regardless of the specific aim(s) of the study, all scheduled analyses must be specified prior to initiating the study, e.g. a strategy for statistical analysis of the data must be made. If the study includes an analysis of the association between the biomarker candidate and patient outcome the statistical planning must

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include a power calculation, specification of clinical end-points to be evaluated, and a description of the cut-points that will be applied in the study. 3.6. Refine the assay and perform an additional retrospective study if necessary 3.6(a) If necessary modify analytical platform, study preanalytical variables. 3.6(b) Perform an additional retrospective study employing the improved assay. Planning of this step will be based on the results obtained during the first clinical study. As described above, the first study might generate information on the association between the candidate biomarker and outcome and if this is the case the need for additional retrospective analysis might be limited. However, most often the assay and the specimen type need to be evaluated and modifications might be necessary. In addition, the influence of preanalytical variables on the candidate biomarker should be thoroughly investigated. For example, this includes the investigation of the influence on biomarker levels of different processing steps such as fixation conditions with paraffinembedded tissue or centrifugation speed and freezing of blood-based samples. It should be kept in mind that the use of the biomarker in daily clinical routine is facilitated if the biomarker can be determined in specimens, which are routinely prepared or readily available. For most new biomarkers this means that very special requirements for pretreatment are unlikely to be met on a daily basis. An additional retrospective study might be warranted. If considerable changes have been made to the assay, the modified assay must be tested in a clinical study. In addition, the first clinical study might have shown that the biomarker appears useful in a subgroup, or that the association studied is stronger or weaker than expected. Thus, new questions arising after the first clinical study could be addressed by a second retrospective study. In addition, for many new biomarkers establishing a cut-point for identification of responders and non-responders is complicated and often more studies are necessary to identify and validate cut-points.

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Clinical use of a biomarker requires a defined cut-point but choosing this cut-point is often difficult. Expert statistical assistance is needed to do this and the statistical approach must be combined with biological and clinical information. When an apparently appropriate cut-point has been determined it needs to be validated in an independent patient cohort. It must be emphasized that results of preliminary clinical studies should be published. When clinical studies fail to confirm the initial hypothesis, thus indicating that the candidate biomarker is not clinically useful, publication of the results obtained is just as important as publishing positive results. To improve the quality in biomarker studies, general recommendations for reporting of biomarker prognostic studies have been published4 (“REMARK”). Adherence to such common principles when reporting results of biomarker studies ensures availability of all relevant information and facilitates subsequent metaanalyses. 3.7. Develop the assay to standards compatible with daily clinical routine Before embarking on prospective clinical studies the assay should be evaluated again. If preliminary studies have been performed employing an assay, which is not readily transferable into clinical routine laboratories no further studies should be performed until such an assay is available. If this requires substantial changes, this has to be taken into consideration at an earlier stage as significant modifications might warrant additional retrospective testing before development of the biomarker can be continued. At this stage, it should be assessed whether the assay can fulfill the standards for Good Laboratory Practice and Good Manufacturing Practice (GLP/GMP). In addition, procedures for quality assurance and quality control must be established. 3.8. Perform a prospective study with the biomarker as a secondary aim With a well-validated assay available the candidate biomarker can now be tested in a prospective study. However, for most biomarkers evidence

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from retrospective studies is insufficient to design a prospective study that applies only the candidate biomarker in decision-making. Therefore, analysis of the biomarker as a secondary parameter in a prospective study, which is designed with another primary goal, is warranted. For example, the candidate biomarker can be investigated in a clinical trial testing different treatment combinations. Thus, if the marker is expected to separate patients who respond to a certain drug from patients who have no benefit from this particular drug, then the marker can be investigated in a randomized trial comparing this particular drug with standard treatment. The standard treatment arm will serve as a control and if predictive the candidate biomarker will divide the patients who received the particular drug into distinct groups regarding disease free survival, progression free survival and/or overall survival while this is not the case for patients receiving the standard treatment. 3.9. Design a clinical trial including the biomarker Provided that all studies performed until now have been confirmatory of the initial hypothesis, a prospective trial can now be designed employing the marker in clinical decision-making. Thus, if successful this trial will provide final proof that the marker is clinically useful. The trial should be thoroughly planned and statistical advice must be sought in the very first planning of the study to ensure that an adequate number of patients will be included and that an analysis strategy is defined before starting the trial. There are several options when designing a clinical trial to evaluate a predictive biomarker including indirect as well as direct tests. 3.9.1. Indirect tests The study population is divided into a marker positive and a marker negative group. Patients in each group are then randomized to treatment A or treatment B. This trial will answer the question whether one treatment is better than the other treatment within each marker group:

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Treatment A P A T I E N T S

Marker positive

Randomization

Treatment B

Treatment A Marker negative

Randomization

Treatment B

A possible result from a clinical trial.

Disease free survival (%)

Marker pos, A

Marker pos, B Marker neg, A Marker neg, B

Years

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This imaginary plot shows that in marker positive patients treatment A is better than treatment B, but in marker negative patients there is no extra benefit from treatment A as compared to treatment B. Thus, the marker predicts that patients with the marker have an increased likelihood of responding to treatment A, whereas marker negative patients have the same outcome. One can, however, use the same design as described above and apply a formal statistical test of interaction between marker status and treatment assignment. This will demonstrate if the treatment effect observed in the marker positive group is statistically different from the effect seen in the marker negative group. 3.9.2. Direct tests In direct testing patients can be randomized to receive treatment based on marker status or to receive treatment independently of the marker. Patients randomized to marker-based therapy will thus receive treatment A if they are marker positive and treatment B if they are marker negative. Patients randomized to marker-independent therapy will all receive standard treatment B. The predictive value of the marker is assessed by comparing the outcome of all of the patients in the marker-based arm to that of all of the patients in the non-marker-based arm. However, if treatment A is superior in all patients regardless of marker status, this will not show up in this design:

P A T I E N T S

R A N D O M I Z A T I O N

Marker pos

Treatment A

Marker neg

Treatment B

Markerbased therapy

Nonmarkerbased therapy

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A very important issue when designing a clinical trial including a possible predictive marker is the consultation of a biostatistician, who should be involved from the very beginning. Clinical trials are complicated as they require a large number of patients and several parameters need to be considered. These include the statistical values alpha and beta, the prevalence of the biomarker in the population, the expected hazard ratios, the number of events needed and the follow up time. 3.10. Implement the biomarker in daily routine diagnostics With sufficient evidence available the candidate biomarker will now be ready for implementation in daily clinical practice. Continuous use of the biomarker requires well-functioning procedures for quality assurance and quality control. Moreover, it is advised that data are collected during the use of the biomarker to ensure that its clinical usefulness is retained over time. Furthermore, the clinical question that initiated the development of the biomarker may change over time, requiring adjustments of the test. It should be noted that development and validation of a predictive biomarker applies only to the specific disease and patient subgroup included in the process. Thus, further use, for example in other cancer types or patient subgroups or even in other settings such as metastatic versus adjuvant treatment, requires further studying, often including additional prospective testing. 4. From Discovery to Clinical Utility — An Example In the following, the steps outlined above will be further described by a clinical example. This example illustrates the ongoing development and validation of the protein Tissue Inhibitor of Metalloproteinases-1 (TIMP-1) as a predictive marker for anthracycline sensitivity in patients with breast cancer. 4.1. Describe the clinical need The following example illustrates a dilemma faced by oncologists daily.

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4.1.1. Clinical case A 54-year old woman had surgery for primary breast cancer. She was categorized as high risk due to 5/16 tumor-positive lymph nodes, estrogen receptor negativity and malignancy grade III. The oncologist must recommend systemic adjuvant therapy for this patient, and the choice is between six cycles of anthracycline-containing chemotherapy, six cycles of taxane-containing chemotherapy or three cycles of each. A number of potential predictive markers for anthracycline sensitivity have been proposed; the most prominent of these are aberrations in the topoisomerase IIα gene (TOP2A) and over-expression or amplification of the human epidermal growth factor receptor protein/DNA (HER2). The TOP2A gene codes for the enzyme topoisomerase IIα, which is a molecular target of anthracyclines. Both TOP2A and HER2 identify approximately 20% of the high-risk breast cancer population as having a high likelihood of obtaining benefit from anthracycline-based therapy. At the same time, clinical studies suggest that approximately 50% of highrisk breast cancer patients benefit from anthracyclines and consequently, neither TOP2A nor HER2 identifies all responders. No additive effect has been observed between TOP2A and HER29, the explanation for this could be the frequent co-aberration of the genes, which are located in the same region on chromosome 1710. Anthracycline-containing regimens are frequently used in treatment of breast cancer and severe, chronic, late occurring side-effects may be associated with this treatment, e.g. cardiotoxicity. Therefore, predictive markers identifying patients with a high or a low likelihood of obtaining benefit from this particular group of drugs would be very useful. Additional predictive markers identifying patients likely to respond to other drug types, for example taxanes, are also needed. 4.2. Specify a research hypothesis to guide the research Tissue Inhibitor of Metalloproteinases-1 (TIMP-1) is a naturally occurring inhibitor of matrix metalloproteinases (MMPs). In addition to the MMP-inhibitory function of TIMP-1, it has been reported that the TIMP-1 stimulates cell growth, regulates angiogenesis and inhibits

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apoptosis. Moreover, clinical studies have demonstrated that high levels of TIMP-1 in tumor tissue are associated with a poor outcome in breast cancer. Hence, a hypothesis was raised stating that TIMP-1 is capable of inhibiting apoptosis in breast cancer cells thus rendering the cells resistant to chemotherapy-induced cell death. Clinically, this would be seen as a lack of benefit from chemotherapy in patients whose tumors express high levels of TIMP-1 protein. 4.3. Test the hypothesis in preclinical models A large body of evidence is available showing that TIMP-1 is capable of protecting cells against apoptosis-inducing stimuli (for a review, see Würtz11). However, the current preclinical studies were specifically aimed at analyzing the association between TIMP-1 and cell death as induced by treatment of the cells with chemotherapy. The first study employed murine fibrosarcoma cells which were either wild-type or genetically engineered to knock out TIMP-1 expression12. This study showed that with all drugs tested the TIMP-1 wild-type cells were less sensitive to treatment with chemotherapy than cells, which did not express TIMP-1. A second preclinical study aimed at validating the findings described above and, additionally, at transferring the platform to an in vitro system based on human breast cancer cells13. Moreover, this study specifically included the breast cancer relevant drugs epirubicin and paclitaxel along with a well-characterized model drug (etoposide). In human breast cancer cells transfected with TIMP-1 we observed that cells expressing higher levels of TIMP-1 were significantly less sensitive to treatment with epirubicin and etoposide than cells expressing lower levels of TIMP-1 protein. No difference in the amount of cell death induced by paclitaxel was present. Thus, this study supported initial findings and the overall hypothesis and additionally suggested that the TIMP-1 associated with resistance to chemotherapy might be restricted to certain drug types, in this case topoisomerase II-inhibitors which include both etoposide and epirubicin.

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4.4. Select or develop an analytical platform The analytical platform initially chosen for studies of TIMP-1 in patient samples was based on an enzyme-linked immunosorbent assay for measurements of TIMP-1 in extracts of primary tumors. Although not commercially accessible, this ELISA was readily available and had been thoroughly validated for use with tumor tissue extracts14,15. Thus, using this assay would generate valid results, thus allowing for reliable conclusions regarding the association between TIMP-1 in breast tumors and outcome. However, as described in the theoretical part of this chapter, the choice of assay should take into consideration the future clinical implementation of the predictive marker. In the case of this platform, the actual assay (ELISA) could be performed in a routine diagnostic laboratory but the required specimen type, i.e. extracts of primary tumor tissue, is not routinely used. The use of extracts of primary tumors is impaired by limited access to fresh tumor tissue for extraction and by the need for extensive pre-treatment of the tissue in order to extract all proteins and determine their concentration. 4.5. Identify available clinical samples and perform a retrospective study of the potential biomarker Two retrospective studies were planned to investigate the association between tumor tissue TIMP-1 concentration and benefit from chemotherapy. The first one analyzed the outcome after chemotherapy for metastatic disease and it showed that patients whose primary tumors expressed high levels of TIMP-1 protein had a significantly lower probability of obtaining benefit from chemotherapy with anthracyclinecontaining chemotherapy or cyclophosphamide, methotrexate and fluorouracil (CMF)16. The second study showed that in the adjuvant setting, that high tumor levels of TIMP-1 were associated with a poor outcome after anthracycline-based therapy whereas only a very weak association with benefit from CMF was observed17. Accordingly, both these studies were supportive of the research hypothesis stating that high levels of TIMP-1 in tumor tissue are predictive of resistance to chemotherapy; however, the second study suggests that this predictive

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effect might be restricted to certain drug groups, in this case anthracyclines. A large number of samples and follow-up information were readily available for evaluation in the two retrospective studies. The samples, though, were not collected as part of a prospective protocol since they came from a tumor bank of tissue extracts available after routine hormone receptor determinations. As described, access to prospectively collected tissue from a controlled study is preferable. At this early stage, no indications of a cut-point were evident to allow for categorization of patients into responders and non-responders. Thus, both studies analyzed TIMP-1 as a continuous variable and the second one, analyzing the benefit from adjuvant chemotherapy, applied the median TIMP-1 level as an exploratory cut-point. 4.6. Refine the assay and perform an additional retrospective study if necessary 4.6.1. If necessary modify analytical platform, study preanalytical variables Initial studies indicated that there is indeed an association between tumor tissue TIMP-1 levels and benefit from chemotherapy, in particular with anthracycline-based regimens. Prior to further testing the analytical platform had to be evaluated. As described previously, basing determination of the candidate biomarker on an analytical platform, which is suitable for daily clinical use will facilitate use of the marker. The ELISA-based determination of TIMP-1 in tumor tissue extracts was not considered ideal for routine use, mainly due to the limited access to fresh tissue. Thus, it was decided to develop an alternative platform based on immunohistochemical detection of TIMP-1 in formalin-fixed paraffin-embedded tissue. Immunohistochemical analysis of proteins in tumor tissue is widely used and paraffin-embedded tissue is more easily accessible than fresh tumor tissue for extraction. For example, all patients in Denmark have primary tumor tissue available for this method and in most pathology laboratories; paraffin-embedded tissue is kept indefinitely. In addition, for the specific purpose of detecting TIMP-1 a

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number of monoclonal antibodies were available, and one of these was chosen for the assay based on comprehensive testing of several antiTIMP-1 monoclonal antibodies18. The newly developed assay had to be validated prior to use in order to ensure analytical validity. Moreover, changing the analytical platform, including the specimen type employed called for re-evaluation of preanalytical and post analytical variables. 4.6.2. Perform an additional retrospective study employing the improved assay This study was performed in Denmark and included Danish breast cancer patients. All patients with breast cancer have been registered in a national public database since 1977. This database is administered by the Danish Breast Cancer Cooperative Group (DBCG) which also plan, perform and analyze trials. One such trial is the DBCG 89D trial, which was an open label, randomized trial, comparing the combination of adjuvant CMF with the combination of adjuvant cyclophosphamide, epirubicin and fluorouracil (CEF). About 1000 Danish patients were included. In this trial there is now a median of 10 years follow-up with regard to overall survival, which was the secondary end point. Primary end point was invasive disease free survival. Overall there was a small but significant effect of substituting methotrexate with epirubicin19. Formalin fixated paraffin-embedded tissue from nearly all primary tumors was previously collected from the various involved pathology departments. Tissue micro arrays were constructed to ease analyses. In the current study, all tumors were analyzed using the validated TIMP-1 antibody (VT7). Immunohistochemistry slides were scored by two independent pathologists to ensure validity. It was decided to score all tumors as presence or absence of TIMP-1 immunoreactivity in tumor cells. Twenty-five percent of the tumors lacked TIMP-1 immunoreactivity in the tumor cells. This analysis showed that TIMP-1 immunoreactivity in the epithelial tumor cells was associated with less benefit from epirubicin-containing chemotherapy20. The Kaplan–Meier plot below illustrates that patients with tumors lacking TIMP-1 immunoreactivity have an increased invasive disease free survival when

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receiving epirubicin-containing chemotherapy as compared to patients whose tumors contain TIMP-1. 4.7. Develop the assay to standards compatible with daily clinical routine As already described, a panel of anti-TIMP-1 monoclonal antibodies were evaluated for immunhistochemistry and the VT7 clone was selected as the most optimal. We have now transferred the VT7 clone to the diagnostic company Dako A/S, which will secure delivery of high quality VT7 antibodies fulfilling GLP and GMP standards for future studies. We are at the same time analyzing if the TIMP-1 gene is aberrant (amplified or deleted) in breast cancer cells. If so, a new analytical platform compatible with HER2 and Topoisomerase IIα FISH analyses can be developed. It should be mentioned though, that such a TIMP-1

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FISH analyses has to be thoroughly validated and its possible association with therapy response established. 4.8. Perform a prospective study with the biomarker as a secondary aim To take the evaluation of TIMP-1 as a predictive marker of epirubicin resistance further, we are now ready to embark on a prospective study, with a retrospective evaluation of TIMP-1 immunoreactivity. Thus, in this study there will be no clinical decisions made on the basis of TIMP-1. The prospective study chosen for this evaluation is another trial from The Danish Breast Cancer Cooperative Group (DBCG). The study is called READ and is an open label, randomized study in, Topoisomerase 2 alpha DNA normal patients with high-risk primary breast cancer. The study is a superiority study of two different adjuvant chemotherapy regimens. 1900 patients will be randomized to receive either three courses of epirubicin and cyclophosphamide (EC) followed by three courses of docetaxel (D) - (standard treatment) or six courses of docetaxel and cyclophosphamide (DC). In the previously described trial (DBCG 89D) we showed that patients with tumors lacking TIMP-1 immunoreactivity in the tumor cells (TIMP-1 negative) in the primary tumor have significantly more benefit from epirubicin than patients with TIMP-1 positive tumors. One remaining clinical problem is therefore to find a better treatment for patients with TIMP-1 positive tumors among Topoisomerase IIα normal patients. It should be mentioned that as of today the standard treatment for high risk ER negative breast cancer patients and ER positive premenopausal high risk patients is 3EC+3D. The hypothesis is that patients with Topoisomerase IIα normal and TIMP-1 pos. tumors have the same benefit from taxanes than the benefit seen when treating patients with Topoisomerase IIα abnormal and/or TIMP-1 negative tumors with epirubicin.

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The following design can be used to test this hypothesis:

TOP2A normal patients

R A N D O M I Z A T I O N

3EC + 3D

6DC

Test for TIMP-1 immunoreactivity will be performed consequtively but the results will be blinded until the last patient has been included in the study. Patients will be followed up for life and the first interim analysis will be performed five years after the last patient is randomized. As patients with TIMP-1 positive tumors probably account for 60-75% of the patients it is planned that only TIMP-1 positive patients will be included in the analysis. If TIMP-1 negative patients should also be analyzed the samples size would have to be increased by a factor 4. This could however be done by increasing the accrual time and thus the follow up time, but the answer would take many, many years to retrieve.

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The results could look as follows:

Disease free survival (%)

TIMP-1+, DC All, DC All, EC+D

TIMP-1+, EC+D

Years

This imaginary plot shows that patients with TIMP-1 positive tumors receiving six courses of DC have an increased invasive disease free survival compared to TIMP-1 positive patients receiving 3EC+3D (as these patients have only limited benefit from E and receive too few courses of D). If the above should be the outcome of the READ trial, the evaluation of TIMP-1 as a predictive marker, could then be taken to the next step.

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4.9. Design a clinical trial including the biomarker The ultimate step in the development of a predictive marker before taking it into daily clinical practice is a study where the marker is included in the randomization. With regard to TIMP-1 the following trial could be undertaken to provide the final proof of its predictive value for epirubicin sensitivity/resistance.

P A T I E N T S

R A N D O M I Z A T I O N

TIMP-1 pos

6DC

TIMP-1 neg

3EC + 3D

Markerbased therapy

6DC NonMarkerbased therapy

Randomization

3EC + 3D

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Disease free survival (%)

TIMP-1 neg., EC+D TIMP-1 unknown, DC TIMP-1 pos., DC

TIMP-1 unknown, EC+D

Years With the imaginary result presented in the above plot, TIMP-1 is shown to be a predictor of sensitivity/resistance to adjuvant epirubicin. Patients with TIMP-1 negative tumors receiving epirubicin containing chemotherapy will have a superior disease free survival compared to patients with TIMP-1 unknown tumors receiving EC+D as this curve is “pulled down” by the TIMP-1 positive patients receiving EC+D. Patients with unknown TIMP-1 status receiving DC will probably have a disease free survival similar to the TIMP-1 positive patients receiving DC (this only holds true if TIMP-1 is not a predictor of taxane efficacy). Patients with TIMP-1 positive tumors receiving DC will probably have a disease free survival similar to patients with TIMP-1 negative tumors receiving EC+D (again this only holds true if TIMP-1 is not a predictor of taxane efficacy).

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4.10. Implement the biomarker in daily routine diagnostics Based on the above imaginary results, TIMP-1 immunoreactvity together with Topoisomerase IIα DNA analyses are now ready to be implemented in the clinical decision making regarding choice of adjuvant therapy in high risk breast cancer patients. It should be emphasized that a continuous quality assurance and quality control scheme should be installed to secure optimal performance of the assay. In this regard it is recommended to include a diagnostic company who should be responsible for production and supply of the TIMP-1 antibodies including the continuous quality control. Furthermore, there is still several unanswered questions regarding TIMP-1, one of these being whether TIMP-1 neg. versus TIMP-1 pos. patients will respond differently to taxanes. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20.

D.F. Hayes et al, J. Natl. Canc. Inst., 88 (1996). D.F. Hayes et al, Breast Canc. Research and Treatm., 52 (1998). N.L. Henry et al, Oncologist, 11 (2006). L. McShane et al, J. of the Natl. Canc. Inst., 97 (2005). National Cancer Institute, www.cancer.gov. Translational Reserach Working Group (TRWG), www.cancer.gov/trwg. D.J. Sargent et al, J. of Clin. Oncol., 23 (2005). A. Schrohl et al, Molecul. and Cellul. Proteomics, 7 (2008). F.C.G.J. Sweep et al, Internatl. J. of Oncol., 23 (2003). A.S. Knoop et al, J. of Clin. Oncol., 23 (2005). F.P. O’Malley et al, J. Natl. Canc. Inst., 101 (2009). S.O. Würtz et al, Acta Oncologica, 47 (2008). L. Davidsen et al, British J. of Canc., 95 (2006). S.Ø. Würtz et al, Canc. Research, 161s, 69 (Suppl.) (2009). M.N. Holten-Andersen et al, British J. of Canc., 80 (1999). A. Schrohl et al, Clin. Canc. Research, 10 (2004). A. Schrohl et al, Clin. Canc. Research, 12 (2006). A. Schrohl et al, Submit. for publication (2009). I.V. Sorensen et al, J. of Histo. Cytochem., 54 (2006). B. Ejlertsen et al, J. of Canc., 43 (2007). G.L. Willemoe et al, Europ. J. of Canc., (2009).

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CHAPTER 5 PHARMACODIAGNOSTIC TESTING — IMMUNOHISTOLOGY

Anthony S.-Y. Leong MB, BS, MD (Adelaide), FRCPA, FRCPath, FCAP, FASCP, FHKAM (Pathol) Honorary FHKCPath, Honorary FRCPT. Medical Director, Hunter Area Pathology Service, Chair Discipline of Anatomical Pathology, University of Newcastle, Australia, Adjunct Professor, Peking University, Beijing, China Trishe Y.-M. Leong MB, BS (Honours), FRCPA, FCAP. Consultant Pathologist Victorian Cytology Service, Melbourne, Australia The immunohistochemical stain has been rapidly embraced as an integral and often indispensable component of histological assessment. It is a histological assay that is inextricably a part of morphology. For this reason we proposed in 1998 the term “immunohistology” in preference to “immunohistochemistry” and “immunocytochemistry” in order to highlight this important attribute1. Alternative appellations like “immunohistopathology”2, and “immunomicroscopy”3 were subsequently proffered in recognition of the importance of correlating morphologic features with the immunological assay.

1. Introduction The immunohistological test is the application of an antibody to label a protein in tissues or cells based on antibody-antigen recognition. The cells may be fixed and embedded in paraffin, or unfixed as in frozen sections, or as intact cells in cytological preparations. While the test was initially introduced primarily for diagnostic purposes it soon became employed for prognostication and, more recently, as a predictive assay to

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determine the likelihood of response to specific therapeutic regimes and targeted treatments with humanized antibodies (see below). The basic aim of the procedure is the distinct localization of signal to specific cell and tissue components in the section, at the same time retaining good morphological visualization. Amplifying the signal without increasing the non-specific background staining or noise is a major strategy to allow application to routinely processed diagnostic tissues. The direct immunofluorescence technique in which a fluoresceinlabeled primary antibody is employed to detect the protein of interest represents the most basic immunohistological test. However, despite the simplicity of this one-step stain, the insensitivity of this assay that provided a 1:1 antigen to signal ratio and the poor morphologic visualization and preservation of frozen sections, restricted its application. The development of two- and three-step techniques that amplified the antigen to signal ratio, and permanent chromogen systems such as horseradish-peroxidase and diaminobenzidine were important milestones in the evolution of the immunohistological assay as they allowed the procedure to be applied to fixed-paraffin-embedded tissue sections (discussed in detail elsewhere)1,4. Additional developments such as the use of polymers5 and tyramide6 further amplified the antigen to signal ratios, and antigen retrieval methods including enzyme digestion and post-fixation in heavy metal solutions improved the detection of antigens in fixed tissues1,4; however, it was the introduction of the so-called heat-induced antigen retrieval method that had the greatest impact7-9. Pari-pasu with these developments, the range of antibodies and their specificity and sensitivity continued to improve, all contributing to consolidate the pivotal role of the immunohistological assay in both research and diagnosis. 2. Substrate and Chromogen Systems Antibody molecules cannot be seen with the light microscope unless they are labeled. A variety of labels have been used including fluorescent compounds and their active enzymes, all with the property of inducing the formation of a colored reaction product from a suitable substrate

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system to allow visualization. Some of these systems can be employed in electron microscopy by rendering the reaction product electron dense through appropriate treatment. Alternatively, particulate labels such as gold, ferritin, or virus particles can be used. Common chromogen systems currently in use include diaminobenzidine (DAB), 3-amino-9-ethyl-carbazole (AEC), HankerYates reagent, alpha-naphthol pyronin used with peroxidase as substrate; fast blue, fast red, BCIP (5-bromo-4-chloro-3-indolyl phosphate) NBT (nitroblue tetrazolium) used with alkaline phosphatase as substrate; tetrazolium, tetranitroblue tetrazolium used with glucose oxidase as substrate, and immunogold with sliver enhancement1,4. AEC is alcohol soluble and its crisp red color contrasts well against hematoxylin. To avoid removal of the alcohol-soluble product, a nonalcohol based stain like Mayer’s should be used. AEC has two reactive sites so that when one is converted it turns red but when both are saturated a green-brown color results. Aquamount causes slow loss of the stain and glycerol mounting is required, rendered permanent by sealing the edges of the cover slip. AEC may be chosen because it may be a lower-risk carcinogen compared to DAB. The horseradish peroxidase-DAB system is probably the most widely favored as the brown reaction product contrasts well against a wide range of counterstains and mountants. DAB is not only alcohol resistant but can be visualized in the electron microscope. Osmification can produce a more intense dark brown-black color and a similar effect is achieved by post-treatment with nickel sulphate or cobalt chloride but such enhancement is seldom necessary. The reaction product is relatively stable with fading occurring only after years in routine storage. 3. Analytical Characteristics Earlier expectations of the immunohistological assay were based largely on the fluorescein-labeled antibody-antigen stain so that the test was expected to yield no more than a positive or negative result; however, with increasing sensitivity and excellent visualization of morphology, it has become evident that much more information can be obtained, particularly as regards the specific organelle localization of some

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antigens. For example, while the antigen can be generalized as being localized to the cytoplasm, nucleus, or cell membrane, the distinct granular deposition of oxidized diaminobenzidine can be further correlated with specific cell organelle deposition. Antibodies to mitochondria produce a fine speckled or granular cytoplasmic staining corresponding to their specific localization to mitochondria. Antichromogranin antibodies specifically highlight dense core granules in neuroendocrine cells. Dense core granules are localized immediately beneath the plasma membrane so that they occur in greatest density in the tapering cell processes that are characteristic of such cells. Thus the long cell processes that stain for chromogranin and their dot-like appearance seen in cross section are typical of neuro-endocrine cells (Figure 1a). Similarly, staining for α-smooth muscle actin localizes to the dense bodies that are present beneath the cell membrane of myofibroblasts (Figure 1b) as compared to the diffuse cytoplasmic staining in smooth muscle cells. The use of antibodies to enhance the visibility of specific cellular features has also been exploited in diagnosis. Vimentin can be used to confirm the spindled shape of tumor cells whose cytoplasmic outlines are not distinct in hematoxylin-eosin stains. Staining for epithelial membrane antigen demonstrates the long microvillous processes that are pathognomonic of mesothelioma and their juxtaposition to collagen reflects their aberrant neoplastic growth (Figure 2)10,11. Much of contemporary diagnostic histology is based on the identification of tissue patterns and immunolabeling can be used to reveal such distinctive patterns within tumors and tissues that can be exploited for diagnosis. The use of type IV collagen to demonstrate basal lamina distribution is an adjunct for the separation of various soft tissue tumors12. Staining for CD34 demonstrates arterialized vessels that are evenly distributed within a hepatocellular carcinoma, allowing distinction from benign proliferative nodules (Figure 3). The demonstration of the pattern of follicular dendritic cells (FDC) distribution in a lymph node with CD21, CD35 or CD23 can provide useful information about the architecture, contributing to diagnosis. Proliferation of FDCs around high endothelial vessels in the paracortex is

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(a)

(b)

Fig. 1. (a) Staining for chromogranin in a Merkel cell carcinoma showing localization to dense core granules that lie beneath the cell membrane. As such granules are most dense in the cell processes, wispy tapering processes are stained and these appear as dots in cross section; (b) similarly, staining for α-smooth muscle actin in dermatofibroma shows localization to the dense bodies that occur mostly beneath the cell membranes of myofibroblasts.

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Fig. 2. Epithelial membrane antigen highlights the pathognomonic long microvilli around mesothelioma cells. Their juxtaposition to collagen further reflects their aberrant and neoplastic nature.

Fig. 3. Demonstrates a distinctive pattern of arterialization in hepatocellular carcinoma that is not present in the benign liver parenchyma.

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Fig. 4. (a) Proliferating follicular dendritic cells ensheathing high endothelial vessels in the paracortex in angioimmunoblastic T cell lymphoma are decorated by CD35; (b) the pattern of irregular nodular proliferations of follicular dendritic cells with confluence is a feature of mantle cell lymphoma.

a feature of angioblastic T cell lymphoma (Figure 4a), whereas irregular expansions of the FDC network with confluence of adjacent networks is seen in mantle cell lymphoma (Figure 4b). Other distinctive patterns of FDCs can help characterization of the lymphoma, e.g., nodular lymphocyte predominant Hodgkin disease is characterized by large spherical meshworks of follicular dendritic cells that may be confluent and are predominantly filled with small B cells and numerous CD57+ T

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cells. Occasional multilobated B cells characteristic of the condition are also present. 4. Applications With evolution of the technique, diagnostic applications of the immunohistological assay have expanded considerably and include the identification of infective organisms, phenotyping of lymphomas and leukemias, tumor diagnosis and prognosis, and identification of hormones and peptides, predictive factors for targeted therapy, and of carrier states. In tumor pathology, the immunohistological identification of cellular proteins can serve as a surrogate technique for molecular analysis to identify genetic alterations, normal expression, overexpression, aberrant expression, and loss of expression of genes. In addition, antibodies have been produced to fusion proteins that result from the translocation of certain genes in specific tumors13. 5. Expression and Loss of Expression The identification of proteins on the cell membrane, cytoplasm, and less frequently nucleus of hematopoietic cells allows phenotyping of lymphomas and leukemias. Similarly, the immunoexpression of intermediate filament proteins, cytoplasmic proteins, hormones, and peptides assists in the identification and typing of tumors. The absence or loss of immunoexpression of certain proteins is a signature of some tumors. For example, loss of E-cadherin allows separation of lobular carcinoma from ductal carcinoma of the breast. In this case the failure to express E-cadherin protein may result from mutations, allelic loss (loss of heterozygosity), or promoter methylation14. Similarly, the loss of any of the mismatch repair gene proteins, namely MLH1, MSH2, MSH6, PMS2 is a feature of inherited non-polyposis colonic cancer (Figures 5a and 5b); furthermore, it identifies a group of sporadic colon cancer with distinct clinicopathological features including proximal tumor site, high histological grade, early stage and diploidy. Such tumors have

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consistently been associated with a more favorable outcome that is lost if treated with adjuvant chemotherapy15,16. The assessment of mismatch repair proteins therefore is used as a screening test for prognostication in sporadic colorectal carcinoma as well as for identification of the heriditary non-polyposis colonic cancer syndrome in affected patients and family.

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Fig. 5. Sebaceous adenoma in a patient with colonicl carcinoma. (a) The tumor fails to express MSH2 whereas surrounding lymphocytes, stroma, and epidermal cells show distinct nuclear staining. (b) Staining for MLH1 is seen in the tumor and surround cells and positivity was seen with MSH6 and PMS2 (not shown). The same findings were found in the colonicl carcinoma.

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p57Kip2 is paternally imprinted (maternally expressed) and immunoexpressed in the normal placenta at frequencies of up to 100%. Nuclear staining is seen in villous, cytotrophoblast and implantation site interstitial trophoblast as well as villous stromal cells and maternal decidual cells. The protein is present in partial mole but absent in complete mole, the latter an anticipated finding given that the diploid complete mole lacks a copy of the maternal genome, while the partial mole has a haploid copy of the maternal genome and two haploid copies of the paternal genome. When combined with Ki-67 indices, the following profiles allow distinction of the three entities considered in differential diagnosis: hydropic miscarriage – low Ki-67 and high p57Kip2, partial mole – moderate to high Ki-67 and high p57Kip2, and complete mole high Ki-67 and low p57Kip2 (Figures 6a and 6b)17. 5.1. Aberrant expression The expression of a protein at an abnormal site can contribute to our understanding of carcinogenesis. The migration of the cell-cell adhesion proteins E-cadherin and B-catenin into the cytoplasm has been shown to correspond with a change in tumor cells shape and invasiveness from an epithelioid form to spindle form in breast18 and urothelial cancers19 and the aberrant accumulation of B-catenin in the nucleus may trigger nuclear transcription18. Mucosa associated lymphoid tissue (MALT) lymphomas with t(1;14)(p22;q32) or t(11;18)(q21;q21) show unusual nuclear localisation of Bcl10. In normal lymphoid tissue, Bcl10 is weakly expressed in the cytoplasm of germinal and marginal zone cells. Strong nuclear staining of Bcl10 is found in t(1;14)(p22;q32)-positive MALT lymphomas (Figure 7), whereas moderate nuclear expression is associated with t(11;18)(q21;q21) tumors20,21. Bcl10 is a prognostic marker as such MALT lymphomas fail to respond to antibiotic eradication of Helicobacter pylori. Another form of abnormal immunoexpression is the presence of retained antibody-immune complexes on the inert basement membranes and connective tissues, a finding that has long been exploited for the diagnosis of glomerular diseases and bullous eruptions of the skin. More

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recently the demonstration of C4d, a component of complement, on endothelial cells, has been shown to be an important tool for the recognition of humoral rejection in transplantation of kidney, heart, and liver. Complement C4d is the stable remnant of classical complement activation and its presence implicates humoral attacks on the endothelial cell that escape detection by other methods22,23.

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Fig. 6. (a) The Ki-67 count in a complete hydatidiform mole is very high whereas there is no immunoexpression of p57. (b) This distinguishes a complete mole from a partial mole in that the latter shows a low Ki-67 count and positivity for p57.

