Urinary Biomarkers: Methods and Protocols (Methods in Molecular Biology, 2292) 1071613537, 9781071613535

Table of contents :
Preface
Contents
Contributors
Part I: Urinary Biomarkers in Cancer
Chapter 1: Urinary Biomarkers in Tumors: An Overview
1 Introduction
2 Nucleic Acids
2.1 Methylation
2.2 Urine Cell-Free DNA
2.3 RNA-Based Biomarkers: The Example of PCA3
2.4 miRNAs
3 Proteins
4 Exosomes
5 Conclusions
References
Chapter 2: Urinary Cell-Free DNA Integrity Analysis
1 Introduction
2 Materials
3 Methods
3.1 DNA Samples Preparation
3.2 Samples and Primer Preparation for Real-Time PCR
3.3 Real-Time PCR
3.3.1 Data Analysis and Interpretation
4 Notes
References
Chapter 3: Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer
1 Introduction
2 Materials
2.1 ucfDNA Extraction and Quality Assessment
2.2 Mutational Analysis from ucfDNA
3 Methods
3.1 ucfDNA Extraction and Quality Assessment
3.2 ucfDNA Amplification
3.3 Setting Up the Instrument and Real-Time PCR
3.4 Data Analysis and Result Interpretation
4 Notes
References
Chapter 4: Fluorescence In Situ Hybridization in Urine Samples (UroVysion Kit)
1 Introduction
2 Materials
2.1 Materials Provided
2.2 Materials Required But Not Provided
2.3 Preparation of the Solutions
3 Methods
3.1 Specimen Collection
3.2 Specimen Processing
3.2.1 Sample Processing
3.2.2 Slide Preparation
3.2.3 Slide Pretreatment
3.3 FISH Procedure: UroVysion Assay
3.3.1 Manual Assay (See Note 6)
Denaturation of DNA Specimen
Probe Preparation
Hybridization
3.3.2 Optional Automated (HYBrite or ThermoBrite) Codenaturation Assay
Probe Preparation and Application
Denaturation of DNA Specimen and Hybridization on the HYBrite System
Denaturation of DNA Specimen and Hybridization on the ThermoBrite System
Post-hybridization Washes (Manual and Automated Assays)
3.4 Analysis of the Urine Samples
4 Notes
References
Chapter 5: Analysis of Copy Number Variation in Urine: c-Myc Evaluation Using a Real-Time PCR Approach
1 Introduction
2 Materials
2.1 Urinary Supernatant Collection
2.2 UcfDNA Isolation
2.3 UcfDNA Quantification
2.4 Real-Time PCR
3 Methods
3.1 Urinary Supernatant Collection
3.2 UcfDNA Isolation
3.3 ucfDNA Quantification Using Qubit Fluorometer
3.4 Real-Time qPCR Approach
3.4.1 Data Analysis and Interpretation
4 Notes
References
Chapter 6: Urinary microRNA and mRNA in Tumors
1 Introduction
2 mRNAs
3 miRNAs
4 Urinary Nucleic Acids in Bladder Cancer
4.1 mRNAs in Bladder Cancer
4.2 miRNAs in Bladder Cancer
5 Urinary Nucleic Acids in Prostate Cancer
5.1 mRNAs in Prostate Cancer
5.2 miRNAs in Prostate Cancer
6 miRNAs in Renal Carcinoma
7 Urinary miRNAs: A Challenge in Nonurological Tumors
7.1 Urinary miRNAs in Gastric Cancer
7.2 Urinary miRNAs in Female Tumors
8 Conclusions
References
Chapter 7: Long Noncoding RNAs as Innovative Urinary Diagnostic Biomarkers
1 Introduction
2 Long Noncoding RNAs for Bladder Cancer Diagnosis
2.1 UCA1
2.2 H19
2.3 LncRNA-Based Biomarker Panels
3 Long Noncoding RNAs for Prostate Cancer Diagnosis
3.1 PCA3
3.2 MALAT1 and FR0348383
3.3 LincRNA-p21
4 Conclusions
References
Chapter 8: Urinary Nucleic Acid in Tumor: Bioinformatics Approaches
1 Introduction
2 Analysis of Small Variants from ctDNA
3 cfDNA Molecule Tagging
4 Copy-Number Variants from cfDNA
5 Analysis of Epigenetic Profile in cfDNA
6 Conclusions
References
Chapter 9: PCA3 in Prostate Cancer
1 Introduction
2 PCA3
3 PCA3 as a Biomarker
4 The FDA-Approved Progensa PCA3 Assay
5 PCA3 and Prostate Biopsy
6 Conclusions
References
Chapter 10: Urinary Exosomes in Prostate Cancer
1 Introduction
