Innovative leukemia and lymphoma therapy [1st ed.] 9780429524653, 042952465X

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Innovative Leukemia and Lymphoma Therapy

Kaspers_978-0849350832_TP.indd 1

4/23/08 10:13:13 AM

Innovative Leukemia and Lymphoma Therapy Edited by

Gertjan J. L. Kaspers VU University Medical Center Amsterdam, The Netherlands

Bertrand Coiffier

Hospices Civils de Lyon and Claude Bernard University Pierre-Benite, France

Michael C. Heinrich

Oregon Health & Science University Cancer Institute Portland VA Medical Center Portland, Oregon, USA

Elihu Estey

University of Washington Medical Center Seattle, Washington, USA

Kaspers_978-0849350832_TP.indd 2

4/23/08 10:13:15 AM

CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2008 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20130315 International Standard Book Number-13: 978-1-4200-1408-2 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. While all reasonable efforts have been made to publish reliable data and information, neither the author[s] nor the publisher can accept any legal responsibility or liability for any errors or omissions that may be made. The publishers wish to make clear that any views or opinions expressed in this book by individual editors, authors or contributors are personal to them and do not necessarily reflect the views/opinions of the publishers. The information or guidance contained in this book is intended for use by medical, scientific or health-care professionals and is provided strictly as a supplement to the medical or other professional’s own judgement, their knowledge of the patient’s medical history, relevant manufacturer’s instructions and the appropriate best practice guidelines. Because of the rapid advances in medical science, any information or advice on dosages, procedures or diagnoses should be independently verified. The reader is strongly urged to consult the drug companies’ printed instructions, and their websites, before administering any of the drugs recommended in this book. This book does not indicate whether a particular treatment is appropriate or suitable for a particular individual. Ultimately it is the sole responsibility of the medical professional to make his or her own professional judgements, so as to advise and treat patients appropriately. The authors and publishers have also attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

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Foreword

The outcome of therapy for leukemia and malignant lymphoma has improved over the years, mainly in younger patients. Yet, there is no question that the challenges in the area of developmental therapeutics have remained formidable. These challenges relate to the patients who, from the start of treatment, fail to respond to the currently available therapies or combinations of drugs. The outlook of these primarily refractory patients is invariably dismal. Many of the responder patients attaining an initial complete remission, unfortunately, will finally present with relapse of disease. The relapses among the leukemias and high-grade lymphomas usually occur early on, i.e., within the first two years. Both groups, initial nonresponders and secondary failures, pose the notorious difficulty of resistance to conventional therapy. These facts provide an overall notion. Acquired somatic genetic abnormalities of the neoplasms provide keys to the nature of the disease and offer important predictors of treatment failure. They allow to pinpoint individual disease-specific features and distinguish variable disease risks as well as identify those patients with the highest probability of failure. The unmet therapeutic need is, by all standards, greatest among the large population of older patients with hematological cancer in whom response rates are comparatively low, relapse rates are high, and comorbidities prohibit the use of classical chemotherapeutic agents at effective dose levels. Scientists are on the way to discovering new drugs with different modes of action that can overcome the limitations of today’s selection of drugs. Numerous new drugs are currently in early clinical development with the aim of circumventing the clinical bottleneck of chemotherapy resistance. In the coming years, several of these compounds are expected to settle as members of the standard armamentarium of drugs available to the patient with a hematological tumor. New drugs may be designed with the deliberate objective of affecting a known molecular lesion or signaling pathway in the cancer cell, thus critically inhibiting

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Foreword

tumor cell survival. These therapeutic compounds may tackle distinct, molecularly defined subtypes of leukemia or lymphoma, and one would anticipate that their greater specificity will allow for application with enhanced efficacy and reduced toxicity. Currently, we are witnessing the development of diagnostic technologies that directly impact decision making in the clinical management of patients with hematological malignancies. These technologies relate, on the one hand, to more precise tissue diagnosis and involve innovative genomic, proteomic, and immunological techniques. On the other hand, they involve improved in vivo imaging methods, enabling a better and more sensitive visualization of neoplastic deposits in the body. These techniques, when appropriately validated for clinical use, will enable the distinction of prognostic disease subcategories and allow for a specific diagnosis according quantitative, sensitive, and objective parameters. This type of information will guide therapeutic decisions at the outset of treatment. It will also provide substantial insights that will be useful in monitoring treatment effects throughout the therapeutic management of patients and redirect treatment choice. An ambitious diagnostic approach makes sense if there is a choice for the physician among a broader scale of available therapeutic options. One of the major objectives of today’s molecular diagnostics relates to the identification of new druggable targets for pharma developments. Innovative Leukemia and Lymphoma Therapy appropriately and critically deals with each of the issues and challenges as regards developmental therapeutics. The book highlights current, clinically relevant diagnostic strategies for high-throughput diagnosis and disease response monitoring. The book covers, in a series of individual chapters, a collection of overviews that highlight clinically relevant novel therapeutic strategies in concise reviews. It also provides updates on therapeutic compounds with new mechanisms of action that currently raise intense interest and are in active development. This book comes as a timely resource of information that furnishes a state-of-the-art and comprehensive compendium, which will be of value to the interested clinician, researcher, and student. Bob Lo¨wenberg Erasmus University Medical Center Rotterdam, The Netherlands

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Preface

The treatment of leukemia and lymphoma is rapidly developing from conventional chemotherapy toward a more tailored and targeted, innovative therapy. However, conventional therapy is making progress as well. Targeted treatment with increased efficacy and less side effects is becoming more and more a reality, facilitated by fascinating developments such as oncogenomic studies and sophisticated drug engineering. Knowledge on determinants of chemosensitivity is also rapidly increasing. Together with pretreatment individualized tumor response testing and with improved monitoring of treatment response by minimal residual disease measurements, treatment will indeed become more tailored and individualized. This book gives a complete and up-to-date overview of exciting new treatment modalities in leukemia and lymphoma that have been introduced in the clinic or will be introduced in the near future. Well-known international experts summarize clinical studies on drugs such as tyrosine kinase inhibitors, monoclonal antibodies, proteasome inhibitors, farnesyl transferase inhibitors, hypomethylating agents, histone deacetylase inhibitors, mTOR targeting agents, Notch pathway inhibitors, and inhibitors of cyclin-dependent kinases. The first few chapters deal with methodological issues such as gene expression profiling to detect new drug targets, individualized tumor response testing aiming at selecting effective drugs, minimal residual monitoring to adapt treatment based on actual treatment response, and statistical issues concerning clinical studies in small subgroups of patients, while some discuss modulation of drug resistance and improvements in allogeneic bone marrow transplantation. Other chapters summarize targeting regulators of apoptosis, radioimmunotherapy, immunotherapy by vaccination, gene-directed therapy, and anti-angiogenesis approaches. The chapters provide a concise summary of the treatment rationale, of the pathways that are involved, and of relevant preclinical research, whenever relevant.

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Preface

We recommend this well-illustrated, comprehensive book to students, scientists, and clinicians with a special interest in innovative therapy who are involved not only in research and/or treatment of leukemia and lymphoma in particular, but in other malignancies as well. Gertjan J. L. Kaspers Bertrand Coiffier Michael C. Heinrich Elihu Estey

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Contents

Foreword Bob Lo¨wenberg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. v Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1.

2.

Gene Expression Profiling to Detect New Treatment Targets in Leukemia and Lymphoma: A Future Perspective . . . . . . . . . Torsten Haferlach, Wolfgang Kern, and Alexander Kohlmann

1

Individualized Tumor Response Testing in Leukemia and Lymphoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrew G. Bosanquet, Peter Nygren, and Larry M. Weisenthal

23

3.

Minimal Residual Disease ........................... Jacques J. M. van Dongen, Tomasz Szczepa
nski, and Vincent H. J. van der Velden

45

4.

New Methods for Clinical Trials: AML as an Example Elihu Estey

......

85

5.

Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ch. Michel Zwaan and Marry M. van den Heuvel-Eibrink

99

Monoclonal Antibodies in the Treatment of Malignant Lymphomas and Chronic Lymphocytic Leukemia ........ Bertrand Coiffier

125

6.

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Contents

Radioimmunotherapy of Hematological Malignancies Tim Illidge and James Hainsworth

8.

Differentiation Induction in Acute Promyelocytic Leukemia . . . . 185 Adi Gidron and Martin S. Tallman

9.

DNA Methylation and Epigenetics: New Developments in Biology and Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . Jesus Duque, Michael L€ ubbert, and Mark Kirschbaum

207

The Emerging Role of Histone Deacetylase Inhibitors in the Treatment of Lymphoma . . . . . . . . . . . . . . . . . . . . . . Matko Kalac and Owen A. O’Connor

233

10.

......

149

7.

. . . 257

11.

Antileukemic Treatment Targeted at Apoptosis Regulators Simone Fulda and Klaus-Michael Debatin

12.

Angiogenesis in Hematological Malignancies . . . . . . . . . . . . . Alida C. Weidenaar, Hendrik J. M. de Jonge, Arja ter Elst, and Evelina S. J. M. de Bont

13.

Nucleic Acid-Based, mRNA-Targeted Therapeutics for Hematologic Malignancies . . . . . . . . . . . . . . . . . . . . . . . . Alan M. Gewirtz

283

311

14.

Active Specific Immunization by the Use of Leukemic Dendritic Cell Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Ilse Houtenbos, Gert J. Ossenkoppele, and Arjan A. van de Loosdrecht

15.

CDK Inhibitors in Leukemia and Lymphoma Yun Dai and Steven Grant

............

353

16.

FLT3: A Receptor Tyrosine Kinase Target in Adult and Pediatric AML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mark Levis, Patrick Brown, and Donald Small

379

Treatment of Chronic Myeloid Leukemia with Bcr-Abl Kinase Inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Michael J. Mauro and Michael C. Heinrich

411

Tyrosine Kinase Inhibitors: Targets Other Than FLT3, BCR-ABL, and c-KIT ............................. Suzanne R. Hayman and Judith E. Karp

429

17.

18.

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Contents

19.

ix

Tyrosine Phosphatases as New Treatment Targets in Acute Myeloid Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . I. Hubeek, K. Hoorweg, J. Cloos, and Gertjan J. L. Kaspers

20.

Proteasome and Protease Inhibitors . . . . . . . . . . . . . . . . . . . N. E. Franke, J. Vink, J. Cloos, and Gertjan J. L. Kaspers

21.

Farnesyltransferase Inhibitors: Current and Prospective Development for Hematologic Malignancies ............. Judith E. Karp

22.

Targeting Notch Pathways . . . . . . . . . . . . . . . . . . . . . . . . . . Jennifer O’Neil and A. Thomas Look

23.

mTOR Targeting Agents for the Treatment of Lymphoma and Leukemia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Andrea E. Wahner Hendrickson, Thomas E. Witzig, and Scott H. Kaufmann

24.

25.

449 469

491 513

525

Allogeneic Hematopoietic Cell Transplantation After Nonmyeloablative Conditioning ...................... Fre´de´ric Baron, Frederick R. Appelbaum, and Brenda M. Sandmaier

539

Modulation of Classical Multidrug Resistance and Drug Resistance in General . . . . . . . . . . . . . . . . . . . . . . . . . Branimir I. Sikic

563

Index

..............................................

581

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Contributors

Frederick R. Appelbaum Fred Hutchinson Cancer Research Center and The University of Washington, Seattle, Washington, U.S.A. Fre´de´ric Baron Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A. Andrew G. Bosanquet

Bath Cancer Research, Royal United Hospital, Bath, U.K.

Patrick Brown Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, U.S.A. J. Cloos Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Bertrand Coiffier Hematology Department, Hospices Civils de Lyon and Claude Bernard University, Pierre-Benite, France Yun Dai Department of Medicine, Virginia Commonwealth University and Massey Cancer Center, Richmond, Virginia, U.S.A. Evelina S. J. M. de Bont Department of Pediatric Oncology/Hematology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Hendrik J. M. de Jonge Department of Pediatric Oncology/Hematology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Klaus-Michael Debatin

University Children’s Hospital, Ulm, Germany

Jesus Duque Department of Hematology/Oncology, University Medical Center Freiburg, Freiburg, Germany

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Contributors

Elihu Estey Division of Hematology, University of Washington Medical Center, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A. N. E. Franke Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Simone Fulda

University Children’s Hospital, Ulm, Germany

Alan M. Gewirtz Division of Hematology/Oncology, Department of Medicine & Abramson Family Cancer Research Institute, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, U.S.A. Adi Gidron Division of Hematology/Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine and The Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois, U.S.A. Steven Grant Department of Medicine, Biochemistry, and Pharmacology, Virginia Commonwealth University and Massey Cancer Center, Richmond, Virginia, U.S.A. Torsten Haferlach

Munich Leukemia Laboratory, Munich, Germany

James Hainsworth Paterson Institute of Cancer Research, School of Medicine, University of Manchester, Manchester, U.K. Suzanne R. Hayman Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, U.S.A. Michael C. Heinrich Center for Hematologic Malignancies and Departments of Medicine and Cell and Developmental Biology, Oregon Cancer Institute, Oregon Health & Science University and Portland VA Medical Center, Oregon Health & Science University, Portland, Oregon, U.S.A. K. Hoorweg Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Ilse Houtenbos Department of Hematology, VU University Medical Center, Amsterdam, The Netherlands I. Hubeek Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Tim Illidge Paterson Institute of Cancer Research, School of Medicine, University of Manchester, Manchester, U.K. Matko Kalac Herbert Irving Comprehensive Cancer Center, The New York Presbyterian Hospital, Columbia University, New York, New York, U.S.A. Judith E. Karp Division of Hematologic Malignancies, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, U.S.A.

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Contributors

xiii

Gertjan J. L. Kaspers Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Scott H. Kaufmann Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, U.S.A. Wolfgang Kern

Munich Leukemia Laboratory, Munich, Germany

Mark Kirschbaum Division of Hematology and Hematopoietic Cell Transplantation, City of Hope Comprehensive Cancer Center, Duarte, California, U.S.A. Alexander Kohlmann

Roche Molecular Systems, Pleasanton, California, U.S.A.

Michael L€ ubbert Department of Hematology/Oncology, University Medical Center Freiburg, Freiburg, Germany Mark Levis Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, U.S.A. A. Thomas Look Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, U.S.A. Michael J. Mauro Center for Hematologic Malignancies, Oregon Cancer Institute, Oregon Health & Science University, Portland, Oregon, U.S.A. Peter Nygren Department of Oncology, Radiology, and Clinical Immunology, University Hospital, Uppsala, Sweden Owen A. O’Connor Herbert Irving Comprehensive Cancer Center, The New York Presbyterian Hospital, Columbia University, New York, New York, U.S.A. Jennifer O’Neil Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, U.S.A. Gert J. Ossenkoppele Department of Hematology, VU University Medical Center, Amsterdam, The Netherlands Brenda M. Sandmaier Fred Hutchinson Cancer Research Center and The University of Washington, Seattle, Washington, U.S.A. Branimir I. Sikic Oncology Division, Department of Medicine, Stanford University School of Medicine, Stanford, California, U.S.A. Donald Small Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, U.S.A. Tomasz Szczepa
nski Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands, and Department of Pediatric Hematology and Oncology, Medical University of Silesia, Zabrze, Poland

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Contributors

Martin S. Tallman Division of Hematology/Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine and The Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, Illinois, U.S.A. Arja ter Elst Department of Pediatric Oncology/Hematology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Marry M. van den Heuvel-Eibrink Department of Pediatric Oncology/ Hematology, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The Netherlands Arjan A. van de Loosdrecht Department of Hematology, VU University Medical Center, Amsterdam, The Netherlands Vincent H. J. van der Velden Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands Jacques J. M. van Dongen Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands J. Vink Department of Pediatric Oncology/Hematology, VU University Medical Center, Amsterdam, The Netherlands Andrea E. Wahner Hendrickson Rochester, Minnesota, U.S.A.

Department of Medicine, Mayo Clinic,

Alida C. Weidenaar Department of Pediatric Oncology/Hematology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands Larry M. Weisenthal California, U.S.A. Thomas E. Witzig Minnesota, U.S.A.

Weisenthal Cancer Group, Huntington Beach,

Department of Medicine, Mayo Clinic, Rochester,

Ch. Michel Zwaan Department of Pediatric Oncology/Hematology, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The Netherlands

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BASIC AND CLINICAL ONCOLOGY Series Editor Bruce D. Cheson Professor of Medicine and Oncology Head of Hematology Georgetown University Lombardi Comprehensive Cancer Center Washington, D.C.

1. Chronic Lymphocytic Leukemia: Scientific Advances and Clinical Developments, edited by Bruce D. Cheson 2. Therapeutic Applications of Interleukin-2, edited by Michael B. Atkins and James W. Mier 3. Cancer of the Prostate, edited by Sakti Das and E. David Crawford 4. Retinoids in Oncology, edited by Waun Ki Hong and Reuben Lotan 5. Filgrastim (r-metHuG-CSF) in Clinical Practice, edited by George Morstyn and T. Michael Dexter 6. Cancer Prevention and Control, edited by Peter Greenwald, Barnett S. Kramer, and Douglas L. Weed 7. Handbook of Supportive Care in Cancer, edited by Jean Klastersky, Stephen C. Schimpff, and Hans-Jo¨rg Senn 8. Paclitaxel in Cancer Treatment, edited by William P. McGuire and Eric K. Rowinsky 9. Principles of Antineoplastic Drug Development and Pharmacology, rard A. Milano, and Mark J. Ratain edited by Richard L. Schilsky, Ge 10. Gene Therapy in Cancer, edited by Malcolm K. Brenner and Robert C. Moen 11. Expert Consultations in Gynecological Cancers, edited by Maurie Markman and Jerome L. Belinson 12. Nucleoside Analogs in Cancer Therapy, edited by Bruce D. Cheson, Michael J. Keating, and William Plunkett 13. Drug Resistance in Oncology, edited by Samuel D. Bernal 14. Medical Management of Hematological Malignant Diseases, edited by Emil J Freireich and Hagop M. Kantarjian 15. Monoclonal Antibody-Based Therapy of Cancer, edited by Michael L. Grossbard 16. Medical Management of Chronic Myelogenous Leukemia, edited by Moshe Talpaz and Hagop M. Kantarjian

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17. Expert Consultations in Breast Cancer: Critical Pathways and Clinical Decision Making, edited by William N. Hait, David A. August, and Bruce G. Haffty 18. Cancer Screening: Theory and Practice, edited by Barnett S. Kramer, John K. Gohagan, and Philip C. Prorok 19. Supportive Care in Cancer: A Handbook for Oncologists: Second Edition, Revised and Expanded, edited by Jean Klastersky, Stephen C. Schimpff, and Hans-Jo¨rg Senn 20. Integrated Cancer Management: Surgery, Medical Oncology, and Radiation Oncology, edited by Michael H. Torosian 21. AIDS-Related Cancers and Their Treatment, edited by Ellen G. Feigal, Alexandra M. Levine, and Robert J. Biggar 22. Allogeneic Immunotherapy for Malignant Diseases, edited by John Barrett and Yin-Zheng Jiang 23. Cancer in the Elderly, edited by Carrie P. Hunter, Karen A. Johnson, and Hyman B. Muss 24. Tumor Angiogenesis and Microcirculation, edited by Emile E. Voest and Patricia A. D’Amore 25. Controversies in Lung Cancer: A Multidisciplinary Approach, edited by Benjamin Movsas, Corey J. Langer, and Melvyn Goldberg 26. Chronic Lymphoid Leukemias: Second Edition, Revised and Expanded, edited by Bruce D. Cheson 27. The Myelodysplastic Syndromes: Pathology and Clinical Management, edited by John M. Bennett 28. Chemotherapy for Gynecological Neoplasms: Current Therapy and Novel Approaches, edited by Roberto Angioli, Pierluigi Benedetti Panici, John J. Kavanagh, Sergio Pecorelli, and Manuel Penalver 29. Infections in Cancer Patients, edited by John N. Greene 30. Endocrine Therapy for Breast Cancer, edited by James N. Ingle and Mitchell Dowsett 31. Anemia of Chronic Disease, edited by Guenter Weiss, Victor R. Gordeuk, and Chaim Hershko 32. Cancer Risk Assessment, edited by Peter G. Shields 33. Thrombocytopenia, edited by Keith R. McCrae 34. Treatment and Management of Cancer in the Elderly, edited by Hyman B. Muss, Carrie P. Hunter, and Karen A. Johnson 35. Innovative Leukemia and Lymphoma Therapy, edited by Gertjan J. L. Kaspers, Bertrand Coiffier, Michael C. Heinrich, and Elihu Estey

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1 Gene Expression Profiling to Detect New Treatment Targets in Leukemia and Lymphoma: A Future Perspective Torsten Haferlach and Wolfgang Kern Munich Leukemia Laboratory, Munich, Germany

Alexander Kohlmann Roche Molecular Systems, Pleasanton, California, U.S.A.

INTRODUCTION The standard methods for establishing the diagnosis and prognosis of acute leukemias and lymphomas are cytomorphology and cytochemistry in combination with multiparameter immunophenotyping. However, cytogenetics, fluorescence in situ hybridization (FISH), and polymerase chain reaction (PCR)-based assays add important information with respect to biologically defined and prognostically relevant subgroups. Together, a combination of different methods allows a comprehensive diagnosis with relevant clearly defined subentities. It also leads to a better understanding of the respective clinical course of defined disease subtypes and to a more or less disease-specific therapeutic approach. However, not all patients achieve complete remission during treatment, and many of those who do, later develop relapse and treatment-resistant disease. To overcome these problems, the microarray technology, which quantifies gene expression intensities of thousands of genes in a single analysis, holds the potential to become an essential tool for a strictly molecularly defined classification of leukemias and lymphomas.

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It may therefore be used at first as a novel routine method for diagnostic approaches in the near future (1). But even more importantly, it will also reveal new genetic and therapeutically relevant markers and should guide the search for new targets. Gene expression profiling will also improve patient selection to test therapeutic hypothesis most efficiently and may help define dose and schedule determination. This chapter outlines the major steps for gene expression profiling analyses to approach these different goals by starting at a better diagnostic characterization of leukemias and lymphomas hopefully ending up with new targets for individual treatment of the respective patients. MICROARRAYS AND THE ERA OF FUNCTIONAL GENOMICS Both biology and medicine are undergoing a revolution that is based on the accelerating determination of DNA sequences, including the completion of whole genomes of a growing number of organisms (2). In parallel to the sequencing efforts, a wide range of technologies with tremendous potential has been achieved that can take advantage of the vast quantity of genetic information being now available. The field of functional genomics seeks to devise and apply these technologies, such as microarrays, to analyze the full complement of genes and proteins encoded by an organism to understand the functions of genes and proteins (3) (Fig. 1).

Figure 1 Different types of microarray platforms. Microarray platforms vary according to the solid support used (such as glass slides or silicon wafers), the surface modifications with various substrates, the type and length of DNA fragments on the array (such as cDNA or oligonucleotides), whether the gene fragments are presynthesized and deposited, or synthesized in situ, the machinery used to place the fragments on the array (such as ink-jet printing, spotting, mask, or micromirror-based in situ synthesis), and the method of sample preparation. Currently, combinations of these variables are used to generate two main types of microarrays: spotted glass slide arrays (right) and in situ synthesized DNA-oligonucleotide arrays (left).

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Glass Slide Microarrays Glass slide microarrays were first produced in Patrick Brown’s laboratory at Stanford University (4). In glass slide microarray studies, ribonucleic acid (RNA) species from the test sample and from the reference sample are studied pairwise as an equivalent mixture in which the control RNA is the reference for expressing the gene transcript levels in the target sample (Fig. 1). Various direct and indirect labeling methods for the sample have been developed (5). The majority of expression analysis labeling protocols is based on the reverse transcription of mRNA, either from highly purified poly(A) mRNA or total RNA extracts and often include amplification steps. In most protocols, one sample is labeled with the Cy3 (green) fluorochrome, the other with Cy5 (red). The labeled cRNA molecules hybridize to the corresponding cDNA or long oligonucleotides, of which the exact position on the array is known. The binding of the target to the probe is detected by scanning the array, typically using either a scanning confocal laser or a charge coupled device (CCD) camera-based reader. After scanning, software calculations provide the ratios between green and red fluorescence for each spot, corresponding to the relative abundance of mRNA from a particular gene in the target sample versus the reference sample. However, the technical difficulties in the reproducible production of glass slide microarrays should not be underestimated (5). Much of this variation is introduced systematically during the spotting of the DNA onto the slide surface, and many of the initial cDNA clone sets were compromised by contamination with T1 phage, multiple clones in individual wells, and incorrect sequence assignment. Thus, given the lack of a gold standard for the production of glass slide microarrays using current technologies, there is a high degree of variation in the quality of data derived from glass slide microarray experiments. This poor reproducibility not only adds to the cost of a given study but also leads to data sets that are difficult to interpret. MICROARRAYS AS AN INNOVATIVE TECHNIQUE TO DETECT NEW TARGETS For several reasons many investigations using microarrays for biological approaches today are performed on the whole genome Affymetrix U133 set (HG-U133A and HG-U133B or the HG-U133 2.0 plus array; Affymetrix, Santa Clara, California, U.S.). A detailed up-to-date description on sequences and probe selection rules is available as technical note from the manufacturer (www .affymetrix.com). Affymetrix HG-U133A and HG-U133B Microarrays The U133 two-array set provides comprehensive coverage of well-substantiated genes in the human genome. It can be used to analyze the expression level of

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39,000 transcripts and variants, including greater than 33,000 human genes. The two arrays comprise more than 45,000 probe sets and 1,000,000 distinct oligonucleotide features. The sequences from which these probe sets were derived were selected from GenBank, dbEST, and RefSeq. The sequence clusters were created from the UniGene database (Build 133, April 20, 2001) and then refined by analysis and comparison with a number of other publicly available databases, including the Washington University EST trace repository and the University of California, Santa Cruz, Golden-Path human genome database (April 2001 release). In addition, an advanced understanding of probe uniqueness and hybridization characteristics allowed an improved selection of probes based on predicted behavior. The U133 chip design uses a multiple linear regression model that was derived from a thermodynamic model of nucleic acid duplex formation. This model predicts probe binding affinity and linearity of signal changes in response to varying target concentrations. The two arrays are manufactured as standard format arrays with a feature size of 18 mm and use 11 probe pairs per sequence. The oligonucleotide length is 25 mer. Human Genome U133 Plus 2.0 Array In addition to all the sequences represented on the HG-U133A and HG-U133B two-array set, the HG-U133 Plus 2.0 microarray also covers 9921 new probe sets representing approximately 6500 new genes. These gene sequences were selected from GenBank, dbEST, and RefSeq. Sequence clusters were created from the UniGene database (Build 159, January 25, 2003) and refined by analysis and comparison with a number of other publicly available databases, including the Washington University EST trace repository and the NCBI human genome assembly Build 31 (www.affymetrix.com). Thus, in using this comprehensive whole human genome expression array, an extensive coverage of the human genome is reached. HG-U133 Plus 2.0 microarrays are manufactured as standard format arrays with more than 54,000 probe sets of a feature size of 11 mm and use 11 probe pairs per sequence. The oligonucleotide length is 25 mer. MICROARRAY DATA ANALYSIS A wide range of approaches is available for gleaning insights from the data obtained from transcriptional profiling. Data analyses are performed by two different approaches, i.e., the supervised approach and the unsupervised approach (Fig. 2). Unsupervised analyses are used to test the hypothesis whether specific characteristics, e.g., genetic aberrations, are also reflected at the level of gene expression signatures. Supervised analyses identify a minimal set of genes that could be used to stratify those patients after a training of classification engines (6–8). The gene lists from supervised analyses can also be further

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Figure 2 Overview about a common workflow to analyze microarray data. After preparation of corresponding data sets from the main master table, the data are analyzed either unsupervised or supervised. Unsupervised analyses are performed by hierarchical clustering or principal component analysis. In the supervised analyses, differentially expressed genes can be identified by various methods and selected for further interpretations, e.g., visualization by hierarchical clustering, principal component analysis, plotting as bar graphs, or generation of biological networks. In addition, differentially expressed genes can be selected for classification tasks where several different machinelearning approaches have to be applied.