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Fig. 7. Nuclear Bcl-10 is strongly expressed in this case of gastric mucosa-associated lymphoid tissue (MALT) lymphoma that did not respond to antibiotic therapy for Helicobacter and was demonstrated to show t(1;4)(p22;q32).

5.2. Expression of novel fusion proteins The translocation of certain genes may result in the production of novel fusion proteins as in anaplastic large cell lymphoma (ALCL) where the most frequent alteration is a t(2;5)(p23;35). In this translocation there is fusion of the nucleophosmin (NPM) gene on chromosome 5q35 to the anaplastic lymphoma kinase (ALK) gene on chromosome 2p23 resulting in the chimeric NPM-ALK protein that has a key role in the pathogenesis of ALCL. The protein is located in the nucleus, nucleolus and cytoplasm (Figure 8a) but variant translocations involving ALK and other partner genes on chromosomes 1, 2, 3, and 17 can also occur with upregulation of ALK with the ALK protein located often only in the cytoplasm as with other translocations (Figure 8b)24. Desmoplastic small round cell tumor described in the pediatric age group need not be desmoplastic and can occur in an older age group, prompting us to name such tumors “polyphenotypic” small cell tumors to highlight this more important and consistent attribute1,25. While this lesion shares the t(11;22) translocation seen in Ewing’s sarcoma (ES)

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(a)

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Fig. 8. (a) Anaplastic large cell lymphoma with t(2;5) showing characteristic nuclear, nucleolar and cytoplasmic staining for anaplastic large cell lymphoma kinase (ALK) protein compared to (b) another case of anaplastic large cell lymphoma with a t(1;2)(q25;p23) variant translocation involving the TPM3 gene (encodes a non-muscular tropomyosin protein) where the staining is cytoplasmic and membranous.

and primitive neuroectodermal tumor (PNET), it differs fundamentally with respect to the breakpoint on chromosome 11 (t(11;22)(p13;q12)), which results in the unique fusion peptide EWS/WT-126. A portion of this fusion peptide corresponds to the C-terminal portion of the WT-1 gene product allowing immunohistological demonstration of WT-1

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overexpression as a marker of such tumors. With the exception of Wilm’s tumor, the DSRCT is unique among the small blue round cell tumors of childhood in showing positive nuclear immunostaining for the WT-1 gene product27. Other novel fusion proteins include the t(11;22) involving the EWS gene on chromosome 22q12 and other partners, most frequently FLI-1 gene on chromosome 11q24 as is typical for Ewing’s sarcoma/primitive neuroectodermal tumor (ES/PNET). Nuclear overexpression of the FLI-1 protein resulting from this translocation is seen in 70-100% of ES/PNET28 although a number of mimics of ES/PNET including lymphoblastic lymphoma and desmoplastic small round cell tumor also exhibit FLI-1 reactivity29. A third fusion protein occurs in alveolar soft part sarcoma where the specific der(17)t(X;17)(p11;q25) results in fusion between the TFE3 transcription factor gene on Xp11.2 and the novel ASPL gene on 17q25 (the function of which is unknown). The translocation results in overexpression of the C terminal portion of TFE3 allowing a specific assay for this tumor. A subset of renal cell carcinoma in children and young adults have t(X;1)(p11.2;q21) also involving TFE3 (PRCC-TFE3) and nuclear staining for TFE3 has been exploited for the identification of such tumors30. 5.3. Overexpression The immunohistological assay has been employed quantitatively to assess overexpression of specific cellular proteins as a reflection of upregulation of the certain genes. For example, overexpression of Her2 protein is taken to reflect increased copy numbers of the Her2 or cerbB-2 gene. P504S/α-methylacyl-CoA racemase (AMACR) is a mitochondrial and peroxisomal enzyme that is involved in the metabolism of branchedchain fatty acid and bile acid intermediates. P504S mRNA is significantly elevated in prostate cancer compared with benign prostate, a feature that can also be shown by immunostaining (Figure 9). P504S/AMACR immunoreactivity was found in over 94% of carcinomas allowing the identification of small foci of prostatic carcinoma31.

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Fig. 9. The malignant glands in this section of prostate stain for P504S/α-methylacylCoA racemase (AMACR) whereas there is no staining in the adjacent benign glands that display two distinct cell layers.

5.4. Expression of surrogate proteins The immunoexpression of certain proteins has been employed as surrogate markers of changes in deoxyribonucleic acid in the cell. For example, high-risk HPV subtypes, particularly types 16, 18, 31 and 45, integrate with host DNA producing overexpression of viral E6 and E7 genes. HPV E6 protein binds and degrades p53, and E7 protein inactivates the tumor suppressor retinoblastoma protein (pRB). Because p16INK4a undergoes a negative feedback control by pRB, inactivation of pRB resulting from HPV infection or mutations may result in overexpression of p16INK4a. Thus nuclear expression of p16INK4a in dysplastic epithelium serves as a surrogate marker of integration of the virus and a harbinger of progression (Figure 10)32. ZAP-70 is a tyrosine kinase that participates in early B cell differentiation. In chronic lymphocytic leukemia expression of this marker has been demonstrated to be a poor prognostic factor and associated with an unmutated configuration of the IgVH genes33.

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Fig. 10. p16INK4a a surrogate marker of integrated human papilloma virus of aggressive subtypes stains dysplastic cells in the cervix.

6. Targeted Therapies A contemporary trend in cancer therapy is the targeting of specific molecules expressed by the cancer cells; these commonly being molecules on the cell surface that are involved in the regulation of growth and proliferation. Humanised or chimeric monoclonal antibodies have been produced to a handful of these target molecules and the best therapeutic response generally occurs in those tumors expressing large amounts of the target molecules. 6.1. HER2/neu in breast cancer The immunohistological detection of HER2/neu overexpression in breast cancer is now a well-established procedure. HER2/neu is a transmembrane glycoprotein belonging to the epidermal growth factor receptor group and is overexpressed in as many as 25% of invasive ductal carcinomas as a result of amplification of the HER2/neu gene. This overexpression has been confirmed to be an independent prognostic marker in node-positive and more recently, also in node-negative

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patients. More importantly, HER2/neu positive status predicts positive response to adriamycin-based therapies and poor response to tamoxiphen, even in estrogen receptor-positive tumors. Patients with metastatic breast cancer that show HER2/neu overexpression respond to immunotherapy with humanised anti-HER2/neu antibody, Trastuzumab and there is evidence that the treatment is also effective in early breast cancer. Immunostaining for HER2/neu shows good correlation with FISH and PCR assays with concordance rates ranging between 78-98%34. 6.2. C-kit (CD117) in gastrointestinal stromal tumors Imatinib (Glivec, Gleevec, STI571), was initially developed for the treatment of chronic myeloid leukemia (CML). Imatinib works by inhibiting the kinase activity of BCR-ABL, the oncogenic product of the Philadelphia chromosome that is diagnostic of CML. Imatinib is not specific for the ABL tyrosine kinase and has activity against several related kinases including c-Kit and the two receptors for platelet-derived growth factors PDGFRA and PDGFRB. Up to 94% of gastrointestinal stromal tumors (GIST) express the CD117 antigen and harbour activating mutations in the KIT gene. A further 5-7% have mutations in the PDGFRA gene. The kinase isoforms derived from these mutations are oncogenic and play a critical role in the development and progress of these tumors. Fortunately, most of these mutant isoforms are sensitive to Imatinib and more than 80% of patients with GISTs benefit from Imatinib therapy. Immunohistological detection of CD117 is an effective method of identifying tumors treatable with Imatinib35. 6.3. CD20 in aggressive B cell lymphoma The mouse-human chimeric antibodies Retuximab (Retuxam) has been used against aggressive B cell lymphomas such as mantle cell lymphoma and diffuse large cell lymphoma. While these lymphomas immunoexpress CD20 that is detectable by immunostaining, CD20 may be lost following Retuximab treatment and in recurrent tumors. The

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immunohistological identification of this antigen is thus important in the setting of recurrence. 6.4. CD33 in acute myeloid leukemia Gemtuzumab ozogamicin (Myotarg) is the humanized anti-CD33 antibody (hP67.6), which is bound to N-acetyl-γ-calicheamicin, the latter damaging the leukemia cells by becoming internalized following CD33 binding to the cell membrane. CD33 is expressed in about 90% of blasts in acute myeloid leukemia. 6.5. Epidermal growth factor receptor (EGFR) in refractory colorectal carcinoma Cetuximab (Erbitux) has received FDA approval for the treatment of Irinotecan Refractory or Intolerant metastatic colorectal cancer. The EGFR pharmDxTM kit is FDA approved for the detection of EGFR expression to aid patients eligible for treatment with Erbitux which is thought to interfere with the growth of the cancer cells by binding to EGFR and competing with the normal ligand36. 6.6. Tyrosine kinase HER1/EGFR in non-small cell carcinoma of the lung Gefitinin (Iressa) and Erlotinib (Tarceva) target the ATP clefts within the tyrosine kinase HER1/EGFR, which is expressed in 40-80% of non-small cell carcinoma of the lung and many other cancers. Somatic mutations of the EGFR were found in 15/58 unselected non-small cell lung cancers in Japan and in 1/61 from the United States. These mutations lead to increased growth factor signalling and confer susceptibility to the inhibitor, one study with Erlotinib showing a response rate of 12.3%37 and 27.5% with Gefitinib38. Antibodies to EGFR are available but validation of immunohistological staining with therapeutic response to the humanised antibodies is not yet available.

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6.7. Somatostatin receptors in pituitary adenomas and gastroenteropancreatic (GEP) endocrine tumors Somatostatin receptors (SSTRs) are composed of five isoforms, SSTR2 and SSTR5 expressed by pituitary adenomas and SSTR2 by GEP tumors. The somatostatin analogue, Sandostatin (Octreotide) binds to SSTR2 and 5 and inhibits tumor cell growth. SSTRs detection may predict the response to somatostatin and in addition to detection by RT-PCT, isoform-specific antibodies are now available for immunostaining39. 7. Variables that Influence Antigen Preservation The immunostain is vastly different from all other histochemical stains in that it comprises many more steps and unlike other stains, its progress cannot be monitored until the end result. As therapeutic decisions become dependent on immunostaining results, there have been valid calls for standardization of the immunostaining technique40,41. Clearly this is a worthy cause but is it attainable? There are many variables in the test that influence outcome and to achieve standardization it is a prerequisite is that all these variables be equal or similar between laboratories. However there are several variables that can potentially affect the demonstration of the antigen of interest and these are beyond the control of the laboratory42. Briefly, pre-analytical variables such as the interval between excision and placement in fixative, and the nature of fixative and duration of exposure are pivotal factors that influence antigen preservation. Furthermore, selection of the appropriate tissue for examination is in the hands of the surgeon or clinician; all important variables that are not within the control of the laboratory so that it is currently impossible to standardize between laboratories and even between specimens accessioned by the same laboratory. Analytical and post-analytical factors are more likely to be within the influence of the laboratory, but even so many require standardization between laboratories. Analytical variables include processing times, interval between cutting of sections and staining as antigens are vulnerable to ultraviolet light and temperature43,44. Antigen retrieval procedure including time, temperature and pH of retrieval solution45,46,

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and staining procedure. Even with the latter, which can be readily standardized, the intensity of stain is an entirely subjective option and ranges between golden-brown to brown-black for the diaminobenzidine product. Numerous antibody clones for the same target protein are available and detection systems vary, selection being governed by personal choice. In the post-analytical phase, interpretation and validation of the results are dependent on the experience of the reporting pathologist. Selection of the appropriate antibodies to be used and the appropriate area in the section for assessment is dependant on pathologists’ expertise. Cut-off numbers are mostly arbitrary and in scoring, should assessment be made in the areas of highest intensity or is a mean value more appropriate? Some of these issues can be standardized through the use of automated cellular imaging systems that can evaluate the target cells using a large number of different morphologic parameters; however, it is clear that there remain a number of variables that can significantly influence antigen preservation and are beyond the control of the laboratory. While the analytic processes are the most amenable to intralaboratory standardization through the use of automated instruments, standardization of the immunohistological assay currently seems impossible to attain. Indeed among specimens accessioned by the same laboratory, many pre-analytical factors are different and no two specimens are subjected to identical conditions. 8. Quantitative Immunohistology The increasing reliance on immunohistology as an assay to identify both prognostic and predictive factors in a variety of neoplasms has resulted in attempts to quantitate immunostains. Scores assigned to stains for specific proteins are generally based on intensity and percentage of staining cells. As the potential for therapeutic response is dependent on such scores it becomes imperative to critically examine the basis for their derivation and the variables that may affect the scores. Firstly, it has become abundantly clear the many variables that influence the preservation of antigen in fixed, embedded tissues are currently insurmountable obstacles to standardization of the

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immunohistological assay. The same obstacles apply to a lesser extent between different specimens accessioned by the same laboratory. Although a range of scores can be ascribed to a stain, cutoffs are generally arbitrarily set. In the case of Her2 scoring, membrane staining in greater than 10% of the tumor was the initial cutoff but this was recently revised to 30%47. Arbitrary cutoff values and scores do not represent a continuum. Various methods of scoring estrogen and progesterone receptor expression in breast carcinoma have been proposed, mostly based on the sum of staining intensity and proportion of cells stained. The scores are employed clinically to predict response to hormone therapy and for prognosis, despite the observation that a clinical response was obtained with tumors displaying as low as 1% of estrogen receptor positivity48 which led to the National Institutes of Health recommendation that any positive staining for estrogen receptor is considered to be a definitive result and indication for anti-estrogen therapy. More recently, it was demonstrated in a large number of patients that the distribution of estrogen and progesterone receptors is bimodal as is the distribution of Allred scores. This finding implies that these parameters predictive of response to hormonal therapy are generally either negative or positive, with only small number falling in between49. It is interesting to examine the reproducibility of scoring in immunohistochemistry. Several studies using the 10% cutoff scoring method describe a high degree of concordance between pathologists evaluating positive and negative tumors50,51. Percentage scoring of Ki-67, PCNA and MIB-1 expression in non-Hodgkin lymphoma was found to show a strong correlation with the proteins, a finding used to argue the case that immunohistochemical scoring was a reliable and complementary method to assess proliferative activity in such lymphomas52. A more recent study of p53, VEGF, Bcl-2 and APAF-1 immunohistochemistry in colorectal cancer suggested that interclass correlation coefficients for p53 and VEGF indicated substantial agreement between six observers when based on a “positive” score with a predetermined cutoff53. Closer examination of the results reveal that excellent agreement was achieved only when no positivity versus any positivity was evaluated and when 10% cutoff was employed for p53.

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With all the other proteins interobserver agreement was poor especially when staining was weak or variable. In other words, correlation was good for a “yes” or “no” result but when cutoffs were based on different values, correlation was weak supporting the argument that quantitation is unreliable. Such studies expose the subjectivity of quantitative scoring. Qualitative studies assessing “positive” and “negative” staining are reproducible but qualitative analysis is not. Similar results have been demonstrated with immunohistochemical scoring of Her2 in breast cancer. The recommended scoring method is based on a four-tiered system. Scores 0 and 3+ are easy to identify, but it is more difficult to discriminate between 1+ and 2+, and between 2+ and 3+ as it is largely based on subjective perception of the intensity of staining. Another issue of contention is the use of the 10% cutoff for a positive result54. This value is entirely arbitrary and did not take into account the significance of heterogeneity in Her2 staining. When the cutoff was arbitrary raised to 60% in a multicenter study, a concordance of 95% was obtained with fluorescence in situ hybridization (FISH)55. In the National Surgical Adjuvant Breast and Bowel Project studies, when 3+ cases were retested in a central laboratory 18 and 26% were found to be negative56,57. In one quality assurance program, it was found that when using an approved test kit for HER2, only 56% of participating laboratories attained acceptable staining58. In view of the demonstrated subjectivity involved in adjudicating staining intensity and therefore percentages of positive-stained cells, it would be more accurate to associate immunohistochemical labeling with specific cell structures. For example, when Her2 immunoexpression was linked to a specific pattern of membrane localization in that positivity was assigned only when the entire thickness of the section of cell membrane was labeled to produce a band-like staining pattern, a significantly greater degree of concordance with FISH was achieved.59 The HER2 protein is synthesized in the cytoplasm and transported to the cell membrane; so cytoplasmic staining is not an artifact. At the membrane in normal breast epithelium, HER2 aggregates in clusters, located predominantly in the basolateral aspect of the cells59,60. When HER2 is upregulated, these aggregates become larger and eventually coalesce to give the appearance of linear membrane expression.60 As the

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tissue section includes 3-5 micron-thick slices of cell membrane, the appearance of positive staining for HER2 should be visualized as a band (Figure 11a)59, a pattern also seen with the staining of other cellmembrane localized antigens like epithelial membrane antigen, CD79a, CD20 (Figure 11b) and CD3.

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Fig. 11. (a) A band pattern of staining for HER 2 characterizes 3+ positivity in breast cancer and a similar membranous staining is seen in the inset of a cytological smear. (b) A band pattern is seen in a CD20 stain of a large cell lymphoma. Note that the band pattern represents the 3-5 micron slice of the cell membrane in the sections. There is no significant cytoplasmic staining in both pictures.

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9. Controls As with all laboratory procedures positive and negative controls must be employed in immunohistology. Negative controls take the form of tissues that are known not to contain the antigen of interest. Another form of negative control includes the substitution of the primary antibody with antibody diluent, non-immune immunoglobulin or an antibody of irrelevant specificity derived from the same species and at the same dilution. It used to be thought that absorbing the primary antibody with highly purified protein or the peptide antigen that was used to generate the primary antibody produced an ideal negative control. This eliminated the binding of the antibody to the protein in the section. However, it has been shown that the absorption control may not bind to the same protein that was used to generate the primary antibody which, furthermore, may recognize a similar epitope of unrelated protein, especially after tissue fixation61,62. Single tissue sections or composite-tissue blocks known to contain the antigen of interest serve as positive controls and internal controls of non-lesional tissue in the test section also serve as positive controls although such tissue may not always available. Reference standards for quality control for reagents and tests in the clinical laboratory are well established. Such standards can be obtained from pooled serum but the development of reference standard controls for immunohistology is subjected to many more obstacles. Unlike serum samples, pathological tissues cannot be pooled and their supply is not limitless. Furthermore, morphologically similar tumors are not necessarily antigenically identical. The use of multitissue blocks provides a solution to some of these problems. Multi-tissue blocks are prepared by binding together with sausage skin many slivers of a wide range of different tumors to serve as both positive and negative controls. Similar blocks can also be made of non-tumor tissue, however, but all such blocks also contain a limited amount of material. When testing for tumor antigen, it is more appropriate to employ multi-tumor blocks as controls as the level of antigen is more likely to correspond to that in the test section than if multi-non tumor blocks were the control.

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Microtissue arrays are a possible solution to the limited supply of control tissue. Microarray blocks allow the incorporation of 200-300 fine tissue cores into blocks that can be used for controls against a wide variety of antibodies and as the cores are small (0.5-1.5 mm diameter) much of the original tissue block remains preserved. Microtissue arrays should be used with the realization that each core of tissue has been subjected to different fixation and processing so that the level of antigen preservation in each of the 200-300 tissue samples are different and by no means standardized. Recognition of this deficiency in controls led to the development of the “Quicgel” control which was an artificial tissue control block using a breast cancer cell line which was added to the tissue cassette containing the test sample63. This method requires the availability of suitable cell lines expressing the antigen in question, which needs to display consistent behavior under cell culture and storage. An extension of the “Quicgel” method is “histoids” in which three-dimensional pellets are grown in centrifugal cell culture to produce, in theory, an unlimited supply of “faux tissue” controls. If three or more cell lines are cocultured, the faux tissue can be employed as controls for many of the commonly employed antibodies including controls for fixation and the processing steps. Alternatively, it has been suggested that it is possible to develop preparations of purified protein that can be diluted to produce a series of known reference standards for both Western blotting and immunohistochemistry64. In quantitative procedures, a validated control expressing the range of scores is included alongside the test section so that it is subjected to the identical staining procedure. Closer examination of this common practice reveals that such “controls” are not optimal simply because they have been subjected to different pre-analytical variables, e.g., fixation and tissue processing may be quite different to the test tissue. As such, titrating the staining procedure to such “controls” can be misleading and inappropriate. Ideally, both controls and test must be subjected to identical pre-analytical and analytical conditions so that the state of antigen preservation in both tissues is identical. It is clear from the foregoing discussion that the question of appropriate controls has not been satisfactorily answered. As a

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consequence, this deficiency and the fact that variables cannot presently be standardized between laboratories and between specimens, all quantitative measures in immunohistology, many of which are subjective, should be viewed with skepticism and caution especially if therapeutic decisions are based on assigned scores65,66. 10. The Way Forward With the realization that the level of antigen preservation varies between specimens received by different laboratories and also within the same laboratory, what remedial measures can be taken to minimize the influence of such variables? For consistency, the laboratory should, as far as possible, adopt standard procedures for fixation, tissue processing, sectioning, and staining. Automated immunostaining devices provide a method of standardization of the staining protocol within the laboratory but the availability of several such commercial devices using different protocols makes uniformity between laboratories difficult. Antibodies should be stored in the appropriate manner at –70°C, with only aliquots of working dilutions stored at 4°C. Sections should be subjected to immunostaining as soon as possible after preparation. If delays are anticipated, the cut sections should be wrapped in foil and kept in a dark container at –20°C. As it is not possible to standardize the many variables that can influence antigen preservation in the sample, the best alternative at present is to optimize the staining procedure in order to obtain the best results, and this is done against known positive controls of appropriate tissues. Immunostaining should be optimized to known positive controls of similar tissue. Optimizing of any new antibody should be performed against known positive controls of appropriate tissues that have been fixed and processed in a similar if not identical manner to the test tissue. In addition, the antigen retrieval method and performance of immunohistochemical staining should also be similar. To ascertain the most optimal method of antigen retrieval the following methods should be routinely tested for each new antibody. Antigen retrieval should be tested with citrate buffer, 10 mmol/l at pH6.0, and citrate/EDTA at pH8.0 as retrieval solutions. This should be performed at both 98°C and

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110°C, with varying concentrations of primary antibody, starting with that suggested by the product manufacturer. If satisfactory results are not obtained, enzyme digestion (with protease or similar) should be introduced as an additional step before and after the heat-induced antigen retrieval. Assessment is based on the signal to noise ratio and the integrity of tissue morphology. It may be necessary with less sensitive antibodies to experiment with different buffers with different pH as retrieval solutions and another variable is the duration of heating. Optimization of the antigen retrieval procedure for each antibody is, at present, the most important step towards standardization. The optimization process merely ensures that the procedure is at its greatest sensitivity but cannot verify proper tissue fixation or processing and is not a method of standardization. References 1. Leong AS-Y, Wick MR, Swanson PE. Immunohistology and Electron Microscopy of Anaplastic and Pleomorphic Tumours. Cambridge: Cambridge University Press; pp2-35, 1997. 2. Elias JM. Immunohistopathology. A Practical Approach to Diagnosis. Chicago: ASCP Press, 2003. 3. Taylor CR, Cote RJ. Immunomicroscopy, A Tool for the Surgical Pathology, 3rd Edition. Edinburgh: Elsevier, 2005. 4. Leong AS-Y. Immunohistochemistry – Theoretical and Practical Aspects. Applied Immunohistochemistry for the Surgical Pathologist. Leong AS-Y (ed). London: Edward Arnold; pp1-22, 1994. 5. Sabattini E, Bisgaard K, Ascani S, et al. The EnVision system. A new immunohistochemical method for diagnostics and research: Critical comparison with the APAAP, ChemMate, CSA, LABC, and SABC techniques. J Clin Pathol 1998;51:506-510. 6. King G, Payne S, Walker F, et al. A highly sensitive detection method for immunohistochemistry using biotinylated tyramine. J Pathol 1998;183;237-241. 7. Shi SR, Key ME, Kalra KL. Antigen retrieval in formalin-fixed, paraffin-embedded tissues: An enhancement method for immunohistochemical staining based on microwave oven heating of tissue sections. J Histochem Cytochem 1991;39: 741-748. 8. Leong AS-Y, Milios J. An assessment of the efficacy of the microwave antigen retrieval procedure on a range of tissue antigens. Appl Immunohistochem 1993;1:267-274.

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9. Gown AM, de Wever N, Battifora H. Microwave-basd antigenic unmasking: A revolutionary new technique for routine immunohistochemistry. Appl Immunohistochem 1993;1:256-266. 10. Leong AS-Y, Parkinson R, Milios J. ‘Thick’ cell membranes revealed by immunocytochemical staining. A clue to the diagnosis of malignant mesothelioma. Diagn Cytopathol 1990;6:9-13. 11. Leong AS-Y, Stevens MW, Mukherjee TM. Malignant mesothelioma: Cytologic diagnosis with histologic, immunohistochemical and ultrastructural correlation. Sem Diagn Pathology 1992;9:141-150. 12. Leong AS-Y, Vinyuvat S, Suthipintawong C, Leong FJ. Patterns of basal lamina immunostaining in soft tissue tumours. App Immunohistochem 1997;5:1-7. 13. Leong AS-Y and Leong TY-M. Invited Review: Newer developments in immunohistology. Journal of Clinical Pathology 2006;59:1117-1126. 14. Wood B, Leong AS-Y. Cell adhesion proteins – Biology, detection and applications. Pathology 2003;35:101-105. 15. Ribic CM, Sargent DJ, Moore MJ, et al. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med 2003;349:247-257. 16. Popat S, Hubner R, Houlston RS. Systematic review of microsatellite instability and colorectal cancer prognosis. J Clin Oncol 2005;23:609-18. 17. Crisp H, Burton JL, Stewart R, Wells M. Refining the diagnosis of hydatidiform mole: image ploidy analysis and p57KIP2 immunohistochemistry. Histopathology 2003;43:363-373. 18. Sormunen R, Leong AS-Y, Vaaraniemi J, et al. Immunolocalisation of fodrin, ECadherin and beta-catenin adhesion complex in infiltrating ductal carcinoma of the breast – Comparison with an in vitro model. J Pathol 1999;187:416-423. 19. Yin H, Leong AS-Y. Histological grading of non-invasive papillary urothelial tumors. Validation of the 1998 WHO/ISUP system by immunophenotyping and follow-up. Am J Clin Pathol 2004;121:679-687. 20. Ye H, Gong L, Liu H, et al. MALT lymphoma with t(14;18)(q32;q21)/IgH-MALT1 is characterised by strong cytoplasmic MALT1 and Bcl10 expression. J Pathol 2005;205:293-301. 21. Kou SH, Chen LT, Yeh KH, et al. Nuclear expression of Bcl10 or nuclear factor kappa B predicts Helicobacter pylori-independent status of early-stage, high-grade gastric mucosa-associated lymphoid tissue lymphomas. J Clin Oncol 2004;22: 3491-3497. 22. Nickeleit V, Zeiler M, Gudat F, et al. Detection of the complement degradation product C4d in renal allografts: diagnostic and therapeutic implications. J Am Soc Nephrol 2002;13:242-251. 23. Regele H, Bohmig GA, Habicht A, et al. Capillary deposition of complement split product C4d in renal allografts is associated with basement membrane injury in

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25. 26.

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peritubular and glomerular capillaries: A contribution of humoral immunity to chronic allograft rejection. J Am Soc Nephrol 2002;13:2371-2380. Stein H, Foss HD, Durkop H, et al. CD30+ anaplastic large cell lymphoma: A review of its histopathologic, genetic, and clinical features. Blood 2000;96: 3681-3695. Leong AS-Y. Immunohistology of small round cell tumors in childhood. J Histotechnol 1999;22:239-246. Folpe AL, Goldblum JR, Shehata BM, et al. Morphologic and immunophenotypic diversity in Ewing family tumors: A study of 66 genetically confirmed cases. Am J Surg Pathol 2005;29:1025-1033. Barnoud R, Delattre O, Peoc’h M, et al. Desmoplastic small round cell tumor. RTPCR analysis and immunohistochemical detection of the Wilm’s tumor gene WT1. Pathol Res Pract 1998;194:639-700. Folpe AL, Hill CE, Parham DM, et al. Immunohistochemical detection of FLI-1 protein expression: A study of 132 round cell tumors with emphasis on CD99positive mimics of Ewing’s sarcoma/primitive neuroectodermal tumor. Am J Surg Pathol 2000;24:1657-1662. Rossi S, Orvieto E, Furlanetto A, et al. Utility of the immunohistochemical detection of FLI-1 expression in round cell and vascular neoplasm using a monoclonal antibody. Mod Pathol 2004;17:547-552. Argani P, Lal P, Hutchinson B, et al. Aberrant nuclear immunoreactivity for TFE3 in neoplasms with TFE3 gene fusions. A sensitive and specific immunohistochemical assay. Am J Surg Pathol 2003;27:750-761. Jiang Z, Wu C-L, Woda E, et al. P5045/α-methylacyl-CoA racemase. A useful marker for diagnosis of small foci of prostatic adenocarcinoma on needle biopsy. Am J Surg Pathol 2002;26:1169-1174. Negri G, Egarter-Vigl E, Kasal A, et al. p16INK4a is a useful marker for the diagnosis of adenocarcinoma fo the cervix utei and its precursors. Am J Surg Pathol 2003;27:187-193. Crespo M, Bosch F, Villamor N, et al. ZAP-70 expression as a surrogate for immunoglobulin-variable-region mutations in chronic lymphocytic leukemia. N Engl J Med 2003;348:1764-1775. Press MF, Slamon DJ, Flom KJ, et al. Evaluation of HER-2/neu gene amplification and overexpression: Comparison of frequently used assay methods in a molecularly characterised cohort of breast cancer specimens. J Clin Oncol 2002;20:3095-3105. Nowain A, Bhakta H, Pais S et al. Gastrointestinal stromal tumors: Clinical profile and pathogenesis, treatment strategies and prognosis. J Gastroenterol Hepatol 2005;20:818-824. Goldstein NS, Armin M. Epidermal growth factor receptor immunohistochemical reactivity in patients with American Joint Committee on Cancer Stage IV colon adenocarcinoma: Implications for a standardized scoring system. Cancer 2001;92:1331-1346.

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37. Sandler A. Clinical experience with the HER1/EGFR tyrosine kinase inhibitor erlotinib. Oncology 2003;16(11 Suppl 12):17-22 38. Fukuoka M, Yano S, Giaccone G, et al. Multi-institutional randomised phase II trial of Gefitinib for previously treated patients with advanced non-small cell lung cancer. J Clin Oncol 2003;21:2237-2246. 39. Volante M, Bozzalla-Cassione F, Papotti M. Somatostatin receptors and their interest in diagnostic pathology. Endocr Pathol 2004;15:275-91. 40. Taylor CR. An exaltation of experts: Concerted efforts in the standardization of immunohistochemistry. Hum Pathol 1994;25:2-4. 41. Shi SR, Gu J, Cote RJ, et al. Standardization of routine immunohistochemistry: Where to begin? Shi SR, Gu J, Taylor CR, eds. Antigen retrieval technique: Immunohistochemistry and molecular morphology. Natick, MA: Eaton. 2000: 255-272. 42. Leong TY-M, Leong AS-Y. Variables that influence outcomes in immunohistology. Aust J Med Sci 2007;28:47-59. 43. Jacobs TW, Prioleau JE, Stillman IE, et al. Loss of tumor marker-immunostaining intensity on stored paraffin slides of breast cancer. J Natl Cancer Inst 1996;88: 1054-1060. 44. Wester K, Wahlund E, Sundstrom C, et al. Paraffin section storage and immunohistochemistry. Appl Immunohistochem Mol Morph 2000;8:61-66. 45. Evers P, Uylings HB. Microwave-stimulated antigen retrieval is pH and temperature dependent. J Histochem Cytochem 1994;42:1555-1560. 46. Shi SR, Imam SA, Young L, et al. Antigen retrieval immunohistochemistry under the influence of Ph using monoclonal antibodies. J Histochem Cytochem 1995;43:193-198. 47. Wolff AC, Hammond ME, Schwartz JN, et al. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 2007;25:118-145. 48. Hervey JM, Clark GM, Osborne CK, et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 1999;17:1474-1481. 49. Collins LC, Botero ML, Schnitt SJ. Bimodal frequency distribution of estrogen receptor immunohistochemical staining results in breast cancer: An analysis of 825 cases. Am J Clin Pathol 2005;123:16-20. 50. Cascinu S, Staccioli MP, Gasparini G, et al. Expression of vascular endothelial growth factor can predict event-free survival in stage II colon cancer. Clin Cancer Res 2000;6:2803-2807. 51. Kay EW, Walsh CJ, Whelan D, et al. Interobserver variation of p53 immunohistochemistry – An assessment of a practical problem and comparison with other studies. Br J Biomed Sci 1996;53:101-107.

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52. Pich A, Ponti R, Valente G, et al. MIB-1, Ki-67 and PCNA scores and DNA flow cytometry in intermediate grade lymphoma. J Clin Pathol 1994;47:18-22. 53. Zlobec I, Steele R, Michel RP, et al. Scoring of p53, VEGF, Bcl-2 and APAF-1 immunohistochemistry and interobserver reliability in colorectal cancer. Mod Pathol 2006;19:1236-1242. 54. Leong TY-M, Leong AS-Y. Controversies in the assessment of HER-2. More questions than answers. Adv Anat Pathol 2006;13:263-269. 55. Vincent-Salomon A, MacGrogan G, Couturier J, et al. Calibration of immunohistochemistry for assessment of Her2/neu in breast cancer: Results of the French Multicentre GEFPICS Study. Histopathology 2003;42:337-347. 56. Paik S, Bryant J, Tan-Chiu E, et al. Real-world performance of HER2 testing – National Surgical Adjuvant Breast and Bowel Project experience. J Natl Can Inst 2002;94:852-854. 57. Roche PC, Suman VJ, Jenkins RB, et al. Concordance between local and central laboratory HER2 testing in the Breast Integroup Trial N9831. J Natl Cancer Inst 2002;94:855-57. 58. Miller K, Ibrahim M. The breast HER-2 module. Immunocytochemistry 2004;3:147-150. 59. Leong AS-Y, Formby M, Haffajee Z, Morey A. Refinement of immunohistologica parameters for Her2/neu scoring. Validation by FISH and CISH. Appl Immunohistochem Mol Morph 2006;14:384-389. 60. Nagy P, Jenei A, Kirsch AK, et al. Activation-dependent clustering of the erbB2 receptor tyrosine kinase detected by scanning near-field optica. l microscopy. J Cell Sci 1999;112:1733-41. 61. Willingham MC. Conditional epitopes: Is your antibody always specific? J Histochem Cytochem 1999;47:1233-1239. 62. Burry RW. Specificity controls for immunocytochemical methods. J Histochem Cytochem 2000;48:163-168. 63. Riera J, Simpson JF, Tamayo R, et al. Use of cultured cells as a control for quantitative immunocytochemical analysis of estrogen receptor in breast cancer. The Quicgel method. Am J Clin Pathol 1999;111:329-332. 64. Taylor CR. Personal communication. 2006. 65. Leong AS-Y. Editorial: Quantitation in immunohistology – Fact or fiction? A discussion of factors that influence results. Appl Immunohistochem Mol Morph 2004:12:1-7. 66. Leong AS-Y: Pitfalls in diagnostic immunohistology. Adv Anat Pathol 2004;11: 86-93.

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CHAPTER 6 IN SITU HYBRIDISATION FOR PHARMACODIAGNOSTIC TESTING

John M. S. Bartlett and Fiona M. Campbell Endocrine Cancer Research Group Cancer Research UK Building Western General Hospital Crewe Road South, Edinburgh, EH4 2XR, UK E-mail: [email protected] In situ hybridisation has become central to the clinical management of cancer. ISH can be used to detect gene amplification, duplication or deletion, chromosomal aberrations and translocations frequency using interphase nuclei in routinely processed pathological specimens. Both fluorescent and colorimetric ISH assays provide the basis for selecting appropriate treatment or management schedules in cancers as diverse as leukemias, breast and bladder. The technological and technical background to ISH is reviewed, with a focus on methodological aspects critical to the delivery of pharmacodiagnostic assays. Sample preparation methods, assessment and reporting guidelines are reviewed with particular attention to quality assurance and reproducibility in laboratory procedures.