2 Materials
2.1 Urinary Supernatant Collection
2.2 EVs Isolation
2.3 NanoSight Check
2.4 MACSPlex Exosome Kits
3 Methods
3.1 Urinary Supernatant Collection
3.2 EVs Isolation
3.3 Nanosight Check
3.4 MACSPlex Approach
3.4.1 Data Analysis and Interpretation
4 Notes
References
Chapter 11: Urinary Biomarkers In Bladder Cancer
1 Introduction
2 Current FDA-Approved Urinary Biomarkers
2.1 Fluorescent In Situ Hybridization (FISH): UroVysion
2.1.1 Defining the Cutoff for a Positive FISH Result
2.2 Nuclear Matrix Protein 22
2.3 Bladder Tumor Antigen
2.4 ImmunoCyt/uCyt +
3 Commercially Available (Not FDA-Approved) Urinary Biomarkers for Bladder Cancer
3.1 Cxbladder
3.2 AssureMDx
3.3 XPert BC
3.4 Urinary Bladder Cancer Rapid Test
3.5 Telomerase
4 Conclusion
References
Chapter 12: Telomerase Activity Analysis In Urine Sediment for Bladder Cancer
1 Introduction
2 Materials
2.1 Protein Isolation
2.2 TRAP ASSAY with Real Time PCR
3 Method
3.1 Urine Collection
3.2 Proteins Isolation and Quantification
3.3 Protein Quantification
3.4 TRAP Assay with Real Time PCR
4 Notes
References
Chapter 13: Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarker...
1 Introduction
2 Materials
2.1 Urine Sample Storage
2.2 Methanol/Chloroform Precipitation
2.3 Estimation of Protein Concentration
2.4 In-Solution Tryptic Digest
2.5 Stage-Tip Desalting
3 Methods
3.1 Urine Sample Handling
3.2 Methanol-Chloroform Precipitation
3.3 In-Solution Tryptic Digest
3.4 Desalting Tryptic Peptides with C18
3.5 LC-MS/MS Measurement
4 Data Analysis
5 Notes
References
Part II: Urinary Extracellular Vesicles
Chapter 14: Current Methods for the Isolation of Urinary Extracellular Vesicles
1 Introduction
2 Serial Ultracentrifugation
3 Ultrafiltration
4 Polymer-Based Precipitation Methods
5 Density Gradient
6 Size-Exclusion Chromatography
7 Immuno-Bead Precipitation
8 Conclusion
9 Protocols
9.1 Serial Ultracentrifugation
9.2 Ultrafiltration
9.3 Polymer-Based Precipitation
9.4 Optiprep Density Gradient Ultracentrifugation
9.5 Size Exclusion Chromatography
9.6 Immunobead Protocol
References
Chapter 15: Urinary Extracellular Vesicles: Ultracentrifugation Method
1 Introduction
2 Materials
2.1 Collection and Preparation of Urine Samples
2.2 Ultracentrifugation with Sucrose-D2O Cushion
2.3 PS Affinity Method
3 Methods
3.1 Collection and Preparation Steps for Urine Samples
3.2 Ultracentrifugation
3.3 Sucrose-D2O Cushion Ultracentrifugation
3.4 PS Affinity Method for Isolation of EVs from Urine
3.5 Immobilization of the Biotinylated Tim4 to Streptavidin Magnetic Beads
3.6 Affinity Reaction
3.7 Washing of the EV-Binding Beads
3.8 Elution of the EVs
4 Notes
References
Part III: Physical Activity and Urinary Markers
Chapter 16: Urinary Catecholamines as Markers in Overtraining Syndrome
1 Introduction
2 Blood Biochemical Markers: An Overview
2.1 Plasma Glutamine
2.2 Blood Lactate Profile
2.3 Plasma Hormones
3 Urinary Markers: The Catecholamines
3.1 Urinary Catecholamines in Overtraining Syndrome
4 Conclusions
References
Chapter 17: Urinary Markers and Chronic Effect of Physical Exercise
1 Introduction
2 Markers of Kidney Disease and Damage
3 Physical Exercise and Urinary Biomarkers
4 Conclusions
References
Part IV: Urinary Metabolic Markers
Chapter 18: Urinary Metabolic Biomarkers in Cancer Patients: An Overview
1 Introduction
2 Hepatopancreatic Tract Cancers
2.1 Hepatocellular Carcinoma
2.2 Pancreatic Ductal Adenocarcinoma
3 Urinary Tract Cancers
3.1 Renal Cancer
3.2 Prostate Cancer
3.3 Bladder Cancer
4 Future Directions
References
Index