interpreted in terms of underlying biology. For all gene expression profiles, master data tables have to be maintained. In these tables, rows represent all genes for which data have been collected and columns represent microarray experiments from individual patients. Each cell represents the measured fluorescence intensity from the corresponding target probe set on the microarray. Before analyzing the data, it is a routine procedure to normalize the data. This procedure is a mandatory step in the data-mining process to appropriately compare the measured gene expression levels. U133 set microarray signal intensity

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values can be normalized by scaling the raw data intensities to a common target intensity using a recommended mask file. Some Examples of Software to Identify Genes of Interest Several software packages are used for principal data acquisition (GCOS), storage (MicroDB), and analysis (DMT). The following tables give only some examples to approach data. Individual gene expression profiles can also further be prepared as Microsoft Excel tables. Software

Source

Internet

GCOS MicroDB DMT

Affymetrix, Inc. Affymetrix, Inc. Affymetrix, Inc.

www.affymetrix.com/support/ www.affymetrix.com/support/ www.affymetrix.com/support/

The following packages can be applied for the identification of differentially expressed genes and classification: Software

Source

Internet

SAM

Stanford University

Bioconductor q-Value

Open source University of Washington National Taiwan University

www-stat.stanford.edu/~tibs/SAM/ index.html www.bioconductor.org faculty.washington.edu/~jstorey/qvalue/

LIBSVM

www.csie.ntu.edu.tw/~cjlin/libsvm/

SAM is available as Microsoft Excel Add-in (9). Bioconductor is an open source and open development software project for the analysis and comprehension of genomic data. Bioconductor packages provide statistical and graphical methodologies for analyzing genomic data. LIBSVM (Version 2.6) is a software solution for SVM-based classification. The q-value software takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values (10). In addition, further third party software packages can be used for statistical analyses and data visualization. Software

Source

Internet

SPSS Pathways Analysis GeneMaths Genomics Suits

SPSS, Inc. Ingenuity Systems Applied Maths, Inc. Partek, Inc.

www.spss.com/ www.ingenuity.com www.applied-maths.com www.partek.com/

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Tools for Pathway Analyses to Detect New Targets and Correlations The identification of diagnostic, prognostic, or therapeutic markers in leukemia and lymphoma following microarray experiments and their biostatistical read outs have to then focus on the discovery of important pathways in these tumors. Several programs exist in order to identify pathways involved. These include Pathway Assist (http://www.ariadnegenomics.com/products/pathway.html), DAVID (http:// apps1.niaid.nih.gov/david/), and Ingenuity (http://www.ingenuity.com/). As one example, Ingenuity enables researchers to model, analyze, and understand complex biological systems foundational to human health and disease. This includes pathways analysis software and knowledge databases for biologists and biostatisticians and enterprise knowledge management infrastructure. Today, Ingenuity is a useful knowledge base of biological networks with curated relationships between proteins, genes, complexes, cells, tissues, drugs, and diseases. Increasingly, also bioinformaticians are interested in developing analytical tools that help scientists interpret experimental data especially in the context of pathways and biological systems. These analytical tools have broad application throughout research and development, from validating targets by uncovering disease-related pathways to predicting pathways perturbed by therapeutic compounds. As one example in Ingenuity, a broad genome-wide coverage of over 25,900 mammalian genes (11,100 human, 5500 rat, and 9300 mouse) can be found and millions of pathway interactions extracted from literature are managed interactively and web based. At a basic level, an understanding of functions and pathways associated with genes identified within an early-stage candidate region may assist in prioritizing portions of this region for further investigation, e.g., targeted association using higher densities of single nucleotide polymorphisms (SNPs). This type of approach may even assist in identifying which genes to resequence in an attempt to identify further SNPs for association studies. This is achievable now with the ability to upload, for example, Affymetrix SNP identifiers directly into pathway software such as Ingenuity. Future developments may increase the mapping coverage of SNPs beyond the simple 1:1 gene to SNP mapping available today. Beyond this, future functionality may even allow for the correlation between multiple regions of the genome identified at a functional level and findings of a genetic association study that identifies multiple, low scoring regions. Previously, these may not have warranted further investigation based solely on association scores. However, functional, process, pathway, or disease annotations may implicate multiple regions as being relevant to a particular phenotype by virtue of their compound effect. Evidence is already emerging from the HapMap project that there are significant SNPs that are genetically indistinguishable across large regions of individual chromosomes or even different chromosomes. It is anticipated that further development of software and pathway analyses tools to approach the huge sets of data generated in microarray experiments will lead to deeper insights.

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DETECTION OF NEW TARGETS IN LEUKEMIA AND LYMPHOMA As has been outlined before, gene expression profiling has been extensively used for tumor classification (8,11–15) and is on the way to add important information to predict response to therapy as well as for outcome in leukemia and lymphoma patients. As these data are not in the focus of this article, they will only be cited, if they add information also for new target detection. Furthermore, there are only limited efforts yet to incorporate microarrays into clinical trials in hematology and oncology because of several reasons: (1) prospective sample acquisition parallel to the gold standard diagnostic procedures is needed, (2) standardized equipment and software has to be used, (3) experienced scientists and technicians with respect to microarray analyses have to be involved, and (4) funding is mostly lacking and would be best if academic institutions and industry combine efforts. Other factors like intra-laboratory and inter-laboratory comparability have also to be taken into account. This leads to the following relation according to Weeraratna (16): More than 9000 references are available that concern microarrays, but only around 20 are clinical trials, and less than 10 of these pertain to cancer. As currently no single prospective trial has been conducted to our knowledge to address the use of microarrays within a clinical trial in leukemia and lymphoma, we only can rely on information that was published in papers referring to diagnostic or prognostic questions. On the basis of their findings, some preliminary statements can also be made for the use of gene expression profiling to define new targets and drugs in leukemia and lymphoma (17). The following chapters will comment on these aspects and will be subdivided disease specifically. Detection of New Targets in Lymphoma Alizadeh et al. (13) defined distinct subtypes of diffuse large B-cell lymphoma (DLBCL) by specific gene expression signatures. Although this paper mostly focuses on newly defined biological subgroups of DLBCL, different prognosis was also detected. This again leads to the detection of genes that are responsible not only for a better and novel subclassification but also transfer into striking differences in prognosis if patients are treated uniformly. Thus, the authors concluded that a respective gene expression pattern and the IPI score for NHL in combination will guide therapeutic decisions including bone marrow transplantation as one option for high-risk patients. Furthermore, expression profiling may also help to detect homogeneous groups of patients to improve the likelihood of observing treatment efficacy in specific disease entities. This study was the first to show that the two DLBCL subgroups differentially expressed entire transcriptional modules composed of hundreds of genes. Polo et al. identified a discrete subset of DLBCL that are reliant on Bcl6 signaling and uniquely sensitive to Bcl6 inhibitors (18). Therefore, successful new therapeutics may be aimed at the upstream signal-transducing molecules and further investigations are needed.

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Microarrays have also been used to study the targets of c-Myc, a transcription factor that plays a role in Burkitt’s lymphoma as c-Myc is involved in the chromosomal translocation t(8;14). In this study genomic targets including genes involved in cell cycle, cytoskeletal organization, cell growth, and adhesion were identified (19). However, these structures have to be tested again as drug targets after having been detected by gene expression profiling. Detection of New Targets in Acute Myeloid Leukemia Yagi et al. (20) analyzed 54 pediatric acute myeloid leukemia (AML) using Affymetrix U95A arrays and focused on the reproducibility of some FAB subtypes and especially on gene patterns to predict outcome. After unsupervised clustering, they were able to differentiate patients with t(8;21) from those with inv(16) and from those demonstrating an AML M4/5 or AML M7 phenotype or immunophenotype by specific gene expression signatures. Within this unsupervised analysis, no specific profile was found that correlated to the prognosis of the patients. Since the inclusion of further cases with other FAB subtypes and cytogenetic abnormalities (no karyotype was available in 9 of 54 cases) resulted in an increased heterogeneity, the authors restricted their further analyses to the genetically and morphologically better-defined subentities. For further calculation, data were analyzed and supervised with respect to outcome and prognosis. A subset of 35 genes that were independent from the morphology or karyotype of the patients was selected; some of them are associated with the regulation of the cell cycle or with apoptosis. By hierarchical cluster analysis, patients could be classified into high-risk and low-risk groups with highly significant differences in event-free survival (EFS) ( p < 0.001). Another approach was described by Qian et al. (21) in therapy-related AML and myeloid cell lines focussing on CD34-positive selected cells. They were the first ones to define a specific pattern of gene expression for t-AML in comparison with other AML subtypes. The most discriminating genes were found to be involved in arrested differentiation of early progenitor cells. A higher expression of cell cycle control genes such as CCNA2, CCNE2, and CDC2 and genes for cell cycle checkpoints such as BUB1 or growth (Myc) were found. Furthermore, downregulation of transcription factors involved in early hematopoiesis (TAL1, GATA1, EKLF) and overexpression of FLT3 was detected. The authors concluded that these genes may be further investigated for new targets and drugs in this very unfavourable subtype of AML. As a further hallmark in AML, Bullinger et al. analyzed 65 peripheral blood and 54 bone marrow samples in patients with AML (12). On the basis of 6283 most variably expressed genes they were able to reproduce cytogenetically defined AML subgroups and, in addition, to define two different groups with highly differing prognosis on the basis of gene expression profiles. While both groups mainly included AML cases with normal karyotypes without differences in many prognostic parameters, it is noteworthy that the group with the poorer

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prognosis included more patients with monosomy 7, complex aberrant karyotypes, and length mutations of FLT3, while the group with the better prognosis included more patients with inv(16). Thus, the observed differences in the prognosis between both groups may be largely due to imbalances in profiles of established prognostic factors rather than due to the identification of a newly characterized biological subgroup of AML. Genes as published by Bullinger et al. should be tested in independent cohorts of AML patients to further support their prognostic power, and further investigations are again warranted for the definition of such genes as new targets in AML treatment. Similar results have been reported by Valk et al. (11) who discovered 16 groups of AML featuring distinct gene expression profiles on the basis of microarray analysis, which, in addition, showed significant differences in clinical course. However, while many of the identified groups were characterized by specific cytogenetic aberrations known to be highly predictive of outcome, none of the groups were restricted to cases without cytogenetic abnormalities. Thus, the task remains to identify markers capable of discriminating prognostically different cases out of the heterogeneous group of AML with normal karyotype and to use these for target testing. An improvement in this direction has been reported by Kern et al. who analyzed gene expression profiles in 205 patients with AML and normal karyotype (22). In order to identify genetically defined subgroups, an unsupervised principal component analysis revealed 79% of cases clustering together, while a subgroup comprising 21% of cases formed another cluster. Importantly, the analysis of known genetic markers, including the presence of length mutations and point mutations of FLT3, partial tandem duplications of MLL, or mutations of CEBPA, NRAS, or CKIT, did not reveal differences between both groups. Significant differences were found, however, in their phenotypes with more monocytic features in the smaller group. Analysis of differentially regulated genetic pathways revealed CD14, WT1, MYCN, HCK, and SPTBN1 as discriminating genes. Stressing the potential impact of this analysis on the clinical management of AML, these two groups significantly differed in the EFS. Thus, it was demonstrated here also that within the group of AML with normal karyotype highly needed novel molecular markers with prognostic impact can be identified by using gene expression profiling. Some of the discriminating structures defined here may also be used for future targets in specific AML subtypes. However, regarding the biological heterogeneity of AML in general and of AML with normal karyotype in particular, it is anticipated that further largescale studies in the context of clinical trials are needed to fully characterize and validate novel and clinically relevant subgroups in AML and by doing so to define new targets for individual treatment. A recent example is the study of Bullinger et al. who further subclassified 93 patients with core binding factor (CBF) leukemias (AML1-ETO and CBFB-MYH11) in different risk groups (23). Another structure identified by gene expression profiling is the ubiquitinactivating enzyme E1-like (UBE1) gene that is induced by all-trans retinoic acid

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(ATRA) in NB4 cells (24). Detailed investigation revealed that ATRA activates the UBE1 promoter and the overexpression of UBE1 therefore triggers the degradation of promyelocytic leukemia-retinoic acid receptor alpha (PMLRARa) and leads to apoptosis in acute promyelocytic leukemia (APL) cells (25). Clinical studies with UBE1 in leukemia are however missing. Andersson et al. in a recent study compared (26) the molecular signatures in childhood acute leukemias and their correlations to expression patterns in normal hematopoietic subpopulations. 87 B-lineage acute lymphoblastic leukemia (ALL), 11 T-cell ALL, 23 AML, and 6 normal bone marrows, as well as 10 normal hematopoietic subpopulations of different lineages and maturations were ascertained by 27K cDNA microarrays. Not surprisingly, segregation according to lineage and primary genetic changes was achieved. However, several genes were identified that were preferentially expressed by the leukemic cells and not by their normal counterparts. These genes suggest an ectopic activation and are likely to reflect regulatory networks that may provide attractive targets for future directed therapies. However, although this study clearly points to the right direction, targets that were defined in this study have to be tested in an independent cohort of patients before they may be used for drug design. This again demonstrates that even if a variety of markers can be defined by gene expression signatures in addition to the diagnostic pattern of a specific leukemia subtype, the use of such information to find therapeutic structures or even targets is still limited, which emphasizes the need for better support of translational research and drug development in the future. A possible approach to use expression profiling in a high-throughput screening was published by Stegmaier et al. (27). They used HL-60 cells in 384-well culture plates and cultivated them with uniform concentrations of 1739 compounds to induce differentiation. By including different gene expression signatures of AML-versus-monocyte and AML-versus-neutrophil distinctions as measured by DNA microarrays, data were complemented by reverse transcriptionpolymerase chain reaction (RT-PCR) and matrix assisted laser desorption/ ionization time-of-flight (MALDI-TOF). Because of this approach, finally eight compounds were identified that reliably induced the differentiation signature. As a result, a modest number of genes were sufficient to capture a complex cellular response. However, the authors concluded that further investigations are needed to identify the optimal gene signature. This work points to a possible scenario for the identification of new targets and drugs by gene expression profiling. However, it again demonstrates the complex problem to combine different highly sophisticated methods in a high-throughput investigation to define at the end drugs to be tested in a clinical trial. Detection of New Targets in ALL For sure, one of the most important questions posed by the use of gene expression profiling is the identification of new targets for the further development of highly

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specific antileukemic drugs. One striking example is based on the results from Armstrong et al. who found mutations and high-level expression in the FLT3 tyrosine kinase receptor gene in MLL-rearranged ALLs (28,29). FLT3 is known as a tyrosine kinase receptor that is frequently activated by mutations in patients with AML (30) but is rarely activated in ALL. However, by gene expression profiling it was demonstrated that FLT3 was the gene most strongly associated with the presence of MLL gene rearrangements in ALL. This leads to the idea (28,31,32) to further investigate the potential role of this oncogene in the pathogenesis of MLL tumors per se. Mutational analyses of FLT3 in MLL gene–rearranged leukemias clearly showed the presence of activating mutations in the activation loop of this tyrosine kinase receptor in 5 of 30 cases studied. This was further validated by treating leukemia cells with PKC412, a specific inhibitor of the FLT3 tyrosine kinase. It was shown both in vivo and in vitro that PKC412 has differential cytotoxic effects on MLL rearranged leukemia cells harbouring FLT3 activation (28). Furthermore, it was demonstrated that also in ALL with hyperdiploid cytogenetics, the FLT3 receptor is frequently expressed at a higher level. This again reinforces the value of gene expression profiling as a powerful approach for the identification of novel drugs also in ALL (32–34), which should motivate an urgent translation into clinical trials including high-risk patients. Another approach in ALL to use gene expression for further insights in biology of the disease was described by Zaza et al. (35). After intravenous administration of thioguanine nucleotide (TGN), the TGN concentration was determined in the leukemic blasts of 82 children with newly diagnosed ALL. After analyses of 9600 genes, they identified 60 probes that were significantly associated with TGN accumulation if patients were treated with mercaptopurine (MP) alone and another 75 genes in patients treated with a combination of metotrexate (MTX) and MP. There was no overlap between these two sets of genes. The investigation was performed in parallel in vivo and in vitro and gene expression profiling led to new insights into the genomic basis of interpatient differences with respect to different treatment options. Through gene expression profiling, clear correlations between a specific drug’s level in vivo and increased expression of specific genes were detected. It was even visible that expression profiles correlated to mono or combined treatment modalities. Prospective studies are needed to test these results. Another outstanding investigation was conducted by Holleman et al. (36) who identified a set of differentially expressed genes in B-lineage ALL being sensitive or resistant to several drugs such as prednisolone (33 genes), vincristine (40 genes), asparaginase (35 genes), and daunorubicin (20 genes). A score of genes combined to define overall sensitivity or resistance to all four drugs was tested in a multivariate analysis and predicted outcome of 173 children investigated ( p ¼ 0.027). Although these genes do not per se define new targets of treatment, gene expression profiling clearly demonstrated in a prospective setting which treatment may or may not be successful. This may serve as an example for the application of gene expression profiling to improve treatment and to define targets

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and drugs against these targets in ALL. The authors further point to the aspect that it may be important to determine whether specific modulation of proteins encoded by genes that were found may describe treatment response best. These proteins may also point to previously unrecognized potential targets and new agents to augment the efficacy of current chemotherapy for ALL. Brown et al. (37) investigated FLT3 inhibition by the selective inhibitor CEP-701 in ALL. In this study eight ALL cell lines and primary ALL cells from 39 patients were evaluated and a high potency for this drug especially in ALL cells overexpressing FLT3, i.e., MLL rearranged cases, as well as ALLs with hyperdiploid karyotypes was identified. Seven of seven sensitive samples examined by immunoblotting demonstrated constitutively phosphorylated FLT3 that was potently inhibited by CEP-701, whereas zero out of six resistant samples expressed constitutively phosphorylated FLT3. The authors concluded that the compound CEP-701, a potent and selective FLT3 inhibitor, effectively suppresses FLT3driven leukemic cell survival and clinical testing of this compound as a novel molecularly targeted agent for treatment of ALL is warranted. However, in most cases the candidate targets identified in expression studies (28) using relapse or treatment outcome as endpoints of their observation and independent verification is missing (38). Therefore, conflicting results are largely due to differences in treatment and biology of enrolled patients. The gap between gene expression profiling to characterize biological entities in leukemia and lymphoma and the targets to be tested is still not closed, and translation from data management to drug design is still missing. However, the characterization of molecular mutations and of pathway alterations in the leukemias proceeds with high velocity as can be demonstrated by the recent study of Mullighan et al. who revealed the PAX gene as the most frequent target of molecular mutation in ALL and showed that direct disruption of pathways controlling B-cell development and differentiation contribute to B-progenitor ALL pathogenesis (39). This is just one more example of the recent progress in the identification of new molecular targets in ALL. Detection of New Targets in Chronic Myeloid Leukemia McLean et al. (40) intended to define specific gene expression profiles in chronic myeloid leukemia (CML) patients all treated with imatinib. In correlation to cytogenetic response data, the expression pattern of a subset of 55 out of more than 12,000 genes was identified that best predicted response to therapy. The sensitivity to predict the individual response was 93.4%; however, the specificity was only 58.3%. The authors further found that many of the genes identified appeared to be strongly related to BCR-ABL transformation mechanisms. Thus, these genes may need further investigation as potential new drug targets in CML. Diaz-Blanco et al. described several novel transcriptional changes in primary CD34 positive CML cells in comparison with normal CD34-positive cells including an upregulation of components of the TGFB signaling pathway or

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candidate genes such as the leptin receptor (LEPR), thrombin receptor (PAR1), or the neuroepithelial cell transforming gene 1 (NET1) (41). It was further possible to define differentially regulated candidate genes discriminating chronic from blast phase of CML such as PRAME (preferentially expressed antigen of melanoma) (42) or CAMPATH (CD52) (43) or deregulation of pathways, e.g., the WNTB catenin signaling system (42). These studies thus might be helpful for definition of new novel stem or progentior cell-associated targets and of mechanisms being responsible for the higher malignant transformation of CML (41,42). Detection of New Targets in Chronic Lymphocytic Leukemia One highlight to establish the use of gene expression profiling to define new targets in leukemia was the detection of ZAP70 to be expressed in a large proportion of chronic lymphocytic leukemia (CLL) (14). As the expression of ZAP70 was high in IgVH-unmutated cases of CLL, this gene was further correlated to distinction within CLL cases with respect to prognosis. This finding also led to the investigation of the ZAP70 antigen expression by antibodies in CLL using multiparameter immunophenotyping (44). Recently, it was demonstrated that ZAP70 can also be successfully screened by a quantitative RT-PCR method (45). After definition of CLL signature genes, the protein products of these genes may represent such new targets for monoclonal antibodies or for vaccine approaches. Another aspect detected in this investigation was the fact that B-cell activation genes were upregulated in Ig-unmutated patients. Thus, pathways downstream of the B-cell receptor may contribute to aggressive clinical cases. It may be beneficial to target these signaling pathways. However, again, gene expression profiling so far was helpful in finding new epitopes in strict correlation to a specific disease or even subgroups within such diseases, but targeted drugs are still under investigation. Future Investigations to Diagnostic and Therapeutic Use of Gene Expression Profiling: The MILE Study The (microarray innovations in leukemia) MILE study is a cooperation of the European Leukemia Network (ELN, work package 13) together with Roche Molecular Systems. This innovative study was designed to test microarrays in parallel to gold standard diagnostics in 4000 patients with leukemia in 11 different sites (7 from ELN, 3 in United States, 1 in Singapore). At least 18 different classes of leukemia shall prospectively be defined for diagnostic use in the MILE study by their respective gene expression signatures. The ELN work package 13 is per se the head of these activities. In order to set up a clearly defined study with comparable sample quality, as a first step, a prephase was conducted to harmonize laboratory workflows. This prephase included tests of similar aliquots of two cell lines and three

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Figure 3 Example for inter-laboratory reproducibility in MILE study: Center 7 versus all other nine centers was calculated, all genes (38,000) were included in calculation.

leukemia samples—AML, CML, and CLL. A first interim analysis was able to demonstrate a very high inter- and intra-laboratory reproducibility (46). Figure 3 is an example of the data generated. Stage I of the study now includes 2000 samples of leukemia all analyzed in parallel with gold standard methods. After clarification of discrepant results between gene expression analyses and gold standard report forms, the most discriminating genes will be used to design a specific custom microarray for the diagnosis of leukemias. This new microarray will then be tested prospectively in stage II of the study by including another set of 2000 leukemia samples. It is further intended to use a subset of this data to address further questions like response to specific treatment as many patients are enrolled in prospective clinical trials. Only studies like this may define new targets for treatment, because information will be available on diagnosis, prognostic parameters, treatment, and response as well as ultimately for treatment outcome. The power of gene expression profiling may help in approaching such data sets from different perspectives and may therefore be used to address several questions in parallel. SUMMARY AND FUTURE TRENDS As new drugs are classically tested in clinical trials, this may be an interesting scenario for further use of microarrays. In many early clinical phase I/II studies response rate is low and many patients have received some other treatment before. However, if one is coupling clinical trials with gene expression profiling, the investigators may enhance their information, as the identification of specific gene expression profiles may correlate to drug response or resistance of the individual patient. Products of such differentially expressed genes represent at least plausible targets for inhibitors that may reverse the drug-resistance

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phenotype. Thus, these markers may be prospectively used to identify those patients who are likely to respond to the new agent. In follow-up studies far fewer patients would then be required to prove efficacy (38,47–50). Today, we are on the way to design and to use specifically developed microarrays with thousands of genes for the subclassification of leukemias and lymphomas. The ongoing MILE study is one example of an international approach to use gene expression profiling for the first time in a routine diagnostic setting. Also, a lymphoma microarray is already investigated for diagnosis (51). Importantly, this information always includes information about a patient’s prognosis, as clearly defined biological entities in leukemia and lymphoma lead to disease-specific treatment and data are therefore also related to prognosis and outcome. Some examples for this thesis are APL to be treated with ATRA or arsenic trioxide, or BCR-ABL-positive leukemias that can be specifically treated with imatinib or other tyrosine kinase inhibitors. Other examples are the use of CD33-targeted treatment in AML with gemtuzumab ozogamicin, or anti-CD20 and anti-CD52 antibody-related treatment in lymphomas. In addition, several studies were able to define a subset of genes that are not linked to a diagnostic profile but can be also used for outcome prediction. These studies can even demonstrate different marker genes that predict response to specific drugs. So far, one has to accept that much less is known about the use of gene expression profiling in finding new targets in leukemia and lymphoma. One nice example may be the detection of ZAP70 in CLL that not only predicts the IgVH status of the disease but can also be used as an antibody target to discriminate patients at diagnosis. However, new treatment opportunities have not been developed for this gene so far. Of course this does not mean that gene expression profiling will never add information for new targets. By identifying new players and pathways for resistance to therapy, DNA repair, and apoptosis, microarrays open up new avenues for any targeted therapy that had not even existed a few years ago. There is no evidence for any other technique today with so much power for specific and less toxic treatment for cancer patients in the future. However, the exact definition of the difference between a normal and a cancer cell in all details is essentially required for the solution. The goal must be to diagnose and stratify patients according to their disease-specific gene expression profile before treatment starts and to treat individually with drugs specific for such clearly defined biological entities. This does not mean that these drugs will be individually defined for each patient but for a newly defined disease not based only on morphology or cytogenetic parameters. Models for the development of new targets in leukemia and lymphoma should be adapted to large-scale clinical trials and have to focus in detail on new medications tested. Thus, strong links between academic and industry initiatives are urgently needed (52,53) to be the driving force behind the science. As cancer pathways such as Ras, Src, or Myc are known and can be linked to several tumors, their interaction and involvement can be studied by gene expression profiling best. Therefore, not only single genes being over- or underexpressed

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but altered pathways in leukemia and lymphoma also may lead to new targets in the near future. CLINICAL PERSPECTIVES FOR THE NEXT FIVE YEARS Following its fast integration in hematological research we can expect gene expression profiling to be included in clinical procedures already in the very near future. First, it might soon support the classification and risk stratification of hematological malignancies as it provides a high degree of correlation with other diagnostic methods such as flow cytometry (54,55) or PCR (56) and shows a high diagnostic accuracy and reproducibility (7,57). The robustness of the method is a further argument for its applicability in the clinical field (58). Moreover, gene expression profiling is able to further subclassify distinct entities such as chronic myelomonocytic leukemia, which could not be previously subdivided by classical techniques (59). Although it is improbable that the new technique will substitute all established methods such as cytomorphology, cytogenetics, or PCR in the next years, it has to be expected that gene expression profiling will become part of the diagnostic panel of hematological malignancies and will be increasingly correlated with other methods or support those in case of difficult differential diagnoses or decisions. Second, a further step in the near future might be the inclusion in minimal residual disease strategies. In combination with real-time PCR gene expression profiling is able to serve for the definition of molecular markers, which can be monitored during follow-up of the disease. This might be exemplified in AML in the WT1 and PRAME genes (60). Third, gene expression profiling will probably find its way in individualized treatment planning as specific gene expression signatures are associated with poor chemotherapy response and with drug resistance. These processes are, e.g., mediated by a transcriptional program active in hematopoietic stem and progenitor cells as was demonstrated in AML (61) and being associated with nucleotide metabolism, apoptosis, and oxygen species metabolism (62). The finding of such signatures therefore might be an indication for immediate planning of allogeneic stem cell transplantation. However, such application of gene expression profiling for the definition of chemosensitivity for individualized treatment planning will probably have to be prepared somewhat longer than the above mentioned indications and will probably be only part of research studies rather than of routine strategies in the near future. REFERENCES 1. Haferlach T, Kohlmann A, Kern W, et al. Gene expression profiling as a tool for the diagnosis of acute leukemias. Semin Hematol 2003; 40:281–295. 2. Wheeler DL, Church DM, Edgar R, et al. Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res 2004; 32:D35–D40.