1. Introduction to In Situ Hybridisation In situ hybridisation (ISH) was first described in 19691 using radiolabelled RNA and DNA probes to detect in situ (on cytological and later pathological sections) expression and amplification of specific genes. The basic principle behind the technique has not changed during the last 40 years although there has been a rapid diversification of methods and applications for ISH. The fundamental advantage of ISH is the ability to localise, “in situ”, within tissues or cells single gene or whole 119

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chromosomal abnormalities which can be related to the pathology of disease. Experimental approaches using in situ techniques for expression analysis are also widespread. Non-isotopic in situ hybridisation methods are now extensively used for the detection of normal and aberrant DNA sequences in intact cells. The development of interphase ISH methods in the early 1980’s (reviewed by Ref. 2) meant that analysis of chromosomal and genomic alterations could be performed in conventional pathological specimens. However, the relatively recent advent of fluorescence and chromogenic in situ hybridisation methods, applicable to both freshly prepared and formalin fixed preparations, has been the fundamental advance that has led to the diagnostic application of in situ techniques. Both fluorescence (FISH) and chromogenic (CISH) methods are used to detect gene amplification (HER2 in breast cancer3), gene rearrangements (BCR-ABL in leukaemia’s)4, microdeletions5, chromosomal duplication6,7, and viral infections (HPV) demonstrating the broad applicability of this methodology in molecular diagnostic medicine. Wider research applications, such as karyotyping and gene mapping8 are providing further impetus to the application of diagnostic FISH. However experimental extensions of ISH such as comparative genomic hybridisation (CGH) and spectral karyotyping (SKY) and expression analysis have yet to be widely applied within the diagnostic arena9 largely due to the loss of the in situ component that makes ISH such an invaluable diagnostic approach. ISH techniques used in the molecular diagnosis of cancer are predominantly applied to formalin fixed tissue samples, although analysis of cytology samples (for example in bladder cancer) or blood leukocytes (leukaemia) is increasingly common. Since the major technical advances required for the widespread application of ISH techniques in cancer diagnostics have been in regard to the analysis of formalin fixed tissues this chapter will focus on this aspect of the methodology with reference to simpler approaches for cytology and other applications as appropriate. 2. Methodological Considerations ISH is based on the ability of complimentary single-stranded nucleic acid molecules to hybridise to each other and therefore allow the

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demonstration of specific genes or chromosomes in the context of their cellular environment. Whilst early applications were limited to cell lines modern techniques are simple and can be applied to a range of tissue types3,10-12 following formalin fixation and involve the pre-treatment of the tissue or cellular preparation to un-mask the target DNA and the hybridisation of a specific DNA probe, of complimentary base sequence, to this target with subsequent detection by either fluorescent or chromogenic detection systems. Standard ISH protocols can be divided into a series of basic steps as follows; probe and sample preparation, DNA unmasking, denaturation of probe and target DNA, in situ hybridisation, post-hybridisation wash and visualisation of hybridised probes. 2.1. Probes and samples The primary requirement for DNA ISH is the development of a nucleic acid probe with homology to the target region. For the majority of clinical applications probes are commercially available. Commercial developed probes are quality assured and often accredited by national bodies (e.g. FDA). Similar quality assurance procedures would be required for “in house” probes. Probes can be synthesised by using DNA templates (BACs, cosmids, PCR products etc.) labelled by nick translation13, random priming, chemical modification or polymerase chain reaction (PCR). The majority of probe templates derived from long human DNA sequences contain repetitive sequences (e.g. Alu repeats), addition of Cot-1 DNA to probes can suppress non-specific hybridisation5. Probes targeting DNA repetitive sequences can be valuable for pharmacodiagnostics. Probes targeting telomeric or pericentromeric repetitive sequences such as the peri-centromeric alpha satellite tandem repeats, approximately 171 base pair repetitive sequences found at the pericentromeric regions of many human chromosomes14,15, can provide valuable information on tumour ploidy or the deletion/duplication of individual chromosomes6,7,10,14,16,17. Chromosome specific alpha satellite or “classical” satellite (types II and III) markers are available for the majority of human chromosomes.

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Recently surrogates for DNA probes using a protein backbone have been synthesised for many such repeat sequences18. Diagnostic ISH methodologies are most commonly applied either to isolated cells from whole blood or other body fluids19 or tissues which have been preserved by formalin fixation3,20. More recently alternative fixatives have been validated for ISH detection of HER2 gene amplification21. It appears that ISH techniques can be applied to tissues fixed in a variety of ways, however we remain unaware of any methods which are applicable to Bouins fixed tissues. Most current diagnostic applications for ISH require the use of formalin fixed tissues. The effect of fixation, in particular using formalin fixatives, on tissues is one of the key barriers to successful ISH. Most diagnostic samples are fixed using neutral buffered formalin which provides excellent tissue morphology and preservation. Subsequent mounting of tissues in paraffin wax is essential to provide structural support during sectioning of tissue blocks. To embed tissues in wax they must be dehydrated and immersed in liquid paraffin wax to allow complete tissue permeation. This process causes extensive protein denaturation (due to the heating required during embedding in wax) above the effects of formaldehyde which causes extensive proteinprotein and protein-DNA cross-linkages due to the chemical reaction between the oxidising aldehyde group and amino acids. The denaturation of proteins within fixed tissues, combined with the cross-linking caused by the oxidative action of fixatives effectively “masks” nuclear DNA. The network of cross-linked proteins acts as a barrier to the diffusion of macro-molecules, e.g. ISH probes, into the nucleus for subsequent hybridisation to DNA. To overcome this effect samples must be both rehydrated and permeabilised by breaking down (using chemical and enzymatic steps) protein-protein and protein-DNA cross links. A balance must be struck between maximal permeabilisation (to allow probe access) and preservation of tissue morphology. Most ISH protocols use a combination of chemical pre-treatment (often with heating) and enzymatic digestion. In our experience this enzymatic digestion step is the single most critical step in producing high quality ISH results20 (Figure 1).

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(a)

(b)

(c)

Fig. 1. Examples of Digestion: (a) Slightly under-digested nuclei are hidden beneath proteinaceous material, probe access will be reduced, (b) Optimally digested, with distinct nuclei with clearly defined boundaries, and (c) Slightly over-digested, nuclei are starting to be digested and have a “donut” appearance.

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When using unfixed cells or tissues samples must be pretreated to avoid damage during the ISH protocols which frequently involve high temperatures and high salt washes (see below). Light fixation with acid ethanol or formalin and the use of charged slides is generally sufficient for such applications19. 2.2. Tissue permeabilisation The goal of tissue permeabilisation is to maximise probe penetration into tissues – providing the most optimal probe hybridisation possible. Failure to permeabilise tissue will prevent probe reaching and binding to target DNA. However, most methods for tissue permeabilisation will, if taken to extremes, cause significant tissue damage and lead to loss of morphology. Excessive pre-treatment to maximise tissue permeability can cause loss of nuclear structure and ultimately loss of tissue resulting in a failure of in situ analysis. Step by step evaluation of pretreatment protocols, critically assessing the impact on successful ISH analysis in terms of tissue preservation and permeabilisation, can be invaluable in optimising assay performance. There are increasing numbers of different pretreatment protocols for ISH analysis and many diagnostic laboratories introduce minor modifications to existing protocols to optimise performance. Such protocols incorporate one or more permeabilisation procedures aimed at increasing probe access to DNA and these are almost universally followed by a protease digestion step to remove cellular proteins further increasing probe access and, in the case of fluorescence based methods, reducing autofluorescence. These procedures include acid treatment, treatment with reducing agents and detergents and protease digestion (with either pepsin, or less commonly proteinase K). Acid permeabilisation is often performed using 0.2N HCl, which is thought to reverse the effects of formalin fixation22,23. Sodium metabisulphite22 and sodium isothiocyanate3,4,19 break down protein disulphide bridges and are often applied as a precursor to proteolytic treatment3,20,24. Use of either acids or reducing agents, or a combination of both, is thought to facilitate proteolysis and thereby reduce tissue damage during exposure to proteases in ISH protocols.

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2.3. Protease digestion As with tissue permeabilistation the goal of protease pre-treatment is to maximise probe access with minimal tissue damage. In most cases sufficient tissue morphology can be retained to allow tissue morphology to be correlated with ISH results. Varying digestion times can be used to produce the optimal combination of methodological success with preservation of critical tissue structures. In our extensive experience of ISH techniques the careful optimization of protease digestion represents the single most important step for analytical success. The extent of digestion which is optimal varies with tissue type11, 12, 24-26 and to a lesser extent is impacted by differential fixation procedures within tissue types (See Figure 1). Frequently protocols published with some of the commercially available assays suggest inadequate protease digestion times and failure to adapt these to routine practice can cause assay failure. Protease concentrations and activity also vary from batch to batch and digestion times should be re-assessed if protease activity changes. 2.4. Hybridisation and post-hybridisation Whilst adequate sample preparation is essential for successful application of ISH, results can be severely compromised by inappropriate probe selection, hybridisation temperatures and under or over stringent posthybridisation washes (using inappropriate temperature, duration or buffers). For all ISH applications both “in house” and commercially sourced probes should be checked for specificity and applicability to the diagnostic scenario. ISH probes tend to be derived from BAC or similar sources and, in particular in the context of gene amplification and microdeletion assays, sequences should be checked to ensure specificity for the target gene in question. In particular probe sequences which overlap for more than one gene target should be avoided. Hybridisation temperatures for ISH assays aim to maximise hybridisation with a strong reliance on post hybridisation washes to ensure adequate stringency. Therefore in practice hybridisation temperatures are lower that would be required for similar sequence based

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assays (such as PCR). In most procedures sample DNA is denatured for 5-10 minutes at 72-80°C with probe DNA (plus or minus COT-1) being either co-denatured or denatured separately. Even when probe is supplied “ready denatured” we would recommend re-denaturing as some strand to strand annealing will occur over time. The duration of hybridisation can vary from 12-72 hours, research into shorter periods would be of value where critical diagnoses are time dependent. However for most applications this is not required. Since the hybridisation step of ISH assays is designed to maximise probe binding to target DNA there is inevitably a large degree of nonspecific probe binding which must be removed by post-hybridisation washes. The goal of this step (or steps) is to preserve the specifically bound probe whilst disrupting the binding of non-specifically bound probe which would otherwise lead to false positive signals. This step is achieved by using a combination of buffers with varying salt/formamide concentrations and temperature to increase the “stringency” of probe/target binding (stringency is a term used as an arbitrary measure of specificity)2. Decreasing salt (measured as sodium ion concentration) concentrations lead to reduced hybridisation (i.e. increased stringency) as do increased temperature and formamide concentrations. The result of each of these changes (increased temperature, decreased salt concentration etc.) is to destabilise the hydrogen bonds between nucleotide bases which are essential for probe binding to target DNA/RNA. For DNA probes the optimum hybridisation conditions can be defined by the following formula: T = 81.5°C + 16.6(log10[Na+]) + 0.41(%G+C) - 820/l - 0.6(%F) - 1.4(%mismatch). Where [Na+] = molar salt concentration, l = probe length (bases), %F = % formamide, % mismatch = % non-complementary base pairs between hybridising strands15. Specific changes to the buffers used can alter conditions required significantly, for example the inclusion of formamide which allows post-hybridisation washes to proceed at low temperature. A further critical aspect of post hybridisation washes is their duration. Even relatively low stringency washes can, if prolonged, reduce specific probe-DNA binding.

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2.5. Signal detection The earliest approaches to ISH used radioisotopically labelled (predominantly tritium) DNA probes. In addition to the hazards presented by working with radioactivity the prolonged exposure times (6-12 weeks) for such techniques make them impracticable for pharmacodiagnostic assays. The next major development occurred in the mid 1980’s with the development of digoxigenin and biotin labelled DNA probes which could be detected using anti-digoxigenin or antibiotin fluorescently tagged antibodies2,24. This approach was then superseded by directly fluorescently labelled probes (both DNA and PNA)3,25,27. Fluorescence-based ISH systems remain the most widely used in pharmacodiagnostics at the time of writing. However, with the development of chromogenic detection systems we have come full circle with a reversion to digoxigenin and biotin labelled probes detected by enzyme-linked antibodies to produce chromogenic signals which can be scored using light microscopy28-30. A range of detection systems now allow gold-, silver- or chromogen-based ISH detection with nomenclature expanding apace (SISH – silver-enhanced ISH, GOLDFISH, gold-enhanced ISH, CISH – chromogenic ISH; (Figures 2-4). For the sake of simplicity, since in effect silver, gold and other detection methods are all chromogenic in nature we will regard FISH and CISH (fluorescent and chromogenic ISH) as the two main ISH variants currently in use for pharmacodiagnostics.

(a)

(b)

Fig. 2. Examples of FISH signals in breast cancer cell lines: (a) Amplified for HER2 using a ratio of orange signals (HER2) to green signals (chromosome 17) and (b) “normal” polysomy signals of HER2 and chromosome 17.

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Chromosome 17

HER2

Fig. 3. Two-slide SISH staining, amplified case (Ventana INFORM™).

Non-amplified Case

Amplified Case

Fig. 4. Single-slide dual colour CISH (Dako DuoCISH™). (Supplied by Sven Müller DAKO).

3. FISH versus CISH There are strong proponents for both sides of the debate regarding the relative merits of FISH versus CISH. There exists a strong prejudice against the use of fluorescent-based systems which appears, to be the best of our judgement to be largely attributable to a healthy aversion to spending long periods in the dark! No strong cost or technical arguments against either CISH or FISH based systems have been put forward. There are equally strong prejudices against CISH technologies which are

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regarded as unproven and lest robust – whilst the former charge may be partly true at present it is readily addressed and there is little evidence that CISH is less robust than FISH. We therefore propose to briefly review the potential advantages of each approach and leave the reader to decide themselves which is most applicable to their current practice. The majority of the debate is centred around HER2 diagnostics in breast cancer, which currently remains the largest (in terms of sample volume) area of ISH based pharmacodiagnostics and which illustrates many of the key points in this area. 3.1. FISH Fluorescence in situ hybridisation remains the most widely used method for pharmacodiagnostic determination of HER2 status. Surveys of kit usage from programmes such as the UK National External Quality Assurance Scheme (UK-NEQAS) program for ISH31,32 show an overwhelming preference for this long established method. The assay has been shown to be technically robust33,34 (Bartlett et al. 2008 AJCP in press) and reproducible and to provide more accurate and reproducible results than IHC3,35. One of the key advantages which remains for FISH is the ability to use multicolour probes for the simultaneous detection of genes/chromosomes36,37. This multicolour capability is essential for a number of current and future molecular diagnostic approaches such as the bladder cytology screening test UroVysion™ (Abbott Molecular), interphase translocation assays (e.g. BCR-ABL) and multigene assays (HER2/TOP2A/Chromosome 17)38 in breast cancer. This remains probably the strongest argument for the retention of FISH as a key diagnostic tool in molecular pathology. 3.2. CISH Chromogenic assays have been previously hampered by the restriction to assays with single colour. This is particularly critical in situations where gene/chromosome ratios are an important step in diagnostic decisions (for example HER2) and for multicolour assays discussed above. Indeed the key argument against the use of CISH until recently was the omission

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of chromosome 17 in HER2 diagnostics or the use of a separate slide for the determination of this critical component of HER2 ISH assays (shown in Figure 3). Many have suggested that in fact chromosome 17 is important in only a minority of cases, however recent data from our own laboratory (AJCP in press) suggests that for almost 50% of cases assessment of chromosome 17 is essential to achieve an appropriate diagnosis of HER2 gene amplification. However, recent improvements in dual colour CISH systems (Dako, Roche, Zymed) are likely to address this balance and provide opportunities for CISH based dual colour assessments of HER2 status (as shown in Figure 4). One of the key technical challenges for CISH methods is that, since detection systems have reverted to enzymatic detection, it is possible to both “under” and “over” develop signals leading to in appropriate results. To date, however, there is no evidence that such challenges have compromised the quality of data developed by CISH (either single or dual colour). We have recently completed a multicentre study of CISH technology with excellent concordance between centres and across samples (AJCP in prep). Dual colour CISH approaches will address the long standing prejudice held by many diagnosticians against the use of fluorescent microscopy in molecular pathology. Another advantage includes the ability to archive slides (as opposed to images which are routinely archived for FISH) and potential cost savings relating to reagent costs although this remains to be assessed. 3.3. Summary Although there are strong prejudices regarding the relative merits of CISH versus FISH as pharmacodiagnostic applications of ISH there is no technical evidence that one method performs more accurately or robustly than another. With the recent development of dual colour CISH approaches, particularly for HER2 pharmacodiagnostics, there is likely to be a further expansion of dual colour CISH methods for EGFR and other oncogenes. The relative ease of interpretation of “break apart” and “translocation” assays using CISH should be evaluated and it may be that the merged signal colour observed with FISH based approaches

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continues to support use of fluorescence based assays in this context (e.g. BCR-ABL etc.). However for the majority of single or dual colour ISH approaches FISH and CISH will likely be equivalent and usage will depend as much upon cost and personal choice as technical precision. At present FISH is the only approach which can be robustly applied to multicolour (three or more) assays such as UroVysion™ and HER2/TOPA/Chromosome 17 and this is likely to be an area where FISH will dominate for the foreseeable future. However further improvements in colourimetric detection may ultimately lead to development of multicolour CISH assays. 4. Scoring and Interpretation of Results FISH signals are visualised using fluorescence microscopy with a light source emitting high energy photons to excite fluorophores. Most systems use 100W mercury or xenon vapour lamps although more modern non-mercury burning light sources are being increasingly used. For each fluorophore detection filters with appropriate wavelengths are used to focus the emitted band shifted photons. Multicolour signals are detected with multiple band pass filters. The increasing use of image analysis to capture, document and more recently evaluate FISH results is an important ongoing development in this area. For CISH evaluation of either single or dual colour CISH can be performed using simple light microscopy. For both methods underlying tissue pathology can be assessed with experience and care is required to appropriately score signals. Wide variation in approaches to the assessment of ISH results exists between different laboratories. Whilst this is acceptable, if not ideal, in research settings for diagnostic approaches a unified approach is clearly required although criteria may differ for individual tests. Assessment of aneuploidy in shed tumour cells from bladder cancer patients requires different criteria for those applied to detection of gene/chromosomal deletions and amplifications. Also, different approaches are required for tissue sections, which must reflect the phenomenon of nuclear truncation, and for fresh whole nuclear preparation from urine, blood etc. For tissue sections the effect of nuclear truncation means that control genes are

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required to detect gene deletions or amplifications and that methods reflect the loss of signal associated with sections. In our experience we have used fluorescent and chromogenic (including SISH) methods to assess chromosome copy number in whole cells and tissue sections and both gene amplification and deletion3,11,14,20,24-28. We studied the effects of both intra and inter observer variation, coupled with the number of cells which must be scored to achieve an accurate and reliable result3,11,14,24,27. Whilst an overview of this nature cannot provide strict guidelines which are applicable to all pharmacodiagnostic ISH assays there are a number of key principles which will apply in the vast majority of situations. If genomic abnormalities, be they translocations, fusions, aneusomy of particular chromosomes or gene deletions or amplifications, are to be accurately determined evaluation of assay performance in normal cells will be of value. This is most critical where fixed tissue sections are used, as in the majority of diagnostic applications, to correct the effects of nuclear truncation and sub-optimal hybridisation. To illustrate this point we have shown that, when assessed in normal tissue sections (which are by definition disomic for all chromosomes), the observed mean chromosomal copy number (MCCN) ranges between 1.4-1.7 copies per cell (depending on the probe used). This is clearly, for all chromosomes, markedly below the predicted value of 2.0 copies per cell (disomy)6,10,12,14,16,39. Thus for many chromosomes assessed by ISH observed MCCN’s of around 2.0-2.2 are frequently indicative of trisomy and incorrectly interpreted as being disomic. This applies where analysis of all cells within a tissue is included. Different values will apply if assay conditions are changed (e.g. ignoring cells with less than two signals for chromosomes in assessment of HER240, however this is not recommended as it biases against the detection of chromosomal losses. It is often valuable to characterise normal tissues, if possible using two observers (see below), to define the “normal range” for chromosomal and gene copy number. We have used the mean+/-standard deviations derived from normal tissues to identify chromosomal abnormalities, this provides a clear definition of assay performance essential for clinical diagnostics. Clearly such restraints apply much less to whole cell preparations (e.g. urines/bloods). However, in the latter context,

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discrimination between diseased and non-diseased cells may require that larger numbers of cells are scored and a low proportion of abnormal cells is regarded as still indicative of abnormalities. These observations impact equally on the detection of gene amplifications and deletions. Tumours which contain gene specific defects are more often than not aneusomic for one or more chromosomes, including those with the targeted gene amplifications3,16,27,39,41 (Jensen et al. in press). Approaches to the analysis of gene specific amplifications or deletions must discriminate between gain or loss of the whole chromosome and targeted subchromosomal changes. It is our view that the use of a specific control, either centromeric or telomeric chromosomal enumeration probes, is essential where the discrimination between amplification and simple duplication is required. For tests specific for gene amplification (e.g. for HER2) an increase in gene:chromosome ratio to >2.0 is taken as indicative of amplification. Interestingly an increase to >1.5 is regarded as sufficient for diagnosis of amplification of androgen receptor and topoisomerase IIα3,11. Conversely a decrease in ratio to 0.8 or less appears to be accepted for the definition of deletions of genes such as TOP2A38. In each case careful correlation with normal samples should also be performed. 5. Assay Variation All assays exhibit technical variation which must be acknowledged and controlled for. Currently almost all diagnostic ISH methods are analysed by human observers adding a further degree of complexity and potential error. Inter-observer variation is widely documented in other pharmacodiagnostic assay systems which are reliant on immunehistochemical staining and assessment. Until recently there has been relatively little evidence on multi-site assessment of intra- and interobserver variation for CISH and FISH based assays. Almost all has been from our own experience over a number of years. Recently however we have completed two multi-centre ring studies for FISH and CISH respectively (submitted). These have provided excellent information on intra-observer variation and site to site variation in the context of ISH based testing for HER2 gene amplification in breast cancer. Over

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nine sites the mean intra-observer variation was established as being approximately 5% for HER2 copy, chromosome 17 copy and HER2/chromosome 17 ratio. Inter-site variation ranged from 9.5-22%. In our experience this matches well with intra-observer variation within a single site of 10 signals/nucleus are seen in gene amplified samples, so does the inter-observer variation3. However, despite early reports of “clumps” of signals in CISH based assays we have not found any problems in quantifying as many as 50 signals per cell using current CISH based assays. In terms of both accuracy and reproducibility, therefore, dual observer scoring is a valuable method of achieving close concordance of results and validation of what is otherwise a purely objective interpretation of results. Where close concordance between observers is achieved and maintained, the number of cells scored for a clear diagnosis can be reduced. Ultimately, however, the use of objective systems, such as image analysis, will reduce the error rate and also the eye strain associated with most current FISH diagnostic procedures. 6. Pharmacodiagnostics and Quality Assurance Some pharmacodiagnostic ISH reagent suppliers also supply, either as an integral part of the kit or separately, control slides with known positive and negative controls. This is particularly true for the area of HER2 testing where known amplified and non-amplified cell line pellets are frequently used as controls (Ventana, DAKO, Vysis kits31,32,42,43). For all pharmacodiagnostic tests it is mandatory to include positive and negative controls to monitor local assay performance on a run by run basis32,34. This may include surrogates such as the cell lines described above for HER2 testing but should, whenever feasible, also include local samples with known positivity (e.g. amplified for HER2 in breast cancer) and normal (e.g. non-amplified)32,34. Results should be monitored both qualitatively and quantitatively to control assay performance. These internal quality assurance measures allow the monitoring of both technical performances of the assay and of subjective variation in assessment introduced by manual interpretation of results.

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Additionally, wherever such schemes exist, it is mandatory that all laboratories performing pharmacodiagnostic ISH procedures participate in external quality assurance schemes (EQA). In areas where such schemes do not yet exist partnerships between existing ISH EQA schemes and experts within the field may offer ideal opportunities to extend the role of current schemes to cover novel diagnostic methods. We are aware that UK NEQAS31,32,42,43 offers schemes for both HER2 testing, by any ISH method, diagnosis of BCR-ABL translocations and quantisation of BCR-ABL31,42. There is a Nordic EQA scheme which covers HER2 as does the CAP in the US31,42. However there is no-pan European scheme and much of Asia is not covered by formal EQA schemes. Although UK NEQAS welcomes participants from any country the scheme has no regulatory authority outside the UK at present31,42. Evidence from recent publications relating to performance in EQA schemes31,42 suggests that participation plays a significant role in improving and maintaining quality in pharmacodiagnostics. Such schemes also suggest that, contrary to recent reports44, many routine clinical diagnostic laboratories are able to achieve and maintain high quality diagnostics over prolonged periods of time31,42. References 1. Gall JG, Pardue ML: Formation and detection of RNA-DNA hybrid molecules in cytological specimens. Proceeding of the National Academy of Sciences USA 1969;63:378-383. 2. Wolfe KQ, Herrington CS: Interphase cytogenetics and pathology: A tool for diagnosis and research. Journal of Pathology 1997;181(4):359-361. 3. Bartlett JMS, Going JJ, Mallon EA, Watters AD, Reeves JR, Stanton P, Richmond J, Donald B, Ferrier R, Cooke TG: Evaluating HER2 amplification and overexpression in breast cancer. Journal of Pathology 2001;195(4):422-428. 4. Nolte M, Werner M, Ewig M, vonWasielewski R, Link H, Diedrich H, Georgii A: Megakaryocytes carry the fused bcr-abl gene in chronic myeloid leukaemia: A fluorescence in situ hybridization analysis from bone marrow biopsies. Virchows Archiv-An International Journal Of Pathology 1996;427(6):561-565. 5. Lavarino C, Corletto V, Mezzelani A, Della Torre G, Bartoli C, Riva C, Pierotti MA, Rilke F, Pilotti S: Detection of TP53 mutation, loss of heterozygosity and DNA content in fine-needle aspirates of breast carcinoma. British Journal of Cancer 1998;77(1):125-130. 6. Watters AD, Ballantyne SA, Going JJ, Grigor KM, Bartlett J: Aneusomy of chromosomes 7 and 17 predicts the recurrence of transitional cell carcinoma of the urinary bladder. BJU International 2004;85:42-47.

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7. Watters AD, Going JJ, Cooke TG, Bartlett JMS: Chromosome 17 aneusomy is associated with poor prognostic factors in invasive breast carcinoma. Breast Cancer Res Treat 2003;77(2):109-114. 8. Bell SM, Zuo JA, Myers RM, Knowles MA: Fluorescence in situ hybridization deletion mapping at 4p16.3 in bladder cancer cell lines refines the localisation of the critical interval to 30 kb. Genes Chromosomes and Cancer 1996;17(2):108-117. 9. Houldsworth J, Chaganti RSK: Comparative genomic hybridization: An overview. Am J Pathol 1994;145:1253-1260. 10. Bartlett JMS, Watters AD, Ballantyne SA, Going JJ, Grigor KM, Cooke TG: Is chromosome 9 loss a marker of disease recurrence in transitional cell carcinoma of the urinary bladder? British Journal of Cancer 1998;77(12):2193-2198. 11. Edwards J, Krishna NS, Mukherjee R, Watters AD, Underwood MA, Bartlett JMS: Amplification of the androgen receptor may not explain development of androgen independent prostate cancer. British Journal of Urology 2001;88:1-10. 12. Brown RSD, Edwards J, Dogan A, Payne H, Harland SJ, Bartlett JMS, Masters JRW: Amplification of the androgen receptor gene in bone metastases from hormone-refractory prostate cancer. Journal of Pathology 2002;198(2):237-244. 13. Watters AD, Stacey MW, Bartlett JMS: A modified Nick translation method used with FISH that produces reliable results with archival tissue sections. Molecular Biotechnology 2002;20(3):257-260. 14. Bartlett JMS, Adie L, Watters AD, Going, JJ, Grigor KM: Chromosomal aberrations in transitional cell carcinoma that are predictive of disease outcome are independent of polyploidy. BJU International 1999;84(7):775-779. 15. Weier HUG, Gray JW: A degenerate alpha satellite probe, detecting a centromeric deletion on chromosome 21 in an apparently normal human male, shows limitations of the use of satellite DNA probes for interphase ploidy analysis. Analytical Cellular Pathology 1992;4:81-86. 16. Watters AD, Going JJ, Grigor KM, Bartlett JMS: Progression to detrusor-muscle invasion in bladder carcinoma is associated with polysomy of chromosomes 1 and 8 in recurrent pTa/pT1 tumours. European Journal of Cancer 2002;38(12):1593-1599. 17. Weier HUG, Kleine HD, Gray JW: Labeling of the centromeric region on human chromosome 8 by in situ hybridization. Human Genetics 1991;87:489-494. 18. Osamura RY: Recent technical developments of immunomolecular histochemistry and its applications to diagnostic pathology. Acta Histochemica et Cytochemica 1999;32(2):151-152. 19. Bubendorf L, Grilli B, Sauter G, Mihatsch MJ, Gasser TC, Dalquen P: Multiprobe FISH for enhanced detection of bladder cancer in voided urine specimens and bladder washings. American Journal of Clinical Pathology 2001;116(1):79-86. 20. Watters AD, Bartlett JMS: Fluorescence in situ hybridization in paraffin tissue sections - Pretreatment protocol. Molecular Biotechnology 2002;21(3):217-220. 21. Willmore-Payne C, Metzger K, Layfield LJ: Effects of fixative and fixation protocols on assessment of Her-2/neu oncogene amplification status by fluorescence in situ hybridization. Applied Immunohistochemistry and Molecular Morphology 2007;15(1):84-87. 22. Pahphlatz MMM, de Wilde PCM, Poddighe P, Dekken Hv, Vooijs GP, Hanselaar AGJM: A model for evaluation of in situ hybridisation spot-count distributions in tissue sections. Cytometry 1995;20:193-202.

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23. Wolman SR: Fluorescence in situ hybridisation; a new tool for the pathologist. Human Pathology 1994;25:586-590. 24. Bartlett JMS, Watters AD, Ballantyne SA, Going JJ, Grigor KM, Cooke TG: Estimation of chromosome 9 copy number using quantitative fluorescence in situ hybridisation fish as a marker of disease recurrence in transitional cell carcinoma of the bladder. J Pathology 1997;182:A20. 25. Watters AD, Stacey MW, Going JJ, Grigor KM, Cooke TG, Sim E, Bartlett JMS: Genetic alterations of N-acetyl transferase in transitional cell carcinoma of the bladder. British Journal of Cancer 1998;78(2):154. 26. Latif Z, Watters AD, Dunn I, Grigor KM, Underwood MA, Bartlett JMS: HER2/neu gene amplification and protein overexpression in G3 pT2 transitional cell carcinoma of the bladder: A role for anti-HER2 therapy? European Journal of Cancer 2004;40(1):56-63. 27. Watters AD, Latif Z, Forsyth A, Dunn I, Underwood MA, Grigor KM, Bartlett JMS: Genetic aberrations of c-myc and CCND1 in the development of invasive bladder cancer. British Journal of Cancer 2002;87(6):654-658. 28. Isola J, Tanner M, Forsyth A, Cooke TG, Watters AD, Bartlett JMS: Interlaboratory comparison of HER-2 oncogene amplification as detected by chromogenic and fluorescence in situ hybridization. Clin Cancer Res 2004;10(14):4793-4798. 29. Tubbs R, Pettay J, Skacel M, Powell R, Stoler M, Roche P, Hainfeld J: Goldfacilitated in situ hybridization: A Bright-Field autometallographic alternative to fluorescence in situ hybridization for detection of HER-2/neu gene amplification. Am J Pathol 2002;160(5):1589-1595. 30. Tubbs R, Pettay J, Hicks D, Skacel M, Powell R, Grogan T, Hainfeld J: Novel bright field molecular morphology methods for detection of HER2 gene amplification. Journal of Molecular Histology 2004;35(6):589-594. 31. Bartlett JMS, Ibrahim M, Jasani B, Morgan JM, Ellis I, Kay E, Magee H, Barnett S, Miller K: External quality assurance of HER2 fluorescence in situ hybridisation testing: Results of a UK NEQAS pilot scheme. J Clin Path 2007;60(7):816-819. 32. Ellis IO, Bartlett J, Dowsett M, Humphreys S, Jasani B, Miller K, Pinder SE, Rhodes A, Walker R: Updated recommendations for HER2 testing in the UK. J Clin Path 2004;57(3):233-237. 33. Dowsett M, Bartlett J, Ellis IO, Salter J, Hills M, Mallon E, Watters AD, Cooke T, Paish C, Wencyk PM, Pinder SE: Correlation between immunohistochemistry (HercepTest) and fluorescence in situ hybridization (FISH) for HER-2 in 426 breast carcinomas from 37 centres. Journal of Pathology 2003;199(4):418-423. 34. Ellis IO, Dowsett M, Bartlett J, Walker R, Cooke T, Gullick W, Gusterson B, Mallon E, Barrett-Lee P: Recommendations for HER2 testing in the UK. J Clin Path 2000;53(12):890-892. 35. Press MF, Sauter G, Bernstein L, Villalobos IE, Mirlacher M, Zhou JY, Wardeh R, Li YT, Guzman R, Ma YL, Sullivan-Halley J, Santiago A, Park JM, Riva A, Slamon DJ: Diagnostic evaluation of HER-2 as a molecular target: An assessment of accuracy and reproducibility of laboratory testing in large, prospective, randomized clinical trials. Clin Cancer Res 2005;11(18):6598-6607. 36. Di Leo A, Gancberg D, Larsimont D, Tanner M, Jarvinen T, Rouas G, Dolci S, Leroy JY, Paesmans M, Isola J, Piccart MJ: HER-2 amplification and topoisomerase II{alpha} gene aberrations as predictive markers in node-positive breast cancer patients randomly treated either with an anthracycline-based therapy or with

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J. M. S. Bartlett & F. M. Campbell cyclophosphamide, methotrexate, and 5-fluorouracil. Clin Cancer Res 2002;8(5): 1107-1116. Schwarz S, Rechenmacher M, Filbeck T, Knuechel R, Blaszyk H, Hartmann A, Brockhoff G: Value of multicolour fluorescence in situ hybridisation (UroVysion) in the differential diagnosis of flat urothelial lesions. J Clin Path 2008;61(3):272-277. Bartlett JMS, Munro AF, Cameron DA, Thomas JS, Prescott RJ. Twelves, C. Type I receptor tyrosine kinase profiles identify patients with enhanced benefit from anthracyclines in the BR9601 adjuvant breast cancer chemotherapy trial. Journal of Clinical Oncology 2008, (in press). Watters AD, Stacey MW, Going JJ, Grigor KM, Cooke TG, Sim E, Bartlett JMS: Genetic aberrations of NAT2 and chromosome 8: Their association with progression in transitional cell carcinoma of the urinary bladder. Urologia Internationalis 2001;67(3):235-239. Wang S, Saboorian MH, Frenkel EP, Haley BB, Siddiqui MT, Gokaslan S, Hynan L, Ashfaq R: Aneusomy 17 in breast cancer: Its role in HER-2/neu protein expression and implication for clinical assessment of HER-2/neu status. Mod Pathol 2002;15(2):137-145. Kirkegaard T, Nielsen KV, Jensen LB, Campbell FM, Muller S, Tovey SM, Brown S, Cooke TG, Bartlett JMS: Genetic alterations of CCND1 and EMSY in breast cancers. Histopathology 2008;52(6):698-705. Bartlett JMS. Ibrahim M, Jasani B, Morgan JM, Ellis I, Kay E, Connolly Y, Campbell F, O’Grady A, Bartnett S, Miller K. External quality assurance of HER2 FISH and ISH Testing: Three years of the UK National External Quality Assurance Scheme. American Journal of Clinical Pathology 2008. Walker RA, Bartlett JMS, Dowsett M, Ellis IO, Hanby AM, Jasani B, Miller K, Pinder SE: HER2 testing in the UK: Further update to recommendations. J Clin Path 2008;61(7):818-824. Wolff AC, Hammond ME, Schwartz JN, Hagerty KL, Allred DC, Cote RJ, Dowsett M, Fitzgibbons PL, Hanna WM, Langer A, McShane LM, Paik S, Pegram MD, Perez EA, Press MF, Rhodes A, Sturgeon C, Taube SE, Tubbs R, Vance GH, van de Vijver M, Wheeler TM, Hayes DF: American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer. Journal of Clinical Oncology 2007;25(1):118-145.