Citation preview

Methods in Molecular Biology 2292

Samanta Salvi Valentina Casadio Editors

Urinary Biomarkers Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, UK

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-bystep fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Urinary Biomarkers Methods and Protocols

Edited by

Samanta Salvi Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy

Valentina Casadio Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy

Editors Samanta Salvi Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS Meldola, Italy

Valentina Casadio Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS Meldola, Italy

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1353-5 ISBN 978-1-0716-1354-2 (eBook) https://doi.org/10.1007/978-1-0716-1354-2 © Springer Science+Business Media, LLC, part of Springer Nature 2021 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Urine represents a convenient source of biomarkers for different diseases and clinical applications, mostly for cancer diagnosis, prognosis, and treatment monitoring but also for other important areas such as physical activity. Some important characteristics such as noninvasiveness and high patient compliance render urine an ideal body fluid to be analyzed in order to find biomarkers derived from proteins, DNA, mRNA, miRNA, and extracellular vesicles, coming from different body tissues and organs. The technological advancement allows to study urinary biomarkers even when they are present at low concentrations, obtaining a great number of data. The increasing number of publications in the last decades on this topic makes it mandatory to straighten up the main important concepts and findings, from the methodological approaches to the clinical applications. The present volume aims at describing some of the most important techniques used for studying urinary biomarkers that are routinely used in the clinical practice (e.g., PCA3 in prostate cancer) or whose validation is still ongoing (e.g., urinary extracellular vesicles). It will also provide information regarding the different alterations that could be found and studied for different cancer diseases and physiological conditions as physical activity. The target audience will be biologists, technicians, or clinicians that are interested to study and deepen the knowledge on urinary biomarkers and potential applications. Meldola, Italy Meldola, Italy

Samanta Salvi Valentina Casadio

v

Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

PART I

v ix

URINARY BIOMARKERS IN CANCER

1 Urinary Biomarkers in Tumors: An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ilaria Cimmino, Sara Bravaccini, and Claudio Cerchione 2 Urinary Cell-Free DNA Integrity Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Valentina Casadio and Samanta Salvi 3 Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chiara Molinari and Elisa Chiadini 4 Fluorescence In Situ Hybridization in Urine Samples (UroVysion Kit) . . . . . . . . Sara Bravaccini 5 Analysis of Copy Number Variation in Urine: c-Myc Evaluation Using a Real-Time PCR Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Valentina Casadio, Filippo Martignano, Roberta Gunelli, and Samanta Salvi 6 Urinary microRNA and mRNA in Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Erika Bandini 7 Long Noncoding RNAs as Innovative Urinary Diagnostic Biomarkers. . . . . . . . . Giulia Brisotto, Roberto Guerrieri, Francesca Colizzi, Agostino Steffan, Barbara Montico, and Elisabetta Fratta 8 Urinary Nucleic Acid in Tumor: Bioinformatics Approaches. . . . . . . . . . . . . . . . . . Davide Angeli 9 PCA3 in Prostate Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ` , and Massimo Fiori Roberta Gunelli, Eugenia Fragala 10 Urinary Exosomes in Prostate Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samanta Salvi, Erika Bandini, and Francesco Fabbri 11 Urinary Biomarkers In Bladder Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matteo Costantini, Graziana Gallo, and Giovanna Attolini 12 Telomerase Activity Analysis In Urine Sediment for Bladder Cancer. . . . . . . . . . . Valentina Casadio and Sara Bravaccini 13 Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarkers of Schistosoma-Associated Bladder Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tariq Ganief, Bridget Calder, and Jonathan M. Blackburn