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23. Bullinger L, Rucker FG, Kurz S, et al. Gene-expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia. Blood 2007; 110: 1291–1300. 24. Tamayo P, Slonim D, Mesirov J, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A 1999; 96:2907–2912. 25. Kitareewan S, Pitha-Rowe I, Sekula D, et al. UBE1L is a retinoid target that triggers PML/RARalpha degradation and apoptosis in acute promyelocytic leukemia. Proc Natl Acad Sci U S A 2002; 99:3806–3811. 26. Andersson A, Olofsson T, Lindgren D, et al. Molecular signatures in childhood acute leukemia and their correlations to expression patterns in normal hematopoietic subpopulations. Proc Natl Acad Sci U S A 2005; 102:19069–19074. 27. Stegmaier K, Ross KN, Colavito SA, et al. Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat Genet 2004; 36:257–263. 28. Armstrong SA, Kung AL, Mabon ME, et al. Inhibition of FLT3 in MLL. Validation of a therapeutic target identified by gene expression based classification. Cancer Cell 2003; 3:173–183. 29. Armstrong SA, Staunton JE, Silverman LB, et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet 2002; 30:41–47. 30. Schnittger S, Schoch C, Dugas M, et al. Analysis of FLT3 length mutations in 1003 patients with acute myeloid leukemia: correlation to cytogenetics, FAB subtype, and prognosis in the AMLCG study and usefulness as a marker for the detection of minimal residual disease. Blood 2002; 100:59–66. 31. Kohlmann A, Schoch C, Dugas M, et al. New insights into MLL gene rearranged acute leukemias using gene expression profiling: shared pathways, lineage commitment, and partner genes. Leukemia 2005; 19:953–964. 32. Ferrando AA, Look AT. DNA microarrays in the diagnosis and management of acute lymphoblastic leukemia. Int J Hematol 2004; 80:395–400. 33. Armstrong SA, Mabon ME, Silverman LB, et al. FLT3 mutations in childhood acute lymphoblastic leukemia. Blood 2004; 103:3544–3546. 34. Taketani T, Taki T, Sugita K, et al. FLT3 mutations in the activation loop of tyrosine kinase domain are frequently found in infant ALL with MLL rearrangements and pediatric ALL with hyperdiploidy. Blood 2004; 103:1085–1088. 35. Zaza G, Cheok M, Yang W, et al. Gene expression and thioguanine nucleotide disposition in acute lymphoblastic leukemia after in vivo mercaptopurine treatment. Blood 2005; 106:1778–1785. 36. Holleman A, Cheok MH, den Boer ML, et al. Gene-expression patterns in drugresistant acute lymphoblastic leukemia cells and response to treatment. N Engl J Med 2004; 351:533–542. 37. Brown P, Levis M, Shurtleff S, et al. FLT3 inhibition selectively kills childhood acute lymphoblastic leukemia cells with high levels of FLT3 expression. Blood 2005; 105:812–820. 38. Cheok MH, Lugthart S, Evans WE. Pharmacogenomics of acute leukemia. Annu Rev Pharmacol Toxicol 2006; 46:317–353. 39. Mullighan CG, Goorha S, Radtke I, et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 2007; 446:758–764.

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40. McLean LA, Gathmann I, Capdeville R, et al. Pharmacogenomic analysis of cytogenetic response in chronic myeloid leukemia patients treated with imatinib. Clin Cancer Res 2004; 10:155–165. 41. Diaz-Blanco E, Bruns I, Neumann F, et al. Molecular signature of CD34(þ) hematopoietic stem and progenitor cells of patients with CML in chronic phase. Leukemia 2007; 21:494–504. 42. Radich JP, Dai H, Mao M, et al. Gene expression changes associated with progression and response in chronic myeloid leukemia. Proc Natl Acad Sci U S A 2006; 103:2794–2799. 43. Zheng C, Li L, Haak M, et al. Gene expression profiling of CD34þ cells identifies a molecular signature of chronic myeloid leukemia blast crisis. Leukemia 2006; 20: 1028–1034. 44. 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. 45. Dicker F, Schnittger S, Kern W, et al. Complex aberrant karyotypes and unbalanced translocations in CLL correlate with an unmutated IgVH status: a study on 133 patients studied with chromosome banding analysis, interphase FISH, IgVH mutation status, ZAP-70 RNA exrpession and immunophenotyping. Blood 2005; 106:825a. 46. Haferlach T, Kohlmann A, Basso G, et al. A multi-center and multi-national program to assess the clinical accuracy of the molecular subclassification of leukemia by gene expression profiling. Blood 2005; 106:224a. 47. Cheok MH, Yang W, Pui CH, et al. Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Nat Genet 2003; 34:85–90. 48. Golub TR. Mining the genome for combination therapies. Nat Med 2003; 9:510–511. 49. Evans WE, Guy RK. Gene expression as a drug discovery tool. Nat Genet 2004; 36:214–215. 50. Corchero J, Fernandez-Salguero PM. Improving cancer therapeutics by molecular profiling. Curr Drug Metab 2005; 6:553–568. 51. Staudt LM. Molecular diagnosis of the hematologic cancers. N Engl J Med 2003; 348:1777–1785. 52. Altman RB, Flockhart DA, Sherry ST, et al. Indexing pharmacogenetic knowledge on the World Wide Web. Pharmacogenetics 2003; 13:3–5. 53. Downward J. Cancer biology: signatures guide drug choice. Nature 2006; 439: 274–275. 54. Kern W, Kohlmann A, Schoch C, et al. Comparison of mRNA abundance quantified by gene expression profiling and percentage of positive cells using immunophenotyping for diagnostic antigens in acute and chronic leukemias. Cancer 2006; 107: 2401–2407. 55. Basso G, Case C, Dell’Orto MC. Diagnosis and genetic subtypes of leukemia combining gene expression and flow cytometry. Blood Cells Mol Dis 2007; 39:164–168. 56. Sala-Torra O, Gundacker HM, Stirewalt DL, et al. Connective tissue growth factor (CTGF) expression and outcome in adult patients with acute lymphoblastic leukemia. Blood 2007; 109:3080–3083. 57. Kohlmann A, Schoch C, Schnittger S, et al. Molecular characterization of acute leukemias by use of microarray technology. Genes Chromosomes Cancer 2003; 37: 396–405.

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58. Kohlmann A, Schoch C, Dugas M, et al. Pattern robustness of diagnostic gene expression signatures in leukemia. Genes Chromosomes Cancer 2005; 42:299–307. 59. Gelsi-Boyer V, Cervera N, Bertucci F, et al. Gene expression profiling separates chronic myelomonocytic leukemia in two molecular subtypes. Leukemia 2007; 21: 2359–2362. 60. Steinbach D, Schramm A, Eggert A, et al. Identification of a set of seven genes for the monitoring of minimal residual disease in pediatric acute myeloid leukemia. Clin Cancer Res 2006; 12:2434–2441. 61. Heuser M, Wingen LU, Steinemann D, et al. Gene-expression profiles and their association with drug resistance in adult acute myeloid leukemia. Haematologica 2005; 90:1484–1492. 62. Eisele L, Klein-Hitpass L, Chatzimanolis N, et al. Differential expression of drugresistance-related genes between sensitive and resistant blasts in acute myeloid leukemia. Acta Haematol 2007; 117:8–15.

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2 Individualized Tumor Response Testing in Leukemia and Lymphoma Andrew G. Bosanquet Bath Cancer Research, Royal United Hospital, Bath, U.K.

Peter Nygren Department of Oncology, Radiology, and Clinical Immunology, University Hospital, Uppsala, Sweden

Larry M. Weisenthal Weisenthal Cancer Group, Huntington Beach, California, U.S.A.

INTRODUCTION Individualized tumor response testing (ITRT) has a long history, with a number of different technologies and many different tumor types tested. Almost all technologies used for hematological malignancies are identical in their logic and similar in their execution. The concepts underlying cell death assays are relatively simple, even though the technical features and data interpretation can be complex. The logic is that if the drug kills tumor cells from an individual patient in a ‘‘test tube,’’ then it is more likely to be effective when administered directly to a patient. Conversely, a drug that does not kill the patient’s cells, even at concentrations significantly higher than can be achieved in the patient, is unlikely to be effective. Considerable work based on these assays has been reported during the past 25 years, and recently an ad hoc group of 50 scientists from 10 countries agreed on the term ‘‘individualized tumor response’’ for these

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tests, describing them as the ‘‘effect of anticancer treatments on whole living tumor cells freshly removed from cancer patients’’ and not including tests with ‘‘subcellular fractions, animals or cell lines’’ (1). We present results for hematological neoplasms, but note that analogous results have been published for a variety of solid tumors in substantial numbers of patients (2). TOTAL CELL KILL/CELL DEATH ASSAYS There is a clear divide between the two main technologies used in this work: an ITRT endpoint can be based either on reduction of cell proliferation or on cell death (3–6). Historically, the cell proliferation endpoint received great attention as a result of studies by Salmon, Von Hoff, and others during the late 1970s and early 1980s (7,8). These studies occurred during the heyday of the oncogene discovery period in cancer research, when oncogene products were frequently found to be associated with cell growth and when cancer was most prominently considered to be a disease of disordered cell growth. In contrast, the concept of apoptosis had yet to become widely recognized. Also unrecognized were the concepts that cancer may be a disease of disordered apoptosis/cell death and that the mechanisms of action of most, if not all, available anticancer drugs are mediated through apoptosis. When problems with cell proliferation assays emerged (9,10), there was little enthusiasm for studying cell death as an alternative endpoint (11). These factors explain many abandoning research into ITRT during the 1980s. As opposed to measuring cell proliferation, there is a family of assays based on the concept of total cell kill or, in other words, cell death occurring in the entire population of tumor cells (3–6). The basic technology concepts are straightforward. Cells are isolated from a fresh specimen obtained from a viable neoplasm. These cells are cultured in the continuous presence or absence of a drug, most often for three to seven days. At the end of the culture period, a measurement is made of cell injury, which correlates directly with cell death, almost always by apoptosis (12–14). Although there are methods for specifically measuring apoptosis per se, there are practical difficulties in applying these methods to mixed (and sometimes clumpy) preparations of tumor cells and normal cells. Thus, more general measurements of cell death have been applied. One of these measurements is the delayed loss of cell membrane integrity, which has been found to be a useful surrogate for apoptosis. This is measured by differential staining in the Differential Staining Cytotoxicity (DiSC) assay method, which allows selective drug effects against tumor cells to be recognized in a mixed population of tumor and normal cells (6,15). More recently the Tumor Response to Antineoplastic Compounds (TRAC) assay was described as a streamlined version of the DiSC assay (16). Other cell death endpoints include loss of mitochondrial Krebs cycle activity, as measured in the Methylthiazol Tetrazolium (MTT) assay (17), loss of cellular adenosine triphosphate (ATP), as measured in the ATP assay (18), and

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loss of cytosolic esterase activity and cell membrane integrity, as measured by the Fluorometric Microculture Cytotoxicity Assay (FMCA) and similar assays (19–21). Most recently, other methods including assays to measure apoptosis more specifically have been described, although it remains to be seen if these will offer any real advantages over the other measurements of cell death (22–26). These four endpoints produce valid and reliable measurements of cell death. They also correlate well with each other on direct comparisons of the different methods (17,19,20,27–29). For instance, Weisenthal and associates have performed direct correlations between the DiSC and MTT assays in approximately 5,500 fresh human tumor specimens, testing an average of 15 drugs per specimen at two different concentrations. Although these endpoints agree with each other in most solid tumors (overall correlation coefficient ¼ 0.85), we consider that the MTT assay is more problematic in hematological neoplasms. For example, correlations between treatment outcomes and assay results have been more consistent in acute nonlymphocytic leukemia (ANLL) with the DiSC assay endpoint (30–32) than with the MTT endpoint (22,33,34). Additionally, there is a clear relationship between prior treatment status and assay results for anthracyclines in the case of the DiSC assay (relapsed patients having blast cells that are clearly more resistant than those in previously untreated patients, Table 1), which was not evident when ANLL was tested with the MTT assay (35). The absolute magnitudes of drug effects (cell kill) are substantially greater when scored in the DiSC assay than in the MTT assay in the case of ANLL (Table 1). Finally, the correlation coefficient between DiSC and MTT assays was weaker in the case of ANLL (median r ¼ 0.75), than in other classes of neoplasms that Weisenthal had tested (median r ¼ 0.85). There are at least two explanations for the greater drug effects detected in the DiSC endpoint. Firstly, the DiSC assay is a more specific endpoint for drug effects on blast cells (as opposed to drug effects on blast cells plus the normal cells frequently present in ANLL specimens). Table 1 In Vitro Activity of Anthracyclines in ANLL As a Function of Prior Treatment Status and Individualized Tumor Response Testing Endpoint

Drug/assay Doxorubicin/DiSC Doxorubicin/MTT Idarubicin/DiSC Idarubicin/MTT

Number untreated

Number treated

Cell fraction surviving (untreated)

Cell fraction surviving (relapsed)

12 12 10 10

16 16 16 16

0.11 0.34 0.06 0.35

0.33 0.42 0.25 0.45

P 0.020 0.428 0.0015 0.180

Abbreviations: ANLL, acute nonlymphocytic leukemia; DiSC, differential staining cytotoxicity; MTT, methylthiazol tetrazolium. Source: From Ref. 35.

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Secondly, it takes longer for cells to lose the ability to produce a signal in the MTT assay than it does for them to be scored as dead in the DiSC assay [e.g. (36)]. It is possible that the MTT assay would be more useful in ANLL (i) were it applied only in cases in which there was a ‘‘pure’’ (>90%) population of blast cells at the end of the assay, and/or (ii) were the duration of the cell culture (and drug exposure) extended beyond the typical 96-hour period of these assays. With regard to the first of these latter possibilities, it is notable that Hongo, et al. (who contributed a disproportionate share of weak clinical correlations in Table 2) did not attempt to determine the percentage of blast cells at the time the MTT endpoint was measured (34). A final point of emphasis is that it is important to rigorously standardize assay conditions, including precisely controlling the duration of drug exposure and cell culture. Thus, the DiSC assay and similar tests have some advantages over the other short-term assays. COMPLETED STUDIES OF CORRELATION BETWEEN CELL DEATH ASSAY RESULTS AND CHEMOTHERAPY RESPONSE As with other laboratory tests, the determination of the efficacy of ITRT is based on comparisons of laboratory results with patient response (commonly referred to as ‘‘clinical correlations’’). The hypothesis to be tested with clinical correlations is a simple one—that above-average drug effects in the assays correlate with above-average drug effects in the patient, as measured by both response rates and patient survival. Table 2 and Figure 1 show that, with respect to response, the above hypothesis has been confirmed to be true in all published studies. At each point in the distribution of overall response rates, patients with test results in the ‘‘sensitive’’ range were more likely to respond than the total patient population as a whole. Conversely, patients with test results in the ‘‘resistant’’ range were less likely to respond than the patient population as a whole. On average, patients with assays in the test sensitive range were threefold more likely to respond than patients with assays in the test resistant range (see the ‘‘Overall relative risk’’ column in Table 2). Considering this evidence as a whole, can it be inferred with confidence that the cell death measured in the assays correlates with tumor cell death measured in the patient? Comparing the chronic lymphocytic leukemia (CLL) and acute lymphoblastic leukemia (ALL) data with the more limited but also consistent data in non-Hodgkin’s lymphoma (NHL), a powerful case is made to support the clinical relevance of this testing in human lymphatic neoplasms. Considering the ANLL data in the context of the lymphatic neoplasm data, a powerful case is made to support the clinical relevance of this testing in hematological neoplasms in general. The body of literature supporting cell death assays in lymphatic neoplasms dates to studies in CLL published by Schrek in the 1960s (37,38). Schrek

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measured the in vitro cell death effects of drugs, heat, and radiation on CLL cells by means of phase contrast microscopy. He measured what we would today recognize as apoptosis and undoubtedly being precisely congruent with the DiSC assay. Radiation effects in vitro were strongly correlated with clinical outcome (37,38). In the late 1970s, Durkin compared in vitro drug effects in NHL and CLL by means of trypan blue dye exclusion with clinical drug effects and reported good correlations in a small study (39). The DiSC assay was developed independently as an improved variation of the trypan blue test in which suspension cultures of cells were first exposed to trypan blue, cytocentrifuged onto microscope slides, and counterstained with either Haematoxylin/Eosin or Wright/ Giemsa (to identify the non-trypan blue-stained cells with respect to whether these surviving cells were tumor cells or normal cells). With further improvement (substitution of fast green stain for trypan blue and the addition of acetaldehyde-fixed duck erythrocytes as an internal standard to aid in scoring the Cytospin slides), clinical correlations in CLL and other neoplasms were first reported in an abstract form and at meetings in the United States and Europe in 1981. The first publications of clinical correlations with the DiSC assay, in 1983 and 1984, included studies of the activity of glucocorticoids and standard cytotoxic agents, which were correlated with prior therapy and with clinical outcome in ALL and CLL (3,15,40). In 1986, these were followed by a study showing the clinical relevance of the DiSC assay in CLL, ALL, and NHL using several clinical endpoints: (i) Correlations with known disease-specific activity profiles, (ii) individual patient correlations with clinical response, (iii) greater resistance of specimens from previously treated patients versus previously untreated patients, and (iv) a shift to significantly greater drug resistance in metachronous assays in the presence of intervening chemotherapy, but no shift in the absence of intervening chemotherapy (41). These early findings were subsequently independently confirmed by other investigators in more comprehensive studies (19,28,42–51). Additionally, studies in pediatric ALL reported that resistance to dexamethasone in the DiSC assay predicted for poor survival (52), findings also independently confirmed. By the late 1980s, a number of other scientists were investigating the DiSC assay and related cell death assays. These began with a head-to-head comparison of the DiSC assay with the MTT assay in established cell lines by the National Cancer Institute (NCI) lung cancer group (17). These studies established the comparability of these endpoints in homogeneous cell populations. A group at the VU University Medical Center of Amsterdam carried out a head-to-head comparison of the DiSC endpoint with the MTT endpoint in ALL (29). This group showed that the endpoints were comparable in specimens in which the proportion of leukemia cells (relative to normal cells in the specimen) was greater than 80% (29,53). They found the MTT endpoint to be less labor intensive. They used the same general conditions originally described for the DiSC assay (including a 96-hour continuous drug exposure, followed by

Beksac, et al. Bosanquet Hongo, et al. Hongo, et al. Kaspers, et al. Lathan, et al. Nygren, et al. Weisenthal, et al. Weisenthal, et al. Kirkpatrick, et al. Beksac, et al. Bosanquet Hongo, et al. Hongo, et al. Yamada, et al. Langkjer and Norgaard Larsson, et al. Lathan, et al. Norgaard, et al. Nygren, et al. Santini, et al. Sargent and Taylor Staib, et al. Staib, et al. Stute, et al. Tidefelt, et al.

ALL ALL ALL ALL ALL ALL ALL ALL ALL ALL/CLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL ANLL

3 15 25 65 125 4 36 29 2 55 12 5 14 43 124 11 21 17 59 38 27 21 83 79 33 34

Response rate (test sensitive)

Response rate (test resistant)

67 87 68 82 90 75 64 76 100 69 58 20 79 58 86 55 52 71 53 66 52 48 89 90 64 62

100 92 76 95 94 100 80 90 100 79 78 0 82 67 93 100 85 92 71 86 82 83 100 96 73 91

0 0 25 10 81 0 38 44 N/A 0 0 50 67 30 76 0 0 20 35 10 0 0 0 0 56 8

Relative risk (test sensitive)

Relative risk (test resistant)

Overall relative risk

0.87 0.89 0.86 0.97

Infinite 2.72 8.15 1.12

Infinite 3.05 9.45 1.16

0.80 0.84

2.34 1.71

0.87 0.75

Infinite Infinite

Infinite Infinite

0.96 0.87 0.93 0.55 0.62 0.77 0.74 0.77 0.63 0.57 0.94 0.89 0.87 0.68

1.18 1.94 1.14 Infinite Infinite 3.53 1.48 6.58 Infinite Infinite Infinite Infinite 1.15 7.41

1.23 2.22 1.23 Infinite Infinite 4.58 2.01 8.57 Infinite Infinite Infinite Infinite 1.32 10.91

2.93 2.03

Reference number 92 42 49 59a 93 30 19 41 15 28 92 42 49 59a 34 66 47 30 33 19 94 64 63 32a 95 31

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Diagnosis

Clinical response rate (RR)

Bosanquet et al.

Author

Number of patients

28

Table 2 Correlations of Individualized Tumor Response Test Results with Clinical Response to Chemotherapy and Relative Risk of Failure to Achieve Remission

Diagnosis

Bosanquet Bosanquet, et al. Bosanquet, et al. Bosanquet, et al. Larsson et al. Morabito, et al. Nerenberg, et al. Silber, et al. Weisenthal, et al. Weisenthal Bosanquet Weisenthal, et al. Bosanquet Weisenthal, et al. Weisenthal, et al Larsson, et al. Beksac, et al. Bosanquet Leone, et al. Nygren, et al. Strauss, et al. Weisenthal, et al. Weisenthal, et al.

CLL CLL CLL CLL CLL CLL CLL CLL CLL CLL CLL/ALL/NHL CML (blastic) Myeloma Myeloma Myeloma Myelofibrosis NHL NHL NHL NHL NHL NHL NHL

Totals

Clinical response rate (RR)

Response rate (test sensitive)

Response rate (test resistant)

Relative risk (test sensitive)

Relative risk (test resistant)

Overall relative risk

73 34 66 442 1 31 40 15 15 3 107 9 16 6 5 1 1 10 3 50 8 10 3

19 76 55 79 100 74 60 80 67 67 71 22 13 50 0 100 100 40 67 58 50 60 0

48 93 69 84 100 95 94 100 90 100 82 67 29 67 N/A 100 100 50 67 71 100 86 N/A

4 0 7 31 N/A 36 32 25 20 0 17 0 0 33 0 N/A N/A 25 N/A 17 0 0 0

0.82 0.79 0.40 0.94

Infinite 8.18 4.60 2.54

Infinite 10.29 11.52 2.71

0.78 0.64 0.80 0.74

2.04 1.89 3.20 3.33

2.61 2.97 4.00 4.50

0.87

4.26

4.92

0.44

Infinite

0.82

3.48

4.26

1929

70.4

84.6

28.3

0.83

2.48

2.99

Infinite

Reference number 42 96 70 83 47 71 25 51 41 15 97a 41 42 41 15 47 92 42 98 27 99 41 15

Notes: Relative risks are calculated for experiments containing more than 10 patients. a To avoid duplication, clinical correlations recorded here from these papers do not include previously published correlations. The overall results: (TP ¼ 1220, TN ¼ 349, FP ¼ 222, FN ¼ 138) result in: Specificity (for drug resistance) of 0.61 and Sensitivity (for drug resistance) of 0.90.

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Number of patients

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Figure 1 Correlations between ITRT results and clinical response. The data points are the results of each of 33 individual studies that had at least 10 patients per data subset (see Table 2). The data are plotted in order of the increasing response rate in the total patient cohort studied (x-axis). The crosses (all of which lie on the x ¼ y line) represent the response rates of patients in each study if the assay was of no value or not performed. The squares represent the response rates for patients with assay results classified as in vitro sensitive (&) versus resistant (&). The triangles show the weighted mean of all sensitive results (~, n ¼ 1020)) and all resistant results (~, n ¼ 419) from Table 2. The greater the vertical distance between the sensitive and resistant results for an individual study, the more accurate the test results.

comparisons between drug exposed and control cultures with the cell death endpoint). These Dutch authors continue to publish an extensive, elegant, and ongoing series of rigorous studies. They have established that the assay results correlate with and predict both response and survival in ALL and that the assay results are only two factors (the other being minimal residual disease), which independently predict for survival in pediatric ALL (43,44,46,54–57). They have also extended this work to ANLL (22,35,45,58). Taken in the context of the entire literature, these studies in pediatric ALL provide further support for the validity of complementary studies in CLL. Other investigators have also shown strong correlations between cell death assay results and the clinical outcome (response and/or survival) in pediatric ALL (19,49,50,52,55,59), adult ALL and ANLL (20,28,30–32,60–67), CLL

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(42,51,68–71), and adult NHL (15,27,41,48). These studies included further confirmation of the comparability between DiSC and MTT endpoints in assays on clinical specimens. Larsson and Nygren also introduced the fluorescein diacetate cell death endpoint (FMCA), which, like the DiSC endpoint, measures cell membrane integrity and which correlates well with the DiSC endpoint in homogeneous cell populations (19,27). In 1991, Bosanquet, et al. published in Lancet a relatively large number of correlations between clinical response and DiSC assay results, chiefly in CLL (42). He showed, furthermore, highly significant correlations between assay results and patient survival. This paper also confirmed the relevance of the ‘‘EDR’’ (extreme drug resistance) endpoint, which is defined as an assay result greater than 1 SD more resistant than the median of comparison assays. He later described a paradoxical shift toward increased methylprednisolone sensitivity in previously treated CLL and used the DiSC assay to identify high-dose methylprednisolone as an effective treatment for otherwise refractory CLL (72,73). These studies with the DiSC and MTT assays are further supported by studies with the FMCA. Fluorescein diacetate is a lipid soluble material that readily penetrates cell membranes. Viable cells contain cytosolic esterases that cleaves the dye to non–lipid soluble fluorescein, which is concentrated in cells containing a functionally intact membrane. Thus, the assay is conceptually similar to the DiSC assay, which measures the ability of cells with functionally intact membranes to exclude non–lipid soluble dyes. Delayed loss of this membrane integrity is a marker of apoptotic cell death (74). Investigators at Uppsala University in Sweden began work in the 1980s by comparing the DiSC and FMCA assays and establishing their comparability (19,27,48). They proceeded to publish a series of studies showing correlations between assay results and treatment outcomes in NHL and ANLL (19,20,47,48,61,62,75), confirming the specificity of the EDR endpoint in predicting clinical nonresponse (62), and confirming and extending earlier reports on the capability of the cell death endpoint to identify the general disease-specific activity patterns of a diverse spectrum of drugs (76). Within the past several years, additional studies have provided strong support for the clinical relevance of the information provided by cell death assays in hematological neoplasms. Table 2 shows response correlations for ALL, CLL, NHL, and ANLL and may be summarized as follows: l

ALL: n ¼ 304 published correlations between assay results and response l l

l

81% overall response rate for patients studied 91% response rate for patients treated with ITR test–sensitive (ITRþ) drugs; relative risk of failure to achieve remission 0.90 [95% confidence interval (CI) 0.84–0.96, P ¼ 0.002] 49% response rate for patients treated with ITR test–resistant (ITR–) drugs; relative risk 1.65 [95% CI 1.29–2.11, P < 0.0001]

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Bosanquet et al.