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CHAPTER 7 MAMMAPRINT® TRANSLATING RESEARCH INTO A DIAGNOSTIC TEST

Annuska M. Glas, Leonie Delahaye and Oscar Krijgsman Agendia BV, SciencePark 406 1098 XH Amsterdam, The Netherlands E-mail: [email protected] Gene expression profiles that provide clinical utility have been developed in the last decade. However, only a handful are currently available for broad clinical use through commercial enterprises. Once a test does enter the clinical setting it is imperative to get the correct answer even under conditions of varying quality of sample input. In the transition phase of the development of a commercial available test there are a number of requirements that need to be met. In particular, for a gene expression microarray test using a multiple gene index as readout, a customized array including control systems needs to be designed, validated and shown to be reproducible, robust, accurate and stable over time. In addition, regulatory approval is needed. This chapter describes all important critical steps being met for the implementation of a multi-marker microarray (MammaPrint®) as a routine diagnostic tool.

1. Introduction Measuring the expression of thousands of genes of many samples at the same time has become available in the last 20 years1,2. Large scale use of microarrays has unraveled many questions that had been impossible to answer in former years. A PubMed3 search for microarrays leads to over 28,000 hits, indicating its widespread use. For example, disease types with similar characteristics that have strikingly different outcomes

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(e.g. breast cancer, lymphoma) were unraveled by gene expression profiling into different entities that allows for stratification and improved diagnosis. A large number of gene expression profiles have been discovered over the years and the ability to use these profiles in a high throughput diagnostic setting would be a great advancement for patient management. It took approximately six years from the time the first microarray profiles were published in 19984,5 until the first microarray diagnostic tests appeared on the market6-8. For example, in 2002 Van’t Veer et al. published a paper in which the development of a prognostic breast cancer profile was described and within two years this profile became available on the market as MammaPrint®. The most important steps in development of a diagnostic test (Figure 1) are described in this chapter and MammaPrint® is used as an example.

Fig. 1. Phases in the development of a microarray-based diagnostic test.

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2.1. Clinical need and profile development The first step towards the development of a diagnostic gene expression profile is to clearly define the use of the profile in clinical practice, such as prognosis or treatment response predictions. MammaPrint® was developed specifically to distinctively separate patients in groups at low and high risk of developing metastases7. The development of distant metastases is the primary cause of death in breast cancer patients; approximately one third of women with lymph node negative breast cancer will develop distant metastases. The challenge therefore is to predict the risk of metastasis at the time of primary diagnosis and accurately manage those patients identified as high-risk. Adjuvant systemic therapy has proven to be effective in preventing the outgrowth of distant metastases9,10. Both hormonal therapy for estrogen receptor positive breast cancer and chemotherapy for all breast cancer are widely used. However, to treat those women that are at risk current guidelines recommend adjuvant systemic therapy in the large majority of stages 1 and 2 breast cancer11,12. As only about 40% of these patients are at risk, a large proportion of patients is currently overtreated, thereby exposing those women with unnecessary side-effects. The biology of the disease reflected in the gene expression was employed to improve the classification. The MammaPrint® test has been developed by using genome wide expression data and a data driven approach13. In this approach the genes are selected in an unbiased fashion; there are no predefined assumptions on whether certain genes are more likely to be involved in a process of interest. The data driven approach correlates gene expression to certain tumor traits, such as the risk of development of distant metastases in patients with early stage breast cancer7. Using this method 231 genes were found to be significantly correlated to disease outcome. The number of genes was optimized using a nearest mean classifier in a leave one out cross validation method to reach an optimum performance at 70 genes. In this study it was important to set the classification threshold in such a way that the lowest false negative rate was reached, rather than average sensitivity and specificity rates.

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Fig. 2. Expression data matrix of 70 prognostic markers genes from tumors of 78 breast cancer patients. Each row represents a tumor and each column a gene. Genes are ordered according to their original ordering. Tumors are ordered by their correlation to the average profile of the good prognosis group). The metastases status for each patient is shown in the right panel. White indicates patients who developed metastases within five years after the initial diagnosis, black indicates patients who continued to be metastasis free for at least five years.

The data driven approach for development of a gene expression profile is however a difficult one. The first prerequisite is the use of high quality well annotated samples and high quality microarray data. A more intrinsic problem of these development studies entails the high number of analyzed genes versus the usually small number of tumor samples. A number of publications have placed critical notes on the use of microarray data with regard to over estimation of the performance due to over-fitting of the profile, selection bias of genes and error estimation14-17. Careful sample selection criteria as well as multiple cross-validation procedures during the initial development of the classifier can lower the chance of overtraining. However, in order to verify whether the developed profile truly classifies patients according to patient outcome one or preferable more independent validation studies are required.

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2.2. Validation of the profile The first validation of the MammaPrint® signature was a retrospective analysis in a consecutive series of 295 patients diagnosed between 1984 and 199518. In the original study patients were specifically selected on the basis of outcome: distant metastasis development within five years or remaining metastasis free for at least five years, therefore this cohort of patients was used to provide a more accurate estimate of the risks of metastases associated with the signature. This study showed that the 70 genes that make up the MammaPrint® profile outperformed the analyzed clinical criteria that are currently used to classify breast cancer patients into risk groups. Technically this retrospective validation is not a true independent validation since some of the samples that were used for the development of the signature were part of the validation cohort as well. Leaving these samples out would have resulted in selection bias of the validation series, since it would have depleted disproportionately a large number of patients in whom distant metastases developed within five years. Corrected values (the “leave-one-out” cross-validated classifications) for these samples were used, thus minimizing the possibility of overestimating the value of the prognosis profile while keeping the consecutive series complete. Also, even after the original training samples were removed, the 70 genes still showed a significant prognostic value. Given that the profile was now validated, the transition to the development of a commercially available test was now feasible. The 70-gene classifier was subsequently translated into a diagnostic test (described in the paragraph 2.5). Using the diagnostic custom arrays, MammaPrint® was then independently validated in samples from five different institutions in Europe19. This study used frozen tumor samples that were send to a central facility for analysis (Agendia B.V.), blinded from clinical data. An independent statistical office had simultaneous access to both the microarray data and the clinical data. This independent validation confirmed the prognostic value of MammaPrint®.

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The prognostic value of MammaPrint® has since been validated in many series: in a relatively recently diagnosed, representative nodenegative breast cancer patient series20, in two series of older and post menopausal women (Europe and US)21,22, and in patients with 1-3 positive lymph nodes23. In these independent studies (Table 1), MammaPrint® validated as an independent prognostic factor. Although the test was originally designed and validated in samples that had been stored in the freezer for many years (and possibly resulting in gene expression differences because of tissue handling changes over time), in this study using more recent tissue samples MammaPrint® performance validates. The more recent studies also indicate that the test validates in more recently diagnosed patients. ®

Table 1. Overview of the MammaPrint validation studies. MammaPrint Validation Van de Vijver et al. (2002) New Eng J Med 347, 1999-2009 Buyse et al. (2006) J Natl Cancer Inst. 17, 1183-1192 Wittner et al. (2008) Clin Cancer Res 14, 2988-2993 Mook et al. (2008) Breast Cancer Res Treat, Jul 27 Bueno-de-Mesquita et al. (2008) Breast Cancer Res Treat, Sep 26 Mook et al. (2008) Poster presentation at San Antonio Breast Cancer Symposium Bueno-de-Mesquita et al. (2007) Lancet Oncol 8, 1079-1087

Details 295 patients 151 patients LN7.3 yrs follow-up

Metastasis-Free Survival by MammaPrint at 10 yrs for LN- patients: good prognosis signature 87% poor prognosis signature 44%

302 patients LN0, 0.25, 0.5, 0.75, and 0.9). This design may serve as a middleground between the unselected and enrichment clinical trial designs, at the cost of a somewhat larger sample size and/or redundant power dictated by the strategy of partitioning the overall type I error rate. In addition, any trial that uses the data from the same trial to both define and validate a marker cut-point will likely face skepticism from the external community.

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4. Biomarker Assays In addition to the design considerations discussed so far for a marker validation trial, multiple additional issues require consideration. These include the marker assessment methods, feasibility of obtaining the specimens, the reliability and reproducibility of the assay, and additional cost involved with assessing the marker status on every patient, some of which are outlined below. 4.1. Tissue versus serum based biomarkers While the ultimate decision to use a serum or tissue based biomarker is scientifically driven, serum based biomarkers may be preferred for the simplicity, cost, feasibility in a clinical setting (if validated) and from an ethical perspective of obtaining multiple biopsies versus multiple blood draws from a patient. The logistics of sample collection, storage and shipment, and longevity of specimens might not differ between these two, except that a blood sample is undoubtedly more accessible than a tumor sample. 4.2. Local versus central testing The determination of whether within the conduct of a prospective clinical trial, local or central testing of a possible predictive biomarker should be allowed depends on many factors. A key driver of the decision is the intended ultimate clinical use of the biomarker. If it is envisioned that central testing of the marker will always be required, then the choice is clear. Similarly, if the assay technique is well established, the results unambiguous (such as presence or absence of a specific mutation), and biospecimens readily suitable for assay without significant preprocessing, then local biomarker determination for trial eligibility is likely preferred. In our experience, however, in many cases the situation is less clear: the assay may be most optimally centrally performed at the present time, but with the intention of local testing if the biomarker is validated. In this case, when feasible, local testing may be acceptable to

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allow patients to enter the trial, with central confirmation required for the patient to be included in the trial’s primary analysis. In this way, data can be generated on the concordance rate between local and central testing, which may help guide the future use of the biomarker. An example of where the central testing was done after the fact, and was discordant in a high proportion of cases compared to the local testing results, is HER2 positivity in breast cancer, described in detail below. Trastuzumab (Herceptin), a human monoclonal antibody, is currently approved for treatment of HER2 positive breast cancer patients in the advanced as well as the adjuvant setting19. Subsequent analyses of the data from NCCTG N9831 and NSABP B-31 (data from the trials that resulted in the approval of Herceptin), however, have raised the possibility of a beneficial effect of trastuzumab in a more broadly defined patient population. Specifically, post-hoc central testing for HER2 expression from the available tumor tissue blocks from the NSAPB B-31 trial has demonstrated that patients with tumors that were negative for FISH and had less than IHC 3+ staining by central testing also derived benefit from trastuzumab, thus suggesting that the definition for HER2 positivity based on FISH or IHC for the adjuvant disease setting may need to be refined31. Similarly, in the cohort of patients entered on NCCTG N9831 trial based on local HER2 positivity but found to be HER2 negative by central testing, the observed benefit of trastuzumab was similar to the HER2 positive patients by central testing32. Additionally, early in the NCCTG N9831 study, there was a high degree of discordance (approximately 25%) in the HER2 results between central and local testing for IHC and FISH33. Since patients deemed HER2 negative based on the local evaluation were not enrolled onto the NSABP B31 and NCCTG N9831 trials, the validity of HER2 as a predictive marker could not be fully established, and the question remains open whether trastuzumab therapy may benefit a potentially larger group than the approximately 20% of patients defined as HER2 positive by central testing in these two trials. The assay methodology also plays an important role in this determination. For example, is the biomarker status determined based on immunohistochemistry, FISH, or other microarray based high

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dimensional classifiers? The sophistication of some of these techniques makes a local assessment of the marker status infeasible, expensive, and/or unreliable. The use of commercially available kits might be a solution, but again the acceptance of this for routine clinical use is dependent on the simplicity of the kit, timeliness of the results, and the cost to the patient. 4.3. Chemotherapy sensitivity assays Sometimes multiple markers may be hypothesized to jointly predict a clinical outcome or to define an optimal regimen. In such situations, one can either perform a sequential testing procedure (i.e., testing one marker at a time) to select the treatment for the patients in the marker based arm, or one can assign treatment to patients using an algorithm based on multiple markers. One such example is the use of tumor chemosensitivity assays designed to predict which chemotherapy agent a patient’s tumor may be sensitive to based on a direct assessment of the patient’s tumor. A randomized multinational trial was recently reported comparing assay directed chemotherapy (using an assay known as the ATP-TCA assay) versus clinician’s best choice of treatment in platinum refractory recurrent ovarian carcinoma patients. In this trial, no benefit was observed for assay directed therapy, possibly due to an increased use of combination and/or more effective chemotherapy in the physician choice arm as the study progressed. This illustrates a potential challenge to the marker based strategy design when the non-marker based arm is not randomized34. 5. Conclusion The design of a clinical trial is driven by a combination of scientific, clinical, statistical and ethical considerations to advance new discoveries from the bench to the bedside. Predictive biomarkers move us a step closer toward individualized medicine, whereby individuals who are likely to have a favorable clinical outcome such as improved survival and/or decreased toxicity from a treatment can be prospectively identified. The fundamental questions such as: “Is the new treatment

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effective in all patients or just in the marker-defined subgroup”, and “what is the added value of marker assessment in every patient in relation to the prevalence of the marker, the incremental benefit of treatment selection based on the marker, and the added costs of the assay”?, need to be carefully considered in the determination of the clinical utility of a biomarker. Acknowledgments Supported in part by the National Cancer Institute Grants: Mayo Clinic Cancer Center (CA-15083), and the North Central Cancer Treatment Group (CA-25224). References 1. Conley BA, Taube SE. Prognostic and predictive markers in cancer. Dis Markers. 2004;20(2):35-43. 2. Taube SE, Jacobson JW, Lively TG. Cancer diagnostics: Decision criteria for marker utilization in the clinic. Am J Pharmacogenomics. 2005;5(6):357-64. 3. Sequist LV, Bell DW, Lynch TJ, Haber DA. Molecular predictors of response to epidermal growth factor receptor antagonists in non-small-cell lung cancer. J Clin Oncol. 2007 Feb 10;25(5):587-95. 4. Bonomi PD, Buckingham L, Coon J. Selecting patients for treatment with epidermal growth factor tyrosine kinase inhibitors. Clin Cancer Res. 2007 Aug 1;13(15 Pt 2): s4606-12. 5. Slamon D. Herceptin: Increasing survival in metastatic breast cancer. Eur J Oncol Nurs. 2000 Mar;4(Sa):24-9. 6. Paik S. Clinical trial methods to discover and validate predictive markers for treatment response in cancer. Biotechnol Annu Rev. 2003;9:259-67. 7. Prentice RL. Surrogate endpoints in clinical trials: Definition and operational criteria. Stat Med. 1989;8:431-440. 8. Buyse M, Burzykowski T, Carroll K, et al. Progression-free survival as a surrogate for survival in advanced colorectal cancer. J Clin Oncol. 2007;25:5218-5224. 9. Bruzzi P, Del Mastro L, Sormani MP, et al. Objective response to chemotherapy as a potential surrogate end point of survival in metastatic breast cancer patients. J Clin Oncol. 2005;23:5117-5125. 10. Burzykowski T, Buyse M, Piccart-Gebhart MJ, et al. Evaluation of tumor response, disease control, progression-free survival, and time to progression as potential surrogate endpoints in metastatic breast cancer. J Clin Oncol. 2008:26:1987-1992.

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11. McLeod HL, Watters JW. Irinotecan pharmacogenetics: Is it time to intervene? J Clin Oncol. 2004;22(8):1356-1359. 12. Goetz MP, Safgren S, Goldberg RM, Grothey AF, Mandrekar SJ, Reid JM, Erlichman C, Adjei AA, Rubin J, Ames MM. A phase I does escalation study of irinotecan (CPT-11), oxaliplatin (Oxal) capecitabine (Cap) within three UGT1A1 TA promoter cohorts (6/6, 6/7, and 7/7). J Clin Oncol. 2005 ASCO Annual Meeting Proceedings, Orlando FL; 28(16S):138s (abs 2014). 13. Elsaleh H, Joseph D, Grieu F, Zeps N, Spry N, Iacopetta B. Association of tumour site and sex with survival benefit from adjuvant chemotherapy in colorectal cancer. Lancet. 2000;355:1745-1750. 14. Elsaleh H, Powell B, McCaul K, Grieu F, Grant R, Joseph D, Iacopetta B: p53 alteration and microsatellite instability have predictive value for survival benefit from chemotherapy in stage III colorectal carcinoma. Clin Cancer Res. 2001; 7:1343-1349. 15. Amado RG, Wolf M, Peeters M, Van Cutsem E, Siena S, Freeman DJ, Juan T, Sikorski R, Suggs S, Radinsky R, Patterson SD, Chang DD. Wild-type KRAS is required for panitumumab efficacy in patients with metastatic colorectal cancer. J Clin Oncol. 2008;26(10):1626-34. 16. Freeman D, Juan T, Meropol NJ, Hecht JR, Berlin J, Van Cutsem E, Reiner M, Radinsky R, Amado RG, Peeters M. Association of somatic KRAS gene mutations and clinical outcome in patients with metastatic colorectal cancer receiving panitumumab monotherapy. 14th European Cancer Conference. 2007, Abstract 3014. 17. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res. 2004 Oct 15;10(20):6759-63. 18. Maitournam A, Simon R. On the efficiency of targeted clinical trials. Stat Med. 2005 Feb 15;24(3):329-39. 19. Romond EH, Perez EA, Bryant J, Suman VJ, Geyer CE Jr, Davidson NE, Tan-Chiu E, Martino S, Paik S, Kaufman PA, Swain SM, Pisansky TM, Fehrenbacher L, Kutteh LA, Vogel VG, Visscher DW, Yothers G, Jenkins RB, Brown AM, Dakhil SR, Mamounas EP, Lingle WL, Klein PM, Ingle JN, Wolmark N. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med. 2005 Oct 20;353(16):1673-84. 20. Simon R, Wang SJ. Use of genomic signatures in therapeutics development. The Pharmacoenomics J. 2006;6:1667-73. 21. Song Y, Chi GYH. A method for testing a prespecified subgroup in clinical trials. Statistics in Medicine. 2007;26:3535-49. 22. Bauer P. Multiple testing in clinical trials. Statistics in Medicine. 1991;10:871-890. 23. Mandrekar SJ, Grothey A, Goetz MP, Sargent DJ. Clinical trial designs for prospective validation of biomarkers. Am J Pharmacogenomics. 2005;5(5):317-25. 24. Sargent DJ, Conley BA, Allegra C, Collette L. Clinical trial designs for predictive marker validation in cancer treatment trials. J Clin Oncol. 2005 Mar 20;23(9): 2020-7.

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25. Cappuzzo F, Ligorio C, Toschi L, Rossi E, Trisolini R, Paioli D, Magrini E, Finocchiaro G, Bartolini S, Cancellieri A, Hirsch FR, Crino L, Varella-Garcia M. EGFR and HER2 gene copy number and response to first-line chemotherapy in patients with advanced non-small cell lung cancer (NSCLC). J Thorac Oncol. 2007 May;2(5):423-9. 26. Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst RS, Ince WL, Jänne PA, Januario T, Johnson DH, Klein P, Miller VA, Ostland MA, Ramies DA, Sebisanovic D, Stinson JA, Zhang YR, Seshagiri S, Hillan KJ. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol. 2005 Sep 1;23(25):5900-9. 27. Hirsch FR, Scagliotti GV, Langer CJ, et al. Epidermal growth factor family of receptors in preneoplasia and lung cancer: Perspectives for targeted therapies. Lung Cancer. 2003;41 Suppl 1:S29-42. 28. Hirsch FR, Varella-Garcia M, Bunn PA Jr., Di Maria MV, Veve R, Bremmes RM, et al. Epidermal growth factor receptor in non-small-cell lung carcinomas: Correlation between gene copy number and protein expression and impact on prognosis. J Clin Oncol. 2003;21:3798–807. 29. Wang SJ, O’Neill RT, Hung HMJ. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharmaceutical Statistics. 2007; 6:227-44. 30. Jiang W, Freidlin B, Simon R. Biomarker-adaptive threshold design: A procedure for evaluating treatment with possible biomarker-defined subset effect. J Natl Cancer Inst. 2007 Jul 4;99(13):1036-43. 31. Paik S, Kim C, Jeong J, Geyer CE, Romond EH, Mejia-Mejia O, Mamounas EP, Wickerham D, Costantino JP, Wolmark N. Benefit from adjuvant trastuzumab may not be confined to patients with IHC 3+ and/or FISH-positive tumors: Central testing results from NSABP B-31. J Clin Oncol. 2007 ASCO Annual Meeting Proceedings Part I. Vol 25, No. 18S (June 20 Supplement), 2007:511. 32. Perez EA, Romond EH, Suman VJ, Jeong J, Davidson NE, Geyer CE, Martino M, Mamounas EP, Kauffman PA, Wolmark N. Updated results of the combined analysis of NCCTG N9831 and NSABP B-31 adjuvant chemotherapy with/without trastuzumab in patients with HER2-positive breast cancer. J Clin Oncol. 2007 ASCO Annual Meeting Proceedings Part I. Vol 25, No. 18S (June 20 Supplement), 2007:512. 33. Perez EA, Suman VJ, Davidson NE, Martino S, Kaufman PA, Lingle WL, Flynn PJ, Ingle JN, Visscher D, Jenkins RB. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J Clin Oncol. 2006 Jul 1;24(19):3032-8. 34. Cree IA, Kurbacher CM, Lamont A, Hindley AC, Love S. A prospective randomized controlled trial of tumour chemosensitivity assay directed chemotherapy versus physician’s choice in patients with recurrent platinum-resistant ovarian cancer. Anticancer Drugs. 2007 Oct;18(9):1093-101.

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CHAPTER 12 PHARMACOGENOMIC MARKERS FOR CANCER THERAPY

Sharon Marsh Genome Quebec and Montreal Heart Institute Pharmacogenomics Centre 5000 rue Bélanger, Montreal, Quebec H1T 1C8, Canada E-mail: [email protected] Pharmacogenomic markers from germline or tumour DNA can provide useful information pertaining to response or toxicity from chemotherapy drugs. The US Food and Drug Administration (FDA) has begun to approve clinical tests for pharmacogenomics markers and also to approve changes to package inserts to reflect pharmacogenomics knowledge. This chapter discusses the current understanding of cancer pharmacogenomics, with reference to commonly used anti-cancer agents such as mercaptopurine, tamoxifen, irinotecan and 5-fluorouracil. Data for markers in TPMT, DPYD, CYP2D6, and TYMS is described.

1. Introduction Advances in technology along with the vast amount of information available from the Human Genome Project have allowed for significant progress in the goal of personalized medicine in the past few years. Cancer therapy offers an ideal platform for personalized medicine. The availability of multiple treatment regimens for many cancers means that identifying markers for therapy selection could have a rapid and significant clinical impact. Pharmacogenomics encompasses the search for genetic markers of drug resistance and/or toxicity.1,2 These markers can be in germline DNA (inherited markers accessible from blood, saliva, tissue) or somatic

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S. Marsh Table 1. Types of genetic variation in germline and tumour.

Type of variant

Description

Germline (G) or Tumour (T)

Amplification/deletion

Regions of the genome that are duplicated one to many times or deleted. Can be a small DNA segment, a whole gene, or part or all of a chromosome

G, T

Chromosome rearrangements

Regions of chromosomes that are translocated in the tumour genome

T

Loss of heterozygosity

Loss of an allele in the tumour genome. Often measured using microsatellite markers

T

Methylation

Addition of a methyl group to the cytosine base of CG dinucleotides (CpG islands) switching off transcription. Hyper and hypo-methylated regions can occur in the tumour genome.

T

Microsatellite

Typically 1-4 bases of polymorphic tandem repeats occurring throughout the genome

G, T

Microsatellite instability

Variation in length of microsatellite between tumour and germline in the same individual

T

Non-synonymous SNP

SNP within an exon leading to an amino acid substitution in the protein

G, T

Polymorphism

Mutation present at a frequency of at least 1% in the population

G, T

SNP

Single nucleotide polymorphism: DNA differs between individuals at one base

G, T

Somatic mutation

Acquired variation at the DNA level

Synonymous SNP

SNP within an exon that does not lead to an amino acid substitution in the protein

G, T

Tandem repeat or VNTR

Repeated sequence (number of bases can vary widely), number of repeats is polymorphic

G, T

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(accessible from tumour tissue). They can take the form of variations in a single nucleotide (single nucleotide polymorphisms (SNPs) – a polymorphism is an inherited mutations present at a frequency of at least 1% in the general population), variable number tandem repeats (VNTRs), microsatellites, and regions of DNA duplication or amplification. Within the tumour genome variability can also include somatic mutations, epigenetic regulation (methylation), loss of heterozygosity (LOH), microsatellite instability, and regions of chromosome rearrangements (Table 1). 2. Pharmacokinetics and Pharmacodynamics The activity and toxicity of most drugs is not the result of the actions of one gene.3,4 Most drugs undergo metabolism (either to activate or inactivate) and transport (both influx and efflux), and downstream events from the drug target (e.g. cell death pathways) all play a role in drug efficacy. Variation in any one of these genes at the DNA level could lead to alterations in function causing increased or decreased activity, subsequently affecting their role in the efficacy of the drug. Variation in the DNA of pharmacokinetic genes affecting drug metabolism (e.g. cytochrome P450 enzymes) and transport (e.g. multidrug transporters such as ABCB1) can have relevance not only to the activity of a drug in terms of the patients response to therapy, but can also cause severe toxicity due to excess activation of the drug (effectively causing an overdose) or an excess build-up of toxic metabolites. DNA variations in these genes can often be assessed in germline genome with the aim of predicting toxicity in advance of therapy so alternate treatment or reduced dosage can be offered. Pharmacodynamic effects are a result of the drug’s effect on the cell, the genes involved including the drug target (e.g. thymidylate synthase for anti-folates, ERBB2 for herceptin (as discussed in Chapter 4), and downstream genes (e.g. cell death pathways). DNA variations in these genes can provide markers for clinical outcome to therapy. They can be measured in the germline genome but often the tumour genome can play a larger role.5 Consequently, pharmacogenetic markers of

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pharmacodynamics may often rely on the availability of tumour tissue for analysis. 3. Pharmacokinetic Markers for Toxicity Severe toxicity from cancer chemotherapy is both deleterious to the patient and a burden to health care systems. Adverse drug reactions (ADRs) resulting from cancer chemotherapy are estimated to increase treatment costs by 15%.6 Identifying genetic markers for toxicity would help to decrease the cost burden and, more importantly, improve quality of life for cancer patients by preventing the administration of potentially life-threatening doses. Markers of toxicity have been identified for some chemotherapy drugs, including mercaptopurine, 5-fluorouracil and irinotecan. 3.1. TPMT and azathioprine toxicity Thiopurine methyltransferase (TPMT) methylates 6-mercaptopurine, a drug commonly used in the treatment of childhood acute lymphocytic leukemia. Reduced TPMT activity is significantly associated with severe haematological toxicity, and reduction of mercaptopurine dosage is required in patients with low or intermediate TPMT activity.7 3.1.1. TPMT polymorphisms Three common non-synonymous variant alleles (TPMT*2; A80P, TPMT*3A; a combination of A154T and Y240C, and TPMT*3C; Y240C) account for up to 95% of low TPMT activity phenotypes.8 The A80P causes a cleft in the protein, the A154T allele is in the co-substrate binding site, and the Y240C allele causes the loss of protein side-chain contacts. The combination of A154T and Y240C in TPMT*3A causes a distorted protein structure.9 Patients heterozygous for any of these alleles have intermediate TPMT levels and require approximately 65% of standard mercaptopurine dosage, patients homozygous for the variant TPMT alleles require significant (1/10 to 1/15) reduction of mercaptopurine to prevent

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potentially fatal toxicity.7 In 118 children with acute lymphoblastic leukaemia (ALL) it was shown that TPMT genotype only correlated with TPMT activity before the start of mercaptopurine treatment.10 Consequently, pre-screening patients prior to therapy is the most appropriate way to determine the likelihood of haematological toxicity. The identification of the association between TPMT polymorphisms and mercaptopurine toxicity is considered a classic model for pharmacogenomics studies. 3.1.2. TPMT copy number Gain of chromosomes can occur in leukaemia cells. Gain of chromosome 6 (containing the TPMT gene) was shown in a study of 147 children with ALL. The chromosome 6 that was amplified was random, and in patients where a chromosome containing a variant TPMT allele was amplified a significant decrease in TPMT activity was identified (p = 0.007).11 This highlights the need to incorporate the tumour genome into pharmacogenomics studies, as screening germline DNA alone would miss the increase in variant allele copy number and lead to an underestimate of TPMT activity, and the mercaptopurine dose would not be appropriately reduced. 3.1.3. Other TPMT variants The commonly studied variants TPMT*2, *3A and *3C are convincingly associated with azathioprine toxicity. However, ethnic variation in allele frequencies must be taken into account. For example, in sub-Saharan populations, TPMT*8 accounts for the majority of non-functional alleles,12 but this would not be routinely screened for as it is uncommon in other populations. 3.1.4. Other genes The gene for inosine triphosphate pyrophospatase (ITPA) has been associated with mercaptopurine toxicity. A coding variant, ITPA 94C > A was associated with adverse drug reactions (p = 0.0034) in 62 patients

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with inflammatory bowel disease, another disease commonly treated with mercaptopurine.13 Although this finding was not replicated in a subsequent study.14, and another study found an association between the intronic variant ITPA IVS2 + 21A > C and thrombocytopenia in patients with ALL (p = 0.012).15 If ITPA is validated as a marker for toxicity from mercaptopurine, variants will need to be screened in conjunction with TPMT polymorphisms to determine the likelihood of toxicity prior to therapy selection. 3.2. UGT1A1 and irinotecan toxicity Irinotecan is a chemotherapy drug typically used in combination with 5-fluorouracil or oxaliplatin. Side-effects include severe, life-threatening diarrhea and/or neutropenia.16 The active form of irinotecan, SN-38, is be inactivated through glucuronidation by a member of the UDP-glucuronosyltransferase family, UGT1A1. The UGT1A1 enzyme is responsible for hepatic bilirubin glucuronidation and is the main UGT1A enzyme involved in the glucuronidation of SN-38. 3.2.1. UGT1A1*28 The most commonly studies variant in UGT1A1 is a dinucleotide TA repeat in a TATA box of the promoter. The polymorphism results in altered UGT1A1 expression.17 The number of TA repeats ranges from five to eight copies, 6 TA repeats represents the most common allele (wild-type UGT1A1 expression), and the 7 TA repeat (UGT1A1*28) is the most common variant allele (reduced UGT1A1 expression).18 Reduced UGT1A1 expression is linked to an increased risk of severe toxicity (diarrhea and neutropenia) from irinotecan.19 In a prospective study of 66 patients with advanced disease treated with irinotecan, patients homozygous for UGT1A1*28 had a significantly greater risk of grade IV neutropenia compared to heterozygous or homozygous wildtype patients.19

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3.2.2. FDA approval In 2005 the US Food and Drug Administration (FDA) approved a genetic test for UGT1A1*2820 and included toxicity and dosing warnings relating to the UGT1A1*28 allele in the irinotecan package insert.21 Although this is excellent progress towards the integration of pharmacogenomics into clinical practice, there are concerns over the actual dosing required per genotype. Subsequent data suggested the relationship between UGT1A1*28 and irinotecan toxicity may be dependant on the irinotecan regimen used. At lower doses (50180 mg/m2) the relationship between genotype and toxicity does not appear to be clinically significant, whereas at moderate to high doses (200-350 mg/m2) the risk of severe (grade III/IV) hematological toxicity in patients homozygous for UGT1A1*28 is 27.8x higher than for patients heterozygous or homozygous for UGT1A1*1.22 Consequently, it might be necessary to further amend the irinotecan package insert to include dose/genotype guidelines. 3.2.3. Other UGT1A1 polymorphisms In populations with a low frequency of UGT1A1*28, for example Asian populations,23 other variants within UGT1A1 may also play a role in irinotecan toxicity.24-29 For example, in 81 Korean patients with nonsmall cell lung cancer treated with irinotecan and cisplatin, there were associations between UGT1A1*6 (G71R), irinotecan pharmacokinetics and toxicity from irinotecan therapy.29 3.2.4. Other genes Preliminary data suggests that polymorphisms in other metabolism and transport genes may also play a role.26,30 Variants in UGT1A7 and UGT1A9 are associated with irinotecan toxicities.31 In addition, a haplotype in the multidrug transporter ABCC2 predicts toxicity in patients withoutUGT1A1*28.28 Pharmacodynamic irinotecan genes have not been well characterised, but pilot data suggests neutropenia associated with TOP1 variants (p = 0.04)32 All these studies require

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validation in further sample sets, but it is likely that a panel of markers will need to be identified to accurately and consistently predict irinotecan toxicity. 3.3. DPYD and 5-fluorouracil toxicity 5-fluorouracil (5FU) was developed as a chemotherapy drug in the 1950s33 and is still one of most commonly used treatments for colorectal cancer in combination with irinotecan or oxaliplatin. However, some patients experience severe toxicities from 5FU treatment, including severe gastrointestinal toxicities and neutropenia. Dihydropyrimidine dehydrogenase (DPD), encoded for by the gene DPYD on chromosome 1p2034 is responsible for the degradation of over 80% of all 5-fluorouracil. Decreased DPD activity is associated with a greater than four-fold risk of severe or fatal toxicity from 5FU administered at standard doses.35 Over thirty polymorphisms in the gene encoding DPD (DPYD) have been described,36-38 and some of these variants (e.g. DPYD*2A, DPYD*5, DPYD*6, DPYD*9A) have been associated with either severe toxicity after 5FU therapy or an effect on DPD activity.36-38 3.3.1. DPYD*2A DPYD*2A is a G to A transition in an intron/exon splice junction which results in skipping of exon 14.36 Patients heterozygous for this polymorphism have low DPD activity and typically experience severe, life-threatening toxicity from 5FU therapy.35,39 Although the evidence for the link between DPYD*2A and 5FU toxicity is compelling, the incidence of DPYD*2A in the general population is very low ( A) was identified in control populations,44 a larger study is needed to determine its frequency in the general population. Deficiency of β-ureidopropionase could potentially lead to severe 5FU toxicity. A patient with severe 5FU toxicity had a polymorphism in the DPYS gene (G278D; 0.5% frequency in a Caucasian population).45 This polymorphism was shown in vitro to lead to mutant dihydropyrimidinase protein.45 In vitro data also demonstrated reduced dihydropyrimidinase activity for DPYS polymorphisms at L7V (frequency 0.2%) and 1635delC (frequency 0.2%).45,46

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A further mutation, W360R, was identified to be associated in vitro with reduced dihydropyrimidinase enzyme activity. This was identified in a patient with DHP-deficiency and has yet to be studied in the context of 5FU toxicity.47 These studies require validating in a large data set to elucidate the role of variants in genes other than DPYD in 5FU toxicity. 4. Pharmacokinetic Markers and Outcome Pharmacokinetic genetic markers are commonly associated with toxicity through the build-up of toxins via the excess or poor metabolism of either the parent drug or its metabolites. However, the same processes can also affect outcome to therapy. 4.1. CYP2D6 and tamoxifen outcome Tamoxifen has been used in the treatment of breast cancer (advanced and adjuvant treatment) for over 20 years.48 Side effects include hot flushes, thromboembolic events, endometrial cancer, endometrial polyps, irregular menses, and ovarian cysts. Acquired resistance, possibly due to cancers becoming hormone-independent or altered expression/mutated oestrogen receptors is also a problem with tamoxifen therapy.48 CYP2D6 catalyses the conversion of tamoxifen into the active metabolite 4-hydroxytamoxifen (4-OH TAM),49 and also converts the metabolite N-desmethyltamoxifen to 4-hydroxy-N-desmethyltamoxifen (endoxifen).50 There are multiple functional polymorphisms in the CYP2D6 gene, ranging from single nucleotide polymorphisms to gene deletion and variations in CYP2D6 gene copy number.51 These polymorphisms have a range of effects and can cause poor, intermediate, extensive (considered wild-type), and ultra rapid metaboliser status.52 Polymorphisms in CYP2D6 have been associated with outcome to tamoxifen therapy.