vii

3 17

23 35

49

57 73

95 105 115 121 133

143

viii

Contents

PART II 14

15

Current Methods for the Isolation of Urinary Extracellular Vesicles . . . . . . . . . . . 153 Serena Maggio, Emanuela Polidori, Paola Ceccaroli, Andrea Cioccoloni, Vilberto Stocchi, and Michele Guescini Urinary Extracellular Vesicles: Ultracentrifugation Method . . . . . . . . . . . . . . . . . . 173 Eisuke Tomiyama, Kazutoshi Fujita, and Norio Nonomura

PART III 16 17

PHYSICAL ACTIVITY AND URINARY MARKERS

Urinary Catecholamines as Markers in Overtraining Syndrome . . . . . . . . . . . . . . . 185 Marina Casadio Urinary Markers and Chronic Effect of Physical Exercise . . . . . . . . . . . . . . . . . . . . 193 Leydi Natalia Vittori, Jenny Romasco, Andrea Tarozzi, and Pasqualino Maietta Latessa

PART IV 18

URINARY EXTRACELLULAR VESICLES

URINARY METABOLIC MARKERS

Urinary Metabolic Biomarkers in Cancer Patients: An Overview . . . . . . . . . . . . . . 203 Serena De Matteis, Massimiliano Bonafe`, and Anna Maria Giudetti

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

213

Contributors DAVIDE ANGELI • Unit of Biostatistics and Clinical Trials, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST), Meldola, Italy GIOVANNA ATTOLINI • Department of Pathology, Azienda Ospedaliera-Universitaria Policlinico di Modena, Modena, Italy ERIKA BANDINI • Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy JONATHAN M. BLACKBURN • Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa MASSIMILIANO BONAFE` • Department of Experimental, Diagnostic and Specialty Medicine, AlmaMater Studiorum, University of Bologna, Bologna, Italy SARA BRAVACCINI • Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy GIULIA BRISOTTO • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy BRIDGET CALDER • Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa MARINA CASADIO • Ravenna, Italy VALENTINA CASADIO • Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy PAOLA CECCAROLI • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy CLAUDIO CERCHIONE • Hematology Unit, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy ELISA CHIADINI • Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” – IRST S.r.l., Meldola, Italy ILARIA CIMMINO • Department of Translational Medicine, University of Naples “Federico II”, Naples, Italy ANDREA CIOCCOLONI • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy FRANCESCA COLIZZI • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy MATTEO COSTANTINI • Department of Pathology, Azienda Ospedaliera-Universitaria Policlinico di Modena, Modena, Italy SERENA DE MATTEIS • Department of Medicine, Section of Oncology, University of Verona, Verona, Italy FRANCESCO FABBRI • Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy MASSIMO FIORI • Department of Urology, GB Morgagni Hospital, Forlı`, Italy EUGENIA FRAGALA` • Department of Urology, GB Morgagni Hospital, Forlı`, Italy ELISABETTA FRATTA • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy

ix

x

Contributors

KAZUTOSHI FUJITA • Department of Urology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan GRAZIANA GALLO • Department of Pathology, Azienda Ospedaliera-Universitaria Policlinico di Modena, Modena, Italy TARIQ GANIEF • Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa ANNA MARIA GIUDETTI • Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy ROBERTO GUERRIERI • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy MICHELE GUESCINI • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy ROBERTA GUNELLI • Department of Urology, GB Morgagni Hospital, Forlı`, Italy PASQUALINO MAIETTA LATESSA • Department for Life Quality Studies, University of Bologna, Rimini, Italy SERENA MAGGIO • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy FILIPPO MARTIGNANO • Core Research Laboratory, ISPRO, Florence, Italy; Department of Medical Biotechnologies, University of Siena, Siena, Italy CHIARA MOLINARI • Biosciences Laboratory, IRCCS Istituto Romagnolo per lo Studio dei Tumori “Dino Amadori” – IRST S.r.l., Meldola, Italy BARBARA MONTICO • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy NORIO NONOMURA • Department of Urology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan EMANUELA POLIDORI • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy JENNY ROMASCO • Department for Life Quality Studies, University of Bologna, Rimini, Italy SAMANTA SALVI • Biosciences Laboratory, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy AGOSTINO STEFFAN • Immunopathology and Cancer Biomarkers, Centro di Riferimento Oncologico di Aviano (CRO), IRCCS, Aviano, Italy VILBERTO STOCCHI • Department of Biomolecular Sciences, University of Urbino Carlo Bo, Urbino, Italy ANDREA TAROZZI • Department for Life Quality Studies, University of Bologna, Rimini, Italy EISUKE TOMIYAMA • Department of Urology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan LEYDI NATALIA VITTORI • Department for Life Quality Studies, University of Bologna, Rimini, Italy