CLL: n ¼ 720 l l

l

l

l

69% overall response rate 83% response rate with ITRþ drugs; relative risk 0.83 [0.78–0.89; P < 0.0001] 19% response rate with ITR drugs; relative risk 3.66 [2.6–5.1; P < 0.0001]

NHL: n ¼ 85 l 54% overall response rate l 73% response rate with ITRþ drugs; relative risk 0.74 [0.58–0.95; P ¼ 0.025] l 12% response rate with ITR– drugs; relative risk 4.69 [1.59–13.8; P < 0.0001] ANLL: n ¼ 621 l 72% overall remission rate l 88% response rate with ITRþ drugs; relative risk 0.82 [0.77–0.90; P < 0.0001] l 36% response rate with ITR drugs; relative risk 2.01 [1.65–2.45; P < 0.0001]

Thus, there is a long, extensive, and consistent body of evidence supporting the clinical relevance of cell death assays in human hematological neoplasms. Gene Expression Profiling Over the last few years, gene expression profiling has been suggested as the best or only way of determining ex vivo drug sensitivity (77), and there has been some recent progress for the concept of prediction of cytotoxic drug activity in individual patients with solid tumors based on genomic signatures (80). However, due to almost all patients being treated with combination chemotherapy, without ITRT, there are calibration difficulties with this methodology. Thus, in one of the best papers on the subject, originating from work with childhood ALL (78), the supervised cluster analysis was ‘‘based on in vitro drug sensitivity’’ (79). This editorial then continued by erroneously suggesting that ITRT was ‘‘more cumbersome’’ than gene expression profiling (79), whilst ITRT is actually integrating all the gene expression into one convenient test. As a result, and because it is as near real time and real life as is possible for a laboratory test, we believe that ITRT may be most clinically relevant to the patient (Fig. 2). COMPLETED STUDIES OF PATIENT SURVIVAL In 1999, in a study of 243 CLL patients (70), Bosanquet identified 66 patients who received fludarabine within a year of the performance of the DiSC assay— 51 fludarabine test–sensitive patients had a 69% response rate (80% for untreated

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Figure 2 Clinical relevance of various endpoints.

patients; 64% for previously treated patients), while 15 fludarabine test–resistant patients had a 7% response rate (25% for previously untreated; 0% for previously treated). None of these 15 fludarabine-resistant patients treated with fludarabine survived 17 months; median survival was 7.9 months. In contrast, the fludarabinesensitive patients treated with fludarabine had an 80% chance of surviving beyond 17 months, a median survival of 41.7 months, and a 43% chance of surviving beyond four years. Patients (n ¼ 42) with DiSC assay resistance to fludarabine but treated with regimens other than fludarabine had a median survival of 16.3 months and 10% survived beyond four years. The relative risk of death for patients with DiSC assay resistance to fludarabine treated with fludarabine versus those treated with a non-fludarabine regimen was 2.9. On multivariate analysis, fludarabine test resistance was a more important determinant of survival in patients treated with fludarabine than was any other clinical characteristic, including gender, Binet stage, prior chemotherapy, and patient age. In a separate analysis, DiSC assay– directed therapy of CLL was calculated to be cost effective at only $2500 per quality life-year saved (81). Other investigators, as noted, have reported that assay results are important predictors of patient survival in pediatric ALL and ANLL (33,49, 56–58,82). Similar studies from a number of different groups have published correlations between ITRT results and survival in adult ANLL (32,33,63). Correlations between DiSC assay results and patient survival in ANLL were first published by a Swedish group in 1989 (31). These results were confirmed and extended by a group at the University of Cologne (32), in a follow-up to their earlier report of strong correlations between DiSC assay results and clinical remission of adult ANLL a decade earlier (63). In their recent studies, the DiSC assay results 100% accurately predicted clinical outcome and identified a group of patients with a 100% early death rate, when treated with conventional induction therapy (32,63). These studies are very analogous to the above-cited work identifying a group of CLL patients in whom conventional treatment is uniformly inactive (70).

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The University of Cologne group followed up with a presentation at the American Society of Hematology meeting in December, 1999, in which multivariate analysis showed DiSC assay results to be the strongest factor predicting for clinical outcome (both complete remission and long-term patient survival) in adult ANLL, using a novel and sophisticated method for calculating sensitive/ resistant cutoff boundaries (32). Additionally, a Danish group reported studies correlating MTT assay results with both overall and relapse-free survival in 85 adult ANLL patients (33). Assay results remained significantly correlated with survival on multivariate analysis. This work on ANLL is analogous and complementary to the studies by the Dutch (Amsterdam) group in pediatric ALL, discussed above. ONGOING CLINICAL STUDIES A major international clinical trial on ITRT in CLL is ongoing. The first randomization has closed and comparisons of ITRT by TRAC assay (83,84) and patient response (85) have been published. The second randomization (at nonresponse or relapse after first treatment) is between ITRT-directed therapy and protocol-guided treatment. This second randomization continues to accrue patients and will determine to what extent ITRT can improve response and survival of CLL patients at first relapse. EXPERT OPINION: CURRENT USE OF ITRT IN CLINICAL ONCOLOGY The American Society of Clinical Oncology (ASCO) working group recommended against the use of ITRT in oncology practice, stating that ‘‘Oncologists should make chemotherapy treatment recommendations on the basis of published reports of clinical trials. . . .’’ (86). In a published objection to this recommendation, Castro wrote: ‘‘Paradoxically, as the number of possible treatment options supported by completed randomized clinical trials increases, the scientific literature becomes increasingly vague in guiding physicians . . . moreover, clinicians are confronted on nearly a daily basis by decisions that have not been addressed by randomized trial evaluation’’ (87). The data in Table 3 support Castro’s point of view. These data are taken from the United States NCI Web site, in which the so-called state-of-the-art, standard therapy options are reviewed. It can be readily seen that 50 years of prospective, randomized trials have failed to identify clear-cut ‘‘best’’ standard therapies, even in the setting of first-line treatment (Table 3). In each class of adult hematological neoplasms, there are a variety of choices, similar in some respects but with key differences. One conservative application of the assays would be to identify the most active of the otherwise equally acceptable regimens. Another would be to eliminate the most inactive of the regimens and choose from among the rest on the basis of other clinical factors, including cost. In the setting of relapsed, refractory disease, ITRT

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Table 3 Equally Acceptable ‘‘Standard’’ Chemotherapy Options, According to the United States National Cancer Institute ALL (adult)

AML

CLL

NHL (indolent)

D/V/P

C

R/F

R

R/F/Ctx F/Ctx Ctx/V/P Ctx/Dox/V/P

R/F R/Ctx/V/P R/Ctx/Dox/V/P R/F/M

F/Chl

Chl (P or Dex)

D/V/P/A C/D D/V/P/A/Ctx C/D/T M/E C/I

R/Ctx/F/M Cda Ctx (P or Dex) F Ctx/V/P C/V/P/Procarb Ctx/Dox/V/P F/M/Dex

NHL (aggressive) R/Ctx/Dox/ V/P Ctx/Dox/V/P D/Ctx/V/B/P Ctx/M/V/P Mtx/B/Dox/ Ctx/V/Dex P/Dox/Ctx/E/ C/B/V/Mtx

Myeloma Dex or P Ctx/P Melph/P Bortezomib V/Carm/ Melph/Ctx/P V/Melph/ Ctx/P/C/ Carm/Dox/P Thalidomide

Abbreviations: A, Asparaginase; B, Bleomycin; C, Cytarabine; Carm, Carmustine; Cda, Cladribine; Ctx, Cyclophosphamide; Chl, Chlorambucil; D, Daunorubicin; Dex, Dexamethasone; Dox, Doxorubicin; E, Etoposide; F, Fludarabine; I, Idarubicin; M, Mitoxantrone; Melph, Melphalan; Mtx, methotrexate; P, Prednisone; Procarb, Procarbazine; R, Rituximab; T, Tioguanine; V, Vincristine.

provides a mechanism for choosing from an even larger number of potential choices, many of which will not be tested in prospective, randomized trials for years to come. Beyond the potential advantage to the patient is the progress, which would be fostered with regard to improving methodology, to make it available for future applications to handle the explosive growth of new, expensive, potentially toxic, and only partially effective drugs. Castro further argued (87): ‘‘Until the trialist approach has delivered curative results with a high success rate, the clinical autonomy to integrate promising insights and methods, including [ITRT], remains an essential component of patient advocacy.’’ The members of the ASCO working group who formulated the ASCO recommendations concerning ITRT agreed with Castro, stating: ‘‘It is certainly each practitioner’s prerogative to order [ITRT] . . . However, it is important to specify to the patient what the treatment would be in the absence of the assay and to be clear about if and how the information will be used to inform treatment decision making’’(88).

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The authors of this chapter agree completely with the above quoted viewpoints, as expressed by Castro and as modified by the ASCO working group. CLINICAL PERSPECTIVES FOR THE NEXT FIVE YEARS Progress in the treatment of cancer has been surprisingly modest in light of the rapid progress in tumor biology, although several new ‘‘targeted’’ drugs have been introduced over the last few years. Most of them have so far been developed for use in solid tumors, but new drugs have also emerged for hematological malignancies, for instance imatinib for chronic myeloid leukemia (CML), rituximab for lymphomas and bortezomib for myeloma. With the exception of the new treatment situation in CML, there is little reason to believe that established chemotherapy will not continue to form the basis of medical treatment of hematological as well as solid tumors in the next five years. The new targeted drugs mostly need to be combined with active chemotherapy to provide any benefit and, thus, the need for predictive tests for individualized therapy selection has increased. Disappointingly, the introduction of the new drugs has not been accompanied by specific predictive tests allowing for a rational and economical use of the drugs. On the other hand, preliminary data indicate that ITRTs also adequately reflect the clinical activity of, for instance, various tyrosine kinase inhibitors. Given also the technical and conceptual advantages of ITRTs together with their performance and the quite modest efficacy of therapy prediction based on analysis of genome expression as published so far, there is reason for a renewal in the interest for ITRTs for future optimized use of medical treatment of malignant disease. Thus, the current and potential role of ITRT in the management of patients with hematological neoplasms remains controversial, although, for some years, ITRT has been approved for reimbursement by Medicare in the United States. Specifically excluded from consideration by the two editorial reviews published in the Journal of Clinical Oncology (86,89) were studies which related ‘‘only’’ to the performance characteristics of ITRT—predictive accuracy, sensitivity, and specificity. The only studies considered were the very few which attempted to show if treatment outcomes could be improved through the use of ITRT. These criteria were surprising, as there are virtually no published studies with any other laboratory test in which patients were randomized to treatment with and without test information. The traditional (and heretofore only) criteria on which other laboratory, clinical, and radiographic tests have been judged are the performance characteristics (predictive accuracy, sensitivity, and specificity) and perceived utility in the judgment of the clinician who orders the tests. Only when these assays are widely performed and used and routinely included as an integral part of clinical trials will these already promising technologies be improved and only then will their role in patient management become better defined. But this is true for all complex laboratory technologies. A good example is immunohistochemical staining for batteries of cell antigens, the

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use of which has never been shown by means of randomized controlled clinical trials to improve treatment outcomes. By raising the bar of acceptance to levels unprecedented for a laboratory test, in essence a tariff has been erected to protect the paradigm of the ‘‘best’’ empiric treatment for the average patient, as identified in the all-too-commonlynonproductive clinical trials. This tariff also discourages discovery of new, effective drug regimens through the use of ITRT to guide drug selection. With greater use of these assays in hemato-oncology and the everincreasing list of licensed drugs, it is very likely that the activity of new drugs and new regimens would be identified at a much earlier time than with the current system relying exclusively on usually empiric phase II trials (90,91). If test results are used to assist in the selection of a regimen chosen from a series of otherwise reasonable alternatives, then patients will never be harmed by using the test result and best available evidence strongly indicates they will often be helped. In conclusion, there is a 45-year history of highly positive studies in hematological neoplasms showing consistent, strong correlations between the results of cell death assays and clinical outcomes (both initial response and longterm patient survival). A similarly convincing body of evidence in solid tumors (2) suggests these technologies are relevant for most, if not all, tumor types. Thus, there is strong scientific rationale and good documentation for these tests in a collectively large and diverse literature about hematological neoplasms for the clinical relevance of the information provided by the tests. Their use, particularly where equally effective treatment options are possible, could improve the rationale of treatment choice as well as probability of response and survival. REFERENCES 1. Bosanquet AG, Kaspers GJ, Larsson R, et al. Individualized tumor response (ITR) profiling for drug selection in tailored therapy: meta-analysis of 1929 cases of leukemia and lymphoma. Blood 2007; 108:1017A (abstr). 2. Weisenthal LM, Nygren P. Current status of cell culture drug resistance testing (CCDRT). Human Tumor Assay Journal 2002. Available at: http://www.weisenthal. org/oncol_t.htm. Accessed February 2008. 3. Weisenthal LM, Shoemaker RH, Marsden JA, et al. In vitro chemosensitivity assay based on the concept of total tumor cell kill. Recent Results Cancer Res 1984; 94:161–173. 4. Weisenthal LM, Lippman ME. Clonogenic and nonclonogenic in vitro chemosensitivity assays. Cancer Treat Rep 1985; 69:615–632. 5. Weisenthal LM. Cell culture assays for hematologic neoplasms based on the concept of total tumor cell kill. In: Kaspers GJL, Pieters R, Twentyman PR, et al., eds. Drug Resistance in Leukemia and Lymphoma: The Clinical Value of Laboratory Studies. Chur, Switzerland: Harwood Academic Publishers, 1993:415–432. 6. Weisenthal LM, Kern, DH. Prediction of drug resistance in cancer chemotherapy: the Kern and DiSC assays. Oncology (U.S.A.) 1992; 5:93–103.

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3 Minimal Residual Disease Jacques J. M. van Dongen Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands

Tomasz Szczepan´ski Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands, and Department of Pediatric Hematology and Oncology, Medical University of Silesia, Zabrze, Poland

Vincent H. J. van der Velden Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands

MINIMAL RESIDUAL DISEASE Current cytotoxic treatment protocols induce complete remission (CR) in most acute leukemia patients [both acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML)], in some patients with chronic lymphocytic leukemia (CLL), and in most non-Hodgkin’s lymphoma (NHL) and chronic myeloid leukemia (CML) patients. Introduction of allogeneic and autologous hematopoietic stem cell transplantation (HSCT) in treatment protocols has further increased the remission rates in ALL, AML, CML, and NHL. Nevertheless, many of these patients ultimately relapse. Apparently, the treatment protocols are not capable of killing all clonogenic malignant cells in these patients, even though they reached CR according to cytomorphological criteria. The detection limit of cytomorphological techniques is not lower than 1% to 5% of malignant cells, implying that these techniques can provide only superficial information

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about the effectiveness of the treatment. More sensitive techniques are required for the detection of low frequencies of malignant cells during and after treatment, i.e., detection of minimal residual disease (MRD). MRD techniques should reach sensitivities of at least 103 (one malignant cell within thousand normal cells), but sensitivities of 104 to 106 are preferred. Such sensitivities allow ‘‘true’’ MRD detection and thereby evaluation of the effectiveness of the total treatment and assessment of the contribution of each treatment phase. TECHNIQUES AND TARGETS FOR MRD MONITORING For the detection of MRD, at present at least three methods are sufficiently sensitive (103), quantitative, and broadly applicable: flow cytometric immunophenotyping, polymerase chain reaction (PCR)-based detection of junctional regions of rearranged immunoglobulin (Ig) and T-cell receptor (TCR) genes (mostly in lymphoid malignancies), and PCR-based detection of breakpoint fusion regions of chromosome aberrations (Table 1). Table 1 Applicability of MRD Techniques in Leukemias and Lymphomasa Flow cytometric immunophenotyping

Precursor-B-ALL children adults T-ALL children adults Chronic B-cell leukemias Chronic T-cell leukemias B-NHL T-NHL AML (non-M3) APL CML

PCR analysis

Aberrant immunophenotypes (103–104)

Junctional regions of Ig/TCR genes (103–106)

Chromosome aberrations (104–106)

70–90% 70–80%

*95% *95%

40–45% 40–45%

*95% *95% >95%

>95% >90% >95%

25–30% 10–15% –

–

*95%

–

– 20–25%c 60–90% NR –

70–80%b *95% 10–15% NR –

25–30% 10–15% 15–30% >95% >95%

a The percentages indicate the applicability of the MRD techniques per category of hematopoietic malignancies; J.J.M. van Dongen, unpublished results. b Somatic mutations hamper primer annealing in a part of the patients with B-NHL or B-CLL. c Based on T-ALL-like immunophenotype in lymphoblastic T-NHL and NPM-ALK expression in *50% of large cell anaplastic lymphomas of T-cell lineage. NR No reports on detailed studies.

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MRD Monitoring by Flow Cytometric Immunophenotyping Principles Hematological malignancies can be regarded as malignant counterparts of cells in immature (acute leukemia) or mature (CLL, CML, NHL) stages of hematopoiesis. Although the immunophenotype of the malignant cells is often comparable to their normal counterparts, tumor-associated immunophenotypes can be observed. Such tumor-associated immunophenotypes can be identified in the vast majority of acute leukemias, while they are less common in mature hematological malignancies. Asynchronous antigen expression refers to the coexpression of two or more antigens that are not present at the same time during normal differentiation. Cross-lineage antigen expression represents the expression of typical myeloid antigens on lymphoid cells or vice versa and the presence of B-lineage antigens on T-lineage cells or vice versa. Ectopic antigen expression refers to the presence of particular antigens on cells outside their normal breeding sites or homing areas or to the expression of antigens that are normally only expressed on nonhematopoietic cells. MRD Monitoring in Acute Leukemias Investigation of normal bone marrow (BM) B-cell precursors enabled establishment of templates for normal B-cell development. Malignant precursor-B lymphoblasts frequently display aberrant immunophenotypic features and thereby fall into ‘‘empty spaces’’ outside the normal B-lineage pathways (Fig. 1) (1,2). Flow cytometric investigations based on three- or four-color stainings showed the presence of leukemia-associated phenotypes in 70% to 95% of precursor-B-ALL patients (3,4). It should be noted that the detection of small numbers of precursor-B-ALL cells in regenerating BM after chemotherapy or after HSCT can be hampered by high frequencies of normal, regenerating precursor-B-cells (up to 50%) (5,6). The extent of B-cell regeneration in BM differs per treatment protocol, per phase of treatment, and seems to be dependent on the intensity of the preceding treatment (6). Since nearly all T-ALL express terminal deoxynucleotidyl transferase (TdT) as well as the pan-T-cell antigens CD2, cytoplasmic CD3 (CyCD3), CD5, and CD7, the ectopic TdT expression allows MRD detection in 90% of T-ALL. Flow cytometric analysis based on cross-lineage myeloid antigen expression, asynchronous antigen expression (e.g., CD34-positive/CD3-positive), and antigen overexpression (e.g., CD99 or CD7) can also be used for MRD detection in TALL (7,8). Similar to precursor-B-ALL, multiparameter flow cytometry in T-ALL reveals empty spaces outside normal T-cell development pathways. Together the various leukemia-associated immunophenotypes can be employed for MRD detection in virtually all T-ALL and lymphoblastic T-NHL. In AML, tumor-associated immunophenotypes can be observed in about 70% to 85% of patients (9–13). However, it should be noted that the immunophenotype of the AML cells may be heterogeneous and that several subpopulations

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Figure 1 Flow cytometric detection of minimal residual disease in a precursor-B-ALL patient, 5756, by use of the CD45/CD10/CD19 (A,B) and TdT/CD10/CD19 (C,D) triple labelings. The leukemia-specific immunophenotypic detection was based on CD10 overexpression and CD45 negativity. In the follow-up BM sample, taken 4.5 years from the diagnosis of ALL, the population of cells with leukemia-specific immunophenotype comprised 0.2% of BM cells, i.e., 1.5% of CD19-positive cells. At that time, the patient was in complete clinical remission of the leukemia. However, he underwent the overt hematological relapse of precursor-B-ALL, nine months after this positive MRD test.

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can be present (14). For reliable MRD analysis, all leukemic subpopulations should be monitored. The detection limit of current flow cytometric MRD methods varies between 0.1% and 0.01% for most precursor-B-ALL and AML, while in virtually all T-ALL a detection limit of 0.01% can be reached (7,15–17). It can be expected that the recent introduction of flow cytometers with six to eight colors will further improve the applicability and sensitivity of MRD detection in ALL and AML. Furthermore, on the basis of a comparison of gene expression profiles of normal and leukemic cells, new and widely applicable markers for MRD studies in acute leukemias are being identified. The applicability of this approach has already been proven for CD58, which is now one of the most useful markers to study MRD in precursor-B-ALL (18). MRD Monitoring in Mature B- and T-Cell Malignancies In B-CLL, quantitative differences in the levels of antigen expression as compared with normal B-cells can be observed in the vast majority of patients. This approach, analogous to detection of empty spaces outside the normal B-cell development in ALL, can potentially reach sensitivities of 104 to 105 (19–21). Especially the combination of CD19/CD5/CD20/CD79b or CD43 antibodies seems to be informative for MRD detection in PB of B-CLL patients. For MRD monitoring in BM, at least one of the combinations CD19/CD5/CD20/CD79b, CD19/CD5/CD38/CD79b, CD19/CD5/CD38/CD20 was found sufficiently sensitive and specific (19). A combination of CD81/CD22/CD19/CD5 is particularly useful for MRD detection in patients treated with CD20 antibody (rituximab) (22). In B-NHL, the sensitivity of immunophenotypic MRD analysis is often hampered by the lack of a tumor-associated immunophenotype and the presence of normal B-cells with a comparable immunophenotype. Nevertheless, by the use of markers that are normally only expressed on a subpopulation of B-cells, sensitivities of 103 can be achieved. Examples include the CD103 antigen on hairy cell leukemias and the CD5 antigen on mantle cell lymphoma (MCL) cells (23). Furthermore, protein products from particular chromosome aberrations may also be used as additional markers in flow cytometric analysis. BCL2/B-cell antigen/Ig light chain stainings may be employed for MRD detection in patients with follicular lymphomas (FL), since BCL2 overexpression is observed in this type of B-NHL with t(14;18) (24). Similarly, the overexpression of CyclinD1 in MCL with t(11;14) or of MYC in Burkitt’s lymphomas with t(2;8), t(8;14), or t(8;22) may be employed for MRD detection of these types of B-NHL (25). The vast majority of chronic T-cell leukemias and T-NHL belong to the TCRab lineage, whereas only a minor fraction expresses TCRgd. Antibodies against the protein products of V gene segments of TCR beta (TCRB) V gene families, recognizing 60% to 70% of normal and malignant T-cells with TCRb chain expression, can be used for detecting malignant cells (26). Also, Vg and Vd antibodies might be useful for detecting malignant (clonal) TCRgdþ T-cells, although the presence of normal TCRgdþ T-lymphocytes will interfere with these

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applications (27,28). This especially concerns Vg9/Vd2þ phenotypes, because most normal TCRgdþ T-lymphocytes have this TCR phenotype (29). The application of Vb, Vg, and Vd antibodies in well-chosen multicolor stainings can result in sensitivity levels of approximately 102. Such sensitivities do not allow true MRD detection but may enable monitoring of T-cell leukemia patients during treatment or predicting the possible outgrowth of a dominant subclone in case of oligoclonal T-cell proliferations. Detection of lower MRD levels (*103) in chronic T-cell leukemia or T-NHL is only possible upon analysis of tumor-associated immunophenotypes (e.g., lack of CD2 expression) or translocation-specific fusion proteins such as the NPM-ALK fusion protein in anaplastic large cell lymphoma (ALCL) with t(2;5) (30). Immunophenotypic Shifts A potential pitfall of immunophenotypic MRD detection in hematological malignancies is the occurrence of immunophenotypic shifts during the course of the disease. Differences in immunological marker expression are particularly frequent in acute leukemias and may concern up to 90% of patients (31–33). However, at least one leukemia-specific marker combination is retained by leukemic cells at relapse in at least 80% of patients (31,34). In order to limit the risk of false-negative results, at least two marker combinations per patient should be used for immunophenotypic MRD monitoring. Furthermore, since in AML shifts toward a more immature phenotype of the myeloblasts, consistent with clonal evolution of a leukemic stem cell, are frequently observed (33), antibody panels used for MRD monitoring in AML should preferably not be restricted to the immunophenotype detected at presentation but should also include markers of lineage immaturity. Immunophenotypic shifts may also occur during the early phase of treatment. Such immunophenotypic shifts have been reported in ALL and may be a direct result of the effect of the drugs on the expression level of various antigens or may be related to drug-induced cell kill (35,36).

MRD Monitoring by PCR Analysis of Junctional Regions Principles During early B- and T-cell differentiation the germline V, (D), and J gene segments of the Ig and TCR gene complexes rearrange, and each lymphocyte thereby obtains a specific combination of V-(D-)J segments that codes for the variable domains of Ig or TCR molecules. The random insertion and deletion of nucleotides at the junction sites of V, (D), and J gene segments make the junctional regions of Ig and TCR genes ‘‘fingerprint-like’’ sequences, which

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are different in each lymphocyte and thus also in each lymphoid malignancy. Therefore, junctional regions can be used as tumor-specific targets for MRDPCR analysis. Such targets can be identified (e.g., by PCR heteroduplex analysis or GeneScan analysis) at initial diagnosis in more than 95% of lymphoid malignancies and in approximately 10% of AML (37,38). Subsequently, the precise nucleotide sequence of the junctional regions can be determined. This sequence information allows the design of junctional region-specific oligonucleotides [either allele specific oligonucleotide (ASO) probes or ASO primers], which can be used to detect malignant cells among normal lymphoid cells during follow-up of patients. At present, real-time quantitative PCR (RQPCR) analysis is the most frequently used approach for MRD monitoring in hematological malignancies (Fig. 2) (39). MRD Monitoring in Acute Leukemias By applying appropriate primer sets, Ig/TCR gene rearrangements can be detected in virtually all precursor-B-ALL and T-ALL patients. The number and type of Ig/TCR rearrangements is however dependent on the age of the patient and the presence of fusion gene transcripts, like TEL-AML1 and MLL-AF4 (40–44). In order to limit the risk of false-negative MRD results due to clonal evolution phenomena (e.g., ongoing rearrangements, loss of subclones), preferably two MRD-PCR targets with sufficient sensitivity (104) should be used for each ALL patient (45–47). MRD Monitoring by PCR Analysis of Junctional Regions in Mature B- and T-Cell Malignancies For MRD studies in B-CLL and B-NHL patients, Ig heavy (IGH) chain gene rearrangements are frequently used, because they are present in virtually all lymphoma patients (37). Also Ig kappa (IGK) light chain and Ig lambda (IGL) light chain gene rearrangements can be applied as MRD-PCR target in lymphoma patients (48,49). A limitation of Ig gene rearrangements as MRD-PCR target is the occurrence of somatic hypermutations in part of B-CLL patients and in many B-NHL patients, especially FL and postfollicular B-NHL. This does not seriously hamper initial diagnostics because currently available multiplex PCR assays allow identification of clonal IGH, IGK, and/or IGL gene rearrangements in more than 95% of mature B-cell malignancies (37,50). However, when the somatic mutation process is active like in FL, this might result in the formation of subclones, which are no longer recognized by the applied primer-probe set. Since IGK-Kde and DH-JH rearrangements are not prone to somatic hypermutations, theoretically they are preferred targets for MRD analysis. Clonal evolution phenomena are rare in mature B-cell malignancies, and consequently this does not hamper MRD monitoring in lymphomas.

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TCR gamma (TCRG) gene rearrangements are found in virtually all mature T-lineage malignancies, whereas TCRB gene rearrangements can be detected in all malignancies belonging to the TCRab lineage (51). Thus, MRD studies in mature T-cell malignancies can generally use junctional regions of rearranged TCRG and TCRB genes as PCR targets, whereas TCR delta (TCRD) gene rearrangements are less often available (52). In mature T-cell malignancies, TCR genes are not affected by somatic mutations and are not susceptible to ongoing or secondary rearrangements. Consequently, one MRD-PCR target should be sufficient for reliable monitoring of mature T-cell malignancies during and after treatment. RQ-PCR Analysis of Ig/TCR Gene Rearrangements Several primer-probe sets for RQ-PCR-based detection of tumor-specific IGH gene rearrangements have been described, particularly for application in ALL patients (reviewed by van der Velden et al.) (39). In principle, these sets can also be applied for MRD studies in mature B-cell malignancies, although the presence of somatic hypermutations might particularly hamper efficient annealing of germline VH primers and probes. Application of 30 -minor groove binding (MGB) probes allows design in small areas of the VH gene segments that are less susceptible to somatic hypermutations (53). Also, for IGK, IGL, TCRG, TCRD, and TCRB, several germline primer-probe sets have been designed (39). In order to determine the sensitivity of the RQ-PCR assay, serial dilutions of the diagnostic sample are generally used (Fig. 2). For defining the sensitivity, several criteria (including reproducibility of the measurement, the difference

< Figure 2 RQ-PCR assay for detection of MRD using IGK-Kde gene rearrangement as a patient-specific target. (A) Schematic representation of an IGK-Kde rearrangement. The position and sequences of the primers used for target identification at diagnosis are indicated. (B) Sequences are given of the germline TaqMan1 probe and the germline Kde reverse primer used for RQ-PCR analysis during follow-up of patients. All sequences are given from 50 to 30 . For each patient, a patient-specific forward primer is designed. (C) RQ-PCR analysis of the Vk-Kde rearrangement in a precursor-B-ALL patient. Tenfold dilutions of the diagnostic sample in normal MNC DNA were analyzed at an annealing temperature of 608C; a quantitative range of 104 was reached. Normal MNC DNA did not show amplification in any of the four wells tested. (D) Applicability of RQ-PCR analysis of IGK-Kde rearrangements for MRD detection in follow-up samples of two patients with precursor-B-ALL an MRD high-risk patient, 5257, with high MRD levels (103) at the early time points and a low-risk patient, 5397, with undetectable MRD already at the end of induction treatment. RQ-PCR analysis (black diamonds) was compared with classical dot blot analysis (open squares). Abbreviations: RQ-PCR, real-time quantitative polymerase chain reaction; MRD, minimal residual disease; ALL, acute lymphoblastic leukemia; MNC, mononuclear cells.