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4.1.1. CYP2D6*4 Several studies have assessed CYP2D6 polymorphisms in breast cancer patients treated with tamoxifen. Comparing CYP2D6 genotype with tamoxifen metabolism in 80 patients found that individuals homozygous for CYP2D6*4 (an allele common in Caucasian populations leading to lack of CYP2D6 function) had significantly lower 4-hydroxyN-desmethyltamoxifen levels than patients wild-type for CYP2D6.53 In 223 oestrogen receptor positive patients receiving tamoxifen, those individuals homozygous for CYP2D6*4 experienced significantly shorter relapse-free time (p = 0.023) and reduced disease-free survival (p = 0.012) than patients with at least one wild-type CYP2D6 allele.54 4.1.2. CYP2D6*10 CYP2D6*4 is relatively common in Caucasian populations (up to 21%), however, it is less common in many African populations and occurs at typically less than 1% frequency Asian populations (see http://pgeni.unc.edu/snp.asp?sid=775 for allele frequencies across multiple populations). A reduced function (but not absence of function as is the case for CYP2D6*4) allele, CYP2D6*10 is common in Asians and less frequent in Caucasian and African populations.52 This polymorphism has been identified in two studies of Asian breast cancer patients to be associated with tamoxifen response. In 293 patients who received tamoxifen, CYP2D6*10 was significantly associated with reduced disease-free survival (p = 0.04), this finding was not mirrored in patients who did not receive tamoxifen. In addition, serum 4-OH TAM was significantly lower (p-0.04) in patients receiving tamoxifen who were also homozygous for CYP2D6*10 (12/37 tested).55 A similar finding was seen for risk of recurrence-free survival in 67 breast cancer patients treated with tamoxifen (p = 0.036).56 These studies highlight the need to look across populations to identify common functional variants associated with outcome/toxicity, rather than relying on single populations to derive data.57,58

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4.1.3. Contradictory data Not all studies have seen the same associations with CYP2D6*4. In 162 breast cancer patients CYP2D6*4 was not associated with overall survival,59 and in 226 breast cancer patients CYP2D6*4 was associated with a decreased risk or recurrence (p = 0.0989). When assessed in combination with the SULT1A1*1 (wild-type) allele the same patients had a significantly reduced risk of distant recurrence (p = 0.0041).60 4.1.4. Other genes Metabolites of tamoxifen are conjugated via SULT1A1 leading to excretion by sulfurylation, and are also subjected to glucurondation, via UGT2B15.61 SULT1A1*2 (R213H) is associated with a reduced SULT1A1 protein half-life62 and decreased 4-OH TAM sulfation.62,63 Studies have demonstrated an association between SULT1A1*2 and survival in a study of 160 breast cancer patients receiving tamoxifen therapy (p = 0.02).59,64 and an increased risk of recurrence in 226 patients (p = 0.074).60 However, no effect of SULT1A1*2 genotype on tamoxifen metabolite concentration was seen in a study of 80 patients receiving tamoxifen,53 and no prognostic association was seen in 238 patients who had received tamoxifen.65 A non-synonymous polymorphism in UGT2B15 (UGT2B15*2, corresponding to D85Y), was assessed in 165 tamoxifen-receiving breast cancer patients. When assessed in patients with SULT1A1*2 alleles, patients carrying at least one UGT2B15*2 allele had significantly reduced five year survival rates (p = 0.003).59 No association with prognosis was seen for UGT2B15*2 in 238 patients who had received tamoxifen.65 4.1.5. FDA approval CYP2D6 genotyping currently provides the most compelling evidence for pharmacogenetic markers for tamoxifen treatment selection, and in October 2006 an advisory committee for the FDA recommended an update to the tamoxifen package insert to include CYP2D6

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pharmacogenetic information.66 The FDA has also approved an AmpliChip CYPP450 test through Roche for screening CYP2D6 (27 alleles, including *4 and *10), and CYP2C19 polymorphisms.67 However, it is worth noting that CYP2D6*4 may not provide definitive information for every population, and if findings for other genes such as SULT1A1 and UGT2B15 are validated, these may also have to be included in the screen to maximize the likelihood of predicting which patients would benefit from tamoxifen therapy. 5. Pharmacodynamic Markers and Outcome Although there are examples of pharmacokinetic markers being associated with outcome, it is likely that pharmacodynamic markers will play a large role.68 As the cancer genome has its own variability, measuring pharmacodynamic variants in germline may not always give the whole story. In some instances the tumour genome may also need to be screened.5,69 5.1. TYMS and 5-fluorouracil The main mechanism of action of 5FU (see section 3.3) is to form a table ternary complex with thymidylate synthase (TYMS) and the co-factor 5,10-methylenetetrahydrofolate, blocking the conversion of dUMP to dTMP. This leads to a depletion of thymidine in the cell and consequently inhibits DNA synthesis. Over-expression of TYMS has been linked to resistance to 5FU.70,71 Three common polymorphisms in the TYMS gene have been identified.72 5.1.1. TYMS TSER A polymorphisms in a promoter enhancer region of the 5’untranslated region (5’UTR) of the TYMS gene has been identified with predicted effect on TYMS expression.72,73 This polymorphism (TYMS TSER) consists of a 28bp tandem repeat resulting in between two up to nine copies of the sequence.73-75 Studies have suggested that increased number

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of repeats increases TYMS RNA and protein expression.73,76,77 Frequency of the TYMS TSER alleles among different ethnic groups shows an unusual pattern. Caucasian and African populations have similar frequencies of the TSER*3 (three repeats) allele (49-54%),74,78,79 however, Asian populations have significantly higher frequency of TSER*3 (62-95%).75,78,80 TSER*2 (two repeats) makes up the majority of the other alleles. The TSER*4 (four repeats) alleles are mainly found in African populations at low frequency (1-7%),74,79 TSER*5 (five repeats) has been identified in a Chinese population (up to 4%),75 and TSER*9 (nine repeats) alleles have only been identified in a population from Ghana (1%), although many African populations remain unstudied for this polymorphism.74 The effect on expression of the nine repeat remains unknown. A pilot study of 24 metastatic colorectal cancer patients found that patients had a stepwise decrease in median survival with increasing TSER repeat genotype (TSER*2/*2, TSER*2/*3, TSER*3/*3).79 In 117 patients receiving 5FU adjuvant therapy compared to 104 patients receiving surgery alone, no significant benefit of chemotherapy on survival was observed for homozygous TSER*3 patients. 5FU provided improved survival compared with surgery alone to patients with at least one TSER*2 allele (p = 005).81 A further report from 50 patients receiving 5FU for metastatic colorectal cancer found a 41% higher response rate in patients homozygous for TSER*2 compared to patients homozygous for TSER*3,82 and in 58 patients treated with capecitabine (fluorouracil-based therapy) and raltitrexed (a direct thymidylate synthase inhibitor) patients who were homozygous for TSER*3 had significantly worse response (p < 0.01).83 The TYMS TSER is also linked to tumour downstaging (a measurement of response) in 65 patients with rectal cancer who were treated preoperatively with 5FU-based chemoradiation.84 Patients with at least one TSER*2 allele had significantly increased frequency of tumour downstaging compared to patients homozygous for TSER*3 (p = 0.002).84 Not all studies show associations with TSER and 5FU outcome. In a study of 135 Japanese colorectal cancer patients there was no association between efficacy of oral 5FU chemotherapy and TSER genotype.85

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5.1.2. TYMS TSER*3G>C A single nucleotide polymorphism (SNP) has been identified within the TYMS TSER.86,87 This is an unusual variant in that the SNP lies in the 12th nucleotide of the second repeat of the TSER*3 allele, and is consequently only seen in patients with at least one TSER*3 allele. Although a further polymorphism in the TSER*2 allele has also been described, however it’s function is currently unknown.88,89 The TSER*3G > C polymorphism disrupts a USF1 transcription factor binding site.86 In tumour samples from 48 colorectal cancer patients the patients homozygous for TSER*3G had significantly higher RNA expression (p = 0.017),77 whereas when comparing TSER*2 to TSER*3 without stratifying for the G > C SNP there was a trend towards increased RNA expression for TSER*3 patients but it did not reach statistical significance.77 Consequently, it is possible that this SNP will allow TSER*3 carriers to be further stratified into “good” and “bad” risk categories for 5FU response. In 89 metastatic colorectal cancer patients receiving 5FU, patients without any TSER*3G alleles had improved overall response (p = 0.035),90 however, in 129 colorectal cancer patients treated with 5FU the TSER*3G > C SNP did not add any prognostic information beyond that found for the TSER polymorphism.91 5.1.3. TYMS 3’UTR deletion A 6bp deletion located in the 3’UTR, 447bp downstream from the stop codon (TYMSdel)92 was significantly associated with red blood cell folate levels (p = 0.002) and homocysteine concentrations (p = 0.03).93 This variant was shown to express around three-fold less TYMS RNA than patients homozygous for the presence of the 6bp (p = 0.017) in 43 colorectal cancer patients.94 It is possible that the deletion affects RNA stability in the cell. The deletion occurs at an allele frequency of 27-29% in Caucasians.92,94 In 58 patients receiving capecitabine and raltitrexed a significant association was seen with the TYMSdel homozygous deletion and poor response to therapy (p < 0.05).83 Conversely, a study of 129 colorectal

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cancer patients treated with 5FU found the homozygous deletion allele was protective (p = 0.0034),91 and in 146 patients receiving 5FUcontaining therapy patients homozygous for the insertion were significantly associated with poor progression free survival (p=0.002).95 5.1.4. TYMS haplotype Looking at a combination of the three common TYMS polymorphisms may be more informative than assessing each variant separately. In 197 colorectal cancer patients who had received 5-fluororacil containing therapy, patients with a combination of TSER*3G and the presence of the 6bp in TYMSdel were at significantly higher risk for tumour recurrence (p = 0.044).96 5.1.5. TYMS genotype-guided trial Studies of the TSER polymorhosm led to the introduction of a genotypeguided clinical cancer trial consisting of two Phase II studies.97 Rectal cancer patients with at least one TSER*2 allele were treated with standard therapy (radiation and 5FU). Patients homozygous for TSER*3 received the standard therapy in combination with irinotecan.84 Preliminary data suggests an improved response rate in both Phase II studies,97 indicating that stratifying patients with respect to genotype might improve response rates to rectal cancer. 5.1.6. TYMS in tumour Despite the data suggesting polymorphisms in TYMS might predict 5FU response, the correlation is far from 100%. TYMS status in the tumour genome also needs to be taken into account. TYMS amplification has been shown in vitro and in vivo to influence resistance to 5FU.98,99 Conversely, loss of the TYMS-containing region of chromosome 18p is common in colorectal cancer,100 and loss of heterozygosity (LOH) of TYMS could also affect it’s expression in tumour,101 although the true extent of LOH at the TYMS region of chromosome 18 remains unclear due to inconsistencies in the assays and reporting of LOH in TYMS

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studies. It is likely that a comprehensive pharmacogenomics approach, incorporating germline TYMS polymorphisms with tumour TYMS copy number will provide a stronger indication of the role of TYMS variation on outcome to 5FU therapy. 5.1.7. Other genes Genes do not act in isolation, and contributions from other genes in the 5-fluorouracil pathway could play a role in predicting outcome to 5FU. In 125 rectal cancer patients who received 5FU-based chemoradiation a combination of two polymporphisms (677C > T and 1298A > C) in the methylenetetrahdrofolate reductase gene (MTHFR), specifically patients with both the 677T and 1298A alleles, was associated with tumour regression (p = 0.004) and poor response (p = 0.002).102 The impact of variants in other genes from the 5FU pathway remains to be elucidated. 6. Conclusions and Outlook A great deal of progress has been made in the field of cancer pharmacogenomics in recent years. The inclusion of genotype information into package inserts by the FDA is a major step towards integrating pharmacogenomics into clinical practice. However, there is still a paucity of positive, well-validated associations in the literature. In addition, the individual genotypes associated with outcome and toxicity to chemotherapy clearly do not provide the whole story. Future studies will identify panels of polymorphisms from multiple genes that can be screened in patients prior to therapy selection. This will maximize the likelihood of a positive outcome and minimise the likelihood of toxic events from chemotherapy treatment. Acknowledgment The author is funded by Genome Quebec.

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34. McLeod HL, Collie-Duguid ES, Vreken P, Johnson MR, Wei X, Sapone A, et al. Nomenclature for human DPYD alleles. Pharmacogenetics 1998;8: 455-459. 35. Van Kuilenburg AB, Meinsma R, Zoetekouw L, Van Gennip AH. High prevalence of the IVS14 + 1G > A mutation in the dihydropyrimidine dehydrogenase gene of patients with severe 5-fluorouracil-associated toxicity. Pharmacogenetics 2002;12: 555-558. 36. Wei X, Elizondo G, Sapone A, McLeod HL, Raunio H, Fernandez-Salguero P, et al. Characterization of the human dihydropyrimidine dehydrogenase gene. Genomics 1998;51: 391-400. 37. Ezzeldin H, Johnson MR, Okamoto Y, Diasio R. Denaturing high performance liquid chromatography analysis of the DPYD gene in patients with lethal 5-fluorouracil toxicity. Clin Cancer Res 2003;9: 3021-3028. 38. Seck K, Riemer S, Kates R, Ullrich T, Lutz V, Harbeck N, et al. Analysis of the DPYD gene implicated in 5-fluorouracil catabolism in a cohort of Caucasian individuals. Clin Cancer Res 2005;11: 5886-5892. 39. Wei X, McLeod HL, McMurrough J, Gonzalez FJ, Fernandez-Salguero P. Molecular basis of the human dihydropyrimidine dehydrogenase deficiency and 5-fluorouracil toxicity. J Clin Invest 1996;98: 610-615. 40. Ahluwalia R, Freimuth R, McLeod HL, Marsh S. Use of pyrosequencing to detect clinically relevant polymorphisms in dihydropyrimidine dehydrogenase. Clin Chem 2003;49: 1661-1664. 41. Schwab M, Zanger UM, Marx C, Schaeffeler E, Klein K, Dippon J, et al. Role of genetic and nongenetic factors for fluorouracil treatment-related severe toxicity: A prospective clinical trial by the German 5-FU Toxicity Study Group. J Clin Oncol 2008;26: 2131-2138. 42. Zhang H, Li YM, Jin X. DPYD*5 gene mutation contributes to the reduced DPYD enzyme activity and chemotherapeutic toxicity of 5-FU: Results from genotyping study on 75 gastric carcinoma and colon carcinoma patients. Med Oncol 2007;24: 251-258. 43. Maekawa K, Saeki M, Saito Y, Ozawa S, Kurose K, Kaniwa N, et al. Genetic variations and haplotype structures of the DPYD gene encoding dihydropyrimidine dehydrogenase in Japanese and their ethnic differences. J Hum Genet 2007;52: 804-819. 44. van Kuilenburg AB, Meinsma R, Beke E, Assmann B, Ribes A, Lorente I, et al. beta-Ureidopropionase deficiency: An inborn error of pyrimidine degradation associated with neurological abnormalities. Hum Mol Genet 2004;13: 2793-2801. 45. van Kuilenburg AB, Meinsma R, Zonnenberg BA, Zoetekouw L, Baas F, Matsuda K, et al. Dihydropyrimidinase deficiency and severe 5-fluorouracil toxicity. Clin Cancer Res 2003;9: 4363-4367. 46. Thomas HR, Ezzeldin HH, Guarcello V, Mattison LK, Fridley BL, Diasio RB. Genetic regulation of dihydropyrimidinase and its possible implication in altered uracil catabolism. Pharmacogenet Genomics 2007;17: 973-987. 47. Hamajima N, Kouwaki M, Vreken P, Matsuda K, Sumi S, Imaeda M, et al. Dihydropyrimidinase deficiency: Structural organization, chromosomal localization, and mutation analysis of the human dihydropyrimidinase gene. Am J Hum Genet 1998;63: 717-726. 48. Osborne CK. Tamoxifen in the treatment of breast cancer. N Engl J Med 1998;339: 1609-1618.

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S. Marsh gene is a determinant of red blood cell folate and homocysteine concentrations. Hum Genet 2005;116: 347-353. Lenz H-J, Zhang W, Zahedy S, Gil J, Yu M, Stoehlmacher J. A 6 base-pair deletion in the 3 UTR of the thymidylate synthase (TS) gene predicts TS mRNA expression in colorectal tumors. A possible candidate gene for colorectal cancer risk (Abstract). Proc Am Assoc Cancer Res 2002;43: 660. Ruzzo A, Graziano F, Loupakis F, Santini D, Catalano V, Bisonni R, et al. Pharmacogenetic profiling in patients with advanced colorectal cancer treated with first-line FOLFIRI chemotherapy. Pharmacogenomics J 2008;8: 278-288. Lurje G, Zhang W, Yang D, Groshen S, Hendifar AE, Husain H, et al. Thymidylate synthase haplotype is associated with tumor recurrence in stage II and stage III colon cancer. Pharmacogenet Genomics 2008;18: 161-168. McLeod HL, Tan B, Malyapa R, Abbey E, Picus J, Myerson R, et al. Genotypeguided neoadjuvant therapy for rectal cancer. Proc Am Soc Clin Oncol 2005;23: 197. Wang TL, Diaz LA, Jr., Romans K, Bardelli A, Saha S, Galizia G, et al. Digital karyotyping identifies thymidylate synthase amplification as a mechanism of resistance to 5-fluorouracil in metastatic colorectal cancer patients. Proc Natl Acad Sci U S A 2004;101: 3089-3094. Rooney PH, Stevenson DA, Marsh S, Johnston PG, Haites NE, Cassidy J, et al. Comparative genomic hybridization analysis of chromosomal alterations induced by the development of resistance to thymidylate synthase inhibitors. Cancer Res 1998;58: 5042-5045. Rooney PH, Murray GI, Stevenson DA, Haites NE, Cassidy J, McLeod HL. Comparative genomic hybridization and chromosomal instability in solid tumours. Br J Cancer 1999;80: 862-873. Kawakami K. Thymidylate Synthase Gene in Pharmacogenetics. Current Pharmacogenomics 2004;2: 137-147. Terrazzino S, Agostini M, Pucciarelli S, Pasetto LM, Friso ML, Ambrosi A, et al. A haplotype of the methylenetetrahydrofolate reductase gene predicts poor tumor response in rectal cancer patients receiving preoperative chemoradiation. Pharmacogenet Genomics 2006;16: 817-824.

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CHAPTER 13 SAFETY AND EFFECTIVE BIOMAKERS IN ONCOLOGY — A REGULATORY DRUG AND DEVICE PERSPECTIVE

Atiqur Rahman and Francis Kalush US Food and Drug Administration, Center for Drug Evaluation and Research Center for Devices and Radiological Health, Silver Spring, Maryland, USA E-mail: [email protected] No official support or endorsement of this article by the Food and Drug Administration is intended or should be inferred The chapter describes the safety and effective biomakers that the US Food and Drug Administration have included in drug or biologics labels for cancer treatment. The chapter includes relevant regulations regarding diagnostics for biomarker testing. The utility of biomarkers in cancer drug development and cancer treatment is described. The chapter provides the regulatory process for the evaluation of biomarkers and rational for inclusion of biomarkers related information in the drug and device label. The importance of biomarker test development along with the therapeutic product development is emphasized when a biomarker test is expected to monitor and/or optimize treatment outcomes by enabling health care providers to choose, avoid a therapy or tailor dose with precision in a selected population. The regulations related to a test development, interactions with the Agency, type and content of submissions for review, and harmonized and concurrent cross center review of the companion drugdiagnostics are described in this chapter.

1. Introduction The mission of the United States Food and Drug Administration (FDA) is to protect and promote public health through application of innovative science in drug development and drug usage. Application of biomarkers 275

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in drug development and drug label aligns with the mission of the Agency.1 Defining a subpopulation that has a better benefit risk profile compared to the overall population based on genetic or proteomic markers and identifying that sub population through an appropriate test for the right therapy aligns with the commitment of the FDA to protect health of each American public. The legal basis for protecting the health of a sub population is stated in US Code of Federal Regulation, 21CFR 201.57,2 which states “If evidence is available to support the safety and effectiveness of the drug only in selected subgroups of the larger population with a disease, the labeling shall describe the evidence and identify specific tests needed for selection and monitoring of patients who need the drug.” To identify a subgroup, biomarker based tests should be developed and adequately assessed for safe and effective use of the test in conjunction with the drug. The labeling requirements for in vitro diagnostic devices are stated in 21 CFR 809.10.3 Development and validation of a test to select a subpopulation who should be eligible for the therapy or who will require a modified dose is integral to the overall drug device development program. 2. Challenges in Cancer Treatment In cancer treatment, the objective is to destroy the malignant cells without harming the normal tissue or organs required for normal function. However, the drugs and biologics used for oncology indications are as well toxic to nonmalignant functional cells. This provides a challenge to initiate aggressive therapy to eradicate all the malignant cells yet protect the normal cells. Therefore, the benefit and risks of a cancer therapy has to be finely balanced so that the therapy provides adequate benefit to justify the risk for toxicity. In cancer, clinical benefit from a treatment is dependent on the stage of cancer and the available effective therapy. Sometimes a 10 to 30% response in the overall population from a given therapy may be considered beneficial for patients with rare or refractory diseases; however, serious toxicities under these circumstances are common. A small number of patients may have fatal consequence from these toxicities. Biomarker helps to optimize the benefit-risks of a therapy for a patient by helping select the

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right drug or an individualized dose for that particular patient. The biomarker information provides an additional tool to the physician to individualize a patient’s treatment and increase the probability of effectiveness with potential reduction of toxicity.4 The scope of this chapter is aimed for drug and devices in companion irrespective of the biomarker test being cleared or approved by the Agency for selecting a drug or biologics in cancer treatment. As for example, Philadelphia Chromosome test which is a laboratory developed Test is included in this chapter. Experimental biomarkers and other approved targeted therapy such as viral DNA testing for HIV treatment or use of HLA testing for carbamazepine treatment is not addressed in this chapter.5,6 This chapter does not address diagnostics tests to determine the future risk of disease, screening for disease, confirming the presence of suspected disease, monitoring disease status, and the prognosis and staging of various types of cancer.7,8 The term “drug” is used in this chapter to include both small molecules and therapeutic biologics. 3. Utility of Biomarkers in Cancer Drug Development and Treatment Biomarkers have been effectively used in detecting the stages of cancer, or prognosis of a particular cancer or in evaluating effect of a therapy.7 Biomarkers are measured to assess disease progression after surgery, such as prostate specific antigen (PSA) testing for prostate cancer after prostatectomy or laparoscopic surgery.9,10 An expression of estrogen and progesterone receptors on tumors and lymph node involvement in breast cancer is used as prognostic factors for selection of therapy.11,12 Biomarkers have led to follow disease recurrence and progression, or response to a therapy such as rise in PSA for prostrate cancer. The biomarkers made a significant impact both in cancer drug development and cancer treatment over the past decade. A number of cancer targets have been identified and agents have been developed that interact with these targets. Biologics and small molecules are approved to treat a specific type of cancer that is defined by a biomarker, such as the use of imatinib in chronic myeloid leukemia patients with Philadelphia positive chromosome.13 Also, biomarkers interact with a target such as binding of

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monoclonal antibody to EGFR receptor and modulate the disease. The understanding of polymorphic nature of drug metabolizing enzymes has allowed a dose of a drug to be tailored for a particular patient or a sub population identified by a test that differentiates the enzyme status and has allowed to deliver a targeted dose.14-18 a. Drug development Over the past decade, individualized therapy based on biomarkers has played a significant role in cancer drug development. Biomarkers are targets for drugs or biologics. Biomarker has the potential to identify sources of inter-individual variability in drug response that affect both efficacy and safety. In addition, biomarker provides a tool to improve the safety outcomes of existing therapies. Recently, biomarkers have been developed and validated to support safety and efficacy of products in a sub population with cancer, such as trastuzumab, cetuximab, imatinib.13,19,20 Various signaling pathways, such as PI3K/AKT, JAK/STAT, MAPK/ERK, and receptors, such as, Vascular Endothelial Growth Factor (VEGF) receptor, Epidermal Growth Factor (EGF) receptors are the targets for new drugs under development.21-23 Antibody against cell surface antigens, such as CD20, CD30, CTLA4/CD152 are also targeted in biomarkers based therapeutic drug development.24 The drugs are expected to modulate these pathways and provide an effective treatment with reduced toxicity to individuals who express the targets or over express a target or express an alternate form of a target. New targeted molecules are sometimes combined with existing therapy and tested in clinical trials for safety and efficacy. b. Cancer treatment Over the last decade, the FDA has approved a number of agents and cleared a number of devices (tests) indicated for a specific population or approved a modified dose for a specific sub-group based on a test (see Table 1).25 In September of 1998, FDA approved Herceptin (trastuzumab) for the treatment of patients with metastatic breast cancer whose tumors over express the HER2 protein; subsequently the

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biologics received approval for adjuvant treatment of breast cancer.19 Approximately, 20 to 30% of the breast tumors over express HER2 protein. Detection of HER2 over expression is necessary for selection of patient for Herceptin therapy. Since the clinical trials included only patients who over expressed HER2 protein, the effect of trastuzumab on patients without over expression of HER2 protein is unknown. The FDA approved Tykerb (lapatinib)26 in 2007 for the treatment of advanced or metastatic breast cancer patients whose tumor over-expresses HER2. In the clinical trial, patients who progressed after treatment with anthracyclines, taxanes, and trastuzumab were treated with lapatinib. The clinical trials demonstrated that breast cancer patients whose tumors over express HER2 protein or mRNA as detected by immunohistochemistry (IHC) or fluorescence in situ hybridization (FISH) test benefit from these treatments. The Agency approved Gleevec (imatinib)13 to treat patients with chronic myeloid leukemia (CML). The drug inhibits Bcr-Abl tyrosine kinase, an abnormal tyrosine kinase created by Philadelphia chromosome abnormality. Subsequently, imatinib received approval for patients with c-Kit (CD117) positive unresectable and/or metastatic malignant gastrointestinal stromal tumors (GIST) and for patients with Philadelphia chromosome positive acute lymphoblastic leukemia (ALL). Recently, imatinib received approval for the treatment of myelodysplastic/ myeloproliferative diseases associated with platelet-derived growth factor receptor (PDGFR) gene rearrangements. The FDA approved Sprycel (dasatinib)27 for the treatment of adults with Philadelphia chromosome positive ALL with resistance or intolerance to prior therapy. In 2007, the Agency approved Tasigna (nilotinib)28 for the treatment of Philadelphia chromosome positive CML patients who are resistant or intolerant to prior therapy that included imatinib. The assessment of target helps select patients who benefit from the treatment with acceptable risk. In 2004, Erbitux (cetuximab)20 was approved for use either alone or in combination with irinotecan for the treatment of patients with EGFRexpressing metastatic colorectal cancer. Clinical trials submitted for regulatory approval studied the safety and effectiveness of the biologics in patients with EGFR-expressing metastatic colorectal cancer. The

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usefulness of this drug in patients who don’t over-express EGFR is unknown and therefore, should not be used in this patient population. In the clinical trial, primary tumor or tumor from a metastatic site was tested with Dako EGFR pharmDx test kit for EGFR over-expression. Since EGFR expression was detected in nearly all patients in the head and neck cancer, clinical trials were not required to screen patients for EGFR expression. Therefore, a test or EGFR expression is not required for treating squamous cell carcinoma of the head and neck patients with cetuximab, but it is required for identifying colorectal cancer patients who can benefit from cetuximab therapy. The FDA recently approved Vectibix (panitumumab),29 a recombinant, human IgG2 kappa monoclonal antibody, for the treatment of EGFR-expressing metastatic colorectal cancer. Tumors from all patients enrolled in the clinical trial expressed EGFR as determined by the Dako EGFR pharmDx test kit. In July of 2004, the FDA approved a labeling supplement for 6mercaptopurine (6-MP)14 to include a recommendation for testing for Thiopurine methyl-S-transferase (TPMT) for optimal dose selection and reduction of risk for life threatening bone marrow suppression in patients with acute lymphatic leukemia (see Table 2). The variation in 6-MP metabolism is one of the major causes in the inter-individual differences in systemic exposure of the drug. Such variations in exposure may result in severe toxicity (anemia, leucopenia, thrombocytopenia) in patients who can not deactivate the drug. TPMT is a polymorphic enzyme that inactivates 6-MP. Approximately, 0.3% (1 in 300) Caucasian or African American patients carry two non-functional alleles (homozygous) of TPMT and approximately 10% patients carry one non-functional allele (heterozygous).15-17 Patients who are homozygous deficient are at risk for life threatening bone marrow suppression and require substantial dose reduction for treatment. Heterozygous patients have variable exposure and may avoid severe myelosuppression and receive continued therapy if recommended a modified dose rather than the full dose of 6-MP. Genotypic testing of TPMT gene and phenotypic testing of TPMT enzyme activity are commercially available. Similar dose modification instructions are included in the label of azathioprine, a precursor of 6-MP. Azathioprine is approved for renal transplantation and rheumatoid arthritis.

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Safety and Effective Biomakers in Oncology Table 1. Biomarker based therapy in Oncology.

Trastuzumab

Drugs/Biologics TM Herceptin

Cancer Types Breast

Biomarkers/Device Classification* Her2/neuClass III

Cetuximab

Erbitux

Colorectal, Head and Neck

Epidermal growth factor receptorClass III

Panitumumab

Vectibix

Colorectal

Epidermal growth factor receptorClass III

Denileukin

Ontak

Cutaneous T-cell lymphoma

CD25 Class III

Imatinib mesylate

Gleevec

CML, ALL, MDs, MPD, GIST, ASM, HES, CEL, DFSP

Philadelphia chromosome, PDGFR c-Kit-Class III

Dasatinib

Sprycel

CML, ALL

Philadelphia positive chromosome

Nilotinib

Tasigna

CML

Philadelphia positive chromosome

Lapatinib

Tykerb

Breast

HER2 Class III

Drug/Biologics

Types of Evidence Adjuvant, first-line metastatic and advanced metastatic breast cancer. Detection of Her2/neu overexpression is required for treatment and patients selection. Metastatic colorectal cancer and head and neck cancer EGFR positive patients in clinical trial were tested for safety and efficacy. Patients with head and neck cancer are not required to be tested. Metastatic colorectal cancer EGFR positive patients in clinical trial were tested for safety and efficacy. B-cell Non-Hodgkin’s lymphoma patients are required to be tested for CD25 expression. Biomarker tested patients were mostly included in the regulatory clinical trials. The clinical trial size varied based on the disease prevalence. Phase 2, single-arm studies were conducted in patients with CML and Ph+ ALL resistant to or intolerant of imatinib. A single open label study was conducted in patients with Ph+ CML resistant to or intolerant of imatinib. A randomized phase 3 trial of lapatinib with capecitabine in HER2 over expressing breast cancer patients.

* Risk-based Device classification. Philadelphia Chromosome test have not been cleared or approved by the FDA.

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A. Rahman & F. Kalush Table 2. Biomarker based dose adjustment in cancer treatment.

6-Mercaptopurine

Drugs/Biologics Cancer Types TM Purinethol ALL

Biomarkers/Device Classification* TPMT

Irinotecan

Camptosar

UGT1A1Class II

Drugs/Biologics

*

Colorectal

Types of Evidence Patients with little or no TPMT activity are at increased risk for sever toxicity from conventional dose and generally require substantial dose reduction. Literature information provided support for label recommendation. Reduction of the starting dose by at least one level should be considered for patients carrying UGT1A1*28 allele. A number of phase 1 study provided evidence for label recommendation.

Risk based classification. TPMT test has not been cleared or approved by FDA.