Part I Urinary Biomarkers in Cancer

Chapter 1 Urinary Biomarkers in Tumors: An Overview Ilaria Cimmino, Sara Bravaccini, and Claudio Cerchione Abstract Recent reports suggest that urine is a useful noninvasive tool for the identification of urogenital tumors, including bladder, prostate, kidney, and other nonurological cancers. As a liquid biopsy, urine represents an important source for the improvement of new promising biomarkers, a suitable tool to identify indolent cancer and avoid overtreatment. Urine is enriched with DNAs, RNAs, proteins, circulating tumor cells, exosomes, and other small molecules which can be detected with several diagnostic methodologies. We provide an overview of the ongoing state of urinary biomarkers underlying both their potential utilities to improve cancer prognosis, diagnosis, and therapeutic strategy and their limitations. Key words Urological cancers, Nonurological cancers, Biomarker, Exosome, miRNA, ucfDNA, Liquid biopsy, Urine

1

Introduction The early diagnosis of cancers is a great challenge, in terms of accuracy, compliance of patients, and costs. The ideal biomarker for cancers should be noninvasive and highly accurate, with a good cost-effectiveness and a simple interpretation of the results, balanced with good compliance for patients. Considering these characteristics, there is increasing need of innovative tools which could have a role in diagnosis and monitoring of cancers, and urine could be an ideal source of innovative biomarkers, which could reduce the necessity of semiinvasive procedures, often required in clinical management of patients for accurate diagnosis and follow-up [1]. Nowadays, liquid biopsy is considered a useful tool for cancer biomarker discovery, as it is a noninvasive and cost-effective alternative to tissue biopsy, which can provide a more personalized overview of the disease and the genetic profile of tumor subclones [2]. For many cancers, urine could represent a “liquid biopsy”, which is easily and repeatedly accessible, with potential

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Ilaria Cimmino et al.

frequent use in different time-points of diagnosis and follow-up. It can be widely used as an ideal biological matrix for the study of urinary system diseases [3]. Urine is a biological fluid composed of several inorganic salts, organic compounds, and a variety of different exfoliated cell types from different sites such as leukocytes, urothelial cells, renal cells, and prostate cells. Although it seems to be a promising source for the discovery of new biomarkers for cancers, the presence of other interfering substances and urine volume could still be a problem for accuracy [1]. During last years, many researchers investigated the ability of new potential urine biomarkers to improve cancers diagnosis in order to avoid unnecessary invasive exams (such as biopsies) and to discriminate between aggressive and benign cancers and, in this way, to prevent overtreatment and to help to correctly diagnose with a cost-effective, simple, and fast exam, which could have not only a diagnostic but also a prognostic role, which should be validated by clinical trials. A summary of the urinary biomarkers explained in this review is reported in Table 1.

2

Nucleic Acids In urogenital tumors, urine is one of the biologic fluids that can be obtained in a noninvasively and repeatedly manner, containing exfoliated tumor cells and tumor cell-free nucleic acids. Tumor-derived DNA, mRNA and miRNAs can be collected in different ways, including whole urine, using centrifugation to obtain urine sediment, and through filtration to get urine supernatant and cells [4].