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between specific and nonspecific amplification, slope, and correlation coefficient of the standard curve) should be taken into account (39). In order to compare data between different studies and/or different laboratories, it is essential to have uniform guidelines for RQ-PCR data interpretation (54). For Ig/TCR-based MRD data in ALL, such guidelines have recently been developed within the European Study Group on MRD detection in ALL (ESG-MRD-ALL; a consortium of 30 international laboratories, coordinated by JJM van Dongen and VHJ van der Velden) (55). These guidelines should be evaluated for use in monitoring of other hematological malignancies as well. The sensitivity of MRD-PCR analysis of junctional regions is dependent on the type of rearrangement, on the size of the junctional region, and on the ‘‘background’’ of normal lymphoid cells with comparable Ig/TCR gene rearrangements (56). One should be aware that the background of normal lymphoid cells is not constant, but can differ per treatment phase. For example, high frequencies of normal T-cells can be detected in postinduction follow-up samples, and substantial expansions of normal precursor-B-cells can be detected in regenerating BM after cessation of therapy (5,6,57). To check the quantity and amplifiability of the DNA samples, a control gene RQ-PCR should always be used. MRD Monitoring by PCR Analysis of Chromosome Aberrations Principles In several hematological malignancies, chromosome aberrations can be detected and may be used as MRD-PCR target. This includes breakpoint regions of fusion genes, fusion gene transcripts, and aberrantly expressed genes (39). An advantage of using chromosome aberrations as tumor-specific PCR targets for MRD detection is their stability during the disease course. However, many hematological malignancies do not have specific chromosome aberrations, which can be detected by PCR. Nevertheless, new techniques for rapid and efficient screening of relatively large breakpoint regions, such as long-distance PCR and long-distance inverse PCR, should render such genomic breakpoint fusion sites into more feasible MRD-PCR targets (58). MRD Monitoring in Acute Leukemias In about 40% of precursor-B-ALL patients and in about 15% of T-ALL patients, fusion gene transcripts can be detected. These particularly concern MLL-AF4, BCR-ABL, TEL-AML1, E2A-PBX1, CALM-AF10, and SIL-TAL1. In AML, CBFB-MYH11 and AML1-ETO can be found in 10% to 25% of patients, the frequency decreasing with age (59). PML-RARA transcripts can be detected in virtually all acute promyelocytic leukemia (APL) patients. Primer-probe sets for the detection of these fusion gene transcripts have been developed within the Europe Against Cancer program (60,61).

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MRD Monitoring in CML The BCR-ABL p210 fusion gene transcript can be detected in over 95% of patients with CML and thereby is an excellent MRD-PCR target in this group of patients. Moreover, both BM and PB can be applied for clinically relevant MRD monitoring in CML (62). Several efforts to standardize the methodology of BCRABL detection have been undertaken or are currently in progress (60,63,64). MRD Monitoring by PCR Analysis of Chromosome Aberrations in Mature B- and T-Cell Malignancies In approximately 30% of B-NHL patients, chromosome aberrations can be employed as tumor-specific MRD-PCR targets in which the PCR primers are chosen at opposite sides of the breakpoint fusion region (65). One of the most widely studied chromosomal translocations is t(14;18), involving the BCL2 and IGH genes, which occurs in 80% of FL patients, 20% of DLBCL patients, and which is detectable by standard PCR procedures in 60% to 70% of cases with t(14;18). The t(11;14) is characteristic for most MCL and involves the BCL1 and IGH genes. In 30% to 40% of MCL patients, the breakpoints are clustered within a restricted area [the major translocation cluster (MTC) region], allowing easy identification at the DNA level by standard PCR analysis. In the vast majority of Burkitt’s lymphoma patients, chromosomal aberrations involving one of the Ig genes in combination with the MYC gene, e.g., t(8;14), t(2;8), and t(8;22), can be found. In all the above-mentioned B-NHL types the breakpoints generally occur outside coding regions, implying that these translocations are not amenable to reverse transcriptase PCR (RT-PCR) analysis for MRD detection, but should be studied at the DNA level. In some lymphomas, aberrantly expressed genes can theoretically be used for MRD detection, although transcripts in normal cells may limit the sensitivity. Examples include the expression of CCND1 transcripts in MCL with t(11;14), and overexpression of MYC in Burkitt’s lymphomas with t(2;8), t(8;14), or t(8;22). Although RQ-PCR assays for such transcripts have been reported, they have not yet been used for MRD detection. In T-NHL only a few well-defined translocations are known so far. This concerns the NPM-ALK fusion gene that is observed in ALCL with t(2;5), and that can be used for RT-PCR analysis and potentially in some cases for PCR analysis at the DNA level as well (66). RQ-PCR Analysis of Chromosome Aberrations Depending on the type of chromosome aberration, detection limits of 103 to 106 can generally be reached. Because of the high sensitivity of PCR techniques, cross-contamination of RT-PCR products between patient samples is a major pitfall in RT-PCR-mediated MRD studies, resulting in up to 20% of falsepositive results (60). Such cross-contamination is difficult to recognize since

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leukemia-specific fusion gene transcript-derived PCR products and wild-type transcript-derived PCR products are not patient specific. This is in contrast to PCR products obtained from genomic breakpoint fusion regions, such as in t(14;18) and TAL1 deletions, which can be identified by use of patient-specific oligonucleotide probes. For quantification of fusion gene transcript, generally plasmid standard curves are used. For patient-specific fusion genes, the standard curve is usually prepared from serial dilutions of the diagnostic sample. To correct for the amount and quality of the RNA, the use of a control gene is essential (39,61). CLINICAL RELEVANCE OF MRD MONITORING IN LEUKEMIAS AND LYMPHOMAS Clinical Relevance of MRD Monitoring in ALL Clinical Value of MRD Detection During Frontline Treatment of Childhood ALL The most significant application of MRD monitoring in childhood ALL is the evaluation of the initial response to chemotherapy, since numerous studies have demonstrated that low levels or absence of MRD after completion of induction therapy predicts excellent outcome (67,68) (Fig. 3). The level of MRD-PCR positivity after induction therapy is independent of other clinically relevant risk factors (e.g., age, blast count at diagnosis, immunophenotype at diagnosis, presence of chromosome aberrations, response to prednizone, and classical clinical risk group assignment) and is the most powerful prognostic factor. Depending on the treatment protocol, the sensitivity of the MRD technique, and the timing of the follow-up BM samples, MRD negativity is associated with overall relapse rates of only 2% to 10% (15,17,69–72). Moreover,

> Figure 3 (A) Hypothetical graph showing the kinetics of leukemic cell decrease and regrowth in several ALL patients during and after treatment with the I-BFM-SG treatment protocol. MRD curves represent individual patients of the three MRD-based risk groups two patients with slow MRD clearance (high-risk group), two patients with moderate MRD clearance (intermediate-risk group), and one patient with rapid MRD clearance (low-risk group) (69). The detection limit of cytomorphologic techniques as well as the detection limit of flow cytometric immunophenotyping and PCR techniques are indicated: I, induction treatment; C, consolidation treatment; and II, reinduction treatment. (B) Relapsefree survival of the three MRD-based risk groups of children treated for ALL according to protocols of the International BFM Study Group. The three risk groups were defined by combined MRD information at the end of induction treatment and before consolidation treatment (69). (C) EFS plot of patients divided according to the MRD prior to hematopoietic stem cell transplantation. The five-year EFS and number of patients for each group are shown at the end of each curve (based on the report of the Pre-BMT MRD Study Group) (82). Abbreviations: EFS, event-free survival; MRD, minimal residual disease.

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sensitive MRD detection during the induction phase seems capable of identifying 20% to 50% of childhood ALL patients with a very rapid leukemia clearance and long-term relapse-free survival (73,74). On the other hand, several studies proved that high MRD levels at the end of induction treatment are associated with high relapse rates of 70% to 100% (15,17,69,71). MRD analysis at a single time point gives highly significant prognostic information, but a single time point is not sufficiently precise to define MRDbased low-risk and high-risk groups (15,17,69–71). Depending on the MRD study, the end-of-induction MRD status either identifies only patients at low risk of relapse (69,70) or more frequently identifies exclusively high-risk patients (3,71). In contrast, combined information on MRD at the end of induction treatment and before consolidation treatment is significantly superior to single time point measurement, which was first demonstrated by the International BFM Study Group (I-BFM-SG) (69). Such combined MRD information distinguishes patients at low risk with MRD negativity at both time points (5-year relapse rate of 2%); from patients at high risk with an intermediate (103) or high (102) degree of MRD at both time points (5-year relapse rate of 80%), and the remaining patients at intermediate risk (5-year relapse rate of 22%) (Fig. 3) (69). The group of MRD-based high-risk patients is larger than any previously identified high-risk group (*15%) and has an unprecedentedly high five-year relapse rate of 80%. In ongoing frontline protocols with MRD-based intervention, the MRD-based high-risk group is subjected to further intensification of treatment protocols, including HSCT during first remission or novel treatment modalities, e.g., imatinib in t(9;22)-positive cases. On the other hand, the MRD-based low-risk patients make up a group of a substantial size (*45%), comparable to the frequency of survivors of childhood ALL before treatment intensification was introduced. Therefore, low-intensity standard-risk protocols may be sufficiently effective to cure such patients. MRD-based risk-group distribution is even more striking in T-ALL: with fewer (*25%) low-risk patients with virtually no relapses, more (*25%) high-risk patients uniformly relapsing, and approximately 50% intermediate-risk patients with 25% relapses (72). Most ALL patients on conventional frontline chemotherapy protocols reach MRD negativity at some point during the treatment, while approximately 10% of patients remain continuously MRD positive until the end of treatment. These patients are usually at high or intermediate risk of relapse on the basis of MRD monitoring. Future MRD-based protocols should demonstrate whether continuous MRD monitoring in MRD-based high- and intermediate-risk patients could be also used for treatment intervention. Clinical Value of MRD Detection After Relapse of ALL After first relapse MRD monitoring has strong predictive value by assessing early treatment response after second induction treatment, although reported studies involved small groups of patients and need to be confirmed (75,76). In the BFM

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ALL-REZ treatment protocol, patients with MRD levels less than 103 at day 36 had a probability of relapse-free survival of 86%, whereas MRD levels more than or equal to 103 were uniformly predictive of dismal outcome (probability of relapse-free survival of 0%) (75). Similarly, the study from St. Jude’s Children’s Research Hospital showed that MRD investigation at day 36 of their protocol could largely distinguish patients with relatively good prognosis (MRD 104, 2-year incidence of relapse of 28%) and bad prognosis (MRD > 10-4, 2-year incidence of relapse of 70%). Many current ALL relapse treatment protocols include MRD measurements, which can be particularly useful for patients with late (off-therapy) relapses for optimal qualification for and timing of HSCT (76). Clinical Value of MRD Detection Before and/or After Stem Cell Transplantation in Childhood ALL Several studies have demonstrated that MRD monitoring is highly significant for ALL patients undergoing HSCT (77–81). Multicenter data combined by the PreBMT MRD Study Group showed that the level of MRD prior to allogeneic HSCT identifies a group of patients with a high risk of relapse after transplantation (Fig. 3) (82). The five-year event-free survival (EFS) of the group with negative-, low-, and high-level positive-MRD approximated 75%, 40%, and 20%, respectively (82). MRD-PCR positivity in ALL patients after HSCT is also suggestive of impending relapse (83). MRD was shown to occur in post-HSCT samples in 88% of patients who subsequently relapsed, while only 22% of patients in long-term CR showed MRD at any time after HSCT, mostly at low levels (83). Therefore, the treatment of patients with a high MRD burden before HSCT or persistent MRD positivity after HSCT should be modified (e.g., further cytoreduction before HSCT, intensified conditioning, and/or early post-HSCT immunotherapy to induce ‘‘controlled’’ graft-versus-leukemia effects) in order to improve their generally poor outcome (77–80,82). Clinical Value of MRD Detection in Adult ALL Adult ALL is more frequently characterized by high-risk features with greater drug resistance, poorer tolerance of and compliance with treatment as compared with childhood ALL (84) Also the frequencies of MRD positivity and the MRD levels in adult patients are significantly higher than in comparably treated children (85,86) Therefore, MRD information might be particularly important for standard-risk ALL patient without known factors predictive of resistant disease, which was clearly demonstrated by the study of the German Multicenter Study Group for Adult ALL (87). Similar to studies in childhood ALL, MRDbased measurement of early treatment response in adult ALL resulted in a very precise new risk group definition: low-risk group with MRD less than or equal to 104 at day 11 and day 24 (10%, 3-year relapse rate of 0%), high-risk group with MRD more than or equal to 104 or higher until week 16 (23%, 3-year relapse

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rate of 94%), and intermediate-risk group comprising the remaining patients (3-year relapse rate of 47%) (87). Moreover, among patients who reached MRD negativity, a conversion to MRD positivity (especially within the quantitative range) during the follow-up was associated with significantly increased risk of relapse (88) Another prospective study by Vidriales et al. has shown that MRD detection remains highly relevant for the entire adult ALL group. Patients with low/undetectable MRD early during induction treatment have excellent survival rates, while high MRD positivity at the end of induction treatment was associated with dismal outcomes (89). Introduction of imatinib into therapy of t(9;22)-positive adult ALL brought new promises on improving treatment outcome in this otherwise highly resistant disease (90). Therefore, MRD monitoring becomes of high clinical value to analyze the effectiveness of different combinations of chemotherapy, imatinib, and HSCT in t(9;22)-positive adult ALL (91,92). Clinical Relevance of MRD Monitoring in APL The most extensive MRD studies in AML concerned the RT-PCR monitoring of PML-RARA fusion transcripts in APL patients with t(15;17). The results from several retrospective as well as prospective RT-PCR studies in APL patients showed several distinct molecular characteristics of this AML subtype (93,94), leading to the first successful treatment intervention protocol based on MRD information (95). It was known for many years that treatment with alltrans-retinoic acid (ATRA) alone is insufficient to eliminate all leukemic cells (96) Rapid loss of MRD positivity during the first three months of ATRA and cytotoxic treatment was associated with good outcome, whereas continuous positivity was predictive of relapse (97). Nevertheless, MRD status at the end of induction treatment is clinically insufficient to predict the patient’s subsequent outcome as shown by several large prospective studies (98–100). With modern treatment protocols, combining ATRA with consolidation chemotherapy, PCR negativity is achieved at the end of treatment in virtually all patients. A small subset (*5%) of patients with refractory APL are RT-PCR positive even at the end of treatment consolidation (97–99), while the vast majority of relapsing patients (20–30% of total APL patients) is MRD negative at the end of consolidation treatment (98,101,102). To obtain clinically relevant information, continuous prospective MRD monitoring is required during the first 6 to 12 months after consolidation treatment for early identification of patients at increased risk of relapse (103). This is particularly important for patients with high-risk features at presentation such as hyperleukocytosis, while the need for continuous monitoring of patients with low initial white blood counts (i.e., 0.02%) in two consecutive specimens more than four months after HSCT (136). When donor lymphocyte infusions (DLIs) are administered at the phase of molecular relapse, the outcome after immunotherapy is more favorable (137,138). In some responders, such early treatment results in conversion into sustained PCR negativity (137). Recently, it was demonstrated that imatinib could be an alternative to DLI for the treatment of molecular relapse of CML after HSCT (139). Interestingly, 40% of patients remained in continuous molecular remission after imatinib discontinuation (139). Clinical Relevance of MRD Monitoring in CLL Clinical Relevance of MRD Monitoring in Chronic B-Cell Leukemias B-CLL has a highly variable clinical course and shows heterogeneity in prognosis. Many patients show a rather indolent disease without requiring treatment, whereas others present with more aggressive forms that often lead to early death. In the last few years, more insight has emerged into the biological prognostic factors that determine differences in the disease course. The currently most relevant parameters associated with unfavorable outcome include cytogenetic aberrations (17p deletions, 11q aberrations), unmutated IGH VH segments, and increased CD38 and ZAP70 expression (140). Together with the increased knowledge on prognostic factors, therapy results have further improved over the years. Moreover, the introduction of newer treatment modalities, such as autologous and allogeneic HSCT and especially therapy with antibodies such as rituximab or alemtuzumab (CD52) or chemo-immunotherapy (combination of chemotherapy and antibodies), has resulted in better responses in a significant proportion of B-CLL patients (141,142). In fact, this has shifted therapeutic goals from palliation to cure, at least in subsets of patients. With eradication of MRD becoming a realistic goal in B-CLL, MRD detection has now become a relevant issue (142). To measure MRD in B-CLL, both qualitative and quantitative approaches have been applied. Qualitative MRD studies show variable sensitivities and generally fail to show increased progression-free survival. Quantitative approaches show higher sensitivities and seem more predictive (19,21). B€ ottcher et al. have shown that four-color flow cytometry and RQ-PCR with ASO primers have largely comparable sensitivities (around 104) (21). Measurement of MRD is not relevant in conventionally treated B-CLL patients, because patients still show a relatively high tumor burden, even in CR. In

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contrast, several studies have demonstrated a role for MRD status evaluation in clinical trials employing newer treatment modalities (142,143). Combination chemotherapy (e.g., fludarabine and cyclophosphamide) has been shown to result in MRD negativity in previously untreated patients or relapsed/refractory patients (144). Furthermore, it has also been demonstrated that achieving an MRD-negative response after alemtuzumab was the best predictor of survival in relapsed or refractory B-CLL. Chemoimmunotherapy (e.g., fludarabine, with or without cyclophosphamide, and rituximab, or fludarabine and alemtuzumab) also leads to high overall response rates, including MRD-negative patients. Other studies showed that molecular remission (MRD negativity) could be achieved upon alemtuzumab consolidation therapy and after autologous and allogeneic HSCT (144,145). Despite the molecular remissions that can be achieved in the above studies, it remains to be shown that MRD negativity also correlates with improved and prolonged survival rates in all these therapeutic approaches. In addition, MRD detection has entered B-CLL treatment protocols only recently and is often limited to some B-CLL patient groups. Two important actions are therefore essential in the coming years. One is the further standardization of sensitive methods for MRD detection in multicenter laboratory networks. The other is the implementation of standardized MRD detection in multicenter clinical trials to prove its clinical application (142). Several initiatives in these directions have already been taken. One interesting trial in this respect could be the Nordic/ HOVON-68 trial in which fludarabine and cyclophosphamide will be compared with fludarabine, cyclophosphamide, and alemtuzumab in biologically defined [mutation status and fluorescence in situ hybridization (FISH) aberrations] highrisk B-CLL patients. Included in the response criteria of this trial is MRD detection via ASO-PCR and multiparameter flow cytometry in patients in CR. Results from this and similar trials should help to reveal the true value of MRD detection in clinical management in B-CLL patients as well as define the most sensitive and practical approach for routine MRD analysis. Clinical Relevance of MRD Monitoring in Chronic T-Cell Leukemias In chronic leukemias of the T-cell lineage, such as T-cell large granular lymphocyte (T-LGL) leukemia, MRD evaluation has never been a goal so far; firstly, because these diseases are mostly very indolent and secondly, as chronic T-cell leukemias are relatively rare, large clinical studies with accurate follow-up analysis have simply not been performed. Recently, a clinical trial for standardized treatment of T-LGL leukemias has been initiated in Germany in which methotrexate and fludarabine are being evaluated. In this study, MRD detection via TCRB/TCRG ASO RQ-PCR and four-color flow cytometry will be implemented and be used to assess the treatment response. Eventually, such clinical studies should reveal whether there is a true value for MRD detection as prognostic factor or for evaluating treatment response in a relatively indolent disease as chronic T-cell leukemia.

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Clinical Relevance of MRD Monitoring in NHL In NHL patients, it is generally not possible to monitor MRD at the original site of disease. However, in a proportion of lymphomas (particularly high and intermediate-grade lymphomas) malignant cells can clearly be detected in BM and PB at diagnosis (Table 2). Immunophenotypic and molecular studies can on one hand contribute to better recognition of minimal BM infiltration at diagnosis, undetectable with cytomorphological analyses (Fig. 4). On the other hand, the presence of MRD in BM and to a lesser extent in PB might be applied as a surrogate marker for treatment effectiveness (Table 2) (146). Detection of BM Involvement During Initial Staging of NHL Immunophenotypic and molecular detection of BM and/or PB involvement has not yet been routinely implemented into clinical staging of NHL. Nevertheless, the presence of aberrant clonal cells was demonstrated in BM of most children

Figure 4 An example of BM staging in a patient with NHL using flow cytometric immunophenotyping. CD19-positive B-cells were gated and the expression of CD38, SmIgk, and SmIgl were evaluated. In (A) a normal bone marrow sample is shown, with an approximately equal distribution of SmIgk and SmIgl in the mature B-cells (black). In (B) a bone marrow sample of a patient with B-NHL is shown; clearly an aberrant SmIglpositive B-cell population can be detected. The cells that are strongly positive for CD38 and SmIg-negative are precursor-B-cells (light gray); the cells that are very strongly positive for CD38 are plasma cells (dark gray).

Type of lymphoma B-cell lymphomas Small lymphocytic lymphoma/ B-CLL Lymphoplasmacytic lymphoma MALT lymphoma Nodal marginal zone lymphoma Follicular lymphoma Mantle cell lymphoma Diffuse large B-cell lymphoma Mediastinal large B-cell lymphoma Burkitt lymphoma T-cell lymphomas Precursor-T-cell lymphoblastic lymphoma ALCL Mature T-cell lymphomas (except ALCL)

Availability of MRD-PCR targets Occurrence of somatic hypermutation

Ig/TCR

Chromosome breakpoint fusions

Fusion gene transcripts

>50%

>98%

–

–

*70% *10% *40% *40% *60% *20% 95% >95% >98% 90–95% >65% >80%

– 30% – 70–80% 30–40% – – 70%

– 30% – – – – – –

30–60%

*40%

NA

>95%

15%

–

10–30% 30–60%

*10% *40%

NA NA

75–80% >98%

20% –

75% –

Relative frequency

Peripheral blood

Bone marrow

6.7%

>60%

*70%

1.2% 7.6% 1.8% 22.1% 6.0% 30.6% 2.4% 2.1%

>60% 10–30% 30–60% 30–60% 30–60% 10–30% 10% better) than those with TA or TI. Note however that these changes would also reduce the probability of selecting TA when it is better. These considerations illustrate the importance of interaction between the physician and the statistician, an important feature of Bayesian designs with their emphasis of continual updating of data such as CR rates. Another potential disadvantage of adaptive randomized designs such as those used for the IA versus TA versus TI trial is their failure to account for a possible imbalance in prognostic covariates between the arms. This problem can be ameliorated by the use of ‘‘dynamic allocation’’ when patients are randomized (13). Even if not, accounting for such covariates is possible, but would obviously require entry of more patients before an arm might close; the number is however still less than that used in conventional phase 3 trials. Table 2 illustrates the consequences to patients of employing the adaptively randomized design used for the IA versus TA versus TI trial. The design resulted in the administration of IA to 18 patients and of TA or TI to 16. In contrast, use of a 1:1:1 randomization scheme throughout would have resulted in seven fewer patients receiving the seemingly superior IA regimen than TA or TI. Closing accrual to an arm after the entry of a relatively small number of patients

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could lead to failure to detect a small, perhaps biologically unique, subset of patients who might respond to a treatment, even though the average patient is highly unlikely to respond. It is unclear, however, whether any trial should proceed with such a goal in mind. NEED TO STUDY MORE TREATMENTS: SELECTION DESIGNS Proposals for ‘‘quicker’’ phase 3 trials and adaptive randomization enable study of many more drugs. Thus, for example, 200 patients might be randomized among four or five, rather than two, treatments. This is possibly of value given the large number of ‘‘targeted therapies’’ to be tested—a number made all the more larger by the recognized need to combine these with each other and with chemotherapy. While in principle only a few promising treatments might be selected on the basis of preclinical rationale, experience suggests this may not be possible. In addition to the examples (2 CdA, ATO, IFN) of therapies for which the demand of a ‘‘convincing’’ preclinical rationale might have prevented investigation of the therapy, there are numerous examples of treatments that, despite seemingly sound preclinical rationales, have been clinically ineffective. This has led to the proposal of randomized selection designs (5) in which the above 200 patients would be randomized among four or five treatments, with the treatment selected as best subsequently investigated further. Selection designs are intended to choose the best treatment, among those not dropped due to lack of efficacy regardless of the degree of difference between the best and the second best treatment. This is very different from a design in which the goal is to decide whether the best treatment provides a specified degree of improvement over the others. Hypothesis-test-based designs have the latter goal, and thus require a much larger sample size. The OC of selection designs indicate that in the case of a trial randomizing a maximum of 200 patients among four treatments, the probability of selecting a treatment that is 20% superior to the other three is only about 60%. Thus, such selection trials are often criticized as ‘‘underpowered phase 2 studies.’’ Note however that the nominally high false negative rate of 40% must be compared with what would obtain in the absence of the design. Assuming that is very difficult to select the best treatment in the absence of clinical data such as would emanate from a selection design, the effective false negative rate is 75% if one of four new treatments is randomly selected for comparison with standard therapy in a phase 3 study. Indeed, given the uncertainty inherent in selecting the best arm, it might be said that the worse false negative result obtains if a drug is not studied at all; the selection design is intended to prevent this possibility. ARTIFICIALITY OF THE PHASE 2–PHASE 3 DICHOTOMY The phase 2 selection design randomizes patients thus flying in the face of the concept of the phase 2 trial as single arm. However, in reality all phase 2 trials are inherently comparative. In particular, patients are vitally interested in knowing

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which of the several new treatments they might receive is superior—a question that implies comparison. Stopping rules for single-arm phase 2 trials are based on historical data with standard therapy further emphasizing the comparative nature of phase 2 trials (14). However, using data from single-arm phase 2 trials as a basis for comparison introduces bias in the form of trial-treatment confounding. The need to avoid this problem leads to the use of randomization in phase 3. However, it appears irrational to accept randomization in a phase 3 trial, but not in a phase 2 trial intended to determine whether a new therapy should be investigated in phase 3. Certainly the failure to randomize in phase 2 makes it impossible to use any phase 2 data in phase 3 comparisons. Even with randomization, however, decisions to move from phase 2 to phase 3 typically are based on early outcomes in phase 2 rather than survival or disease-free survival (DFS) time. This requires the implicit assumption that the early outcome is to some extent associated with improved survival or DFS. To address these issues ‘‘seamless phase 2–3 designs’’ have been proposed (15). Such a design randomizes between treatments, for example a standard S and an experimental E, throughout and makes repeated interim decisions during the trial based on both early response and survival time data. These decisions include (i) stopping the trial and concluding that E is associated with longer survival than S, (ii) stopping the trial because of futility, i.e., concluding that E and S are associated with similar survival, (iii) continuing the trial, or (iv) expanding the phase 2 trial to incorporate other centers, at which point the ‘‘phase 3’’ trial begins. Accrual continues while the phase 3 trial is being organized, and the ‘‘seamless’’ nature of the phase 2–3 transition allows for the use of all phase 2 data in all phase 3 decisions. This is illustrated by a trial in which patients with stage 3 or 4 unresectable non–small cell lung cancer receive docetaxel and radiation and are randomized to receive or not receive an intratumor injection of an adenovirus containing wild type p53 gene (Ad-p53). The hypothesis is that the transfected p53 will be proapoptotic and also increase the tumor’s sensitivity to docetaxel and radiation therapy. The data on each patient consist of (i) whether a response, defined as local control (LC) of the tumor evaluated by fine-needle aspiration biopsy, is observed at five months and (ii) the patient’s survival time, T. The overall survival distribution is formulated as a mixture of three components: (i) the survival distribution when LC is achieved
the probability of achieving LC, (ii) the survival distribution when LC is not achieved
the probability that LC is not achieved, and (iii) the survival distribution for patients who die in less than five months, before LC is evaluated. The Ad-p53 effect on T may vary as a function of the LC rates, the possibility that LC affects survival, and the possibility that there is a direct Ad-p53 effect on T not mediated by LC. In particular, the model does not assume that LC is a surrogate for T. The design specifies a maximum sample size of 900 patients and maximum duration of 72 months. At each of up to 18 interim times during the trial, decisions are made on the basis of predictive probability that survival in the Ad-p53 arm is greater, assuming either that (i) accrual stops immediately but treated patients are followed for an additional 12 months or (ii) all 900 patients are accrued and followed until 72 months.