Camptosar (irinotecan) label18 includes a recommendation to reduce dose by one level for patients known to be homozygous for the UGT1A1*28 allele. A patient carrying two UGT1A1*28 alleles should start with a 300 mg/m2 of irinotecan dose for every three week regimen. If the patient tolerate the first dose and does not encounter any unacceptable hematologic or non-hematologic toxicities, the patient may receive the full dose of 350 mg/m2 of irinotecan in the subsequent cycle. If the patient does not tolerate the reduced dose and has grade 2 or higher toxicities, the label recommended dose reduction for hematologic and non-hematologic toxicities should be followed. 4. Recent Developments of Biomarkers for Efficacy Recent clinical trials indicate that K-ras may be a predictive biomarker for two biologics cetuximab and panitumumab.20,29 After treatment with cetuximab and panitumumab, some clinical studies have shown that patients carrying normal (wild-type) K-ras gene may have increased

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survival from the treatment with panitumumab or cetuximab compared to patients who carry an altered K-ras gene.30,31 The FDA Oncology Drug Advisory Committee meeting held on December 16, 2008 discussed the type and amount of data needed to support product labeling for new biomarkers in the context of K-ras and the biologics.32 Metabolic enzyme, cytochrome P-450 2D6 (CYP2D6) may play a significant role in the response to tamoxifen therapy for breast cancer patients. Patients with deficient CYP 2D6 activity (poor metabolizers) treated with tamoxifen may have inferior response compared to patients with normal activity of the enzyme.33,34 5. Regulatory Path and Scientific Review to Include Biomarkers in Product Labels The US Code of Federal Regulation, 21CFR 201.572 requires that the Agency includes evidence of safety and effectiveness of a drug in a sub population in the label of a product and includes the test information to identify the subset of the overall population.2 Drugs and biologics intended for targeted therapy have identified and utilized biomarkers in the overall development of a product for oncology indications. In the development strategy, agents are engineered to interact with a specific target, such as EGFR, or to modulate a biological pathway and treat a disease. In these types of development, prospective clinical studies are conducted to co-develop the biomarker and the drug. The clinical studies include the biomarker test to select patients in the pivotal trials designed for regulatory approval. The main objective of the clinical study is to validate the biomarker in the context of the drug use and demonstrate the effectiveness and safety of the drug and the test for the indication. The clinical study collects data to establish the analytical and clinical validation of the biomarker test which is reviewed by the Center for Device and Radiological Health (CDRH), where as the clinical studies to support the effectiveness and safety of the drug is review by the Center for Drug Evaluation and Research (CDER). If the drug demonstrates adequate effectiveness and safety for the indicated population and the performance of the test is satisfactory, the drug label requires dispensing of the drug based on the biomarker test outcome and both the drug and

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the test receives concurrent Agency approval for marketing. For the use of trastuzumab, a patient is selected based on HER2 expression. The types of clinical trials conducted to demonstrate the safety and effectiveness of transtuzumab depended on the indications. In the advanced metastatic breast cancer setting, a single arm trial with overall response rate as the clinical endpoint was sufficient for approval. The overall response rate in 222 HER2 over expressing patients was 14% including 2% complete response.19 In the first-line metastatic breast cancer setting, a multicenter, randomized, open-label trial with time to progression as the primary endpoint was considered for regulatory approval. The study included 469 metastatic breast cancer patients who did not receive prior chemotherapy and their tumors over expressed HER2 based on the IHC test. Patients with greater than 2+ positive tumors by IHC were enrolled in both advanced metastatic and first-line metastatic trials. Patients treated with transtuzumab had a longer time to disease progression, higher response rate and higher one-year survival rate.19 In the adjuvant setting, trastuzumab in combination with chemotherapy was assessed in a total of 3752 breast cancer patients. The HER2 over expression was detected either by IHC test or by gene amplification using FISH test. There was a significant improvement in the disease free- survival of patients treated with trastuzumab.19 Exploratory analysis of disease-free survival as a function of HER2 over expression or gene amplification was conducted. Patients who were both IHC 3+ and FISH positive had a significant reduction in risk of recurrence, second primary malignancy or death. The design of a clinical trial and the level of evidence required to approve a new drug for a sub population depends on various factors; how robust is the test to identify the sub population, the association of the biomarker with the disease or drug response, the understanding of the effect of the drug in patients excluded from the treatment, the stage of the disease, available or alternate therapy, rarity of the disease, etc. For treating a rare population with advanced disease, a single arm trial with surrogate endpoints may allow for accelerated regulatory approval with a condition to demonstrate clinical benefit in subsequent trial. However, when a disease is prevalent in a larger population and other therapeutic interventions are available, randomized controlled trials are expected for

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regulatory approval using endpoints that are associated with clinical benefit. The data submission for regulatory approval of a companion drug-diagnostics is reviewed by respective centers, CDER and CDRH. Based on the complexity of the scientific and/or the regulatory issues, sometimes a submission is discussed at an Advisory Committee. The Advisory Committee is an open public forum, to obtain expert opinion of scientists in the field regarding some critical issues related to drug’s approval. However, the ultimate responsibility for the approval or nonapproval of a drug remains with the Agency and a decision is made after consensus and input from the review team. The review team consists of clinical, chemistry, pharmacology/toxicology, clinical pharmacology, statistics, safety and regulatory reviewers. When the utility of a biomarker is discovered after an agent is approved, the FDA conducts a thorough review of the information that is available in the scientific literature. Based on the strength of the data and completion of the review, the Agency contacts the sponsor of the drug and negotiates a label language. The data is interpreted in a manner that the physicians and the patients can use the information to make an informed decision regarding the use of the biomarker test in selection of a therapy or a particular dose of a drug. If the data is equivocal, the Agency may collaborate with other governmental agency such as NIH, research or academic institute to address the knowledge gap and then update the label with biomarker information. If the reviewed data appears to be controversial, the data may be presented and discussed at an FDA Advisory Committee Meeting consisting of recognized experts in the field. The recommendation of the committee is discussed internally and an action is generated. The action could be an update of the label based on the committee recommendation. Also, there may be a delay in action until adequate information is obtained and science is developed to support the inclusion of the biomarker in the label for the drug use. Inclusion of UGT1A1 genetic testing for identifying patients who may be at a greater risk for grade 4 neutropenia and may require a lower starting dose of irinotecan was based on literature data from peer reviewed journals. Multiple phase 1 studies provided the scientific evidence to link UGT1A1 polymorphism with altered metabolism and plasma levels of irinotecan and clinical evidence to associate UGT1A1

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polymorphism with grade 3/4 neutorpenia.35-37 The Agency presented the scientific and the clinical data to an FDA Advisory Committee with participation from the sponsor of the drug. The committee discussed the strength and the gaps in the knowledge of association between UGT1A1 genotypes of patients and grade 4 neutropenia. The committee recommended that there was sufficient scientific and clinical evidence to include information on the impact of UGT1A1 polymorphism and irinotecan toxicity in the label of Camptosar.38 The committee didn’t discuss the exact label language at the meeting. The label is crafted with the help of appropriate scientists in the Agency and in the pharmaceutical company. Although at the time of the regulatory action, the test for UGT1A1 genotype was not available, there were research institutes and Clinical Laboratory Improvement Amendments (CLIA) certified laboratories that were capable of providing reliable test for UGT1A1 genotype. Subsequent to the label update, the Agency cleared a test for UGT1A1 genotype. Regulations facilitate certain degree of flexibility in assessing the scientific and the clinical data in regulatory submissions and in the scientific literature to approve a drug for a specific subpopulation identified by a biomarker or to update a label with information about a biomarker for optimizing the benefit-risk of a therapy. The data is reviewed by a multidisciplinary team and sometimes discussed in a public forum to obtain expert opinion and an action is taken to include appropriate information in a drug label. 6. Diagnostics in Cancer Treatment When use of a diagnostic test is identified as important in selecting which patients receive or avoid a new drug or be given a higher or lower dose of a drug, the efficacy/safety of the drug becomes inextricably linked to the effectiveness of the diagnostic. Drug performance will only be as good as the ability of the diagnostic to properly select patients for that drug treatment. Drug performance is judged based on an assessment of the drug to meet appropriate clinical endpoints or responses with a reasonable safety profile. Diagnostic studies are based on demonstrating that a test

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properly identifies an outcome or target of interest. When the diagnostic is used to detect drug response, then the outcome of interest becomes the drug response or avoidance of drug toxicity. The test parameters of interest in the context of co-development with a new drug do not differ from those of ubiquitous interest in the study of a new diagnostic. Standardized techniques to establishing test performance have been promoted in the literature and in FDA guidances. Clinical parameters of importance include sensitivity – in this case the ability of the test to identify patients who will exhibit the desired drug response of interest – and specificity – in this case the ability of the test to identify patients who will not demonstrate the drug response of interest. Alternative useful techniques include a definition of the predictive value of a positive result – fraction of test positives who respond, the predictive value of a negative result – fraction of test negatives who do not respond, or the likelihood ratio of drug response after testing. Hazard ratio (HR) in prospective studies/analysis and odds ratio (OR) in retrospective analysis could be used when interpreting probabilities in different context of use. 7. Regulations for Drug and Diagnostic Co-Development for Cancer Indications Overview of the Regulations for In Vitro Devices (IVDs) on Cancer Biomarkers The regulation of IVDs by FDA, like the regulation of all medical devices, is risk-based according to the indication and the intended uses, with devices classified into low-risk (class I, e.g. adjunctive immunohistochemical stains used in conjunction with standard microscopic analysis to subtype tumors), moderate-risk (class II, e.g. prognosis, monitoring in already diagnosed cancer patients), or high-risk (class III, e.g. cancer diagnosis, screening) categories. The FDA regulatory program for medical devices is comprehensive and includes requirements for registration and listing of products, for high-quality production using good manufacturing practices, and for post-market reporting of adverse events. For some class I, most class II, and all

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class III devices, FDA review is required before a new medical device can enter the marketplace. Class I devices – They typically present minimal potential for harm to the user and the person being tested. They are subject to general controls, which include registration and listing, labeling, and adverse event reporting requirements (section 513(a)(1)(A) of the Act). Most Class I devices are exempt from premarket notification (see definition below), subject to certain limitations found in section 510(l) of the Act and in 21 CFR 862.9, 864.9, and 866.9. IVD example of Class I devices includes complement reagent (21 CFR 866.4100). Class II devices – Are devices for which general controls alone are insufficient to provide reasonable assurance of their safety and effectiveness and for which establishment of special controls can provide such assurances. Special controls may include special labeling, mandatory performance standards, risk mitigation measures identified in guidance, and post-market surveillance (section 513(a)(1)(B) of the Act). Most Class II devices require premarket notification IVD examples of Class II devices include glucose test systems (21 CFR 862.1345), antinuclear antibody immunological test systems (21 CFR 866.5100), and coagulation instruments (21 CFR 864.5400). Class III devices – Are devices for which insufficient information exists to provide reasonable assurance of safety and effectiveness through general or special controls. Class III devices are usually those that support or sustain human life, are of substantial importance in preventing impairment of human health, or which present a potential, unreasonable risk of illness or injury (section 513(a)(1)(C) of the Act). Most Class III devices require premarket approval (PMA), defined below. IVD examples of these include automated Papanicolau Cervical Cytology (PAP) smear readers, nucleic acid amplification devices for tuberculosis, and total prostate specific antigen (PSA) for the detection of cancer (21 CFR 866.6010).

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8. Major Elements of an In Vitro Diagnostic Submission Major elements of a submission can be found by browsing the Office of In Vitro Diagnostic Devices and Safety (OIVD) 510(k) data templates posted on the OIVD web page. These summarize information related to specific cleared products including the intended use/indications for use, analytical and clinical validation information, device description including platform and software information, information on instrument and software validation when applicable and labeling (package insert information). IVD labeling is unique among medical devices since it is specifically described in regulation [21 CFR 809.10(b)]. For PMAs, information describing manufacturing, design controls, adherence to quality system regulations are also needed. The intended use should specify what analyte the test measures, the clinical indication for which the test is to be used, and the target population for which the test is intended. It should also indicate whether the test is qualitative or quantitative. Analytical validation includes precision (repeatability and reproducibility), accuracy, limit of detection, interferences, cross reactivity, software, performance around the cut-off, carry-over, cross hybridization, sample preparation/conditions and assay limitations. FDA recognizes dozens of standards by the Clinical Laboratory Standards Institute (CLSI) to assist companies in developing this information. A list of recognized standards can also be found on the OIVD web page. Clinical validation should be established in appropriate clinical trials or studies to support the indication for use and claims of the device. These studies generally need to be provided by the sponsor or, if available, sponsors may also cite applicable clinical literature. In order for clinical literature to be acceptable for product clearance or approval, the quality must be carefully assessed and the link between the published studies and the device being reviewed must be carefully established. In instances where tests are developed based on sequential use of training sets followed by independent test validation, FDA encourages that rigorous and meticulous attention be paid to the training sets used for new diagnostic markers. It is specifically important that materials and data used during the exploratory phase of the device development are not

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carried into the conduct and analysis of pivotal studies for device validation. The FDA review is focused on the independent validation that supports the merits of the device itself based on a clear hypothesis in a defined population under established conditions of use. When the system includes software, software documentation including software design, hazard analysis and complete verification and validation should be provided. Labeling needs to be sufficiently detailed to satisfy the requirements of 21 CFR 807.87(e). Final labeling for in vitro diagnostic devices must comply with the requirements of 21 CFR 809.10 before the device is introduced into interstate commerce. Among the particularly important components of required labeling are: an intended use, device description, directions for use, quality control, precautions, warnings and limitations, performance characteristics, interpretation of results and expected values. 9. Clinical Validation and Qualification of the Predictive Biomarker and Drug Combination Co-developed products that would be used together may or may not be combination products as defined in 21 CFR 3.2(e)39. A combination product is defined to include: a product comprised of two or more regulated components that are physically, chemically, or otherwise combined or mixed and produced as a single entity; two or more separated products packages together in a single package or as a unit and comprised of drug and device products, device and biological products, or biological and drug products; a drug, device or biological product packages separately that according to its investigational plan or proposed labeling is intended for use only with an approved individually specified drug, device or biological product; any investigational drug, device or biological product packaged separately that according to its proposed labeling is for use only with another individually specified investigational drug, device, or biological product.39 FDA anticipates that many therapeutic drug and diagnostic test products will be marketed separately. For the purposes of this document, the emphasis is the predictive biomarker defined as: Single trait or signature of traits that separates different population with respect to a

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particular targeted treatment. Co-development refers to products that raise development issues that affect both the drug therapy and the diagnostic test, regardless of their regulatory status as a combination product or as a non-combination product. For example, when codeveloped products are considered together, unique questions may arise that would not exist for either product alone. Scientific or technologic issues for one product alone may be minimal, but they may have substantial implications for the other product. Also, post-approval changes in one may affect the safety and effectiveness of the other. FDA has recently established the Office of Combination Products (OCP) to work with sponsors and FDA work groups to ensure proper regulatory tools are applied to products used in combination and to assist in addressing problems that joint reviews may encounter. If the diagnostic becomes integral to approval of the drug, then diagnostic approval is needed in parallel with the drug. FDA review centers (CDER and CDRH) are willing to work with single sponsors or collaborating sponsors (drug-diagnostics companies) to coordinate review processes and have in the past had conjoint teams, tandem panel meetings, and same day approval of linked products. If the diagnostic has broad use not specifically linked to the drug or if the drug has use not specifically linked to the diagnostic, approvals would follow usual CDER or CDRH procedures and timelines. In some cases, development of a diagnostic may follow development of a drug and be used to refine or improve the safety or effectiveness profile of the drug. CDER and CDRH will work collaboratively with OCP to determine best practices for addressing these situations in a timely manner. 10. Implementation of Biomarkers in Cancer Treatment Implementation or uptake of biomarker testing for cancer treatment depends on the perspectives of the patients, the physicians and the health care providers. From a patient’s perspective, the critical questions to be addressed; am I getting a better treatment based on a biomarker testing compared to the other options, or will the test reduce acceptable toxicity and avoid life-threatening toxicity, or am I compromising my chances to

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respond to a therapy and cure cancer, or who covers the cost of the test? The physicians are concerned about the reliability of the test (false positive versus false negative), discarding patients based on biomarker test results who otherwise may benefit from the drug, the potential delay in treatment initiation due to time required to obtain the test result, loss of efficacy for using a lower dose in high risk cancer patients, having to explain to the patients the utility of the biomarker, and interpreting the test results. From a clinical practice point of view, a physician may wonder if a patient would need less monitoring if they receive a drug or a particular dose of a drug based on biomarker. Also, if biomarker based treatment allow taking care of more patients since the therapeutic benefit is optimized and the patient may need less follow-up. The healthcare providers are concerned about the scientific and clinical evidence that establish the benefit of the test and the cost-effectiveness of the test, availability of alternate therapy, the relative cost of the drug compared to the test. Biomarker-based therapy selection allows a patient ownership in decision making with regard to the choice of treatment and help avoid a serious toxicity or improve chance of getting a better treatment. The biomarker test result provides a physician with patient specific scientific information to select a therapy or to tailor a dose for an individual patient and improve the benefit risk of a therapy. The biomarker-based therapy allows the healthcare provider to likely reduce the cost associated with the treatment for serious toxicity, such as hospitalization, cost of intervention, expensive medication, etc and provide the best care for an individual patient. If the price of a drug is less and require a test and the drug is equally effective to another drug that is expensive, the healthcare provider may prefer to cover the cost of the drug given with the test rather than the expensive drug. The assumption is this case is that both drugs are equally effective and the safety profiles are not significantly different. Cancer treatment is known to have serious toxicities, and mostly limited benefit in many settings. Early detection of a cancer allows a patient to have a long term remission and a desire to have a quality of life. As such, selection of appropriate therapy at various stages of a cancer is critical. In early stage of disease, if a biomarker-based therapy

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allows for a treatment with minimum toxicity and optimum benefit, a patient is likely to prefer that therapy to maintain quality of life and avoid long-term toxicity. In the later stage of disease, if the benefit of a drug is marginal and risk for toxicity is high, a patient may prefer a biomarker-based approach that would optimize the benefit-risk of that treatment. Patients with poor performance status are likely to benefit most from a biomarker approach to select a treatment. The biomarkerbased dose modification might provide a way to treat successfully a sub population who can not tolerate the fully prescribed dose that would otherwise be recommended for the overall population. The challenges in implementing a biomarker-based therapeutic-based therapy are the understanding of the association of the biomarker with the disease and/or association of the biomarker with the therapeutic response. In many situations, the selective interaction between the biomarker and the drug, and the non-specific interactions of the drug with other targets that may elicit a therapeutic response in the population not selected based on the biomarker remains unknown. It is possible that patients not selected for a therapy may also benefit from the treatment. Similarly, there are concerns about lowering a dose based on biomarker status in cancer patients. The drug efficacy might be compromised if the first dose is reduced or dosing regimen is modified based on biomarker status. In oncology treatment, toxicity based titration of dose is common. Physicians routinely skip a scheduled dose or delay a therapy or reduce a dose to relieve toxicity from conventional dose of a drug in an individual patient. However, there may be reluctance in reduction of a dose based on biomarkers that predicts an increased risk of toxicity in a specific population. The impact of skipped dose or delay of therapy on the overall treatment outcome has not been systematically evaluated. At the same time initial dose reduction and its impact in the overall treatment outcome is usually not addressed through a randomized clinical trial. An important factor that influences the utility and adoption of a biomarker test in clinical practice is the reliability and the validation of the test both from the analytical and clinical perspectives. The analytical performance of a biomarker test has to be extremely robust so that both the physician and the patient can rely on the test results. The clinical validation through adequately well-controlled clinical trials is required

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for regulatory approval as adoption of the test in clinical practice. Such a decision is critical for optimizing the benefit from a therapy with reasonable risks in otherwise limited field of choice of oncology therapy. The overall implementation of biomarker testing in cancer treatment needs a coordinated effort among the government agencies, pharmaceutical companies, researchers, health-care providers, physicians and patients. Risk reduction and improved safety and efficacy although should be the major objective for a biomarker-based therapy, other factors such as reliability of the test, availability of alternate therapy, cost effectiveness will play a role in the implementation of biomarker in clinical practice. Most of all, understanding of pharmacogenetics and its usefulness and limitations will help the acceptance of a test based treatment in cancer. 11. Conclusions Advancement of science identifying targets associated with cellular differentiation and programmed cell death has lead to efficient drug development for oncology indications and anticipation of more effective and safer drugs for cancer treatment. The biomarkers in treatment have allowed design of drugs to treat more safely and/or effectively a sub population expressing the biomarker. Biomarkers are used with the intent to optimize the benefit-risk of cancer therapy either through selection of patients to be treated with a drug that targets a specific biomarker or selection of a dose for a particular patient. The failure rate of cancer drug development is one of the highest among various types of diseases.40,41 The biomarker based development is likely to reduce the risk of drug failing to demonstrate adequate safety and/or efficacy for regulatory approval. Targeted therapy development will also allow patients to participate in clinical trial with an expectation of a benefit from an experimental agent. This may allow for appropriate distribution and availability of limited patient populations for investigational clinical trials. The test development becomes extremely critical with regard to the timeline of the drug development so that adequate data is generated to seek approval for both the test and the drug at the same time. The drug

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and the device labels should include adequate information to convey to the physicians and the patients the need for a test to select therapy or select an appropriate dose for treatment. The FDA is currently working with the pharmaceutical and the diagnostic industries, academics, professional societies, international regulatory agencies, and other governmental agencies to develop guidance and promote use of biomarkers in cancer drug development and cancer therapy. The interaction will lead to the development of innovative drug-diagnostics development programs to establish the safety and effectiveness of drugs/biologics/diagnostics for a targeted patient population and to increase the success rate of cancer drug development and approval and better patient care. Acknowledgment The authors would like to thank Dr. Max Robinowitx for valuable comments regarding this article. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

http://www.fda.gov/opacom/morechoices/mission.html. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfCFR/CFRSearch.cfm?fr=201.57. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?FR=809.10. L.J. Lesko, J. Woodcock. Nat Rev Drug Discov., 3(9) 763-769 (2004). B.R. Schackman, C.A. Scott, R.P. Walensky, E. Losina, K.A. Freedberg, AIDS., 20252033 (2008). J. de Leon, Pharmacol Res., Epub. October 17 (2008). http://www.cancer.gov/cancertopics/factsheet/detection/staging. http://www.asco.org/ASCO/Quality+Care+%26+Guidelines/Practice+Guidelines/Clinic al+Practice+Guidelines. http://www.cancer.gov/cancertopics/pdq/treatment/prostate/patient. http://www.asco.org/ASCO/Quality+Care+%26+Guidelines/Practice+Guidelines/Guidel ine+Clinical+Tools+and+Resources/Genitourinary+Cancer. http://www.asco.org/ASCO/Quality+Care+%26+Guidelines/Practice+Guidelines/Guidel ine+Clinical+Tools+and+Resources/Breast+Cancer. http://www.cancer.gov/cancertopics/understandingcancer/estrogenreceptors. Gleevec Label : http://www.fda.gov/cder/foi/label/2008/021588s024lbl.pdf. Purinethol Label : http://www.fda.gov/cder/foi/label/2004/09053s024lbl.pdf. L. Lennard, J.C. Welch, J.S. Lilleyman. Br. J. Clin. Pharmacol., 44(5) 455-61 (1997).

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A. Rahman & F. Kalush R. Weinshilboum. Drug Metab. Dispos., 29 601-605 (2001). H.L. Leod, C. Siva. Pharmacogenomics., 3 89-98 (2002). Camptosar Label: http://www.fda.gov/cder/foi/label/2006/020571s030lbl.pdf. Herceptin Label: http://www.fda.gov/cder/foi/label/2008/103792s5175lbl.pdf. Erbitux Label: http://www.fda.gov/cder/foi/label/2007/125084s103lbl.pdf. W.A. Messersmith, D.J. Ahnen. N. Engl. J. Med., 359(17) 1834 (2008) J.V. Heymach, B.E. Johnson, D. Prager, E. Csada, J. Roubec, M. Pesek, I. Spasova, C.P. Belani, I. Bodrogi, S. Gadgeel, S.J. Kennedy, J. Hou, R.S. Herbst. J. Clin. Oncol., 25 (27) 4270-4277 (2007). J. Drevs, P. Siegert, M. Medinger, K. Mross, R. Strecker, U. Zirrgiebel, J. Harder, H. Blum, J. Robertson, J.M. Jurgensmeier, T.A. Puchalski, H. Young, O. Saunders, C. Unger. J. Clin. Oncol., 25(21) 3045-3054 (2007). R.O. Dillman. J. Clin. Oncol., 20(16) 3545-3557 (2002). F.W. Frueh, S. Amur, P. Mummaneni, R.S. Epstein, R.E. Aubert, T.M. DeLuca, R.R. Verbrugge, G.J. Burckart, L.J. Lesko. Pharmacotherapy, 28(8) 992-998 (2008). Tykerb Label: http://www.fda.gov/cder/foi/label/2008/022059s004lbl.pdf. Sprycel Label: http://www.fda.gov/cder/foi/label/2006/022072lbl.pdf. Tasigna Label : http://www.fda.gov/cder/foi/label/2007/022068lbl.pdf. Vectibix Label: http://www.fda.gov/cder/foi/label/2008/125147s026lbl.pdf. R.G. Amado, M. Wolf, M. Peeters, E.V. Cutsem, S. Siena, D.J. Freeman, T. Juan, R. Sikorski, S. Suggs, R. Radinsky, S.D. Patterson, D.D. Chang. J. Clin. Oncol., 26(10) 1626-1634 (2008). C.S. Karapetis, S. Khambata-Ford, D.J. Jonker, C.J. O’Callaghan, D.Tu, N.C. Tebbutt, R.J. Simes, H. Chalchal, J.D. Shapiro, S. Robitaille, H.-J. Au, C. Langer, M.J. Moore, J.R. Zalcberg. N. Eng. J. Med., 359(17) 1757-1765 (2008). http://www.fda.gov/ohrms/dockets/ac/08/minutes/2008-4409m1-Final.pdf. W. Schroth, L. Antoniadou, P. Fritz, M. Schwab, T. Muerdter, U.M. Zanger, W. Simon, M. Eicherbaum, H. Brauch. J. Clin. Oncol., 25(33) 5187-5193 (2007). M.P. Goetz, J.M. Rae, V.J. Suman, S.L. Safgren, M.M. Ames. Breast Can. Treat. Res., 101 113-121 (2007). F. Innocenti, S.D. Undevia, L. Iyer, P.X. Chen, S. Das, M. Kocherginsky, T. Karrison, L. Janisch, J. Ramirez, C.M. Rudin, E.E. Vokes, M. Ratain. J. Clin. Oncol., 22(9) 13821388 (2004). E. Marcuello, A. Altes, A. Menoyo, E. del Rio, M. Gomez-Pardo, M. Baiget. Br. J. Can., 91 678-682 (2004). E. Routis, M. Boisdron-Celle, A. Dumont, O. Guerin, A. Morel, E. Gamelin. Clin. Can. Res., 10 5151-5159 (2004). Advisory Committee for Pharmaceutical Science; Clinical Pharmacology Subcommittee: http://www.fda.gov/ohrms/dockets/ac/04/transcripts/2004-4079T1.htm. 21 Code of Federal Regulations 3.2(e) http://www.fda.gov/oc/ombudsman/part3&5.htm. T. Fiorino. Letter to the Editor. J. Clin. Oncol., 25(19) e21-523 (2007). I. Kola, J. Landis. Nature Reviews Drug Discovery, 3 711-715 (2004).

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CHAPTER 14 PERSONALIZING TREATMENT SELECTION FOR BREAST CANCER

Lajos Pusztai Department of Breast Medical Oncology University of Texas M.D. Anderson Cancer Center P. O. Box 301439, Houston, Texas 77230-1439, USA E-mail: [email protected] Chit Cheng Yeoh Center for Molecular Oncology Institute of Cancer and the CR-UK Clinical Centre London, EC1M 6BQ, UK E-mail: [email protected] Breast cancer is the most common malignancy afflicting women, and the second leading cause of cancer-related death. Current treatment modalities include surgical resection, radiation, endocrine- and chemotherapy and an increasing repertoire of biologically targeted drugs. Fifteen new drugs were approved by the United States Food and Drug administration (FDA) for breast cancer in the last decade. The availability of more effective treatments has led to a decline in mortality from breast cancer in all stages of the disease. However, the biology of breast cancer remains poorly understood and the multiple treatment options also created a pressing need for better strategies for treatment selection. Currently, highly accurate markers to predict prognosis and probability of response to a given therapy on an individual basis are lacking, and routinely used clinicopathologic variables fail to capture the heterogeneity of breast cancer. As a result, many patients are over treated and others may receive suboptimal combination of treatments. Molecular differences in breast cancer probably account for most heterogeneity in clinical outcome. Geneexpression profiling and other high-throughput molecular analytical

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methods are increasingly applied in breast cancer research to comprehensively measure molecular differences in cancers in the hope that a combination of multiple genes will be more informative in predicting disease outcome and response to a specific therapies than any single gene alone. Existing and emerging single and multi-gene tests to personalize treatment selection will be discussed in this chapter.

1. Introduction “Personalized therapy” is an important rallying point of current research in oncology. However, this is not a novel idea, it has been the focus of medicine since its inception. Ancient medical texts clearly indicate that different empirical therapies were recommended for different constellations of symptoms in an attempt to tailor treatment to the illness of a particular individual. For example, the Ebers’ papyrus written in 1500 B.C. recommends that “... for a person who suffers from abdominal obstruction and you find [on physical examination] that it goes-andcomes under your fingers like oil-in-tube, then prepare for him fruit-ofthe-dompalm, dissolve in semen, crush and cook in oil and honey”1. On the other hand, if a person suffers from abdominal obstruction but displays different physical findings“… his stomach is swollen and his chest asthmatic, then make for him wormwood, elderberries, sebesten, sesa chips, crush and cook in beer …”1. One could argue that the history of medicine is the history of increasingly more sophisticated personalization of treatment which involves increasingly narrow definitions of diseases and selective treatments based on better understanding of the various biological mechanisms that causes a disease. This process is intricately intertwined with technologic advances in diagnostic medicine. In the past 10 years, several new ground breaking tissue analytical methods were developed that offer a potential to raise “personalized therapy” to the next level. High throughput molecular analytical tools including, gene expression profiling, protein profiling, single nucleotide polymorphism (SNP) analysis and comparative genomic hybridization (CGH) enable investigators to take an almost complete inventory of all genes that are expressed in a particular cancer and to comprehensively examine DNA

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nucleotide sequence and copy number changes in these cells. It is hoped that these technologies will lead to the discovery of new prognostic and predictive markers and can also reveal new biological pathways that cause cancer and therefore could lead to new therapies, hence the current excitement about personalized medicine in oncology. Medical decision making takes place in four-dimensional decision space (Figure 1). Physicians and patients need to consider the clinical outcome in the absence of treatment (i.e. prognosis), the probability of benefit from therapy, and the risk of adverse events from an intervention. An important fourth dimension is patient preference. A person’s willingness to accept therapy is influenced by her/his tolerance for the consequences of the disease (i.e. symptoms, risk of recurrence, death) and adverse events from the treatment2. The more accurate the toxicity, prognostic and response predictions are, the better the personalized treatment recommendation can be made for an individual. Several diagnostic tools are commonly used in the context of breast cancer to gauge patient preference, estimate the risk of recurrence, and estimate the probability of benefit from endocrine-, chemo- or trastuzumab therapy (Table 1). The risk of adverse events is estimated, rather subjectively, based on age, co-morbidities and results from organ function tests. The risk of recurrence is primarily determined from lymph node status, tumor size, and histological grade. Estrogen receptor (ER) and progesterone receptor (PR) immunohistochemical (IHC) results are used to define the subset of individuals who may benefit from endocrine therapy. Human Epidermal Growth Factor Receptor-2 (HER-2) status is determined by fluorescent in situ hybridization (FISH) or IHC in order to select patients for trastuzumab treatment. These clinical and pathologic features can be combined into a multivariate outcome prediction model Adjuvant Onlinea. This freely available web-based tool estimates the risk of recurrence (or death) with locoregional therapy alone and with various systemic adjuvant treatments, including endocrine therapy and chemotherapy for stages I-III breast cancer3.

a

www.adjuvantonline.com.

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Patient 2

Se

iti ns

ty vi

to

en

d

rin oc

e

ap er th

y

Patient 1

Sensitivity to chemotherapy Fig. 1. Three-dimensional treatment decision space. Optimal treatment recommendations for breast cancer require understanding the risk of recurrence and the probabilities of benefit from endocrine- and chemotherapies, respectively. Chemotherapy may be selected for different reasons for different individuals. An important fourth dimension is risk of toxicity from therapy which is not shown on this figure. Novel genomic tests started to enable physicians to estimate these risks separately and somewhat more precisely than by using routine clinical parameters alone.

However, current prediction models have several limitations. Disease outcomes remain heterogeneous within clinical prognostic risk groups. Estrogen receptor and HER2 status are helpful in identifying patients who are not eligible for endocrine or trastuzumab therapies, respectively, but less than half of ER-positive and less than a third of HER2-positive patients actually respond to receptor-targeted therapies. The positive predictive values (PPV) of these tests are modest. Furthermore, currently, there are no accepted molecular predictors of response to chemotherapeutic drugs. We also have limited ability to predict adverse events despite the relatively high toxicity and modest activity of cytotoxic drugs. These shortcomings of current tests motivate research to develop more accurate molecular predictors of clinical outcome.

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Table 1. Routine clinical tools to aid medical decision making in breast cancer. Risk of adverse events

Medical history Organ function tests (BUN, creatinine, liver enzymes, complete blood count) Echocardiogram

Prognosis

TNM stage Nottingham Prognostic Index Adjuvant Online software

Treatment selection

ER and PR immunohistochemical (IHC) analysis for endocrine therapy HER-2 fluorescent in situ hybridization or IHC analysis for trastuzumab therapy

Patient preferences

Medical interview

2. Molecular Classification of Breast Cancer and its Clinical Relevance One of the conceptually most important discoveries of the past 10 years was the realization that invasive breast cancer is not a single disease with different degrees of ER and HER2 expression and variable histologic features but a collection of several molecularly rather distinct diseases. Transcriptional profiling revealed large scale gene expression differences between ER-positive and ER-negative cancers that go far beyond the expression of ER itself. It is plausible that ER-positive and -negative cancers originate from different epithelial precursors, luminal and basal ductal epithelial cells, respectively. Furthermore, among the ER-positive cancers, two distinct subtypes can also be distinguished that show different sensitivities to therapy and have different prognosis. Currently, four different molecular classes of breast cancers can be identified consistently through gene expression profiling. Using the original terminology proposed by Perou et al. these include: (i) “Basal-like” breast cancers that correspond mostly to ER-and HER2-negative, high histologic grade cancers, (ii) “Luminal-A” cancers that are mostly ERpositive and lower grade cancers, (iii) “Luminal-B” cancers that are also mostly ER-positive but often higher grade, and (iv) “HER2-positive” cancers that include most of the HER2 gene amplified cases4. It is

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important to recognize that up to 25-30% breast cancers do not fall into any one of the above robust molecular categories. Some limitations of the current gene expression based classification of breast cancer are also increasingly apparent. Hierarchical clustering and heat maps that were used to define the molecular classes of breast cancer are visually appealing tools to display similarity of samples but are not appropriate statistical tools to assign molecular class to new cases. Clustering algorithms work by linking the two most similar specimens together first, and then successively merge other specimens in order of similarity. Every time new cases are added to an existing data set the previous order of clustering is revised and a completely new dendogram (cluster tree) is generated. Small clusters are highly unstable and even membership in large clusters can shift considerably when new samples are added or if the gene lists that are used for the clustering are altered5. Not surprisingly, several smaller molecular subsets such as normal-like breast cancers, luminal-C and other types that were proposed are not consistently observed by different investigators. For these and other mathematical reasons, hierarchical clustering should not be used to assign molecular class to a new case. Class membership has to be determined by applying a proper class prediction method. One such single sample breast cancer classifier has recently been published6. The four molecular classes of breast correspond closely, but not perfectly, to well-established clinical phenotypes of breast cancer. This correspondence is reassuring and provides a molecular framework to understand clinical phenotype. It is important to consider that while histological grade can not be targeted with therapies; better understanding of the molecular abnormalities that cause high grade morphologic features may lead to new therapeutic targets. On the other hand, the diagnostic relevance of molecular classification is limited by its close association with ER- and HER2-status and histological grade. Basal-like cancers are almost exclusively ER- and HER2-negative and high grade cancers therefore it is expected that they will have poor prognosis in the absence of adjuvant systemic therapy, show have higher sensitivity to chemotherapy in general and do not benefit from endocrine treatment. Conversely, Luminal-A cancers that are mostly ER-positive, HER-2 normal and have lower grade will have the highest endocrine

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sensitivity (but lowest chemotherapy sensitivity) and best prognosis7. To what extent molecular classification provides clinical value beyond routine histopathology parameters remains unknown. However, molecular classification provides a simple summary measure of complex clinical-pathological variables. This is an important potential advantage because considerable variation exists in the assessment of routine histopathological features (i.e. grade and even ER-status) of the same cancer by different pathologists. 3. Gene Expression Signatures and Other Markers to Predict Prognosis Molecular classes that emerge from grouping cancers with similar gene expression profiles together may not necessarily represent the best marker to predict a particular clinical outcome. Two alternative predictor development strategies exist and have been explored. The first one may be referred to as the “supervised” marker discovery strategy. This relies on comparing gene expression data from cohorts of cases with known outcome to identify genes that are associated with prognosis or response to therapy and than combine these informative genes into a multivariate prediction model. The second strategy may be called the “hypothesis testing” approach. This starts with defining a hypothesis that particular genes or molecular pathways may influence a clinical outcome of interest and these genes are used to construct a multi-gene predictive signature. Candidate genes may be selected based on existing biological knowledge or can be identified through experiments in vitro. Regardless of which development strategy is utilized, genomic outcome predictors (i.e. “gene signatures”) are conceptually similar to multivariate clinical prognostic prediction models. These prediction tools apply the mathematical principle that individually weak predictive variables, that are at least partly independent of each other, can be combined into prediction models that are more accurate than any single variable alone. This strategy has been applied successfully to routine histological and clinical variables that were combined into clinically useful predictors of prognosis including the Nottingham Prognostic Index or Adjuvant Online. The main difference is that genomic

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Table 2. Emerging molecular assays to personalize medical decision making in breast cancer. Molecular test Molecular Class Predictor Mammaprint® Oncotype Dx™ Genomic Grade Index TOP2A FISH pharmDx™ * †

Independent validation

Type of tissue

Yes Yes Yes Yes Yes

Frozen Frozen Fixed Frozen Fixed

Prospective clinical evaluation No Underway* Underway† No No

Commercially available No Yes Yes No Yes

MINDACT (Microarray in Node Negative Disease May Avoid Chemotherapy) trial. TAILORX (Trial Assigning Individual Options for Treatment) trial.

predictors combine molecular rather than clinico-pathological variables into a prediction model. Table 2 summarizes several of the published clinical predictors and the development strategy that they represent. 3.1. Prognostic gene signatures Three distinct gene expression profiling-based prognostic tests were recently developed. One of these, MammaPrint (Agendia Inc., Amsterdam, Netherlands), was recently cleared by the U.S. Food and Drug Administration (FDA) to aid prognostic prediction in nodenegative breast cancer. This assay measures the expression of 70 genes and calculates a prognostic score that can be used to categorize patients into “good” or “poor” prognostic risk groups. This test was subsequently evaluated on two separate cohorts of patients that received no systemic adjuvant therapy. The first cohort included 295 patients and showed that those with the good prognosis gene signature had 95% (standard error ±2%) and 85% (±4%) distant metastasis-free survival at 5 and 10 years, respectively. In contrast, the poor prognostic group had 60% (±4%) and 50% (±4.5%) distant metastasis-free survival at 5 and 10 years, respectively8. A second validation study (n=307) confirmed these findings and showed that patients with the good prognosis signature had 90% (95% confidence interval, 85-96%) distant metastasis-free survival at 10 years, whereas it was 71% (65-78%) in the poor prognosis group9. Importantly, the MammaPrint signature could re-stratify patients within clinical risk categories defined by the Adjuvant Online program. Some of

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the clinically low-risk patients were correctly re-categorized as high-risk based on their gene signature, and some clinically high-risk patients were correctly predicted to be low-risk by the genomic test. However, a recent report also highlighted an important limitation of this test, almost all ER-negative cancers (>90%) are classified as high-risk by MammaPrint. Other investigators also identified genes that were associated with relapse in node-negative breast cancer. In another study, prognostic genes were selected separately from ER-negative and ER-positive tumors and were combined into a single 76-gene prognostic signature (VDX2; Veridex, LLC, Warren, NJ). This test was also evaluated in two separate cohorts of patients that received no systemic adjuvant therapy and were not included in the development of the test. The first cohort included 180 patients and showed 5- and 10-year distant metastasis-free survival rates of 96% (89-99%) and 94% (83-98%), respectively, for the good prognosis group and the corresponding rates were 74% (64-81%) and 65% (53-74%) for the poor prognosis group10. The second independent validation included 198 node-negative cases and demonstrated 5- and 10year distant metastasis-free survival rates of 98% (88-100%) and 94% (83-89%), respectively for the good prognosis group and 76% (68-82%) and 73% (65-79%) for the poor prognosis group11. In this instance too, the gene signature could re-stratify patients within the clinical risk categories defined by the Adjuvant Online program, and the hazard ratios for recurrence remained similar after adjustment for tumor grade, size, and ER status that confirms least partly independent added prognostic value. A third prognostic signature utilized the “hypothesis testing” discovery strategy. Investigators set out to define the gene expression differences between low- and high-histological-grade cancers and assumed that these genes would be able to improve prognostic predictions for morphologically intermediate-grade cancers. Using this approach, a 97-gene genomic grade signature was identified that discriminated between low- and high-grade tumors and separated intermediate-grade tumors into two distinct subgroups of lower and higher genomic grade cancers with different prognosis12. These results were observed across multiple independent data sets generated on different microarray platforms. Not surprisingly, the genomic grade

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gene index is dominated by genes involved in cell cycle regulation and proliferation13. It is important to point out that the various prognostic signatures have very few genes in common. This may be surprising at first, but it is a common feature of high-dimensional data that contain large numbers of highly correlated variables. Gene expression values are highly correlated with each other and therefore, if the expression of a particular gene is associated with a particular clinical outcome, all other genes whose expression are closely correlated with that index gene will also correlate with the same clinical outcome. However, the strength of association between any given gene and the clinical outcome varies from training set to training set and therefore, the rank order of the informative genes is unstable when they are ranked by strength of association. Nevertheless, all of these co-expressed genes carry similar information about the outcome of interest and therefore many different statistically equally good predictors can be discovered from the same data set14. A corollary of this is that different predictors that use information from different genes can predict equally well on a given data set. Indeed; investigators applied five distinct multi-gene prognostic predictors to a single breast cancer gene expression data set and found that four of the five predictors showed similar predictive values even though very few genes were common between the predictors15. A limitation of the current microarray-based prognostic assays is that they only provide moderately precise estimates of risk of recurrence. This is evident from the relatively broad confidence intervals around the outcome estimates. Also, almost all ER-negative cancers are assigned to high prognostic risk category by the currently available assays. On the other hand, the genomic predictors seem to complement tumor size- and grade-based prognostic models. This is probably driven by the improved ability of the genomic tests to categorize clinically intermediate risk groups (i.e. intermediate grade cancers) into low or high prognostic categories. What constitutes a low enough risk to forgo systemic adjuvant chemotherapy is influenced not only by the absolute risk of relapse but also by the risk of adverse events, the probability of benefit from therapy, and personal preferences. Many patients are willing to accept adjuvant chemotherapy for rather small gains in survival2.