2.1

Methylation

DNA methylation–based tests improvement provide promising biomarkers for detection of urogenital cancers, especially for early diagnosis because DNA methylation changes are considered one of the first event in tumorigenesis. In prostate cancer, GSTP1 methylation represents a promising urine biomarker particularly detected in urine of cancer patients [5]. Due to its low sensitivity GSTP1 has been recommended in clinical practice as diagnostic markers in combination with other biomarkers [6]. Feng et al. show a potential feasibility of hypermethylated genes detected in urine (DAPK1, RARB , TWIST1, and CDH13) using as molecular markers for cervical cancer screening [7]. Since the detection of epigenetic alterations require different methodological approaches, most of these biomarkers lack of reproducibility. Usually, the methods used to identify DNA methylation changes, is DNA–sodium bisulfite treatment. This compound is able to selectively deaminates unmethylated cytosines to uracil while the methylated form of cytosines escapes from the

Urinary Biomarkers in Tumors: An Overview

5

Table 1 Urinary biomarkers in tumors Biomarker

Tumor type

References

GSTP1 hypermethylation

PCa

[5]

DAPK1, RARB, TWIST1. CDH13

Cervical cancer

[7]

PIK3CA (E545K) FGFR3 (S249C, Y373C) mutations

NMIBC, MIBC

[16, 17]

LINE1

NSCLC

[18–20]

p53 mutations (codon 249)

HCC

[22]

Vimentin Hypermethylation

CRC

[23]

PCA3

PCa

[24–26]

TMPRSS2:ERG

PCa

[32–35]

miR-144-5p, miR-23b/27b, miR-145

Bladder cancer

[41–43]

" miR-107, miR-574-3p

PCa

[44]

# miR-205, miR-214

PCa

[45]

" miR-96, miR-183

Urothelial carcinoma

[47]

" miR-888

PCa

[48]

PSA

PCa

[51, 52]

Sarcosine

PCa

[53, 54]

HE4

Ovarian cancer

[55, 56]

REG1A, TFF1, LYVE1

PDAC

[58]

Fetuin-A, aquaporin.1, ATF3

Kidney cancer

[61–63]

TALDO 1

MIBC

[65]

PCa prostate cancer, PDAC pancreatic ductal adenocarcinoma, MIBC muscle-invasive bladder cancer, NMIBC non–muscle-invasive bladder cancer, NSCLC non–small-cell lung cancer, HCC hepatocarcinoma, CRC colorectal cancer

bisulfite reaction. Then, using PCR-based technologies, these regions can be analyzed to functionally target relevant locations, such as CpG islands, where methylation affects gene expression [8]. During the last years, many clinical studies have been performed to find an “ideal” biomarker but, among the independent studies evaluated, there was great variability in sensitivity and specificity. 2.2 Urine Cell-Free DNA

Liquid biopsy is gaining significant attention for personalized medicine in cancer [2, 9]. Circulating tumor DNA (ctDNA) is a precious tool in cancer research, an indispensable component of liquid biopsy able to overcome traditional biopsy methods, thus

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facilitating the study of tumor dynamics, treatment resistance, and disease progression. Indeed, urinary cell-free DNA (ucfDNA) is believed to have the potential of being a useful and ultranoninvasive tool for cancer screening, diagnosis, prognosis, and monitoring of cancer progression and therapeutic effect. Moreover, ucfDNA has the ability to give important information on DNA derived from cancerous cells, and tumors originate either from cells shedding into urine from the genitourinary tract. Thus, ucfDNA samples seem to provide more representative information that can be comparable to patient biopsy samples [10, 11]. Several papers have described different methods for the detection of ucfDNA in the urine, although until now there are no standard protocols that described a unique methodology for isolation and detection of ucfDNA to identify and detect somatic aberrations, such as single nucleotide variants (SNVs) or copy number alterations (CNAs). UcfDNA could be isolated using either commercial kits or classical laboratory techniques are mainly PCR-based assays [12, 13]. More recently, the rapid development of new molecular assays such as the next-generation sequencing (NGS) has largely improved the sensitivity of ucfDNA detection [14]. UcfDNA has been reported to be clinically useful at every step of the urogenital malignancy treatment, from early diagnosis to the association with tumor burden and prognosis in some cancer sites, monitoring or predicting response to treatment in both the radical and palliative settings. One of the first study that demonstrated the presence of ucfDNA in muscle invasive bladder cancer (MIBC) was described in 1991 by Sidransky et al., that showed the presence of p53 mutations in the urinary sediment of MIBC affecting patients [15]. Furthermore, Christensen et al. detected ucfDNA in patients with non–muscle-invasive bladder cancer (NMIBC) and MIBC, using a targeted approach with ddPCR assays to detect three hotspot mutations in PIK3CA (E545K) and FGFR3 (S249C, Y373C), first in tumor tissue biopsies and then in plasma and urinary supernatant [16]. It has been demonstrated that the availability of ucfDNA in urine could have great importance in bladder cancer prognosis due to the close proximity of the primary tumor to urine in patients with localized disease [17]. In oncology, ucfDNA has been investigated as a promising biomarker in several cancer types, particularly, but not only for genitourinary tract. For example, ucfDNA showed 88% concordance with primary tumor tissue for EGFR mutations in non– small-cell lung cancer (NSCLC) [18] and in hepatocellular carcinoma. Recent data displayed LINE1 as a promising biomarker for early detection of NSCLC which was significantly higher in III/IV stage NSCLC patients than in healthy controls [19]. Moreover, it has been demonstrated an hypomethylation of LINE1 fragments in bladder cancer patients, an epigenetic alteration frequently found in malignancies, compared to healthy individuals [20].