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Table 3 Operating Characteristics of the Bayesian Phase II–III Design and the Conventional Designs Under the Six-Mixture Model-Based Hypotheses Hypothesized Effects Ad-p53 effect on LC rate

LC effect on survival

Ad-p53 direct effect on survival

No

No

No

Yes

No

No

No

Yes

No

Yes

Yes

No

Yes

Yes

Yes

No

No

Yes

Design Bayesian conventional Bayesian conventional Bayesian conventional Bayesian conventional Bayesian conventional Bayesian conventional

Patients (mean number)

Mean duration (mo)

Probability conclude E>S

425 842 453 842 452 854 640 884 525 861 576 873

20.4 28.1 21.6 28.1 21.7 28.5 30.7 29.5 23.2 28.7 29.2 29.1

0.03 0.05 0.04 0.05 0.03 0.05 0.85 0.83 0.97 >0.99 0.56 0.79

This approach may be compared with a conventional frequentist design that uses a log rank test to reject or accept the null hypothesis at up to four successive times with an O’Brien–Fleming test boundary while maintaining overall significance level 0.05 and power 0.80 to detect a 25% increase in median survival time from the null median of 15.5 months. A comparison of the two designs, assuming the same maximum sample size (900) and maximum duration (72 months), is summarized in Table 3. Six different hypotheses are assumed, the first three being different types of ‘‘null’’ cases in which Ad-p53 does not improve survival, whereas the last three are alternatives in which survival is longer in the Ad-p53 arm. The Bayesian design parameters were calibrated to maintain type I error less than 0.05 and power more than 0.80. In all six cases, the Bayesian design has on average a substantially smaller sample size, and a trial duration that is either shorter than or the same as that of the conventional phase 3 design. Although our example involves a lung cancer trial, this sort of seamless phase 2–3 Bayesian design may easily be applied to trials of hematologic malignancies where CR takes the part of LC. MULTIPLE OUTCOMES At the beginning of this chapter, I noted that commonly used statistical designs pay insufficient regard to the complexities of medical practice. The failure to explicitly recognize the inherently comparative nature of phase 2 trials or the

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assumption of a relation between response and survival are examples, as is the failure to provide for monitoring multiple outcomes in phase 2 (16). Specifically, phase 2 trials focus on response and provide explicit stopping rules based on this outcome. In contrast monitoring of toxicity is done informally, i.e., without explicit stopping rules. The assumption is that toxicity rates are already known from the phase 1 study. However, it has been pointed out that the number of patients entered into typical phase 1 trials is often inadequate to estimate the toxicity rate at any given dose (17). Furthermore, the patients treated on phase 2 trials may be quite different with regard to the likelihood of toxicity than those entered on the corresponding phase 1 trial. The following example illustrates the desirability of monitoring multiple outcomes. We conducted a trial in which untreated AML patients under age 50 received ‘‘double induction’’ consisting of a course of idarubicin and ‘‘high-dose’’ ara-C, followed by a second course of this combination 14 days after beginning the first course, regardless of the status of the marrow at that time. While the objective of intensifying therapy was to improve the 90-day CR rate, this approach also carried the risk of increasing the 90-day mortality rate. Furthermore, the second course was to be given only to patients judged ‘‘eligible,’’ i.e., recovered from first course toxicity, by their attending physicians. Accordingly, it was possible that the number of patients eligible for the second course would be sufficiently small that the results would be of little practical significance. Our design formally monitored three outcomes: the course 2 eligibility, and among eligible patients, the CR rate and the death rate within 90 days after the start of course 1. The 90-day window was selected after considering the anticipated accrual rate of three to four patients per month, both because the risks of treatment failure or death within this timeframe are high and for logistical reasons. To account for prognostic heterogeneity, the early stopping rules were applied separately in two subgroups: patients with abnormalities of chromosomes 5 and/or 7 (–5/–7), and patients with all other karyotypes. For illustration, we will focus attention on the latter subgroup. It was decided that the requisite course 2 eligibility rate was greater than or equal to 67%. On the basis of historical rates, a 4% increase in a 90-day mortality rate (‘‘death’’), from 14% to 18%, was considered an acceptable trade-off for a 15% increase in the 90-day CR rate (‘‘success’’), from 67% to 82%. Death and success were not complementary events because an eligible patient could be alive but not in CR at day 90. The trial was to stop if, after evaluating each cohort of five patients, the probability was too high that (i) the eligibility rate was less than 67% or (ii) the death rate among eligible patients was increased by greater than 4%, or (iii) the probability of a 15% increase in the success rate among eligible patients was too low. Criteria probabilities of 95%, 90%, and 5% for these events quantified the terms ‘‘too high’’ and ‘‘too low.’’ These criteria led to three sets of stopping boundaries, one for each rate being monitored. If early termination did not occur, 50 patients would be entered, which would provide a 90% posterior credibility interval for the 90-day success rate having limits within 0.12 of its mean, assuming a mean success rate of 0.82 and mean eligibility rate of 0.68.

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Table 4 Operating Characteristics of the Double Induction Trial

Clinical scenario Same as historical rates 82% success rate and 18% death rate 67% success rate and 33% death rate 50% ineligibility rate

Probability of early termination

Achieved sample size percentiles 25th

50th

75th

0.69 0.21

15 50

25 50

50 50

0.96

10

15

20

0.89

10

15

30

This design’s OC are summarized in Table 4. For example, under the scenario where the true death rate is the unacceptably high value 33%, the probability of early termination (PET) is 96% and 50% of the simulated trials stopped after 15 patients were entered. For higher true death rates, the median sample size becomes smaller. In contrast, if the minimum study goals were met, the trial ran to completion in 79% of the simulated trials, with this percent increasing for higher eligibility rate, higher success rate, or lower death rate. In fact, the trial closed after 14 of the first 25 patients were ineligible although the observed numbers of deaths (2/7) and success (5/7) within 90 days among eligible patients were both acceptable. Such multiple outcome designs allow the investigator to explicitly specify a trade-off between ‘‘efficacy’’ and ‘‘toxicity,’’ which correspond to 90-day success and 90-day death in the double induction trial. Different investigators might have different trade-offs. For example, in the double induction trial, one might consider a 4% increase in death rate acceptable only given a 25% increase in success rate. In the scenario where the true death rate is 33% (row 3, Table 4), on average, 5 of the median sample size of 15 would die compared to 2 of 15 in the historical situation (14% death rate). If the investigator believes this is unacceptable, the criterion probability could be lowered from 90% to 85%. However, making it easier to stop the trial would also increase the PET if the true success rate is the desired 82%. Our experience suggests that the need to specify such trade-offs encourages dialogue between clinical investigators and statisticians. Moreover, multiple outcome designs encourage the use of a wider range of data in therapeutic decision-making. CONCLUSION Statistical methods for conducting clinical trials have remained essentially static for the past 30 years. This phenomenon is difficult to explain in light of the issues raised above and the profusion of papers in the statistical literature describing new designs; I have referenced just a few of these here. It is difficult to reconcile the eagerness with which physician or scientists have adopted new

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molecular biologic techniques with their reluctance to adopt new statistical techniques. Regardless of the reason for the contrast, I believe the consequences of this reluctance are unfortunate from both academic and clinical perspectives. CLINICAL PERSPECTIVES FOR THE NEXT FIVE YEARS Hopefully, the next five years will see the adoption of many new methods for conducting clinical trials. Phase 1 and phase 2 trials will be combined and will monitor both efficacy and toxicity, consistent with patients’ primary reason for entering phase 1 trials: to achieve a response, not ‘‘no toxicity.’’ Factors other than dosage will be considered in assessing toxicity. For example, for purposes of determining the dose for phase 2 trials, it is unrealistic to equate a 70-year-old who has severe toxicity at a given dose with a 50-year-old who has the same toxicity at the same dose. Yet this is what is done currently; ‘‘pharmacogenomics’’ will also be considered as a determinant of toxicity. The 3þ3 design will be replaced with Bayesian methods, such as the clinical reassessment method (CRM), which allow all information to be used, rather than only the information obtained at the most recent dose. Most importantly, given the large number of therapies to be investigated, there will be a shift away from large randomized trials to smaller randomized trials. The invariable need for the ‘‘magic’’ statistical significance level of 0.05 and equally magic power of 80% to 90% will be rethought. The question of whether the optimal biological dose should be investigated rather than the more traditional MTD will be explored, perhaps in separate arms of a randomized trial. Using a single protocol, therapies that do poorly will be replaced by other therapies, while if results are equivocal, more patients will be recruited into the study. In general, there will then be a blurring of the artificial distinctions between phases 1, 2, and 3. The above will only occur given a willingness to forego habit and tradition. However, the recent adoption by the MRC of the small, randomized trial, i.e., the selection design described earlier in this chapter, is encouraging and will hopefully provide a model for others. REFERENCES 1. van der Holt B, Lowenberg B, Burnett A, et al. The value of the MDR1 reversal agent PSC-833 in addition to daunorubicin and cytarabine in the treatment of elderly patients with previously untreated acute myeloid leukemia in relation to MDR1 status at diagnosis. Blood 2005; 106:2646–2654. 2. Baer M, George S, Dodge R, et al. Phase 3 study of the multidrug resistance modulator PSC-833 in previously untreated patients 60 years of age and older with acute myeloid leukemia: Cancer and Leukemia Group B Study 9720. Blood 2002; 100:1224–1232.

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3. Rowe J, Neuberg D, Friedenberg W, et al. A phase 3 study of three induction regimens and of priming with GM-CSF in older adults with acute myeloid leukemia: a trial by the Eastern Cooperative Oncology Group. Blood 2004; 103:479–485. 4. Estey E. Clinical trials in AML of the elderly: should we change our methodology? Leukemia 2004; 18:1772–1774. 5. Estey E, Thall P. New designs for phase 2 clinical trials. Blood 2003; 102:442–448. 6. Berry D. Statistics: A Bayesian Perspective. Belmont, CA: Wadsworth Publishing Company, 1996:124–161. 7. Spiegelhalter D, Abrams K, Myles J. Bayesian Approaches to Clinical Trials and Health-Care Evaluation. Chichester, UK: John Wiley & Sons, 2004. 8. Goodman, SN. Toward evidence-based medical statistics. 2: the Bayes factor. Ann Intern Med 1999; 130:1005–1013. 9. Berger J, Berry D, Statistical analysis and the illusion of objectivity. American Scientist 1988; 76:159–165. 10. Giles F, Kantarjian H, Cortes J, et al. Adaptive randomized study of idarubicin and cytarabine versus troxacitabine and cytarabine versus troxacitabine. J Clin Oncol 2003; 21:1722–1727. 11. Gooley T, Martin P, Fisher L, et al. Simulation as a design tool for phase I/II clinical trials: an example from bone marrow transplantation. Control Clin Trials 1994; 15: 450–462. 12. Thall PF, Estey E. A Bayesian strategy for screening cancer treatments prior to phase II clinical evaluation. Stat Med 1993; 12:1197–1211. 13. Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 1975; 31:102–115. 14. Simon R. Optimal two-stage designs for phase II clinical trials. Control Clin Trials 1989; 10:1–10. 15. Inoue LYT, Thall PF, Berry DA. Seamlessly expanding a randomized phase II trial to phase III. Biometrics 2002; 58:823–831. 16. Thall PF, Simon RM, Estey EH. New statistical strategy for monitoring safety and efficacy in single-arm clinical trials. J Clin Oncol 1996; 14:296–303. 17. Edler, L. Overview of phase I clinical trials. In: Crowley J, ed. Handbook of Statistics in Clinical Oncology. New York, NY: Marcel Dekker, 2002:1–34.

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5 Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia Ch. Michel Zwaan and Marry M. van den Heuvel-Eibrink Department of Pediatric Oncology/Hematology, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The Netherlands

GENERAL INTRODUCTION Monoconal Antibodies Thirty years have passed since Ko¨hler and Milstein first described the possibility of hybridizing murine cell lines, resulting in the expression and production of specific murine antibodies (1,2). In 1984, they were awarded with the Nobel Prize for their discovery. Currently, more than 20 monoclonal antibody preparations are available for clinical use for various diseases, including cancer and autoimmune diseases, of which several are approved by the regulatory authorities, as summarized in Table 1 (this list is neither meant to be exhaustive nor complete) (2). Monoclonal antibody therapy is a type of immunotherapy and is referred to as passive immunotherapy as the antibodies are not produced by the body’s own immune system. Other examples are the use of cytokines such as interferones, interleukines (ILs), or growth factors. This in contrast to cancer vaccines, which are dependent on the host immune system to conquer the disease, are, therefore, referred to as ‘‘active immunotherapy.’’ Another example of active immunotherapy in hematological malignancies is stem cell transplantation (SCT), aiming

99

Brand name

Target(s)

Conjugated

Disease

Rituximab Daclizumab

Rituxan, Mabthera Zenapax

CD20 CD25

no no

Trastuzumab Gemtuzumab ozogamicin Alemtuzumab Ibitumomab tiuxetan Tositumomab

Herceptin Mylotarg

ErbB2 CD33

no To calicheamicin

Non-Hodgkin’s lymphoma Renal allograft rejection and T-cell leukemia Breast cancer Acute myeloid leukemia

Campath, MabCampath Zevalin

CD52 CD20

no 90 Yttrium

Chronic lymphocytic leukemia Non-Hodgkin’s lymphoma

Bexxar

CD20

131

Non-Hodgkin’s lymphoma

Bevacizumab Cetuximab

Avastin Erbitux

VEGF EGFR

no no

Iodine

Colorectal cancer Colorectal cancer

Market authorization status, (yr of approval) FDA (1997), EMEA (1998) FDA (1997), EMEA (1999) FDA (1998), EMEA (2000) FDA (2000), not approved by EMEA FDA (2001), EMEA (2001) FDA (2002), EMEA (2004) FDA (2003), not approved by EMEA FDA (2004), EMEA (2005) FDA (2004), EMEA (2004)

Abbreviations: VEGF, vascular endothelial growth factor; EGFR, Epidermal growth factor receptor; FDA, Food and Drug Administration; EMEA, European Agency for the Evaluation of Medicinal Products.

Zwaan and van den Heuvel-Eibrink

Antibody generic name

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100

Table 1 Monoclonal Antibodies That Have Received Market Authorization from the Regulatory Authorities for Treatment of Cancer, i.e., Either the Food and Drug Administration in the United States and/or the European Agency for the Evaluation of Medicinal Products. Listed by Year of Approval

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at inducing a graft-versus-leukemia effect, in addition to elimination of the malignant stem cell by high-dose chemotherapy and/or irradiation. Monoclonal antibodies have high specificity and are directed against a single antigen. Ideally, they are directed against antigens that are present selectively on tumor cells, which may result in limited side effects and organ toxicity. Hence, broad tissue typing is required for the antigen of interest to study its expression on normal human tissues. In addition, the expression should preferably be strong and homogeneous throughout the tumor. Antibodies targeting antigens that are also present in soluble form in the circulation may be less effective because of rapid antibody clearance after infusion. The use of murine antibodies in the clinical setting has not been very successful, as they will be recognized by the human immune system as foreign proteins, and generate an human anti-mouse antibody (HAMA) immune reaction (3). This may result in inadequate exposure to the antibody due to diminished stability in the circulation, as well as a severe allergic reaction in the recipient, which interferes with repeated dosing. Murine antibodies also have less capacity to recruit effector cells and complement to destruct cancer cells. They are referred to as ‘‘momabs,’’ such as in ibritumomab, which is directed against the CD20 antigen and used in non-Hodgkin’s lymphoma. The ability to construct so-called ‘‘chimeric’’ or ‘‘humanized’’ antibodies by genetic engineering has markedly improved the possibilities to use monoclonal antibodies in the clinic (4). In chimeric antibodies, the variable region is still from mouse origin, whereas in humanized antibodies this is only the hypervariable region. Chimeric antibodies are approximately 60% to 95% human, whereas humanized antibodies are over 95% human (3). In both instances the murine part is responsible for specificity and antigen recognition. They are referred to as ‘‘ximabs’’ and ‘‘zumabs,’’ respectively, as in rituximab (anti-CD20) and gemtuzumab (anti-CD33) or epratuzumab (anti-CD22). Moreover, fully human antibodies are now available by using transgenic mice that have been engineered to synthesize human antibodies. These antibodies usually allow repeated dosing and do not result in severe allergic reactions. Two different types of monoclonal antibodies can be distinguished, i.e., naked versus conjugated monoclonal antibodies: 1. Naked antibodies bind directly to antigens expressed on tumor cells, and may either stimulate the immune system to destroy the cancer cell by antibody-dependent cell mediated cytotoxicity (ADCC), complement dependent cytotoxicity (CDC), or by induction of apoptosis. Examples are rituximab, an anti-CD20 antibody used in the treatment of non-Hodgkin’s lymphoma (5), and the anti-CD52 directed antibody alemtuzumab, which may be used as part of the conditioning regimen in SCTs or to eliminate T-cells from grafts (6). Rituximab was actually the first monoclonal antibody registered (in 1997) by the Food and Drug Administration (FDA) for use against cancer. Alternatively, monoclonal antibodies may exert their effect as competitive antagonists, prohibiting a ligand to bind to a certain receptor,

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and thereby blocking the activity of the ligand, subsequently shutting down the intracellular signaling cascades that would normally result from ligand binding. The antiangiogenesis monoclonal antibody bevacizumab, which interferes with vascular-endothelial growth factor receptor signaling, is an example of the latter (7). 2. Conjugated monoclonal antibodies are antibodies linked to drugs, toxins or radioactive compounds, and basically represent targeted delivery of the conjugate to the tumor cell rather than inducing cell death by the aforementioned mechanisms. An important issue to consider in developing such conjugates is whether the conjugate will be internalized after binding to the tumor cell. Examples are gemtuzumab ozogamicin (GO) (anti-CD33 linked to calicheamicin) for the treatment of acute myeloid leukemia (AML) (8) and radiolabeled antibodies directed against CD45 used in the conditioning regimen of advanced leukemia patients (9). These data show that the development of a monoclonal antibody represents rigorous scientific effort and financial resources. Both the target and the antibody need to be selected very carefully to ensure that the in vivo properties will allow therapeutic efficacy. Hematological malignancies are ideally suited to treatment with monoclonal antibodies because of the accessibility of malignant cells in the blood, bone marrow, spleen, skin, and lymph nodes and the availability of targets that are restricted to the hematopoietic system. Rapid and repeated evaluation of target antigen expression is feasible using flowcytometry. In this chapter, we will review the current status of targeted monoclonal antibody therapy in use or development for the treatment of children and adults with AML, with the exception of radiolabeled antibodies. Acute Myeloid Leukemia AML is a heterogeneous group of diseases and basically comprises all other than lymphocyte-precursor derived acute leukemias. Traditionally, classification is based on morphology according to the French-American-British (FAB) classification. Recently, a new classification for myeloid neoplasms has been introduced by the WHO, which differentiates between AML with recurrent cytogenetic abnormalities, AML with multilineage dysplasia, therapy-related AML, and AML not otherwise specified (10). For stratification of patients in clinical trials, cytogenetic abnormalities and early response to treatment are often used, representing the underlying biology of the disease (11,12). Patients with t(8;21), t(15;17), or inv(16)/t(16;16) are considered good risk by most collaborative study groups, whereas abn(3q), monosomy 5 or 7 and deletion 5(q) or 7(q) and complex abnormalities are considered poor risk by most groups (12). Currently, other molecular abnormalities, such as mutations in C/EBPa, or receptor tyrosine kinases such as FLT3, KIT, and others may also be taken into account (13). It is well known that the prognosis decreases with increasing age, which is

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partially due to an increasing frequency of unfavorable biology with increasing age, but also represents unfavorable host characteristics (11). On the basis of this, many study groups have separate protocols for children, younger adults, and elderly people with AML. Currently, induction regimens usually consist of intensive chemotherapy comprised of cytarabine in combination with an anthracycline, with or without a third drug such as etoposide or 6-thioguanine. Subsequently, the consolidation phase consists of intensive courses of chemotherapy in children and younger adults, with or without SCT, aiming at the eradication of minimal residual disease. Children and adults with acute promyelocytic leukemia [(APL) or AML FAB M3] receive adapted treatment protocols incorporating all-trans retinoic acid (ATRA), which results in much better treatment outcome than in non-M3 AML. Overall, 80% to 90% of children with AML reach complete remission (CR) versus approximately 75% of younger adults and 50% of older adults (11,13,14). This results in survival rates of approximately 50% to 70% for children versus approximately 40% to 50% for younger adults and only 10% for elderly AML patients (11,13,14). Relapse and nonresponse mainly contribute to these high failure rates. Hence, outcome for most patients with AML is still unsatisfactory in terms of antileukemic efficacy. Moreover, especially in children, there is concern over the acute and long-term side effects associated with the intensive chemotherapy (15). Therefore, there is a continuous interest in the development of new antileukemic agents, preferably those that exert their action without causing too many side effects. CD33-DIRECTED MONOCLONAL ANTIBODIES IN ADULT AML CD33 is expressed on the cell surface of malignant blast cells in 80% to 90% of AML cases, but not on normal hematopoietic stem cells or nonhematopoietic tissues. Antibody-based therapies for AML have, therefore, focused on CD33 as a suitable target antigen. CD33 is also a useful target for conjugated antibodies, as binding to CD33 results in internalization of the complex. The natural ligand of CD33 and its function are currently not known (16). Lintuzumab (HuM195) At first, studies have been performed with lintuzumab (HuM195), which is a naked antibody. This did not result in significant antileukemic activity in AML, with the exception of molecular disease control in AML FAB M3 (APL), in patients who were reverse transcriptase polymerase chain reaction (RT-PCR) positive for the APL characteristic t(15;17) fusion transcript after induction chemotherapy plus ATRA (17,18). These APL patients were treated with lintuzumab twice weekly for three weeks. Half of the patients evaluable for conversion of the RT-PCR of the fusion transcript became molecularly negative. This is not surprising, as APL is characterized by relatively strong CD33

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expression. A recent randomized phase III study in relapsed/refractory AML patients did not show a survival advantage in patients stratified to chemotherapy plus lintuzumab (4 days of lintuzumab 12 mg/m2 following induction chemotherapy were given, starting at day 6 after completion of induction chemotherapy and repeated at day 16–18) versus chemotherapy alone (pOS 36 vs. 28%, p ¼ 0.28) (19). The addition of lintuzumab did not result in increased toxicity when compared with the ‘‘chemotherapy-only’’ arm. Gemtuzumab Ozogamicin The most promising results have been obtained with GO, Mylotarg1, a humanized anti-CD33 monoclonal antibody linked to calicheamicin, which is a potent enidyne antileukemic antibiotic. Calicheamicin dissociates from the antibodycalicheamicin complex after internalization, binds to the minor groove of DNA, and results in DNA-double strand breaks (Fig. 1). The results of the various phase I/II studies in adults are summarized in Table 2, and some are discussed in more detail below.

Figure 1 Mechanism of action of gemtuzumab ozogamicin. (A) Gemtuzumab ozogamcin consists of an anti-CD33 antibody linked to the antitumor antibiotic calicheamicin. (B) After binding to CD33, the complex is internalized, after which the calicheamicin is released by hydrolysis in lysosomes. (C) Free calicheamicin then translocates to the nucleus and cleaves double-stranded DNA, resulting in apoptosis. (text continues on page 112)

Relapsed/ refractory AML Phase I study Relapsed/ refractory AMLa Relapsed/ refractory AML Newly diagnosed AML or MDS Newly diagnosed AML

Phase of treatment

Single agent

Population and number of patients

Dosages

Response rate

Median duration of response

Induction

median age 54 yr, n ¼ 40

Dose escalation 0.25–9 mg/m2, up to 3 infusions

20%

NA

Single agent

Induction

median age 61 yr, n ¼ 277

GO 2
9 mg/m2, on day 1 þ 15

Overall 26% (CR 13% and CRp 13%)

22

Single agent

Induction

Median age 64 yr, n ¼ 57

GO 3 mg/m2, day 1, 4, and 7

Overall 33% (CR 26% and CRp 7%)

CR patients: 6.4 mo CRp patients: 4.5 mo Median overall survival 8.4 mo

Single agentb

Induction

Elderly AML, median age 71 yr, n ¼ 51

GO 2
9 mg/m2, on day 1 þ 8 or day 1 þ 15

Overall 22%, i.e., GO only 8%c and GO þ IL-11 36%

GO: 8 wk GO þ IL-11: 15 wk

28

Single agent

Induction

Elderly AML, median age 76 yr, n ¼ 40

GO 2
9 mg/m2, on day 1 þ 15 Followed by 2 more courses in case of responsed

Overall 17% (CR 10% and CRp 7%) Note: in patients aged 61–75 yr 33%, in older patients 5%.

All patients 4.3 mo Age 61–75: 11.4 mo Age >75: 1 mo

29

Ref. 8

32

105

(Continued)

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Phase of the disease

GO monotherapy or in combination

Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia

Table 2 Phase I/II Studies with Gemtuzumab Ozogamicin as Single Agent or in Combination with Regular Chemotherapy in Adults with Acute Myeloid Leukemia

Phase of the disease Newly diagnosed AML Newly diagnosed AML

Phase of treatment

Single agent

Induction

Sequential combination therapy: GO monotherapy for induction followed by chemotherapy GO combined with cytarabine

Induction

GO combined with idarubicin and cytarabine

Population and number of patients Elderly AML, median age 75 yr, n ¼ 12 Elderly AML, median age 68 yr, n ¼ 64

Dosages

Response rate

GO 2
9 mg/m2, on day 1 þ 15

Overall 27%

GO 2
9 mg/m2, day 1 þ 15

After GO monotherapy: 35.1% (CR 22.8, CRp 12.3%) After GO followed by 1 course of chemotherapy: overall 54.4% (CR 35.1%, CRp 19.3%) CR rate 55%

Induction

median age 63 yr, n ¼ 9

GO 1
6 mg/m2, day 1 and 4 mg/m2, day 8 Ara-C 100 mg/m2, day 1–7, cont IV

Induction

median age 61 yr, n ¼ 14

GO 1
6 mg/m2, day 1 þ 15 Idarubicin 12 mg/m2, day 2–4 Ara-C 1.5 g/m2, day 2–5

Overall 42% (CR 21%, CRp 21%)

Median duration of response Available in 2 patients, 11.2 and 4.0 mo 1 yr pOS 34% (SE 7.2%)

Ref. 30

33

NA

63

Median survival time 8 wk, in CR patients 27 wk

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Newly diagnosed and relapsed/ refractory AML Relapsed/ refractory AML

GO monotherapy or in combination

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Myeloid Leukemia (Continued )

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Table 2 Phase I/II Studies with Gemtuzumab Ozogamicin as Single Agent or in Combination with Regular Chemotherapy in Adults with Acute

Relapsed/ refractory AML

Relapsed/ refractory AML or MDS

GO combined with liposomal daunorubicin, cytarabine, and cyclosporine A (MDAC) GO combined with fludarabine, cytarabine, and cyclosporine A (MFAC)

GO combined with cytarabine and topotecan

Population and number of patients

Dosages

Response rate

Median duration of response

Ref.