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Molecular prognostic markers may provide little clinical value for these individuals because no predictive test is accurate enough to completely rule out risk of relapse or some potential benefit from adjuvant therapy. However, many other patients are reluctant to accept the toxicities, inconvenience, and costs of chemotherapy for a small and uncertain benefit. For these individuals, more precise prediction of risk of recurrence and sensitivity to adjuvant therapy with genomic tests can assist in making a more informed decision. 4. Molecular Predictors of Response to Endocrine Therapy 4.1. Estrogen receptor Determination of ER status is an essential part of the diagnostic work up of all breast cancer patients in order to determine whether a patient is a candidate for endocrine therapy or not. The current gold standard to assess ER status is IHC performed on formalin fixed paraffin embedded cancer tissue. Unfortunately, the existing IHC assays have only modest positive predictive value (30-60%) for response to single agent hormonal therapies16,17. Furthermore, there is substantial intra- and inter-laboratory variation in ER results because of fixation, antigen retrieval and staining methods may differ between laboratories18,19. Interpretation of staining can also be subjective and influenced by the training of individual pathologists and by the threshold setting of the image analysis system that is being used. In one study, 200 clinical laboratories received sections from the same three breast cancers that include one case of each low, moderate or high ER expression. Each laboratory performed its own IHC assessment and concordance of results was examined. The results indicated considerable inter-laboratory variation, for example the false negative rates were as high as 30-60% (depending on the cut off) in the low ER-positive case18. The technical variability and discrepancies in reproducibility of results between laboratories is not limited to ER IHC. Substantial discordance also exists for HER-2 results generated in different laboratories from the same specimen. For example, the level of concordance for HER-2 IHC results was 80% and for HER-2 FISH it was 85% when the same specimens were tested in local and central

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laboratories20. This has lead to recent attempts to standardize HER2 evaluation methods21. The modest positive predictive value of current methods and the variable reproducibility of results fuels efforts to develop more accurate and more reliable predictors of benefit from hormonal therapy. There is some data to suggest that the higher the expression of ER, the higher the probability of benefit from adjuvant endocrine therapy22. Therefore, more accurate quantification of ER expression over a broad dynamic range may alone represent an improvement over the semiquantitative ER IHC. There are now several methods that can reliably measure ER mRNA expression over a broad range, foremost of these are quantitative RT-PCR and DNA microarrays23. Since ER protein and ER mRNA levels correlate closely, it is reasonable to hypothesize that determination of ER status by mRNA measurements will be of clinical value. 4.2. Oncotype DX assay and other genomic predictors One of the most important questions for patients with ER-positive breast cancer is whether they should receive adjuvant endocrine therapy alone or also take chemotherapy. Oncotype DX (Genomic Health, Inc., Redwood City, CA), represents a novel and commercially available molecular assay in the United States to assist decision making in this clinical setting. This RT-PCR-based assay represents an important conceptual advance in the diagnosis of ER-positive breast cancers. It measures the expression of 21 genes including ER and HER2 as well as several ER-regulated transcripts and proliferation related-genes including Ki-67. Several of these genes were already known to be associated with outcome and can be assessed with more conventional methods as well. However, an important value of Oncotype DX is that it combines these results into a simple and easily interpretable “recurrence score” (RS). The RS could be used as a continuous variable to estimate the probability of recurrence at 10-years or can be grouped into low-, intermediate- or high-risk categories. Correlation between RS and distant relapse was examined in 668 patients with ER-positive, node-negative cancers

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treated with tamoxifen who were enrolled in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B14 clinical trial. The 10-year distant recurrence rates were 7% (4-10%), 14% (8-20%), and 30% (24-37%) for the low-, intermediate-, and high-risk categories (p31, while there was no benefit for patients with RS 2-fold variation. This means that, 314 genes can be > 2-fold decreased or increased from one experiment to another due to technical noise alone51. Despite these caveats, it is likely that as larger and larger clinical data sets become available empirical discovery of predictive signatures will also become more and more successful. 5.5. Cell line-derived predictive signatures Experimental cancer models are readily available in the laboratory to identify putative markers. For example, cancer cell lines can be treated with drugs in vitro and comparison of gene expression data between sensitive and resistant cells can yield potential predictors of response52. In several studies investigators used publicly available gene expression data and in vitro response information for several different cytotoxic drugs to develop drug-specific pharmacogenomic response predictors and tested these in human data. In one study, cell line-derived predictors were applied to gene expression data from 125 breast cancers who received preoperative chemotherapy53. The pharmacogenomic predictor for a 3-drug combination chemotherapy regimen of 5-fluorouracil, epirubicin and cyclophosphamide had a sensitivity of 96%, specificity of 66% and positive (PPV) and negative predictive (NPV) values of 68% and 96%, respectively. In the same study, a different predictor for epirubicin and docetaxel combination therapy had sensitivity of 93%, specificity of 69% and PPV and NPV of 71% and 92%, respectively when applied to the human cases. However, an independent group of statisticians could not replicate these results using the publicly available information that accompanied these manuscripts54,55.

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How to combine genes identified from gene expression data of cell lines into multivariate prediction models that could be applied to primary breast cancer data is challenging. Gene expression profiles of cell lines are substantially different from primary breast cancers. Hierarchical clustering shows that the global gene expression structure of any breast cancer cell line is more different from any human primary breast cancer than the gene expression profiles of any primary tumor from one another. There are also several theoretical reasons why cell line based predictors may not perform well on human cancers. Established cell lines represent an extremely small and highly selected minority of the heterogeneous neoplastic cell population of human cancers. Cell lines specially selected and adopted for cell culture conditions may not respond similarly to drugs as cancer cells growing in a human host56. Drug exposure in vitro does not mimic the kinetics of drug exposure in human tumors that are influenced by interstitial pressure, blood flow, drug metabolism rate and other host factors. There is a long line of investigations that attempted to correlate ex vivo short-term chemotherapy sensitivity test results (i.e. human tumor cloning assay, differential staining toxicity, extreme drug resistance assay, etc.) obtained from primary tumor cultures with clinical response of the same tumor57. Unfortunately, results from these investigations were largely disappointing. There is modest correlation between clinical response and ex vivo chemotherapy sensitivity of the same cancer58. In the light of these results, one must be cautious to expect that gene signatures associated with response to therapy in cell lines will correlate closely with tumor response in patients. Independent validation of all such claims is essential. 6. Future of Therapy Optimization in Breast Cancer Using Predictive Biomarkers An important promise of the microarray-based tests is that multiple distinct predictions including prognosis, ER- and HER2-status and sensitivity to various treatment modalities may be generated from a single experiment. This assay would use information from different genes or sets of genes for different predictions from the same tissue. This is already technically feasible and could substantially improve the

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cost-effectiveness of a multi-gene assay. In order to provide a truly personalized treatment recommendation it will be important to measure the risk of relapse and the probabilities of benefit from endocrine and chemotherapies separately and consider patient preferences in the light of this information (Figure 1). Another important direction of research is to examine the hypothesis that different markers and biological pathways may be involved in determining prognosis or response or resistance to therapy in different molecular subsets of breast cancers. As larger clinical data sets become available for molecular analysis, it is possible that molecular classspecific prognostic and treatment response predictors may be developed in the future. The microarray platforms that were used to develop the current clinical outcome predictors have a clear limitation; they confine their interrogation to the “mRNA world” as it was known 10-15 years ago. In the past few years, our knowledge of the RNA world evolved rapidly. It is now apparent that a previously unrecognized complex world of small regulatory RNA species exists including microRNA, siRNA, snoRNA and vast regions of non-coding DNA, pseudo-genes and antisense DNA strands are also frequently transcribedc,59. Alternative splice forms of mRNAs are commonly generated from the same gene and can lead to distinct transcripts with different functions. It is almost certain that this extended RNA world contains complementary information not fully captured by measuring the expression of previously know genes. The next generation of DNA arrays (e.g. tiling arrays, miRNA arrays) will enable investigators to study the clinical and diagnostic potential of these new RNA species. In the long run, one might hope that the most important contribution of high throughput genomic technologies will be to define the next generation of drug targets through molecular analysis of human cancers. Current molecular models of breast cancer biology are based on interactions between a few hundred molecules. However, gene expression data indicates that at least 5-10 thousand different mRNA transcripts are expressed in every cancer and most of these have no c

http://www.genome.gov/ENCODE.

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known function in cancer biology. Many of these genes may prove to be important novel drug targets and the technology that has lead to their discovery may also serve to select patients for these future therapies. References 1. Carmichael AG, Ratzan RM (eds) (2001). Medicine, a treasury of art and literature. Beaux Arts Editions. New York, p29. 2. Ravdin PM, Siminoff IA, Harvey JA et al. Survey of breast cancer patients concerning their knowledge and expectations of adjuvant therapy. J Clin Oncol 16:515-521 (1998). 3. Olivotto IA, Bajdik CD, Ravdin PM et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23:2716-2725 (2005). 4. Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 406:747-752 (2000). 5. Pusztai L, Mazouni C, Anderson K, Wu Y, Symmans WF. Molecular classification of breast cancer: Limitations and potential. Oncologist 11(8):868-77 (2006). 6. Hu Z, Fan C, Oh DS et al. The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7:96 (2006). 7. Sotiriou C, Neo SY, McShane LM et al. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100:10393-10398 (2003). 8. van de Vijver, He YD, van’t Veer LJ et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999-2009 (2002). 9. Buyse M, Loi S, van’t VL et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98:1183-1192 (2006). 10. Wang Y, Klijn JG, Zhang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365:671-679 (2005). 11. Foekens JA, Atkins D, Zhang Y et al. Multicenter validation of a gene expressionbased prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24:1665-1671 (2006). 12. Sotiriou C, Wirapati P, Loi S et al. Gene expression profiling in breast cancer: Understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98:262-272 (2006). 13. Desmedt C, Sotiriou C. Proliferation: The most prominent predictor of clinical outcome in breast cancer. Cell Cycle 5(19):2198-202 (2006). 14. Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: Is there a unique set? Bioinformatics 21:171-178 (2005).

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15. Fan C, Oh DS, Wessels L et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 355(6):560-9 (2006). 16. Bonneterre J, Thürlimann B, Robertson JFR et al. Anastrozole versus tamoxifen as first-line therapy for advanced breast cancer in 668 postmenopausal women: Results of the Tamoxifen or Arimidex Randomized Group Efficacy and Tolerability Study. J Clin Oncol 18:3748-3757 (2000). 17. Mouridsen H, Gershanovich M, Sun Y et al. Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: Results of a phase III study of the International Letrozole Breast Cancer Group. J Clin Oncol 19:2596-2606 (2001). 18. Rhodes A, Jasani B, Barnes DM et al. Reliability of immunohistochemical demonstration of estrogen receptors in routine practice: Interlaboratory variance in the sensitivity of detection and evaluation of scoring systems. J Clin Pathol 53:125-130 (2000). 19. Rüdiger T, Höfler H, Kreipe H et al. Quality assurance in immunohistochemistry: Results of an interlaboratory trial involving 172 pathologists. Am J Surg Pathol 26:873-882 (2002). 20. Perez EA, Suman VJ, Davidson NE, Martino S, Kaufman PA, Lingle WL, Flynn PJ, Ingle JN, Visscher D, Jenkins RB. HER2 testing by local, central, and reference laboratories in specimens from the North Central Cancer Treatment Group N9831 intergroup adjuvant trial. J Clin Oncol 4:3032-8 (2006). 21. Wolff AC, Hammond ME, Schwartz JN et al. American Society of Clinical Oncology. College of American Pathologists. American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 25:118-45 (2007). 22. Harvey JM, Clark GM, Osborne CK et al. Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. J Clin Oncol 17:1474-1481 (1999). 23. Gong Y, Yan K, Lin F, Anderson K et al. Determination of oestrogen-receptor status and ERBB2 status of breast carcinoma: A gene-expression profiling study. Lancet Oncol 8:203-11 (2007). 24. Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifentreated, node-negative breast cancer. N Engl J Med 351:2817-2826 (2004). 25. Habel LA, Quesenberry CP, Jacobs M et al. Gene expression and breast cancer mortality in Northern California Kaiser Permanente Patients: A large populationbased case control study. J Clin Oncol 2005 ASCO Annual Meeting Proceedings. Part 1. 23:603 (suppl) (2005). 26. Paik S, Shak S, Tang G et al. A multigene assay to predict recurrence of tamoxifentreated, node-negative breast cancer. N Engl J Med 351:2817-26 (2004).

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27. Goldstein LJ, Gray R, Childs BH et al. Prognostic utility of the 21-gene assay in Hormone Receptor (HR) positive operable breast cancer and 0-3 positive axillary nodes treated with adjuvant chemo-hormonal therapy: An analysis of Intergoup trial E2197. Proc Am Soc Clin Oncol Annual Meeting 2007 Abs 526. 28. Albain K, Barlow W, Shak S et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal, node-positive, ER-positive breast cancer (S8814, INT0100). Breast Cancer Res Treat 106 (supp 1):Abstr # 10 (2007). 29. Ma XJ, Wang Z, Ryan PD et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5:607-616 (2004). 30. Jansen MP, Foekens JA, van Staveren IL et al. Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23:732-40 (2005). 31. Oh DS, Troester MA, Usary J et al. Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers. J Clin Oncol 24:1656-64 (2006). 32. Symmans WF, Hatzis C, Sotiriou C et al. Ability of a 200-gene endocrine sensitivity index (SET) to predict survival for patients who receive adjuvant endocrine therapy or for untreated patients. 2007 Breast Cancer Symposium, abstr #25. http://www.asco.org/portal/site/ASCO/menuitem.34d60f5624ba07fd506fe310ee37a 01d/?vgnextoid=76f8201eb61a7010VgnVCM100000ed730ad1RCRD&vmview=ab st_detail_view&confID=52&abstractID=40353 accessed October 8, 2007. 33. Rouzier R, Perou CM, Symmans WF, Ibrahim N, Cristofanilli M, Anderson K, Hess KR, Stec J, Ayers M, Wagner P, Morandi P, Fan C, Rabiul I, Ross JS, Hortobagyi GN, Pusztai L. Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 11(16):5678-85 (2005). 34. Liedtke C, Mazouni C, Hess KR et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol 26(8):1275-81 (2008). 35. Berry DA, Cirrincione C, Henderson IC et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA 295:1658-67 (2006). 36. Hayes DF, Thor AD, Dressler LG et al. HER2 and response to paclitaxel in nodepositive breast cancer. N Engl J Med 357:1496-506 (2007). 37. Andre F, Mazouni C, Liedtke C et al. HER2 expression and efficacy of preoperative paclitaxel/FAC chemotherapy in breast cancer. Breast Cancer Res Treat 108(2): 183-190 (2008). 38. Rouzier R, Pusztai L, Delaloge S et al. Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol 23:8331-9 (2005). 39. Gruber BM, Anuszewska EL, Roman I, Gozdzik A, Priebe W, Fokt I. Topoisomerase II alpha expression and cytotoxicity of anthracyclines in human neoplastic cells. Acta Pol Pharm 63:15-8 (2006).

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40. Pritchard KI, Shepherd LE, O’Malley FP et al. National Cancer Institute of Canada Clinical Trials Group. HER2 and responsiveness of breast cancer to adjuvant chemotherapy. N Engl J Med 354:2103-11 (2006). 41. Knoop AS, Knudsen H, Balslev E et al. Retrospective analysis of Topoisomerase IIa amplification and deletions as predictive markers in primary breast cancer patients randomly assigned to cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide, epirubicin or fluorouracil: Danish Breast Cancer cooperative Group. J Clin Oncol 23:7483-7490 (2005). 42. Press MF, Sauter G, Buyse M et al. Alteration of topoisomerase II-alpha gene in human breast cancer and its association with responsiveness to anthracycline-based chemotherapy. J Clin Oncol 2007 ASCO Annual Meeting Proceedings Part I. 25:524 (suppl) (2007). 43. Durbecq V, Paesmans M, Cardoso F et al. Topoisomerase-II alpha expression as a predictive marker in a population of advanced breast cancer patients randomly treated either with single-agent doxorubicin or single-agent docetaxel. Mol Cancer Ther 3:1207-14 (2004). 44. Pusztai L. Markers predicting clinical benefit in breast cancer from microtubuletargeting agents. Ann Oncol 18(12):xii15-20 (2007). 45. Rouzier R, Rajan R, Wagner P et al. Microtubule-associated protein tau: A marker of paclitaxel sensitivity in breast cancer. Proc Natl Acad Sci USA 102:8315-8320 (2005). 46. Andre F, Hatzis C, Anderson K et al. Microtubule associated protein (MAP)-tau is a bifunctional predictor of endocrine sensitivity and chemotherapy resistance in ERpositive breast cancer. Clin Cancer Res 13:2061-2067 (2007). 47. Chang JC, Wooten EC, Tsimelzon A et al. Patterns of resistance and incomplete response to docetaxel by gene expression profiling in breast cancer patients. J Clin Oncol 23:1169-1177 (2005). 48. Andre F, Mazouni C, Hortobagyi GN, Pusztai L. DNA arrays as predictors of efficacy of adjuvant/neoadjuvant chemotherapy in breast cancer patients: Current data and issues on study design. Biochim Biophys Acta 1766:197-204 (2006). 49. Hess KR, Anderson K, Symmans WF et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol 24:4236-4244 (2006). 50. Pusztai L, Anderson K, Hess KR. Pharmacogenomic predictor discovery in phase II clinical trials for breast cancer. Clin Cancer Res 13(20):6080-86 (2007). 51. Anderson K, Hess KR, Kapoor M et al. Reproducibility of gene expression signature based predictions in replicate experiments. Clinical Cancer Res 12:1721-7 (2006). 52. Potti A, Dressman HK, Bild A et al. Genomic signatures to guide the use of chemotherapeutics. Nat Med (2006).

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53. Bonnefoi H, Potti A, Delorenzi M et al. Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy: A substudy of the EORTC 10994/BIG 00-01 clinical trial. Lancet Oncology 8(12):1071-8 (2007). 54. Coombes KR. Wang J. Baggerly KA. Microarrays: Retracing steps. Nature Medicine 13(11):1276-7 (2007). 55. Baggerly KA, Coombes KR, Neeley ES. Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. J Clin Oncol 26(7):1186-7 (2008). 56. Shay JW, Wright WE. Tissue culture as a hostile environment: Identifying conditions for breast cancer progression studies. Cancer Cell 12(2):100-1 (2007). 57. Schrag D, Garewal HS, Burstein HJ, Samson DJ, Von Hoff DD, Somerfield MR. ASCO Working Group on Chemotherapy Sensitivity and Resistance Assays. American Society of Clinical Oncology Technology Assessment: Chemotherapy sensitivity and resistance assays. J Clin Oncol 22(17):3631-8 (2004). 58. Mehta RS, Bornstein R, Yu IR et al. Breast cancer survival and in vitro tumor response in the extreme drug resistance assay. Breast Cancer Research and Treatment 66(3):225-37 (2001). 59. ENCODE Project Consortium. Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816 (2007).

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CHAPTER 15 PHARMACODIAGNOSTICS AND PERSONALIZED MEDICINE IN CANCER

Jan Trøst Jørgensen Dx-Rx Institute Baunevaenget 76 DK-3480 Fredensborg, Denmark Denmark E-mail: [email protected] The treatment of cancer will change considerably within the next decade especially due to the development of new improved targeted drugs. The development of these drugs will be depended on a parallel development of a diagnostic assay, as the drugs will only be useful if a pharmacodiagnostic test is able to identify the patients likely to respond. In personalized cancer medicine, drug and diagnostics are linked closely together in order to match the patient with the most optimal treatment. Despite the fact that the concept of personalized medicine was only introduced in the late 1990’s the development towards a more individualized drug therapy was made already several decades ago. The discovery of the estrogen receptor in the 1960’s and the introduction of the anti-estrogen tamoxifen in the 1970’s paved the way for a more individualized treatment of patients within breast cancer. Since then a number of targeted drugs have been introduced which are guided by a pharmacodiagnostic test, the best known example among these is the monoclonal antibody trastuzumab (Herceptin®, Genentech). Co-development of a drug and diagnostics will be the future development model in stratified and personalized medicine, and we will see more and more drugs being developed amid for smaller segments of cancer patients in order to increase the treatment benefit of drug therapy.

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1. Introduction Within the next decade treatment of cancer will undergo extensive changes. Due to new improved targeted treatment modalities our perception of several cancers will change from being potential life threatening diseases to chronic conditions. An important factor in this development will be the molecular diagnostic assays. Biomarker assays will not only contribute to an improved diagnosis of the different types of cancer by supporting a new biological sub classification, but even more with respect to an improved treatment outcome. Targeted cancer drugs will only be useful if pharmacodiagnostic assays are able to identify the appropriate patient population likely to respond1. These assays will be a decisive factor with respect to the future therapy selection, and support making the vision of stratified or personalized cancer medicine a reality. 2. Targeted Cancer Therapy When defining targeted cancer therapy several elements need to be taken into consideration; the molecular target, the pharmacodiagnostic assay that directly or indirectly identify the target, the drug that interacts with the target and the treatment outcome. The first requirement that needs to be fulfilled is that the molecular target must be measurable, either directly or indirectly, and that the drug specifically interacts with this target. Secondly, the treatment outcome such as response rate, disease free survival or overall survival must correlate with the measurable “target”, e.g. the degree of overexpression or the level of gene aberration1,2. In fact, it doesn’t make sense to talk about targeted cancer therapy without taking the pharmacodiagnostic assay into consideration. When developing a new targeted cancer drug an assay to select the appropriate patient population will need to be developed as well. In the absence of such an assay there is no targeted therapy1. The selective estrogen receptor modulator tamoxifen, which was introduced in the late 1970’s, must be characterized as the first targeted cancer therapy where the requirements of the measurable target, the estrogen receptor (ER), and the correlation with treatment outcome seem to be fulfilled. The use of tamoxifen in the adjuvant setting for the treatment of women with ER positive breast cancer is probably among

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the most well-documented cancer treatments available today. Within this type of breast cancer tamoxifen has shown to improve survival and reduce the risk of recurrences of the disease considerably3,4. In the 1990’s another targeted cancer drug was introduced which was aimed at a selected group of cancer patients: women whose breast cancer tumors over-expressed the human epidermal growth factor receptor 2 (HER-2). The drug was the monoclonal antibody trastuzumab (Herceptin®, Genentech) which was specifically targeted towards the HER2 protein of the tumor cells5,6. The effect of trastuzumab is dependent on over-expression of the HER2 protein, or that the gene for HER2 is amplified. Testing for the presence of these changes can be performed by pharmacodiagnostic assays such as the HercepTest™ (Dako) or the HER2 FISH pharmDx™ (Dako). These HER2 protein or gene changes occur in 20-25% of all women with breast cancer7, and the use of trastuzumab has shown to be especially effective as adjuvant treatment in combination with chemotherapy. Results from large phase III studies in HER2 positive women with breast cancer have shown the combined treatment of trastuzumab and chemotherapy can reduce the risk of relapse with approximately 50% compared to conventional treatment8,9. During the last 10 years a number of new targeted cancer drugs have been introduced which are developed for a selected group of patients only. In the package insert for some of these drugs a pre-selection of the patients with a pharmacodiagnostic test is suggested. A number of these drugs, their targets, the indication for use and the type of pharmacodiagnostic assay used for patient selection are listed in the below Table 1. Approximately forty years have passed since the discovery of the estrogen receptor which can be seen as a model for modern molecular medicine, and when looking at Table 1 in this perspective this does not seem very impressive. The development of targeted cancer drug and pharmacodiagnostic assays is a difficult task, and it may be so that neither the knowledge nor the interest has been there until recently. Hopefully, one thing that the last forty years have taught us is that the “one size fits all” model is very rarely an optimal solution with respect to treatment of cancer, and the road to an improved survival goes via a more individualized or tailored therapy.

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J. T. Jørgensen Table 1. Targeted cancer drugs and the corresponding pharmacodiagnostic assays.

Drug

Target

Indication

Assay

Tamoxifen (Nolvadex®, AstraZeneca)

ER

Breast Cancer

IHC

Aromatase Inhibitors: Letrozole (Femara®, Novartis) Anastrozole (Arimidex®, AstraZeneca) Exemestane (Aromasin®, Pfizer )

Aromatase/ ER

Breast Cancer

IHC

Assay mentioned in package insert No

Yes Yes Yes

Trastuzumab (Herceptin®, Genentech)

HER2

Breast Cancer

IHC FISH CISH

Yes

Lapatinib (Tykerb®, GlaxoSmithKline)

HER2 EGFR

Breast Cancer

Yes

Epirubicin (Ellence®, Pfizer) Doxorubicin (Adriamycin®, Pfizer)

Topo IIα

Breast Cancer

IHC FISH CISH FISH

Cetuximab (Erbitux®, BMS/Merck)

EGFR

Colorectal Cancer

IHC

Yes

Panitumumab (Vectibix®, Amgen)

EGFR

Colorectal Cancer

IHC

Yes

Erlotinib (Tarceva®, Genentech )

EGFR

Non-Small Cell Lung Cancer

IHC

Yes

C-KIT (CD117)

Gastrointestinal Stromal Tumor

IHC

Yes

Imatinib (Glivec®, Novartis)

Targets: EGFR = Epidermal Growth Factor Receptor ER = Estrogen Receptor HER2 = Human Epidermal Growth Factor Receptor-2 Topo IIα = Topoisomerase IIα Assays: CISH = Chromogenic In Situ Hybridization FISH = Fluorescence In Situ Hybridization IHC = Immunohistochemistry

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No

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3. From Rational Pharmacotherapy to Personalized Medicine For several decades rational pharmacotherapy – the right drug to the right patient in the right dosage at the right time – has been the headline for the optimal medical cancer therapy and drug therapy in general. Unfortunately, our knowledge at the molecular level about the pathophysiology of the different cancers and the mechanisms of action of the drugs has often been inadequate in order to practice the principles of rational pharmacotherapy. To some extent medical cancer therapy has been empirical despite the fact that healthcare professionals within oncology have always adhered to the principles of “evidence-based medicine”. No new medical cancer treatment has been brought into clinical practice without positive results from well-conducted randomized clinical trials. Despites the limitations in this approach, it has led to considerable improvements in survival within some cancers, like early breast cancer3. But we may now be standing at a crossroad - if survival from cancer should be further improved it may not be sufficient just to base our therapy selection on the traditional methods such as average results from controlled clinical trials and tumor histology2. When treating apparently the same cancer with the same drug, or a combination of drugs, the outcome shows considerable variability among patients. A major proportion of the patients does not respond at all to the treatment and may only suffer the side effects while a small minority experiences the benefit. For cancer chemotherapy general response rates as low as 25% have been reported10 and with a cure rate that is even lower11. Women who receive adjuvant chemotherapy for breast cancer have a similar low benefit rate, and the problem is that until now we have not been able to identify the responders in advance11. In the future the selection of therapy should be based on the knowledge we can obtain from the molecular diagnostic methods to a far greater extent by analyzing the tumor cells from the individual cancer patient. By the use of pharmacodiagnostic assays we should be able to match the patient, or more correctly the patient’s tumor biology, with the most optimal targeted treatment available. In fact, such a match is the essence of personalized medicine. The development towards a more individualized cancer treatment is driven by genuine unmet medical needs.

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The philosophy behind personalized medicine is that every patient has a unique biology and that this should be reflected in the choice of medical treatment, thus resulting in improved efficacy and a reduction of side effects. In many ways personalized medicine can be regarded as the 21st century’s answer to the rational pharmacotherapy. With the new molecular diagnostic methods healthcare professionals will be provided with an objective tool that will enable them to optimize the medical treatment for the individual cancer patients12. It has been said about medicine, if it were not for the big variability between individual patients, medicine might as well be a science and not an art. The principles of personalized medicine, by the use of pharmacodiagnostic testing, may also change this perception and bring a new scientific dimension to medicine13. The definitions of personalized medicine are many, but the use of the molecular diagnostic methods is part of most of them. At a meeting in November 2006 in Boston organized by Harvard Medical School and Harvard Business School, the following definition was suggested: “The management of a patient’s disease or disposition by using molecular knowledge to achieve the best possible medicinal outcome for that individual”14. The molecular techniques that are frequently discussed in relation to personalized medicine are the “omics” like genomics, proteomics and metabolomics, which are factors affected by the underlying genetic variation. With respect to these variations both germline (e.g. hereditary mutations) and somatic (e.g. nonhereditary mutations, such as most cancer diseases) mutations must be taken into consideration13. The implementation of personalized medicine will be a stepwise process where the stratification of patients into biological subgroups will be the first important step2,13,15. Here drug and pharmacodiagnostic testing are very closely linked together, and the test result is used to stratify the patient to the most optimal treatment. The use of the pharmacodiagnostic test for stratification purposes has recently lead to the introduction of the term stratified medicine, which in fact may be a more precise description of the current status of personalized medicine16,17. The move from blockbuster or empirical medicine towards

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Fig. 1. The move from blockbuster or empirical medicine towards personalized medicine will be a stepwise process. We are currently on the first step moving towards stratified 15 medicine .

personalized medicine will be a gradual process, where stratified medicine will be the first goal to be reached, as illustrated in Figure 1. Despite the basis for what we today understand by stratified or personalized medicine was formed in the 1960’s and 1970’s it was not until the turn of the century that it was given its name. In an article in Wall Street Journal in April 1999 Robert Langreth and Michael Waldholz announced the “New Era of Personalized Medicine”, which promised targeting drugs for each unique genetic profile. This article was reprinted in the Oncologist later that year18. 4. Personalizing Medical Cancer Treatment The use of tamoxifen and trastuzumab combined with pharmacodiagnostic tests for ER and HER2, respectively, as described earlier, are the best known examples of stratified or personalized medicine within cancer today. In both instances the targeted drug and the diagnostic test

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were more or less developed simultaneously, certainly this was the situation for trastuzumab and the immunohistochemical assay for HER2, and seen from a medical and regulatory point of view this is the optimal solution. But it is also possible to develop a pharmacodiagnostic assay for a drug that has been used in the clinic for years. A recent example is the fluorescence in situ hybridization (FISH) assay for epirubicin (Ellence®, Pfizer Pharmaceuticals) treatment in primary breast cancer. The first anthracycline was discovered in the 1950’s, but more than two decades would pass before the mechanisms of action were explained. In the 1980’s it was shown that anthracyclines inhibit the topoisomerase IIα enzyme. The interaction with the topoisomerase IIαDNA complex results in double-strand DNA breaks and subsequently apoptosis19. The gene that codes for topoisomerase IIα is TOP2A, and this gene can now be detected in the tumor tissue by FISH or chromogenic in situ hybridization (CISH). Figure 2 shows a fluorescence microscopic image of tumor cells with amplification of the TOP2A gene (TOP2A FISH pharmDx™, Dako). A number of clinical studies have shown that patients who have tumors with TOP2A gene aberrations, especially amplifications, have a significantly better response to anthracycline-based chemotherapy than patients with a normal TOP2A gene status20-23. The TOP2A FISH pharmDx™ kit has recently obtained regulatory approval by the US Food and Drug Administration (FDA) for the use in primary breast cancer24. Most pharmacodiagnostic tests are predictive with respect to efficacy of a given drug, but such tests could also be used to predict toxicity. Many anticancer drugs cause severe side effects and some of them are due to the variation in the metabolism of the drugs. That is the case for irinotecan (Campto®, Pfizer), a topoisomerase-1 inhibitor used for the treatment of colorectal cancer, which is metabolized by the UDPglucuronosyltransferase 1A1 (UGT1A1) enzyme. Genetic polymorphism of the UDP-glucuronosyltransferase 1A1 (UGT1A1) gene is related to severe drug toxicity such as leucopenia and diarrhea25. In order to identify the group of patients with aberration of the UGT1A1 gene who will need a reduced dose of irinotecan, a pharmacodiagnostic test has been developed (Invader® UGT1A1 Molecular Assay, Third Wave

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Fig. 2. Tumor cells with amplification of the TOP2A gene (TOP2A FISH pharmDx™, Dako). The red signal comes from the TOP2A gene and the green signals from the reference probe (centromere 17). Notice that the TOP2A signals are the majority.