Induction

median age 37 yr, n ¼ 11

GO 1
6 mg/m , day 6 Ara-C 1 g/m2, day 1–5 DNX 75 mg/m2, day 6–8 CsA 16mg/kg, day 6–8, cont IV

Overall 18% (1 CR and 1 CRP)

Both responding patients relapsed again

65

Induction

median age 53 yr, n ¼ 31

Overall 34% (CR 28%, CRp 6%)

Median survival time 5.3 mo

36

Induction

median age 55 yr, n ¼ 17

GO 1
4.5 mg/m2, day 1 Fludarabine 15 mg/m2, 2
/day, day 2–4 Ara-C, 0.5 g/m2, 2
/day, day 2–4 CsA 16 mg/kg cont IV GO 1
9 mg/m2, day 1 Ara-C 1 g/m2, day 1–5 Topotecan 1.25 mg/m2, day 1–5

Overall 12% Significant toxicity (transaminites)

Overall survival 8 wk (all patients)

66

2

Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia

Relapsed/ refractory AML

Phase of treatment

(Continued)

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Phase of the disease

GO monotherapy or in combination

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Myeloid Leukemia (Continued )

Phase of the disease

GO monotherapy or in combination

Phase of treatment

Population and number of patients

GO combined with cytarabine and mitoxantrone

Induction

median age 54 yr, n ¼ 17

Relapsed/ refractory AML

GO combined with troxatyl

Induction

median age 65 yr, n ¼ 6

Newly diagnosed AML or MDS

GO combined with fludarabine, cytarabine, and cyclosporine A (MFAC)

Induction

median age 57 yr, n ¼ 59

Response rate

Median duration of response

GO 1
9 mg/m2, day 4 Ara-C 1 g/m2, 2
day, day 1–5 Mitoxantrone 12 mg/m2, day 1–3 GO 2
9 mg/m2, day 1 þ 15, later only day 1 Troxatyl 4 mg/m2 GO 1
6 mg/m2, day 1 (Other drugs see above)

Overall 76% (CR 70%, CRp 6%)

Median survival 11 mo

38

2 patients with VOD, 3 CRp

Not given

67

Overall 48% (CR 46% and CRp 2%)

Median survival time 8 mo

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Relapsed/ refractory AML

Dosages

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Table 2 Phase I/II Studies with Gemtuzumab Ozogamicin as Single Agent or in Combination with Regular Chemotherapy in Adults with Acute

Newly diagnosed AML (randomized)

Several induction and consolidation regimens in combination with GO, followed by regular MRC AML15 chemotherapy Several induction and consolidation regimens in combination with GO, followed by regular MRC AML15 chemotherapy

Response rate

Median duration of response

n ¼ 64 in Induction induction and and/or con31 in solidation consolidation, median age 46.5 yr

Induction: DAT, DA or FLAG-Ida with GO 3 mg/m2 Consolidation: MACE or HiDAC with GO 3 mg/m2

Overall CR 86% of patients

78% in CCR at 8 mo

39

Induction n ¼ 1115, and/or conmedian age solidation 49 yr

Induction 43% DA, 43% FLAG-Ida, 14% ADE,  GO 3 mg/m2

GO vs. no-GO 85% and 85%

Relapse rate: GO 37% vs. noGO 52% at 3 yr, p ¼ 0.01. DFS: GO 51% vs. no-GO 40% at 3 yr, p ¼ 0.008. OS: 53% vs. 46% at 3 yr, p ¼ 0.4

40

Ref. Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia

Newly diagnosed AML (pilot study)

Population and number of patients

Dosages

Phase of treatment

(Continued)

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Phase of the disease

GO monotherapy or in combination

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Relapsed APL

Population and number of patients

Dosages

Response rate

Median duration of response

Ref.

GO combined with fludarabine, cytarabine, and cyclosporine A (MFAC)

Postremission median age therapy 57 yr, n ¼ 22

GO 1
4.5mg/m2, day 1 (Other drugs see above)

NA

Median survival from CR was 16 mo

37

Single agent

Reinduction median age for 52 yr, n ¼ 16 molecular relapse Consolidation Median age 51 yr, n ¼ 8

GO 2
6 mg/m2, day 1 and 15

14/16 patients molecular CR

7 patients (50%) remained in molcular CR

68

Consolidation: – arsenic 0.15 mg/kg
5 days, total 5 courses – ATRA 45 mg/m2, 10 days/mo, for 10 courses – GO 9 mg/m2, once a mo, for 10 courses

NA

NA

43

GO combined with ATRA and arsenic trioxide

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Newly diagnosed AML or MDS, in remission after GO containing regimen Molecularly relapsed APL

Phase of treatment

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Phase of the disease

GO monotherapy or in combination

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Table 2 Phase I/II Studies with Gemtuzumab Ozogamicin as Single Agent or in Combination with Regular Chemotherapy in Adults with Acute Myeloid Leukemia (Continued )

a

GO combined with ATRA

Population and number of patients

Induction and median age postremission 50 yr, n ¼ 19 therapy

Dosages

Response rate

Median duration of response

Ref.

Induction: GO 1
9 mg/m2, day 5 ATRA 45 mg/m2 until CR Postremission: GO 9 mg/m2, every 4–5 wk, maximum 8
ATRA 45 mg/m2, 2 wk on/off schedule

Overall CR rate 84%

NA

42

Combined results of the three phase II studies that followed the initial phase I study. Patients were randomized between GO and GO plus interleukin-11 (IL-11). c There was no difference between GO day 1 and 8 or day 1 and 15. d Only 57% of patients received more than one infusion of GO. In 43% only one infusion was given because of toxicity or progressive disease. Only one patient received the four courses as planned. Abbreviations: CR, complete remission; CRp, complete remission with insufficient platelet regeneration, but platelet transfusion independence; AML, acute myeloid leukemia; MDS, myelodysplastic syndrome; GO, gemtuzumab ozogamicin; m2, body surface area in square meter; pOS, probability of overall survival; NA, not available or applicable; cont IV, continuous intravenous; ATRA, all-trans retinoic acid; APL, acute promyelocytic leukemia. b

Monoclonal Antibody Mediated Treatment in Acute Myeloid Leukemia

Newly diagnosed APL

Phase of treatment

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Phase of the disease

GO monotherapy or in combination

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The results of the adult phase I study in relapsed/refractory AML were published in 1999 and showed that a postinfusion syndrome of rigor and chills was the most frequent side effect (8). Two patients who were treated at the 9 mg/m2 dose level had prolonged neutropenia and thrombocytopenia. Of the 40 patients who were treated, 20% showed a response (i.e., absence of leukemia from bone marrow and peripheral blood). No antibodies against the antibody were detected, but two patients developed antibodies against calicheamicin. The recommended dose for phase II studies was two times 9 mg/m2 with a 14-day interval. At this dose level, greater than 75% saturation of CD33 sites on peripheral blood mononuclear cells was found. Three phase II studies in adults with relapsed/refractory AML were started. It has to be noted that inclusion in these three studies was restricted to a selected population of adults with relapsed CD33-positive AML who had a first CR duration of at least three months, and with a minimum of 80% of blasts staining positive for CD33 at least four times above the background level. On the basis of the preliminary data from 142 patients enrolled in these studies, GO was given accelerated approval by the FDA for patients with CD33-positive AML in first relapse who are 60 years or older and who are not considered candidates for cytotoxic chemotherapy (20,21). Administering two dosages of 9 mg/m2 with a 14-day interval, toxicities were similar as in the phase I study, but in addition hepatotoxicity was noted, including elevated transaminases and hyperbilirubinaemia in 17% and 23%, respectively, as well as one death due to liver failure. Of note, mucositis and severe neutropenic infections were infrequent. CR was obtained in 16% of patients. However, another subset of patients also showed remission but with insufficient platelet recovery, and they were categorized as CR with insufficient platelet regeneration, but platelet transfusion independence (CRp). The duration of response in these two subgroups was similar, and hence the total response rate was considered to be 30% (21). Recently, the final results of these phase II studies were published, now including a total of 277 adults (22). The overall response rate was 26% and included 13% of patients classified as CR and 13% as CRp, but now a difference in median leukemiafree survival time between these two patient categories of 6.4 versus 4.5 months was noted. This suggest that the quality of remission in the CRp patients was less than in patients with sufficient platelet recovery. Toxicities included grade 3 or 4 sepsis in 17% and grade 3 or 4 hyperbilirubinemia in 29%. Approximately 1% of patients who did not undergo SCT (either before or after treatment with GO) also developed hepatic veno-occlusive disease (VOD) following GO treatment. Meanwhile more data became available on the hepatotoxicity of GO, again showing that some patients developed clinical signs of VOD, which is thought to be due to CD33 expression in hepatic sinusoids and perhaps better described as ‘‘sinusoidal obstruction syndrome’’ (SOS) (23). Other factors involved may be the liver leukemia load or circulating soluble CD33 levels. A high incidence of VOD (in this particular single-center study as high as 64% of 14 patients) was noted among patients who were transplanted following reinduction with GO, mainly in patients who were transplanted shortly after GO treatment (24). This

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resulted in the recommendation to delay transplantation for at least 3.5 months following treatment with GO. However, in another series VOD also occurred in approximately 4% of patients without prior SCT history, as was also noted in the phase II studies described above (25). Several case reports suggest that defibrotide may be useful in preventing or treating GO-induced VOD, but no larger prospective or comparative studies have been performed (26,27). Three studies have now been performed using single-agent GO as induction therapy in older patients with newly diagnosed AML. The first study of Estey et al. included 51 adults older than 65 years with either AML or myelodysplastic syndrome (MDS) (28). Patents were randomized to receive GO or GO plus interleukin11 (IL-11). CR rates were 8% and 36%, respectively. In this nonrandomized study, the treatment results were compared with a historical cohort of patients receiving cytarabine and idarubicin, and GO  IL-11 was considered inferior to the standard cytarabine/idarubicin schedule. The authors, therefore, argue against using GO as standard regimen in elderly patients with AML. Another phase II study in 40 newly diagnosed AML patients older than 60 years, who were considered not fit for intensive chemotherapy, showed an overall response percentage (CR and CRp) of 17, although responses were mainly restricted to patients 61 to 75 years old (29). Two doses of GO 9 mg/m2 were given, and it was planned to give a third and fourth dose of GO to responding patients. However, the second dose was only given to 57% of patients, either because of toxicity (n ¼ 9) or progressive disease (n ¼ 8). Toxicity occurred mainly in patients older than 75 years of age, with 23% of patients suffering from induction deaths, which led to the suggestion that a reduced dose should be applied for this age group. Only one patient tolerated a third and fourth dose of GO. The median survival in responding patients was 11.4 months. The third study concerned 12 patients over 65 years of age (30). Three out of 12 patients were in CR after two doses of GO at 9 mg/m2. Five patients had nonarrhythmia cardiac adverse events, although it was not clear if they were attributable to treatment with GO. Amadori et al. also mentioned 10% arrhythmias, 7% left ventricular dysfunction, and 5% hypotension as grades 3 to 4 adverse events (29). Most single-agent studies with GO have used the ‘‘classical’’ dosing schedule of 9 mg/m2 on day 1 and 14. However, based on in vitro data showing rapid reexpression of CD33 after internalization of the CD33/GO complex, a more fractionated dosing schedule may be more efficacious (31). These in vitro data were recently ‘‘translated’’ to the clinic in a phase II study in which GO was administered at 3 mg/m2 on day 1, 4, and 7 in 57 patients with relapsed/refractory AML (32). This schedule was very well tolerated, without hepatoxicity. The response rate was 26% CR and 7% CRp. Several studies have been reported on combination chemotherapy regimens including GO, both in relapsed/refractory and in newly diagnosed patients. Amadori et al. treated 57 newly diagnosed elderly AML patients with two infusions of GO 9 mg/m2 as induction therapy, which was followed subsequently by conventional chemotherapy (33). Response to GO was observed in 35.1% of patients, with an additional 10.5% of partial remissions. In total five patients

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suffered from VOD, with three cases during the initial GO treatment phase. Oneyear survival was 34%. The value of this GO-containing regimen is currently being compared in a prospective randomized study with a chemotherapy-only arm. In another report, 59 patients with previously untreated AML or MDS were treated with a single infusion of 6 mg/m2 GO, combined with fludarabine, cytarabine, and cyclosporine A—Mylotarg, fludarabine, cytarabine (ara-C), and cyclosporine (MFAC) regimen, which induced CR in 46% of patients but also resulted in considerable toxicity (34). Cyclosporine A was added as MDR1 reversal agent, as MDR1 has been reported to be involved in GO resistance (35). This regimen was first tested in relapsed/refractory patients, and additionally as postremission therapy in patients achieving CR after GO-containing induction (36,37). Chevallier et al. have combined GO 9 mg/m2 with cytarabine and mitoxantrone in induction, in patients with relapsed/refractory AML, and report a very promising response rate of 76% with acceptable toxicity, but the number of patients was limited (38). Kell et al. reported a feasibility study in newly diagnosed adult AML patients in which GO was combined with several different standard AML induction regimens (39). Several lessons were learned from this study: (i) the maximum dose of GO in these combination was 3 mg/m2 single infusion; (ii) this dose could not be administered in consecutive courses because of hepatotoxocity and delayed hematopoietic reconstitution, but it was possible to use GO in course 1 and 3; and (iii) GO should not be combined with 6-thioguanine because of hepatotoxicity. A very promising CR rate of 85% was noted, and a randomized phase III trial (study AML15) based on this schedule has recently been completed in newly diagnosed patients with AML in the MRC-group. The first results on the 1115 randomized patients were reported by Burnett et al. at the ASH 2006 meeting, showing similar remission induction rates of 85% both in the GO as well as in the non-GO arm. However, there was a significant reduction in the relapse rate in patients included in the GO-arm (37% vs. 52% at 3 years, p ¼ 0.01), resulting in improved disease-free survival (51% vs. 40% at 3 years, p ¼ 0.008), although not yet in overall survival (40). Considering toxicity, there was a significant increase in transaminase elevation in the GO-arm but no difference in bilirubin elevation. Patients in the GO-arm needed significantly more platelet transfusions (19 vs. 14; p < 0.0001), and more days on IV antibiotics (20.6 vs. 18.6 days, p ¼ 0.001), although bone marrow recovery was similar. GO did not increase the number of patients with death in CR, after a median of 15 months of follow-up. Several other cooperative groups have started similar prospective randomized studies. Treatment of APL with GO seems promising, although the data are still limited. Lo-Coco reported 16 patients with a molecular relapse of APL who were salvaged by two (or more in 3 patients) infusions (6 mg/m2) of GO (41). Approximately half of the responses were sustained for a median of 15 months. Quantitative RT-PCR studies showed that responding patients experienced a dramatic decline (at least 2 logs) of the PML/RARalpha transcript after the first GO dose. In a series from MD Anderson, GO was given to nine patients with

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untreated APL, in combination with ATRA and idarubicin in patients with high white counts (42). After two induction courses, patients received eight maintenance courses. GO was tolerated well and induced CR in 16/19 patients. Followup was too short to determine whether these responses were durable. In a recent study, GO (dose of 9 mg/m2) was given as consolidation in combination with ATRA and arsenic trioxide, following arsenic trioxide reinduction, in patients with relapsed APL, again showing good tolerability of this regimen (43). CD33 DIRECTED MONOCLONAL ANTIBODIES IN PEDIATRIC AML The results of the various studies in children are summarized in Table 3. The first data reported on the use of GO in children concerned 15 patients with relapsed/ refractory AML who were treated on compassionate use basis with one to three infusions of GO at a dosage of 4 to 9 mg/m2 (44). Basically, efficacy and safety mimicked the experience in adults. Durable responses were noted in two of the eight patients who achieved a response, defined as ‘‘no evidence of leukemia.’’ Brethon et al. published similar results in another compassionate use series (45). The pediatric phase I study results were recently published by Arceci et al. (46). They initially started at the 6-mg/m2-dose level, administering two infusions with a 14-day interval, and subsequently escalated to 9 mg/m2. At this level, 3 of 13 patients had grades 3 to 4 transaminase elevations, and one patient developed VOD, after which dose de-escalation to the 6-mg/m2 level was issued. The last two patients received 7.5 mg/m2 for two dosages, before the study was closed. Overall, toxicities included grades 3 to 4 hyperbilirubinemia in 7% and elevated hepatic transaminases in 21%; the incidence of grade 3 to 4 mucositis (3%) or sepsis (24%) was relatively low. Eight of 29 patients achieved overall remission (28%). Remissions were comparable in refractory (30%) and relapsed (26%) patients. Versluys et al. published a case series of another seven children treated with GO prior to SCT as reinduction treatment (26). One to four doses of GO 9mg/m2 were administered in these patients. After the second patient suffered from severe VOD at subsequent SCT, routine defibrotide prophylaxis was given to all patients, and none of them developed VOD. Preliminary results from a phase II study with GO (2 doses of 7.5 mg/m2 IV) in 20 patients who received homogenous pretreatment according to the Relapsed AML 2001/01 protocol, but were either refractory to reinduction or suffered from second relapse, showed a response rate of 40% (including both CR and CRp) (47). The median survival of responders was longer than for nonresponders to GO (median 1.04 vs. 0.4 years, p ¼ 0.04). Transaminase and bilirubin elevation was found in 5% of patients. One out of eight transplanted children developed VOD. A recent pilot study addressed the use of GO following reduced-intensity stem cell transplantation (RIC-allo-SCT), in eight children with CD33þ AML, either in first or second CR (48). The first dose of GO was given after reconstitution

Chemotherapy in Children with Acute Myeloid Leukemia

Phase of the disease

Phase of treatment

Single agent

Induction

Single agent

Induction, com- n ¼ 15, median age 8.9 yr passionate use Induction n ¼ 7, median age 6 yr

Single agent

Population and number of patients n ¼ 29, median age 12 yr

Median duration of response

Dosages

Response rate

Dose escalation from 6 to 9 mg/m2, day 1 þ 15 4–9 mg/m2, up to 3 courses

Overall 28% (14% CR, 14% CRp)

2 patients still alive

46

Overall 33% (33% CRp)

2 patients still alive

44

9 mg/m2, up to 4 courses

Not given

2 patients still alive

26

Median survival 1.04 yr NA

47

Single agent

Induction

n ¼ 20, median age 8.2 yr

7.5 mg/m2, day 1 þ 15

Overall 40%

Single agent

Induction

n ¼ 11, median age 5.5 yr

3–9 mg/m2, once (n ¼ 3), twice (n ¼ 3), thrice (n ¼ 5), or five times (n ¼ 1)

3 CRs

Ref.

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Relapsed/ refractory AML, phase I study Relapsed/ refractory AML Relapsed/ refractory AML Relapsed/ refractory AML Relapsed/ refractory AML

GO monotherapy or in combination

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Table 3 Phase I/II Studies and Compassionate Use Data with Gemtuzumab Ozogamicin as Single Agent or in Combination with Regular

Single agent

Relapsed/ refractory AML

Single agent

Post-SCT, following reduced intensity SCT Induction

Population and number of patients

Dosages

Median duration of response

Ref.

NA

48

– Induction: 2CR, NA 4 CRp, 4 PR After consolidation: 2 PR became CR, 1 PR became CRp

49

Response rate

n ¼ 6, median age 9 yr

4.0–6.0 mg/m , NA 1st dose day 60–180 post-SCT, 2nd dose 8 wk later

n ¼ 17 induction, n¼6 consolidation

– Induction: GO 3 mg/m2, day 1, 4, and 7 Cytarabine 100 mg/m2/day CI for 7 days Consolidation: GO 3 mg/m2 at day 1, plus cytarabine 100 mg/m2/day for 7 days

2

Abbreviations: NA, not available; SCT, stem cell transplant, GO, gemtuzumab ozogamicin.

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Relapsed/ refractory AML

Phase of treatment

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GO monotherapy or in combination

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of neutrophils and platelets at day þ60-180 post RIC-allo-SCT. The second dose was given after recovery of peripheral blood, usually around eight weeks following the first dose. A dose escalation from 4.5 to 6.0 mg/m2 appeared to be safe, and further dose escalation is planned. Toxicity mainly consisted of bone marrow suppression, infections, and transaminase elevations, but no VODs occurred. Further studies are necessary to demonstrate whether GO is able to reduce the relapse rate following RIC-allo-SCT and whether this approach is superior to myeloablative allo-SCT. Brethon et al. presented the first results of a combination of GO with cytarabine, using a fractionated GO schedule; GO was given at 3 mg/m2/day on day 1,4, and 7 plus cytarabine 100 mg/m2/day using continuous IV infusion for seven days (49). Seventeen children received this course as induction regimen, and six patients subsequently received a consolidation course with GO 3 mg/m2 at day 1, plus cytarabine 100 mg/m2/day for seven days. Responses included 2 CRs, 4 CRps, and 4 PRs. Currently, several pediatric studies are underway applying GO in several phases of treatment. This includes the combination of reduced dosages of GO (3 mg/m2) with induction and/or consolidation chemotherapy as well as testing GO as single agent in postremission setting (following intensive AML treatment) with the aim to eradicate minimal residual disease and reduce the relapse rate. CD45-DIRECTED MONOCLONAL ANTIBODIES CD45 is a tyrosine phosphatase and a common leucocyte antigen, expressed in the membrane of all leucocytes, leukemic cells, and erythrocyte progenitors but not outside the hematopoietic system (50). Different isoforms exist because of alternative splicing. Its expression is more dense on lymphoid when compared with myeloid cells, which explains why this antibody is used in conditioning regimens of SCT to deplete the host from lymphocytes and reduce graft failure. Antibody-bound CD45 tends to remain on the cell surface and does not internalize, which makes it an attractive target for radioimmunoconjugates, as the risk of cleavage of the radioisotope and release in the circulation is limited. Several studies on radiolabel-led anti-CD45 antibodies have been reported, but they are outside the focus of this chapter. Experience with unconjugated anti-CD45 antibodies is limited. In a phase I study, patients who were to receive a SCT were treated with the rat anti-CD45 antibodies YTH24 and YTH54. The MTD was defined as 400 mg/kg/day for four days, with bronchospasm as dose-limiting toxicity (50). This resulted in a decline in lymphocytes and granulocytes from the peripheral blood (approximately 1 log reduction), as well as demonstrable antileukemic efficacy (in the 3 patients with measurable disease before the administration of the antibody). In a subsequent study in patients who could not tolerate myeloablative conditioning, anti-CD45 was used in conjunction with alemtuzumab, fludarabine, and total body irradiation, which resulted in successful engraftment (51).

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OTHER ANTIBODIES A phase I study was performed with an anti-GMCSF monoclonal antibody in eight patients with relapsed AML, which was well tolerated but without significant antileukemic activity (52). FLT3 mutations have been detected in approximately 10% to 15% of pediatric AML and 30% of adult AML cases. Apart from inhibitory small molecules that interfere with signal transduction, monoclonal antibodies against FLT3 have been developed (53). These antibodies interfere with ligand-mediated autocrine FLT3 signaling and may induce antibody-dependent cellular cytotoxicity (ADCC). Currently, these antibodies are in the preclinical phase of development, and clinical studies have to be awaited. RESISTANCE MECHANISMS Similar to conventional chemotherapy, the use of monoclonal antibodies may results in clinical resistance against these compounds (16). General mechanisms include clonal evolution of clones that do not (or not high enough) express the selected target antigen. In case of a high tumor load all the antibody may be trapped in the circulation and hence not result in significant antileukemic efficacy, which has also been described for GO (54). Especially when using naked antibodies, immune deficiencies, which may occur secondary to the disease itself or due to immunosuppressive treatment, may impair with successful killing of tumor cells. Of particular relevance for use of monoclonal antibodies in AML is whether the leukemic stem cell itself expresses the target antigen of interest or whether this is only expressed by the more mature bulk of cells (55). This issue is currently unresolved for GO and CD33, although relapses are usually not characterized by CD33-negativity (16,56). For GO, several other mechanisms have been reported, including MDR1 overexpression (35) and differences in cellular calicheamicin sensitivity (57,58). Cell-line studies have shown that GO-induced cytotoxicity was directly related to CD33 expression levels, although several clinical studies have failed to relate CD33 expression to clinical response to GO (59,60). In cell-line studies, apoptosis was not inhibited in the presence of blocking anti-CD33 antibodies in case of continuous exposure to GO in relatively high concentrations, whereas at lower concentrations apoptosis was inhibited (61). This may be explained by other, CD33-independent uptake mechanisms such as endocytosis. A more recent study, in patients undergoing treatment with single-agent GO, showed that responders had higher median CD33 levels and lower P-glycoprotein activity than nonresponders (62). CLINICAL PERSPECTIVE FOR THE NEXT FIVE YEARS The response rates to GO have not been promising enough to justify its use as a single agent. Therefore, the current interest is to incorporate GO in modern multiagent chemotherapy; however, only reduced dosages of GO can be used in

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this setting because of tolerability. Whether combination chemotherapy containing GO will result in improved overall survival rates has to be awaited, although the MRC AML15 study already showed a reduced relapse rate. It is encouraging that the use of GO upfront did not lead to a significant increase in death in CR in this study. The current, conditional approval for GO by the FDA is restricted to elderly AML patients in first relapse who are deemed unfit to receive more intensive therapy only. In addition, there is currently no approval in Europe by the European Agency for the Evaluation of Medicinal Product. This limited approval status may be subject to change once more results become available. GO may very well obtain an important role in the treatment of APL; for instance, as salvage therapy in patients with detectable minimal residual disease after consolidation chemotherapy or in molecular relapse, although larger prospective studies are still lacking. Other options are to combine GO with ATRA or arsenic trioxide, which might be an alternative to the use of regular chemotherapy in APL. GO is still an attractive drug for palliative treatment in AML, given the lack of severe mucositis and alopecia and the possibility to administer it in outpatient basis in most patients.

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41. Lo-Coco F, Cimino G, Breccia M, et al. Gemtuzumab ozogamicin (Mylotarg) as a single agent for molecularly relapsed acute promyelocytic leukemia. Blood 2004; 104:1995–1999. 42. Estey EH, Giles FJ, Beran M, et al. Experience with gemtuzumab ozogamycin (‘‘mylotarg’’) and all-trans retinoic acid in untreated acute promyelocytic leukemia. Blood 2002; 99:4222–4224. 43. Aribi A, Kantarjian HM, Estey EH, et al. Combination therapy with arsenic trioxide, all-trans retinoic acid, and gemtuzumab ozogamicin in recurrent acute promyelocytic leukemia. Cancer 2007; 109:1355–1359. 44. Zwaan ChM, Reinhardt D, Corbacioglu S, et al. Gemtuzumab ozogamicin: first clinical experiences in children with relapsed/refractory acute myeloid leukemia treated on compassionate use basis. Blood 2003; 101:3868–3871. 45. Brethon B, Auvrignon A, Galambrun C, et al. Efficacy and tolerability of gemtuzumab ozogamicin (anti-CD33 monoclonal antibody, CMA-676, Mylotarg) in children with relapsed/refractory myeloid leukemia. BMC Cancer 2006; 6:172–179. 46. Arceci RJ, Sande J, Lange B, et al. Safety and efficacy of gemtuzumab ozogamicin (Mylotarg1)in pediatric patients with advanced CD33-positive acute myeloid leukemia. Blood 2005; 106:1181–1188. 47. Zwaan CM, Reinhardt D, Zimmermann M, et al. A phase II study of single-agent gemtuzumab ozogamicin in relapsed/refractory pediatric AML. J Clin Oncol 2005; 23:S806; (abstr). 48. Roman E, Cooney E, Harrison L, et al. Preliminary results of the safety of immunotherapy with gemtuzumab ozogamicin following reduced intensity allogeneic stem cell transplant in children with CD33þ acute myeloid leukemia. Clin Cancer Res 2005; 11:S7164–S7170. 49. Brethon B, Yakouben K, Oudot C, et al. Efficacy of the combination of gemtuzumabozogamicin (Mylotarg(R)) and cytarabine (GOCYT) in childhood myeloid leukemia: a compassionate use based review in France. Blood 2006; 108:A570; (abstr). 50. Brenner MK, Wulf GG, Rill DR, et al. Complement-fixing CD45 monoclonal antibodies to facilitate stem cell transplantation in mouse and man. Ann N Y Acad Sci 2003; 996:80–88. 51. Popat U, Carrum G, May R, et al. CD52 and CD45 monoclonal antibodies for reduced intensity hemopoietic stem cell transplantation from HLA matched and one antigen mismatched unrelated donors. Bone Marrow Transplant 2005; 35:1127–1132. 52. Bouabdallah R, Olive D, Meyer P, et al. Anti-GM-CSF monoclonal antibody therapy for refractory acute leukemia. Leuk Lymphoma 1998; 30:539–549. 53. Piloto O, Levis M, Huso D, et al. Inhibitory anti-FLT3 antibodies are capable of mediating antibody-dependent cell-mediated cytotoxicity and reducing engraftment of acute myelogenous leukemia blasts in nonobese diabetic/severe combined immunodeficient mice. Cancer Res 2005; 65:1514–1522. 54. van Der Velden V, Boeckx N, Jedema I, et al. High CD33-antigen loads in peripheral blood limit the efficacy of gemtuzumab ozogamicin (Mylotarg) treatment in acute myeloid leukemia patients. Leukemia 2004; 18:983–988. 55. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 1997; 3:730–737. 56. Bernstein ID. CD33 as a target for selective ablation of acute myeloid leukemia. Clin Lymphoma 2002; (suppl 2):S9–S11.

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57. Zwaan ChM, Reinhardt D, Ju¨rgens H, et al. Gemtuzumab ozogamicin in pediatric CD33-positive acute lymphoblastic leukemia: first clinical experiences and relation with cellular sensitivity to single agent calicheamicin. Leukemia 2003; 17:468–470. 58. Goemans BF, Kaspers GJL, Vijverberg SJH, et al. Large interindividual differences in in-vitro calicheamicin sensitivity may underly gemtuzumab ozogamicin resistance in acute myeloid leukemia. Blood 2005; 106:A35; (abstr). 59. Walter RB, Raden BW, Kamikura DM, et al. Influence of CD33 expression levels and ITIM-dependent internalization on gemtuzumab ozogamicin-induced cytotoxicity. Blood 2005; 105:1295–1302. 60. Jilani I, Estey E, Huh Y, et al. Differences in CD33 intensity between various myeloid neoplasms. Am J Clin Pathol 2002; 118:560–566. 61. Jedema I, Barge RM, van Der Velden V, et al. Internalization and cell cycle-dependent killing of leukemic cells by Gemtuzumab Ozogamicin: rationale for efficacy in CD33negative malignancies with endocytic capacity. Leukemia 2004; 18:316–325. 62. Walter RB, Gooley TA, van der Velden VH, et al. CD33 expression and P-glycoproteinmediated drug efflux inversely correlate and predict clinical outcome in patients with acute myeloid leukemia treated with gemtuzumab ozogamicin monotherapy. Blood 2007; 109:4168–4170. 63. Piccaluga PP, Martinelli G, Rondoni M, et al. First experience with gemtuzumab ozogamicin plus cytarabine as continuous infusion for elderly acute myeloid leukaemia patients. Leuk Res 2004; 28:987–990. 64. Alvarado Y, Tsimberidou A, Kantarjian H, et al. Pilot study of Mylotarg, idarubicin and cytarabine combination regimen in patients with primary resistant or relapsed acute myeloid leukemia. Cancer Chemother Pharmacol 2003; 51:87–90. 65. Apostolidou E, Cortes J, Tsimberidou A, et al. Pilot study of gemtuzumab ozogamicin, liposomal daunorubicin, cytarabine and cyclosporine regimen in patients with refractory acute myelogenous leukemia. Leuk Res 2003; 27:887–891. 66. Cortes J, Tsimberidou AM, Alvarez R, et al. Mylotarg combined with topotecan and cytarabine in patients with refractory acute myelogenous leukemia. Cancer Chemother Pharmacol 2002; 50:497–500. 67. Giles F, Garcia-Manero G, O’Brien S, et al. Fatal hepatic veno-occlusive disease in a phase I study of mylotarg and troxatyl in patients with refractory acute myeloid leukemia or myelodysplastic syndrome. Acta Haematol 2002; 108:164–167. 68. Lo-Coco F, Cimino G, Breccia M, et al. Gemtuzumab ozogamicin (Mylotarg) as a single agent for molecularly relapsed acute promyelocytic leukemia. Blood 2004; 104:1995–1999.

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6 Monoclonal Antibodies in the Treatment of Malignant Lymphomas and Chronic Lymphocytic Leukemia Bertrand Coiffier Hematology Department, Hospices Civils de Lyon and Claude Bernard University, Pierre-Benite, France

INTRODUCTION Non-Hodgkin’s lymphoma (NHL) is a heterogeneous group of B- and T-cell cancers, with a large variety of patterns of growth, clinical presentations, and responses to treatment. Chronic lymphocytic leukemia (CLL) is a B-cell chronic proliferation not very different from the small lymphocytic lymphoma, and both diseases are often treated identically (1). The outcome depends on histological subtype, tumor characteristics, host responses, and treatment. About 90% of lymphomas have a B-cell phenotype, and for them recent therapeutic progress came from the introduction of monoclonal antibodies (mAb) alone or in combination with chemotherapy (2–4). The first antigen that has been targeted for therapeutic purpose with success was the CD20 antigen, a trans-membrane protein expressed by more than 99% of B-cell lymphomas. Rituximab was the first mAb engineered to target the CD20 antigen and first approved mAb for the treatment of lymphoma patients. Through the last 10 years, clinical trials with rituximab have confirmed its efficacy in follicular lymphoma (FL) as well as in aggressive lymphomas and its use has expanded significantly beyond the initial indication of indolent B-cell lymphomas to virtually any CD20-positive

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lymphoma. The addition of rituximab to chemotherapy was the first real progress in 10 years that has significantly prolonged the survival of patients with B-cell lymphomas (4,5). In recent years, several other mAb targeting CD20 or other lymphocyte antigens appeared, some of them associated with a toxin or a radioisotope. However, most of the data generated today on mechanisms of action or clinical efficacy have been for rituximab. Thus, rituximab will serve as example in this review, and differences with other mAb will be outlined when necessary. MECHANISMS OF ACTION OF mAb The mechanisms of action of mAb differ with the type of antibody, the antigen they target, and their use: alone, in combination with chemotherapy, or conjugated to a toxin or a radionucleide. In case of a naked antibody, different mechanisms have been identified (6). CD20 binding by rituximab is followed by homotypic aggregation, rapid translocation of CD20 into specialized plasma membrane microdomains known as rafts, and induction of apoptosis. Membrane rafts concentrate Src family kinases and other signaling molecules (phospholipases, caspases), and the anti-CD20-induced apoptotic signals are thought to occur as a consequence of CD20 accumulation in rafts (7). Fas-induced apoptosis occurs with the clustering of Fas molecules that leads to the formation of the death-inducing signaling complex (DISC) and the downstream activation of the death receptor pathway (8). The role of complement-dependent cytotoxicity (CDC) is suggested by the consumption of complement observed after rituximab administration, but in vitro CDC does not correlate with clinical response in lymphomas (9,10). However, CDC seems to be the most important mechanism of cell lysis in CLL patients (11). CDC is probably involved in the cytokinerelease syndrome and its toxicity (12). The importance of antibody-dependent cellular cytotoxicity (ADCC) has been demonstrated in vivo when rituximab is used alone (13). The Fc receptor (FcgR) of effector cells has two alleles and the valine/valine (V/V) allele of FcgRIIIa which confers a higher affinity for immunoglobulin G1 (IgG1) and rituximab is associated with an increased responsiveness to rituximab (13,14). If the clinical relevance of the FcgRIIIa receptor dimorphism was established in a number of studies with rituximab used alone, it does not seem to play a major role when rituximab is used in combination with chemotherapy (15) even if one study showed an increased response for patients with the V/V allele without difference for progression-free survival (PFS) or overall survival (OS) (16). The immune mechanisms are probably valid for other naked antigens but only those targeting the CD20 antigen may have the direct action described here. Finally, evidences that rituximab could synergize with chemotherapeutic agents in B-cell killing were provided by Demidem (17). Subsequent investigations have confirmed synergy of rituximab with fludarabine, doxorubicin, and other anticancer drugs (18–20). In one hypothesis, this synergism is mediated, at

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least in part, via downregulation of interleukin-10 (IL-10) by rituximab, which in turn causes downregulation of the antiapoptotic protein bcl2 and increased sensitivity to apoptosis (21). Another mechanism involves the inhibition of the activity of P-glycoprotein and, thus, the efflux of drugs like doxorubicin or vincristine (22). In cell lines, the P-glycoprotein pump is translocated out of the lipids rafts. Studies performed with cell lines as model systems revealed several mechanisms that are involved in chemo/immunosensitization and the development of resistance to rituximab. Rituximab has been shown to inhibit the p38 mitogenactivated protein kinase, nuclear factor-kB (NF-kB), extracellular signal-regulated kinase 1/2 (ERK 1/2) and AKT antiapoptotic survival pathways, all of which result in upregulation of PTEN and Raf kinase inhibitor protein and in the downregulation of antiapoptotic gene products (particularly Bcl-2, Bcl-xL, and Mcl-1), and resulting in chemo/immunosensitization. Further, rituximab treatment inhibits the overexpressed transcription repressor Yin Yang1 (YY1), which negatively regulates Fas and DR5 expression, and its inhibition leads to sensitization to Fas ligand and tumor necrosis factor-related apoptosis-inducing ligand-induced apoptosis (23). If these mechanisms may have a role when mAb are combined with a radionuclide, most of the antitumor effect resides in their capacity to deliver local radiotherapy after the mAb is attached to tumor cells (24). The choice of the antibody and therapeutic radioisotopes are critical for the success of radioimmunotherapy (RIT). Several radiolabeled mAb have been studied in clinical trial but only two, yttrium-90 (90 Y or Y-90) ibritumomab tiuxetan and iodine-131 (131I or I-131) tositumomab, have been registered for the treatment of lymphoma patients. Both radiolabeled antibodies are mouse antibodies reacting with CD20 expressing tumors. Y-90 is a pure b-emitter, with a half-life of 2.7 days (25). It is a link to the antibody by a chelator (tiuxetan). The long pathlength of its b-particles is particularly advantageous in tumor with heterogeneous or low distribution of the antigen (26). I-131 is an a- and b-emitter that has a half-life of eight days. The path length of its b-particles is relatively shorter than Y-90. Table 1 presents the differences between Y-90 and I-131 radiolabeled antibodies. Table 1 Characteristics of the Two Registered Radiolabeled MmAb 90

Linker Isotope radiation decay Half-life, days Path length, mm Energy, MeV Non tumor distribution Urine excretion Imaging Source: From Refs. 25, 106.

Y-ibritumomab

Tiuxetan Beta 2.7 5.0 2.3 Bone Limited Not possible

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None Beta and gamma 8.0 0.8 0.61 Thyroid Substantial Possible

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An alternative approach to increase the activity of mAbs has been the development of immunotoxin, a construct conjugating the antibody to cytotoxic plant, bacterial toxic proteins, or chemotherapy drugs (doxorubicin) (27). The commonly used toxins, ricin or diphtheria toxin, are highly potent natural products that disrupt protein synthesis. Unlike unconjugated mAb, immunotoxins must be internalized after antigen binding to allow the toxin access to the cytosol. Although the conjugation to mAbs confers some target specificity, the toxin continues to mediate nonspecific toxicity to normal tissues. Deglycosylated ricin A-chain has been used to eliminate such nonspecific toxicity.

MECHANISMS OF RESISTANCE If multiple mechanisms of rituximab action have been reported, it remains unclear which is or are most important in patients, and therefore it is difficult to know the relative importance of potential mechanisms of resistance. This is true for the other mAb too. Conceptual approaches of resistance mechanisms may be resumed as was followed (28). As far as events up to antigen binding are concerned, resistance to rituximab may be secondary to low serum levels or rapid metabolism of the mAb; development of human antimonoclonal antibodies (HAMA), most frequent with nonhumanized antibodies than with rituximab, or human antichimeric antibodies (HACA) (not yet demonstrated in patients); possibly different distribution within malignant nodes, blood cells, marrow, and extranodal sites and responsible for poor tumor penetration; high level of soluble antigen target (not yet demonstrated for CD20 antigen); high tumor burden; and poor surface antigen expression. Events that may induce resistance to rituximab after the antigen binding are alteration of induced intracellular signals; reduction of direct apoptosis effect in cases of elevated bcl-2 protein; inhibition of CDC by complement inhibitors; and alteration of cell-mediated immunity. Gene microarray analysis has shown that patients who failed to respond to rituximab have altered patterns of gene expression, with an overexpression of genes important in cell-mediated immunity (29). In vitro, long exposition to rituximab induced rituximab-resistant clones. These clones exhibited constitutive hyperactivation of the nuclear factor-KB and extracellular signal-regulated kinase 1/2 pathways, leading to overexpression of bcl-2 protein and bcl-2-related genes. These clones can be chemosensitized following treatment with pharmacologic inhibitors like bortezomib (30). In CLL patients, one particular mechanism of resistance has been described where there is a high number of circulating B-cells—the mononuclear phagocytic system is rapidly saturated and rituximab-opsonized cells are not cleared anymore. Then the complex rituximab-CD20 is shaved from the cells that become CD20-negative or low and rituximab losses its efficacy (31).

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SAFETY AND TOLERABILITY The safety of mAb is mainly related to their origin and to the compound attached to them. Radiolabeled mAb have a greater hematological toxicity than naked mAb because of the effect of surrounding normal hematopoietic cells in bone marrow. Immunotoxins also have greater toxicity because of the release of the toxin. Some mAb such as alemtuzumab may have larger hematological toxicity because the target antigen (CD52, in case of alemtuzumab) is not restricted to lymphoid cells. The safety of rituximab is mainly related to infusion toxicity, a toxicity most mAb have in common (32). These side effects are observed during the infusion or in the first hours after drug infusion and particularly for the first infusion. These include fever, chills, dizziness, nausea, pruritus, throat swelling, cough, fatigue, hypotension, and transient bronchospasm in a majority of patients. These symptoms are part of the cytokine-release syndrome. Their intensity correlates with the number of circulating malignant cells at the time of infusion. More severe infusional toxicity includes bronchospasm, angioedema, and acute lung injury, which are often associated with high circulating cell counts or pre-existing cardiac or pulmonary disease. Another common toxicity is the rapid depletion of normal antigen-positive B-lymphocytes from blood, bone marrow, and lymph nodes of the recipient, lasting between six and nine months following the last administration of rituximab. In the case of short rituximab treatment, this depletion does not compromise immunity: Immunoglobulins do not decrease significantly, and patients do not have an increased risk for infections during and after rituximab therapy (32,33); except for some viruses like herpes virus, cytomegalovirus, or hepatitis B virus (HBV). Maintenance treatment, particularly after autologous transplant, might be associated with a decrease in immunoglobulins (34) and late toxicity (32). Rare toxic events associated with rituximab comprised delayed neutropenia and pulmonary reactions. Delayed neutropenia usually occurs in patients treated with rituximab alone or in combination with chemotherapy. It appears between one and six months after the last infusion, may be transient, is rarely associated with infection, and resolves spontaneously in most of the cases (35). The mechanisms are not fully understood. Pulmonary reactions are rare and diverse, and usually related to rituximab because of the temporal relation (32). RIT is associated with secondary myelodysplastic syndromes. Rituximab as chemotherapy may induce a reactivation of hepatitis B in inactive HBV carriers. Lamivudine or other antiviral treatment must be use prophylactically during treatment and the following months to prevent this severe complication (36). CLINICAL STUDIES A few mAb have been registered for the treatment of lymphoma patients: rituximab (Rituxan1 or MabThera1), 90 Y-ibritumomab tiuxetan (Zevalin1), 131I-tositumomab (Bexxar1), and denileukin diftitox (OnTak1), the last two only in the United States. However, a lot of other mAb are currently in preclinical, phase I, or phase II studies.

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Rituximab is certainly the mAb where the largest experience exists and the mAb with several demonstrative randomized studies. We will focus on demonstrated activity (phase III studies) and some phase II studies with promising results. RITUXIMAB IN FOLLICULAR LYMPHOMA Rituximab Alone in Relapse When used alone, rituximab is usually given as four weekly injections of 375 mg/m2 (37). The pivotal multicenter phase II study that included 166 patients treated with four infusions of rituximab showed an overall remission rate of 48% [including 6% of complete response (CR)], and a median time to progression (TTP) of 13 months (38). Elevated b2-microglobulin, elevated lactate dehydrogenase (LDH), bulky disease, and age older than 60 years did not appear to impact response, implying that patients regarded as having a poor prognosis may respond to rituximab. Patients relapsing after initial response to rituximab treatment may be retreated with comparable response rates and adverse side effects, but, interestingly, median time for progression might be longer than after first treatment (39,40). Whether prolonged treatment with rituximab or maintenance is able to further improve response rates and prolong remission duration is of considerable interest. Several arguments are in favor of this approach: the success of re-treatment or the strong correlation between rituximab plasma levels and response rates (41). A recent randomized trial showed that adding maintenance doses of rituximab prolonged response duration (42); 202 patients with newly diagnosed or refractory/relapsed FL were treated with rituximab. Patients responding, or with stable disease, were randomized to no further treatment or prolonged rituximab administration (375 mg/m2 every two months for four times). With a median follow-up of 35 months, the median event-free survival (EFS) was prolonged in the treated group, 23 months versus 12 months in the control group. However, patients relapsed within the six months after stopping rituximab treatment. In another randomized study, Hainsworth showed that re-treatment at relapse or prolonged treatment have the same benefit in terms of duration of rituximab efficacy or time to chemotherapy (43). Several questions remain without clear response: What is the optimal prolonged treatment? What is the optimal duration maintenance? Which patients benefit from prolonged treatment? And, finally, is prolonged treatment or re-treatment at progression better in terms of survival or impact on transformation rate? Rituximab Alone in Untreated FL Usually, patients with no adverse prognostic factors are not treated until they develop such adverse parameters (44). However, because of its low-profile

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toxicity, its presumed low rate of secondary malignancy, and its lack of stem cell toxicity, rituximab single agent was investigated in this setting (45): in a series of 50 patients, a relative risk (RR) of 73% was obtained, with a CR of 26% ; 57% of the informative patients in CR reached a molecular remission. However, even patients in CR and in molecular response did not seem to benefit from this treatment because the median TTP was only two years, which is not longer than that without treatment. A randomized study is currently underway in the United Kingdom challenging this finding in these otherwise ‘‘watch and wait’’ patients. Rituximab alone was also studied in patients with a more aggressive presentation, needing treatment at diagnosis, or after some follow-up without treatment (46). The RR, just after four infusions, was comparable with the one observed in relapsing patients (50% and 50 mutations have been identified (24). Abl kinase mutations cluster within four main regions of the kinase and are associated with particular numbered amino acid residues (25): ATP-binding loop (p-loop), particularly Y253 and E255 mutants; T315 mutants; M351 mutants; and activation loop (a-loop), particularly H396 mutants. Modeling of imatinib and the novel Abl kinase inhibitors with the crystal structure of the Abl kinase (26) suggests that the effect of mutations is to either disrupt critical drug contact points or induce or favor a conformation of the kinase in which drug binding is reduced or precluded. Termed the ‘‘gatekeeper’’ position, mutations at threonine 315 confer resistance both to imatinib as well as nilotinib and dasatinib and represent a unique challenge. Other mechanisms are likely in patients with wild-type Abl; however, the incidence and role of these mechanisms remains unclear. Bcr-Abl amplification at the genomic or transcript level (27,28) has been described, overexpression of other tyrosine kinases such as the Scr-related Lyn kinase has been observed in the case of Bcr-Abl-independent resistance (18), and variability in the amount and function of the drug-influx protein Oct-1 has been linked to relative insensitivity to kinase inhibition by imatinib (29). CML stem cell resistance may also play a role as progenitors may exchange between a cycling and resting or quiescent (G0) state and vary Bcr-Abl expression, resulting in lack of effect of kinase inhibition. The longstanding phenomenon of cytogenetic clonal evolution remains a cause of resistance and likely represents molecular changes potentially active both in the presence or absence of kinase domain mutations. The search for kinase domain mutations should generally be driven by recognition of clinical resistance only. While screening of patients in all phases of CML before imatinib exposure has demonstrated increased likelihood of mutation detection with a more advanced phase of the disease, and correlation has been demonstrated between mutations identified in the setting of clinical resistance with retrospective analysis of pretherapeutic samples, identification of pretherapy high level imatinib resistant mutations did not consistently predict for

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imatinib insensitivity (30). Kinase domain mutations may thus represent a natural element of CML disease progression that may be highlighted by selection pressure with the use of potent Abl kinase inhibitors. RECOGNITION OF CLINICAL RESISTANCE IN CML NCCN guidelines (31) and a recent European LeukemiaNet consensus paper (32) have described target responses at landmark time points and allow triage of patients into categories of failure, suboptimal, and optimal response based on likelihood risk of relapse or progression. CHR by the three-month mark of therapy is considered the minimum initial response and lack of such response as failure. Failure to achieve any reduction in Ph(þ) cells [>95% Ph(þ)] by cytogenetic testing after six months of imatinib and failure to achieve MCyR after 12 months of imatinib therapy suggest a small (7 days) grade 4 hematologic toxicity; for the nilotinib phase II trial, patients with grade 2 nonhematologic toxicity that was recurrent (>3 occurrences) or persistent (>1 month) were also eligible, and all patients deemed intolerant to imatinib in the nilotinib trial were without MCyR at trial entry (thus also resistant). In the ‘‘START-C’’ trial (SRC/ABL Tyrosine kinase inhibition Activity Research Trials of dasatinib in Chronic phase CML) of dasatinib 70 mg BID for imatinib resistant or intolerant chronic phase CML, follow-up data is now available after 15 plus months median therapy in 387 patients (35,36). In this trial, 75% of cases were imatinib resistant and the remainder intolerant. Focusing on the imatinib resistant population (n ¼ 288), 72% had high-dose imatinib (>600 mg) exposure, best response on prior imatinib was MCyR or CCyR in 52%, and mutations in the Abl kinase were found in 52% of cases. Overall CHR was 91%; this included maintenance of response rather than recapturing response in a proportion of patients. Overall cytogenetic response was major in 59% and

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complete in 49%; in the imatinib resistant subset, response was slightly lower at 52% MCyR and 40% CCyR. Major molecular responses (3-log or greater reduction in Bcr-Abl) in the resistant population of 13% at 6 months and 15% at 12 months have been reported (37), as well as a progression free survival of approximately 90% at latest follow-up. Cumulative data from phase II studies of nilotinib in chronic-phase CML (n ¼ 318) continues to be updated (38,39) and the results compared. The proportion of patients with intolerance (31%) was similar, but again in the nilotinib trial these cases also demonstrated cytogenetic resistance. Seventy-three percent of patients either resistant or intolerant had exposure to 600 mg imatinib or higher (vs. 55% of the same group in the dasatinib trial). Of patients lacking CHR at study entry (n ¼ 185), 74% achieved this response on nilotinib. Overall cytogenetic responses in patients with a median of at least six months of therapy (n ¼ 280) were 48% major and 31% complete; similar responses in ‘‘intolerant’’ and ‘‘resistant’’ subsets were seen as expected, given that both groups had documented resistance. Nine percent of complete and 13% of partial cytogenetic responses were based on FISH data rather than metaphase cytogenetics (karyotype), the accepted standard in CML trials. The MCyR rate reported is 41% if partial (1–35%) responses based on FISH analysis, potentially discordant with karyotype, are not included. Of patients achieving major cytogenetic response, 4% progressed or died with six months’ follow-up; an additional 12% lost major response but continued on study. These data are consistent with previously reported progression free survival estimates for nilotinib, which appear somewhat lower than with dasatinib. Building on the experience from phase I studies, toxicity observed in phase II studies of dasatinib (35–37) and nilotinib (38,39) differed, as both agents were positioned to be options not only for imatinib failure but also for imatinib intolerance; ‘‘cross-tolerance’’ from one agent to another was studied as well. Fluid retention and myelosuppression continued to be common toxicities with dasatinib therapy; grades 3/4 neutropenia and thrombocytopenia occurred in nearly one half of treated patients and grades 3/4 fluid retention in approximately 10%, with pleural effusions (all grades) occurring in one-quarter of patients. Other higher grade toxicities with dasatinib in phase II included headache and diarrhea. Nilotinib was associated with less myelosuppression, 25% to 30% grades 3/4 neutropenia and thrombocytopenia, and less than 1% of patients experienced higher grade fluid retention. Grades 3/4 hypophosphatemia and hyperglycemia were observed with nilotinib in 11% of patients and lipase elevation in 15%, but less than 1% with associated pancreatitis. Grades 3/4 elevations in bilirubin levels occurred in 8% and other higher grade toxicities included diarrhea, rash, and arthralgias/myalgias. Evaluation for cardiovascular toxicity revealed a minimal (5 millisecond average) effect on the QTc and no apparent increase in ischemic, arrhythmic, or ventricular dysfunction events. Overall, each agent has new observed toxicities warranting monitoring, and at 70 mg BID dosing of dasatinib, there is a significantly greater need for providers to interrupt and modify dose. In the START-C trial, more than 85% of

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Mauro and Heinrich

patients’ therapy was interrupted and approximately 75% of patients had dose reduction; the actual delivered dose of dasatinib was a median of 100 mg/day. Two other important studies using dasatinib in chronic-phase include a comparison to high-dose imatinib [the ‘‘START-R’’ trial (40)] and a dose optimization trial. The Start R trial randomized patients (n ¼ 150) with resistance to imatinib at doses of 400 to 600 mg, with resistance defined the same as the single arm phase II trials, to high dose imatinib (800 mg target; n ¼ 49) versus a switch to dasatinib 70 mg BID (n ¼ 101). With follow-up now out to a median of 15 months, the dasatinib arm is more favorable, but some caveats bear explanation. The primary endpoint of MCyR three months after randomization was improved with dasatinib over high dose imatinib (36% vs. 29%) but was a trend only; among responding patients however, there was a significant ( p ¼ 0.04) increase in CCyR for dasatinib (22% vs. 8% after 3 months). After a median of 15 months’ follow-up, the gap has widened, and overall MCyR (53% vs. 33%) and CCyR (40% vs. 16%) favor switch to dasatinib. Important subset analysis sorted patients at entry by imatinib dose (400 or 600 mg) and demonstrated that the cytogenetic response advantage of dasatinib was evident mainly in patients entering study on 600 mg imatinib and for whom intervention was a modest increase (200 mg) only; doubling the imatinib dose (400 mg ? 800 mg) overall produced cytogenetic response rates close to that of switching to dasatinib. Perhaps the most relevant advantage to dasatinib is increased durability of response; across all subsets of patients, time to treatment failure and progression free survival were significantly better and support the consideration and use of dasatinib over high dose imatinib in this setting. Dose optimization studies for dasatinib in chronic-phase CML aimed at exploring a modified dose (100 mg) on the basis of the difficulty in delivering 140 mg/day dosing in earlier trials and schedule [once daily (QD) vs. BID], given that responses were schedule-independent in phase I despite the shorter half-life (~4 hours) of dasatinib. A four-arm trial (n ¼ 662 total) for chronic-phase CML (41) distributed evenly doses of 100 mg QD, 50 mg BID, 140 mg QD and 70 mg BID between patients who had demonstrated imatinib resistance [now defined more broadly to include inadequate response on standard (400 mg) imatinib], patients with Abl kinase mutations despite major cytogenetic response, and patients with molecular and low-level cytogenetic relapse. Recent follow-up from this study showed continued equality between the four arms with regard to hematologic, cytogenetic, and molecular response. Response rates were more favorable than the START-C experience likely because of the inclusion of patients with earlier identification of resistance. Comparison of the 100 mg QD arm to all others showed statistically significant reduction in grades 3/4 thrombocytopenia and congestive heart failure events, significant reduction in the need to interrupt, reduce, or discontinue therapy because of toxicity, and superior progression free survival rates. On the basis of this study, the FDA recently approved a change in the recommended dose of dasatinib to 100 mg QD. A summary of the key features and differences between the three currently approved Abl kinase inhibitors is shown in Table 1.

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Treatment of CML with Bcr-Abl Kinase Inhibitors

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Table 1 Comparison of Imatinib, Dasatinib, and Nilotinib Imatinib

Dasatinib

Nilotinib

Kinase targets

Abl, Kit, PDGFR

Abl, Kit, PDGFR, Src family (Src, Lyn, Hck, Lck, Yes, Fyn)

Abl, Kit, PDGFR

Potency vs. nonmutated Abl (IC50, nM)

630