Technologies)26. A similar genetic test to predict toxicity of 5fluorouracil or capecitabine (Xeloda®, Roche) has recently been introduced (TheraGuide 5-FU™, Myriad Genetic Laboratories, Inc.). 5fluorouracil or capecitabine-based chemotherapy are frequently used for the treatment of breast cancer, colorectal cancer and head and neck cancer. The test detects variations in the genes for dihydropyrimidine dehydrogenase (DPYD) and thymidylate synthetase (TYMS)27. There are several other examples where genetic factors can influence the outcome of treatment with cancer drugs, mainly through the effect on the metabolism of the drugs. Among the enzymes involved in drug metabolism, the cytochromes P450 (CYP) family plays a central role. Several factors can influence the CYP enzyme activity such as diet, environmental chemicals, age, gender and not least genetic polymorphisms. The activity of the CYP enzymes has been reported to vary up to 50-fold between individuals for some metabolic reactions28. Among the cancer drugs that are metabolized through the CYP system are cyclophosphamide (CYP2B6), tamoxifen (CYP2D6) and paclitaxel

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(CYP2C8). Aberrations in the genes for these enzymes can contribute substantially to the variability in response to these cancer drugs, which may result in toxic blood levels or ultra-rapid clearance and treatment failure28,29. A microarray-based pharmacodiagnostic test (AmpliChip CYP450 Test, Roche) has recently been introduced which identify patients with polymorphisms of the CYP2D6 and CYP2C19 genes, and thereby reducing the number of drug-induced side effects and drug failures for the drugs metabolized though the corresponding enzymes30. The cancer area has been at the forefront with regards to personalized medicine compared to other disease areas and there are probably several reasons for this. The diagnosis of cancer is nearly always based on a biopsy and a subsequent examination of cells or tumor tissue. The different slide-based technologies, such as Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and chromogenic in situ hybridization (CISH), used in pathology have paved the way for pharmacodiagnostic testing2. Further, cancer is a potential lifethreatening disease that develops from a series of genetic changes and our understanding of the genes and related proteins involved in the disease process have increased considerably over the last 10-20 years. In fact, most of the recently developed anticancer drugs listed in Table 1 reflect this knowledge. 5. Pharmacodiagnostic Testing and Drug Development The traditional randomized clinical trial has probably been the most important single factor with respect to the change of medicine from being empirical or anecdotal to where we are today, which we could call “evidence-based medicine”. For most cancer drugs that have been developed during the last several decades the standard for documenting safety and efficacy and obtaining regulatory approval has been that at least two independent randomized, well-controlled phase III trials should show positive results over standard therapy. Despite the fact that this approach has been a kind of “gold standard” in cancer drug development, we should start to reconsider whether the traditional randomized clinical trial answers the right questions. If a comparative study shows superiority of a new drug over the current standard therapy in a patient

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population, the normal interpretation would be that the new drug is the choice for any given patient in that population. However, a new drug with 10% superiority over the current standard therapy could still be the wrong drug for many patients13. The design of the randomized clinical trial does not answer the question about the individual patient’s response to a given drug, and the extrapolation of the average study result to the whole patient population could be a wrong decision. Generally, the randomized clinical trials are normally designed to investigate differences between treatments and not among the individual responders in the treated groups13. The drug development process needs to address the variability among patients, and the molecular diagnostic methods must be incorporated in the design of the clinical trials to a far greater extent. Pharmacodiagnostic testing can provide important information about the underlying tumor biology and should be used to identify patients that are likely to respond to the drug that is being tested in the study. The importance of using pharmacodiagnostic testing in the clinical drug development process has been illustrated by performing sample size calculations on alternative study designs – targeted and untargeted designs31. In a pivotal trial published by Slamon et al. in 20017 patients with advanced breast cancer were randomized to chemotherapy with and without trastuzumab. In this study, which had a targeted design, only patients with HER2 positive tumors, e.g. overexpression of HER2 with a score of 2+ or 3+ were eligible. All together 469 patients were enrolled, and the result was highly statistically significant in favour of the trastuzumab arm with regards to one-year survival7,31. Without the use of a pharmacodiagnostic test for HER2, a much larger trial would have been needed to detect the survival gain observed in the Slamon trial. Assuming the effect of trastuzumab in the HER2 negative patients is half the effect observed in HER2 positive patients, the number of patients needed in order to show a significant difference in one-year survival was calculated to be 1,256. A second alternative was calculated, and here again an untargeted design was used with no HER2 testing and the assumption that the assay negative patients would not benefit from treatment with trastuzumab. The sample size needed for this alternative

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was 23,586 patients, which were fifty times more patients than were enrolled in the original study31. The development of trastuzumab is often held up as a model for successful development in stratified or personalized medicine. When Genentech developed trastuzumab they realized early on that it was important to have an assay in order to select the patients that might response to the drug, which is clearly substantiated by the above mentioned sample size calculation. Trastuzumab and an immunohistochemistry assay were developed simultaneously, which seems to be an optimal model for drug-diagnostic co-development. In 2005 the US Food and Drug Administration issued a concept paper on drug-diagnostic co-development, and also here the parallel model was chosen32. With respect to this parallel model it is important that the development of the assay is initiated already in the preclinical phase in order to have a prototype assay ready for the phase II clinical trials. At the same time as the clinical phase II studies are conducted the assay is analytically validated with respect to specificity, sensitivity, reproducibility and robustness. Clinical data from phase II will be used to establish the cut-off value for the assay and to give an indication of the

Fig. 3. The drug-diagnostic co-development process. Based on the suggestions from the US FDA Drug-Diagnostic Codevelopment Concept Paper15,32.

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predictive value. The final clinical validation of the assay will be performed during phase III where it is used to stratify patients to the different treatment groups32. By analyzing the clinical outcome data from phase III the final predictive value of the pharmacodiagnostic test can be established. The parallel drug-diagnostic co-development model is briefly described in Figure 3. 6. Future Perspectives There are several reasons why oncology has been at the forefront with regards to stratified or personalized medicine compared to other disease areas. Probably the most important single factor is that the diagnosis of cancer is nearly always based on a tumor biopsy and that the slide-based technologies, such as the IHC and FISH, are now established methods in most pathological laboratories as mentioned earlier. Also different types of gene expression assays are now being introduced and they will undoubtedly play an increasing role in the future33. Further, the implementation of a more individualized therapy has also been driven by a great medical need to improve the treatment modalities within cancer. So far, the larger pharmaceutical companies have not been in the driver seat with respect to the implementation of personalized medicine. Despite the fact that “one-size-fits-all” is very rarely the situation in drug therapy, the pharmaceutical companies seem to hold on to the promise of the blockbusters. Personalized medicine goes against the blockbuster philosophy where a drug should be used broadly by as many patients as possibly and for different indications. It is said that the pharmaceutical industry cannot afford to shift its focus away from pursuing the blockbuster drugs due to the environment of increasing generic competition, tepid innovation, and a slow regulatory environment of today, but this may prove to be a short-sighted strategy34. It is expected that the pressure will increase on the pharmaceutical companies from the patient advocacy groups, the different oncological societies, health insurance companies and others. The use of the principles of personalized or stratified medicine during the development of new cancer drugs may even prove to be an economic advantage for the companies35.

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The larger pharmaceutical companies have problems with the efficiency of the drug development process as described by FDA in relation to the Critical Path Initiative36. Over the last 10 to 15 years the number of new drug applications submitted to the regulatory agencies have declined while the R&D spendings have increased, especially the costs of clinical development have escalated rapidly36,37. In 2007 the FDA approved 17 new drug applications and two biological license applications, which is the lowest number in 25 years34. Rethinking the whole drug development process is urgently needed, and the new molecular diagnostics methods are one of the tools that could improve the efficiency12. A more intensive use of molecular diagnostic methods during preclinical and clinical development could save time and resources and most importantly reduce the failure rate of the drug development programs. The pharmacodiagnostics could help to decrease the variability of treatment response through an identification of responsive subgroups. This would lead to a decrease in the size of clinical trials by reducing the number of patients who fail to respond to therapy as described earlier13,31. By introducing pharmacodiagnostic testing in a drug development program the most optimistic predictions estimate that the development time could be halved and the costs reduced by 50-75% compared to today’s current trend35. If personalized medicine should have a real breakthrough there need to be incentives for those who are going to do the research and development work - the pharmaceutical and diagnostic companies. A reduction in development costs will be an important incentive, but there also need to be a flexible pricing and reimbursement system in place for personalized medicine that is able to reward the higher ratio of benefit to risk for this type of treatment38. The players that have been most active in promoting the idea of personalized medicine are the academic groups, patient advocacy groups, the authorities, and to some extent the health insurance companies. The reason for this interest by the authorities and the insurance companies may to some extent be explained by the high drug costs of the new targeted cancer drugs. The use of a pharmacodiagnostic test will ensure that these expensive drugs are only given to the patients that have a reasonable chance of responding to the treatment. In the UK the annual

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costs including administration and medical care for several of these new cancer drugs exceeds £50,000 (~$100,000) per patient, and the high costs of this type of drugs are likely to create a significant financial pressure on the health care budgets39. A number of these newer cancer drugs and their approximately annual treatment costs are listed in Table 2. Table 2. Approximate annual costs including administration in the UK. Drug

Annual Cost per Patient

Trastuzumab (Herceptin®, Genentech) Imatinib (Glivec®, Novartis)

£50,000 (~ $100,000) £50,000 (~ $100,000)

Cetuximab (Erbitux®, BMS/Merck)

£60,000 (~ $120,000)

Bevacizumab (Avastin®, Genentech/ OSI Pharmaceuticals ) Erlotinib (Tarceva®, Genentech)

£70,000 (~ $140,000) £65,000 (~ $130,000)

Note: The table is based on information available in Sikora K. J R Soc Med 2007; 100: 166-16938.

The interest in personalized medicine is increasing worldwide, and in 2004 a non-governmental organization was formed in the US. The name of the organization is the Personalized Medicine Coalition (PMC), which goal is to advance the understanding and adoption of personalized medicine40. The members of the PMC are a diverse group comprised of participants from industry, universities, academic medical centers, relevant trade associations, patient advocacy groups, government officials, healthcare providers, health insurance companies, information technology companies, venture companies and strategic partners. If we take a closer look at the industry group, this mainly includes the diagnostic companies whereas the large biotech and pharmaceutical companies are more sparsely represented. The membership composition may reflect the interest that the larger pharmaceutical companies still have in personalized medicine. These companies will have to change their attitudes, if they want to be among the winners in the future. The implementation of stratified or personalized medicine will definitely change medical practice, and those pharmaceutical companies that do not understand this message and make changes in their strategies in time may end up as the losers.

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The pharmacodiagnostic tests developed so far only cover a minor part of the cancer drugs that are used routinely in medical oncology today. In the years to come it is expected that a number of new tests will emerge in connection with the targeted cancer drugs that are currently being developed. These tests will be based on both single and multiple proteins or genes and will utilize technologies such as IHC, FISH, CISH and gene profile expression. The gene profile expression, such as DNA microarray or the reverse-transcription polymerase chain reaction (RTPCR), are not yet used for pharmacodiagnostic testing but rather for establishing disease prognosis. Recent examples of such prognostics assays are the Oncotype DX™ (Genomic Health) and MammaPrint® (Agendia). Both assays have recently obtained FDA approval as “prognostic” tests for the use in primary breast cancer in order to determine who should receive adjuvant chemotherapy therapy33. So far the opinion has been that a qualitative difference in the genome in the form of the single-nucleotide polymorphisms (SNP) was the most interesting genetic variation in relation to the development of pharmacodiagnostic tests41. However, it has recently been shown that quantitative differences, such as deletions and amplifications of the genomic sequences, and large scale rearrangements may be even more relevant41,42. In fact, the commercially available FISH assays for HER2 and TOP2A, which are used for the selection of patients for treatment with trastuzumab and anthracyclins, respectively, are tests that look for these types of quantitative gene variations. Based on the results of pharmacodiagnostic testing several cancers, e.g. breast cancer, can now be divided into biological classes to which a particular cancer drug or combination of drugs can be matched. This segmentation of the cancer patients into biological subclasses will continue, and the decisive factor with respect to the future therapy selection will be the molecular or genetic information derived from the patient’s tumor tissue. The traditional classification of cancer based on organ origin will gradually disappear and the targeted cancer drugs will be used across a range of cancers43. The implementation of personalized cancer medicine will lead to a much more rational use of the drugs, and the overtreatment seen today will be reduced. By the use of pharmacodiagnostic tests the treating physician will know whether a

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given cancer drug is justified, and the evidence from the testing will be there to support his or her decision. The principles of personalized cancer medicine should also be able to reduce treatment failures and the imposed sufferings and costs43. In the years to come we will see more and more cancer drugs that are prescribed based on the use of a pharmacodiagnostic test. It is expected that by the end of the next decade very few drugs, if any, will be prescribed without such a test. The pharmacodiagnostic testing has given us a rational tool which has lead to the recognition that the individual cancer patient is unique and that the treatment needs to be individualized to a far greater extent than is the case today. Acknowledgment We would like to thank Dako Denmark A/S for the permission to use the fluorescence microscopic images of tumor cells stained with the TOP2A FISH pharmDx™ kit. References 1. Hortobagyi GN. Opportunities and challenges in the development of targeted therapies. Semin Oncol 2004; 31 (Suppl 3): 21-27. 2. Jørgensen JT, Nielsen KV, Ejlertsen B. Pharmacodiagnostics and targeted therapies – A rational approach for individualizing medical anti-cancer therapy in breast cancer. Oncologist 2007; 12: 397-405. 3. Early Breast Cancer Trialists Collaborative Group. Effect of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomized relational pharmacotherapy. Lancet 2005; 365: 1687-1717. 4. Jensen EV, Jordan CV. The estrogen receptor: A model for molecular medicine. Clin Cancer Res 2003; 9: 1980-1989. 5. Pegram MD, Pauletti G, Slamon DJ. HER-2/neu as a predictive marker of response to breast cancer therapy. Breast Cancer Res Treat 1998; 52: 65-77. 6. Slamon DJ, Leyland-Jones B, Shak S et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001; 344: 783-792. 7. Slamon D, Pegram M. Rationale for Trastuzumab (Herceptin) in adjuvant breast cancer trials. Semin Oncol 2001; 28 (Suppl 3): 13-19.

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8. Romond EH, Perez EA, Bryant J et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005; 353: 1673-1684. 9. Piccart-Gebhart MJ, Procter M, Leyland-Jones B et al. Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med (2005); 353: 1659-1672. 10. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med 2001; 7: 201-204. 11. Sikora K. Personalized cancer therapy – Key to the future. Pharmacogenomics 2004; 5: 225-228. 12. Jørgensen JT. From blockbuster medicine to personalized medicine. Personalized Medicine 2008; 5: 55-63. 13. Woodcock J. The prospects for “personalized medicine” in drug development and drug therapy. Clin Pharmacol Ther 2007; 81: 164-169. 14. Conway D M. Personalized Medicine: Deep Impact on the Healthcare Landscape. Personalized Medicine Conference. The Conference Center at Harvard Medical; Boston, November 29-30, 2006. http://www.hpcgg.org/PM/presentations.jsp#conway. Accessed March 2008. 15. Jørgensen JT. Are we approaching the post-blockbuster era? – Pharmacodiagnostics and rational drug development. Expert Rev Mol Diagn 2008; 8: 689-695. 16. Hu SX, Foster T, Kieffaber A. Pharmacogenomics and personalized medicine: Mapping of future value creation. BioTechniques 2005; 39 (4). 17. Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: Strategic and economic implications of combining drugs and clinical biomarkers. Nature Rev Drug Discov 2007; 6: 287-293. 18. Langreth R, Waldholz M. New Era of Personalized Medicine. Oncologist 1999; 4: 426-427. 19. Tewey KM, Rowe TC, Yang L et al. Adriamycin-induced DNA damage mediated by mammalian DNA topoisomerase II. Science 1984; 226: 466-468. 20. Knoop AS, Knudsen H, Balslev E et al. Retrospective analysis of topoisomerase IIa (TOP2A) amplifications and deletions as predictive markers in primary breast cancer patients randomized to cyclophosphamide, methotrexate, and fluorouracil or cyclophosphamide, epirubicin, and fluorouracil: Danish Breast Cancer Cooperative Group. J Clin Oncol 2005; 23: 7483-90. 21. Nielsen KV, Ejlertsen B, Møller S, Jørgensen JT, Knoop A, Jørgensen HK et al. The value of TOP2A gene copy number variation as a biomarker in breast cancer: Update of DBCG trial 89D. Acta Oncol 2008; 47: 725-734. 22. O’Malley FP, Chia S, Tu D et al. Prognostic and predictive value of topoisomerase II alpha in a randomized trial comparing CMF to CEF in premenopausal women with node positive breast cancer (NCIC CTG MA 5). ASCO Annual Meeting Proceedings, J Clin Oncol 2006; 24 (Suppl) Abstract no. 533. 23. Press MF, Mass RD, Zhou JY et al. Association of topoisomerase II-alpha (TOP2A) gene amplification with responsiveness to anthracycline-containing chemotherapy

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25.

26.

27. 28. 29. 30. 31. 32.

33. 34.

35. 36.

37. 38.

39.

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among women with metastatic breast cancer entered in Herceptin H0648g pivotal clinical trial. ASCO Annual Meeting Proceedings, J Clin Oncol 2005; 23 (Suppl), Abstract no. 9543. New Device Approval - Dako TOP2A FISH pharmDx™ Kit - P050045. US FDA/CDRH. January 11, 2008. http://www.fda.gov/cdrh/MDA/DOCS/p050045.html. Accessed April 2008. Ando Y, Saka H, Ando M, et al. Polymorphisms of UDP-glucuronosyltransferase gene and irinotecan toxicity: A pharmacogenetic analysis. Cancer Res 2000; 60: 6921-6926. Package Insert for the Invader® UGT1A1 Molecular Assay, Third Wave Technologies. http://www.twt.com/pdfs/UGT1A1PackInsert.pdf. Accessed March 2008. Myriad Genetic Laboratories, Inc. http://www.myriadtests.com/hcp/about_theraguide.htm. Accessed March 2008. Frye RF. Probing the world of cytochromes p450 enzymes. Mol Interv 2005; 4: 157-162. Van Schaik RH. Cancer treatment and pharmacogenetics of cytochromes P450 enzymes. Invest New Drugs 2005; 23: 513-522. The CYP450 Gene Family and Drug Metabolism. Roche Diagnostics. http://www.roche.com/final_cyp_gene_family.pd). Accessed March 2008. Simon R, Maitournam A. Evaluating the efficiency of targeted designs for randomized clinical trials. Clin Cancer Res 2004; 10: 6759-63. Drug-Diagnostic Co-Development Concept Paper. Draft. Food and Drug Administration (FDA), April 2005. http://www.fda.gov/cder/genomics/pharmacoconceptfn.pdf. Accessed April 2008. Sawyers CL. The cancer Biomarker problem. Nature 2008; 452: 548-252. Dako scientist says most cancer meds will be Dx based, Personalized in next decade. Pharmacogenomics Reporter. March 26, 2008; (13) 6. http://www.pgxreporter.com/ issues/6_13/. Accessed April 2008. Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: Strategic and economic implications of combining drugs and clinical biomarkers. Nature Rev Drug Discov. 6. Food and Drug Administration. Innovation or stagnation: Challenges and opportunity on the critical path to new medical products. March 2004. (http://www.fda.gov/oc/initiatives/criticalpath/whitepaper.html). Accessed April 2008. Lesko LJ. Personalized medicine: Elusive dream or imminent reality? Clin Pharmacol Ther 2007; 81, 807-816. Garrison LP, Austin MJF. Linking pharmacogenetic-based diagnostics and pharmaceuticals for personalized medicine: scientific and economic challenges. Health Affairs 2006; 25: 1281-1290. Sikora K. Paying for cancer care – A new dilemma. J R Soc Med 2007; 100: 166-169.

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40. Personalized Medicine Coalition (PMC). (http://www.personalizedmedicinecoalition.org/index.php). Accessed June 2007. 41. Lee C, Morton CC. Structural genomic variation and personalized medicine. N Engl J Med 2008; 358: 740-741. 42. Korbel JO, Urban AE, Affourtit JP et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 2007; 318: 420-426. 43. Sikora K. Personalized medicine for cancer: From molecular signature to therapeutic choice. Adv Cancer Res 2007; 96: 345-369.

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INDEX

5-fluorouracil, 233, 258, 263, 314, 316, 333

aromatase inhibitors, 27, 28 ASCO, 57, 189 assay robustness, 5 assay sensitivity, 1, 5, 10, 11 assay specificity, 1, 5, 10, 12 assay standardization, 5, 12 assay variation, 133 ATP-TCA assay, 247 automated IHC, 14 autophosphorylation, 5 azathioprine, 254, 280 AZF, 189

ABL, 170, 173 accuracy, 151– 153 acute lymphoblastic leukemia, 279 adaptive analysis designs, 243 adaptive signature design, 222 adaptively determining predictive biomarker, 221 adjuvant endocrine therapy, 29 AEC, 89 Affymetrix, 146, 147, 157, 158 Agilent, 147, 149, 157 ALCL, 98 Allred, 203, 204 Allred score, 24 AMACR, 100, 101 AmpliChip, 156–158, 160 AmpliChip CYP450, 334 AmpliChip CYPP450, 263 amplification, 165, 167, 169, 170, 177, 193, 194 amplification/deletion, 252 analytical platform, 61, 64, 66, 68, 76–79 analytical specificity, 152 analytical validation, 211 analytically validated assay, 17 anastrozole, 28, 35, 328 aneuploidy, 167, 173 antibody affinity, 10 antigen retrieval, 11–14, 19, 20 antigenicity, 190–192 APC, 169, 175

B5, 189, 193 B-catenin, 96 BcL10, 96, 114 BCR-ABL, 120, 129, 131, 135, 170, 173, 279 BEAMing, 177, 178 BIG1-98, 33 biomarker adaptive threshold design, 244 biomarker assays, 245, 326 biomarker development, 208 biomarker-based therapy, 292 blockbuster drugs, 337 BRAF, 169 breast cancer, 25, 27, 32, 34, 44, 284, 297 C4d, 97, 114 cancer therapy, 251 cancer treatment, 276 CAP, 57, 135, 189 capecitabine, 333

345

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346

CBER, 55 CD20, 103, 109, 278 CD30, 278 CD33, 104 CDER, 283, 291 CDK4, 169 CDRH, 55, 283, 291 CE mark, 151 central laboratory, 145, 151 central testing, 245 centromere 17 (CEN17), 48 cetuximab, 278, 279, 281, 328 chemotherapy sensitivity assays, 247 chromogenic in situ hybridization (CISH), 45, 120, 127–131, 133, 134 chromogranin, 90, 91 chromosome rearrangements, 252 chronic myeloid leukemia, 279 C-kit, 103, 279 Class I devices, 288 Class II devices, 288 Class III devices, 288 Clinical Laboratory Improvement Amendments (CLIA), 286 clinical need, 61, 62, 64, 65, 73, 141 clinical practice, 141, 144, 145, 149, 160 clinical routine, 61, 64, 66, 68, 69, 79 clinical samples, 61, 64, 66, 67, 76 clinical trial, 61, 64, 70, 71, 73, 83, 208, 228 clinical trial designs, 229 clinical validation, 214, 227, 233, 290 clinically validated assay, 18 CMF, 76, 78 CML, 170, 173 colorectal cancer, 279

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Index

companion diagnostic/pharmacodiagnostic assay, 45, 56 companion diagnostics, 3, 16, 20 companion drug-diagnostics, 285 concordance, 51, 53, 55 control material, 187, 192, 196–198, 200 control slides, 134 controls, 2, 14, 15, 18 copy number changes, 299 critical path initiative, 338 cross validation methods, 213 CSQI, 195, 198, 200 CTA, 44, 46, 53 CTLA-4/CD152, 278 CTNNB1, 169 CUPPrint, 157, 158 custom array, 149 cyclophosphamide (CYP2B6), 236, 314, 316, 333 cytochrome P450, 35, 253, 333 DAB, 89 dasatinib, 279, 281 data driven approach, 141, 142 DBCG, 78, 80 decalcification, 191 delayed fixation, 190 deletions, 167, 169, 170 demasking, 186, 189, 190, 192, 197, 202 denileukin, 281 detection systems, 12 diagnostic biomarker, 2, 16, 18 different types of estrogen receptor and progesterone receptor assays, 23 digoxigenin, 127 Dihydropyrimidine dehydrogenase (DPD), 258 doxorubicin, 210, 236, 314, 328

347

Index

DPYD, 258, 333 drug and pharmacodiagnostic co-development, 207 drug development, 278, 334 drug-diagnostic co-development, 336 dual colour CISH, 128, 130, 131 early breast cancer trialists’ collaborative, 26 E-Cadherin, 94, 96 EGF, 278 EGFR pharmDx, 280 endocrine biomarkers, 34 endocrine treatment, 27 endoxifen, 35 endpoints, 229 enrichment design, 215, 218, 235, 237 epidermal growth factor receptor (EGFR), 34, 130, 173, 175, 210, 241, 280 epirubicin, 75, 78– 80, 83, 84, 316, 328, 332 epitope, 6, 7, 9, 11, 13, 15 EQA, 135, 197, 198, 200– 202 ER and PR assays, 23 ER expression, 307 ER mRNA expression, 308 ER positive tumors, 32 ER status, 34 ER-β, 22 ERalpha, 3 ERBB2, 44, 172, 173, 175, 253 erlotinib, 230, 328 ER-negative, 301 ER-positive, 301 estradiol, 22, 27 estrogen, 21 estrogen binding protein, 22

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estrogen receptor (ER), 22, 189, 202, 203, 299, 307, 326 EWS/WT-1, 99 exemestane, 28, 328 external quality assurance, 129, 135, 137, 138 FDA, 16, 18, 44, 45. 146, 150–152, 158, 162, 275 FDA Advisory Committee, 285 FDA review, 290 FDC, 90, 93 feasibility studies, 16, 17 FFPE, 4, 7, 8, 11 FGFR3, 169, 175 fixation time, 189, 204 fixatives, 10, 11 FLI-1, 100, 115 fluorescein-labeled, 89 fluorescent in situ hybridization (FISH), 45, 120, 127–131, 133, 134, 136–138, 230, 242, 279, 299 formalin, 4, 7, 8, 11, 14, 17–20 formalin fixed tissues, 120, 122 frameshift mutations, 169 fulvestrant, 28 fusion proteins, 94, 98, 100 gefitinib, 34 gene amplification, 119, 120, 122, 125, 130, 132, 133, 137 gene chip, 316 gene deletions, 132 gene expression classifiers, 212 gene expression profiles, 139, 140, 149, 159, 164 gene expression signatures, 303 gene rearrangements, 120 gene signatures, 303

348

genomic grade index, 304 germline, 330 GIST, 103 helicobacter pylori, 96, 114 HER2, 34, 44, 74, 79, 100, 102, 103, 107–109, 117, 120, 122, 127–132, 134–136, 138, 139, 215, 236, 327 HER2 amplification, 45, 210 HER2 assays, 45 HER2 expression, 284 HER2 gene, 44 HER2 IHC assay, 58 HER2 overexpression, 45 HER2 positive breast cancer, 246 HER2 positive, 327 HER2 receptors, 45 HER-2, 299, 327 HER2/TOPA, 131 HER2-positive breast cancer, 237 HER2-status, 302 HercepTest™, 43, 56 herceptin, 44, 210 heriditary non-polyposis colonic syndrome, 95 HIER, 186, 189–191, 193, 194 Histochemical (H) score, 24 histoids, 111 horseradish peroxidase, 88, 89 HPV, 101 HRAS, 168, 169, 175 H-score, 203 human genome project, 251 hypomethylation, 171 IGS, 158, 159 IHC assays, 307 IHC HER2 scores, 47 IHC test, 284 Illumina, 147 image analysis, 131, 134

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Index

imatinib, 278, 281, 328 immunohistochemical (IHC) assay, 23, 44, 279 immunohistochemical, 44, 299 In Vitro Devices (IVDs), 287 In Vitro Diagnostic Devices and Safety (OIVD) 510(k), 289 individualized therapy, 337 insertions, 167, 169 interaction test, 218 interference, 152 inter-laboratory, 155, 185, 187 inter-laboratory variation, 307 internal quality assurance, 134, 187 inter-observer, 132, 133, 134 interphase ISH, 120 interphase translocation, 129 interpretation, 130, 131, 134, 185–187, 196, 203, 204 interpretation guidelines, 13, 18 inter-run, 185, 187 intra-laboratory, 155, 185, 187 intra-laboratory variation, 307 intra-observer, 133, 134 IQA, 187, 188, 197 irinotecan, 232, 256, 282, 286, 332 IV Collagen, 90 IVD class I, 2 IVD class II, 3 IVD class III, 3 IVD Directive, 151 IVDMIA, 151, 152, 158 Ki-67, 34, 97, 107, 117 KRAS, 169, 173–175 KRAS mutation, 210, 234 KRAS wild type, 235 K-ras, 282 lapatinib, 56, 279, 281, 328 letrozole, 28, 33, 35, 328 local testing, 245

Index

MammaPrint, 304, 340 MapQuant, 157, 158 marker based strategy design, 239 marker by treatment interaction design, 241 marker-based selection, 236 MCCN, 132 mercaptopurine, 280, 282 methylation, 252 microarray profile, 140 microRNA, 318 microsatellite instability, 167 MINDACT, 145, 146, 161, 162 mismatch repair gene, 94 modification specificity, 6, 9 molecular class predictor, 304 molecular classification of breast cancer, 301 molecular endpoint, 1, 4, 16–18 monoclonal antibodies, 4–9, 19 multicolour, 129, 131, 138 multigene assay, 129 multi-gene assays, 156 mutations, 330 MYC, 170 NBF, 188, 189, 190 NCCLS, 152, 153, 162 nilotinib, 279, 281 nolvadex, 26 non-invasive tests, 174 non-synonymous SNP, 252 NordiQC, 11, 57, 135, 193, 195, 198, 200–202 NRAS, 169 NuvoSelect, 158 Office of Combination Products (OCP), 291 Oncotype DX, 25, 304, 308, 340

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349

optimization, 186–188, 195, 200, 201 overall survival, 229 p15, 171 P16INK4a, 101, 102, 115 P504S, 100, 101 P53, 101, 107, 116, 117 P57Kip2, 96, 114 paclitaxel (CYP2C8), 334 panitumumab, 234, 281, 328 paraffin, 87, 88, 113, 116 paraffin embedding, 190 PathVysion, 52 patient stratification, 228 PCR, 176, 177, 178 PDGFR, 279 personalized medicine, 251, 299, 325, 330 personalized medicine coalition, 339 pharmacodiagnostic assays, 326 pharmacodiagnostic testing, 335 pharmacodiagnostics, 21, 186, 191, 325, 338 pharmacodynamic markers, 263 pharmacogenomic analysis, 315 pharmacogenomic markers, 231, 251 pharmacokinetic markers, 260 phase III clinical trials, 214 phase III trials, 209, 334 Philadelphia chromosome, 167 PIK3CA, 169 PMA, 55 PMC-RARA, 173 point mutations, 165, 169 polyclonal antibodies, 6, 13 polymorphism, 252 positive predictive values, 300 post-analytical, 105, 106, 187, 192 post-hybridisation, 121, 125, 126

350

poto-oncogenes, 167, 169, 170 PR negative, 35 PR-A, 23 PR-B, 23 pre-analytical variables, 61, 64, 68, 77 pre-analytical, 1, 10, 14, 15, 105, 106, 111, 187, 188, 192 precision, 149, 152–154, 162, 185, 187 predictive accuracy, 213 predictive binary classifiers, 211 predictive biomarker, 2–4, 61, 64, 65, 70, 73, 208, 210, 214, 229 predictive biomarker classifier, 214 predictive biomarkers in cancer, 228 predictive classifier, 216 predictive value, 307 predictors for taxane sensitivity, 313 pre-IDE, 55 premarket approval (PMA), 288 pressure cooker, 190 primary antibody, 88, 110, 113, 192, 202 probe, 121–124, 126, 132, 136 progesterone receptor, 21, 23, 299 prognosis, 299 prognostic biomarker, 2, 3, 208,

209 prognostic factor, 144 prognostic gene signatures, 304 prognostic value, 143, 144 progression-frees, 230 promotor hypermethylation, 165, 171, 177 prospective study, 61, 64, 69, 70, 80 prospective trials, 145 prospective validation, 235

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Index

prostate specific antigen (PSA), 277 protease digestion, 124, 125 proteolytic pre-processing, 189 quality assurance, 119, 121, 129, 134, 135, 137, 138, 185, 187, 197, 201 quality control, 147, 155, 162 quantitative immunohistology, 106 Quicgel, 111, 117 quick score, 203 radioactivity, 127 raloxifene, 28 randomized clinical trial, 215, 228, 233, 335 rational pharmacotherapy, 329 READ, 80, 82 regulatory approval, 283 regulatory perspective, 150 reliability, 185, 205 reproducibility, 151, 152, 154, 155, 162, 185, 187, 191, 194, 195, 203 research hypothesis, 61, 64, 65, 74, 76 response to chemotherapy, 311 response to endocrine therapy, 307 retrospective analyses, 241 retrospective analysis, 143, 234 retrospective study, 61, 64, 67, 68, 76–78 retrospective validation, 143, 146, 153 robustness, 185, 187 Rotterdam signature, 159 RT-PCR, 25 sample selection, 142 sample size, 215, 237

351

Index

sample size calculations, 335 scoring, 131, 134 scoring systems for hormone receptors, 24 sequence-specificity, 6, 8, 9 sequential testing strategy, 238 SERM, 28 serum based biomarkers, 245 signal detection, 127 siRNA, 318 snoRNA, 318 SNP, 252, 298 somatic, 330 somatic mutation, 252 specificity, 125, 126 sporadic colon cancer, 94 stability, 152, 155 stability genes, 167 standardization, 185, 187, 188, 191, 192, 203 stratification, 235 stratified medicine, 330 surrogates, 122, 134 surrogate endpoints, 208, 229 synonymous SNP, 252 tamoxifen (CYP2D6), 25, 28, 32, 35, 36, 260, 283, 326, 331, 333 targeted cancer therapy, 326 targeted design, 235, 236 TargetPrint, 156–158 taxol, 210 TheraGuide 5-FU, 333 therapy optimization, 317 Thiopurine methyltransferase (TPMT), 254 tissue based biomarker, 245 tissue of origin test, 157, 158 tissue permeabilisation, 124 tissue-material, 188, 189, 196 TOP2A, 74, 81, 312, 332

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TOP2A amplification, 313 TOP2A FISH pharmDx, 304, 313, 332 Topoisomerase 2A gene amplification, 312 Topoisomerase II, 74, 75, 79, 80, 85 toxicity, 231, 253, 254 TP53, 169, 175 TPMT, 280 translocations, 165, 167, 170, 177 trastuzumab, 35, 44, 215, 236, 246, 278, 281, 299, 327, 328, 331 triple negative, 34 Tris-EDTA, 190, 194 tumor DNA, 174, 176 tumor suppressor genes, 167, 169, 171 TYMS, 263, 333 type I error, 217 type I error rates, 238 UGT1A1*28 polymorphism, 232 UGT1A1, 239, 256, 332 UK NEQAS, 57, 129, 135, 137 UKNeqas, 11 UKNeqas-ICC, 198 unselected designs, 238 untargeted design, 236 UroVysion, 129, 131, 138 US CAP, 11 utility of biomarkers, 277 VEGF, 278 Vimentin, 90 visualization system, 186, 192 WT-1, 99, 100 ZAP 70, 101, 115