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Recently, longitudinal sampling of ucfDNA in patients with MIBC before and after neoadjuvant chemotherapy was shown to help predict outcome [21]. Urine collection offers the advantages of being truly noninvasive and allowing for large sample volumes to be obtained. Lin et al. highlighted the importance of detection of p53 mutations (codon 249) in hepatocarcinoma patients suggesting a potential role for screening [22]. In addition, in colorectal cancer patients the hypermethylation of vimentin gene in ucfDNA has been identified in short fragments [23]. Over the past years, data obtained from several trials suggest that the levels of ucfDNA detected in urine have been associated with tumor burden and may indicate minimal residual disease following surgery, or predict for future disease recurrence with a greater sensitivity compared to current standard radiological assessment. Nevertheless, one of the main problems with the development of ucfDNA-based test is the lack of specificity, because its raised levels are also seen in benign conditions such as pregnancy, trauma, or inflammation and not only in malignancy [12]. However, despite ucfDNA has been explored as noninvasive useful instrument for diagnosis, prognosis, and treatment response prediction in tumor, there is also a lot of work to do for the application of ucfDNA to the clinical setting [12]. 2.3 RNA-Based Biomarkers: The Example of PCA3

Recent advances suggest prostate cancer antigen 3 (PCA3) as a promising urinary biomarker for prostate cancer. The PCA3 gene is a long noncoding mRNA first identified in 1999, mapped to chromosome 9q21–22, that is overexpressed in more than 95% of all prostate cancers [24]. Many clinical studies have correlated higher PCA3 scores with tumor aggressiveness, suggesting PCA3 as a prognostic biomarker [25]. PCA3 test at the first follow-up biopsy was a significant predictor of upgrading to Gleason score  7 among 90 men with a 5α-reductase inhibitor during active surveillance. Whitman et al. demonstrated that the detection of PCA3 in urine before prostatectomy was associated with tumor volume and extracapsular extension [26]. In 2012 the first molecular PCA3 urine test (PROGENSA) was approved in the USA by the FDA for men with a previous negative biopsy. Because PCA3 does not encode a protein, mRNA is the only molecule that could be measured in urine sediment after a digital rectal exam (DRE). It has been demonstrated that overexpression of PCA3 may modulate cell proliferation, while PCA3 knockdown induced the upregulation of several transcripts coding for androgen receptor cofactors and modulated the expression of epithelial–mesenchymal transition (EMT) markers. Moreover, knockdown of PCA3 resulted in the upregulation of E-cadherin, claudin-3, and cytokeratin-18, and the downregulation of vimentin [27].

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Among the urinary tests, the two-gene urinary assay Select MDx (MDxHealth, Irvine, CA, USA) is a very promising test, able to measured HOXC6 and DLX1, having a better diagnostic performance for the identification of clinically significant tumors and a lower cost [28]. The commercially available ExoDx Prostate test is a noninvasive urinary test for the detection of prostate cancer that does not require a DRE for the collection of specimens. This urinary exosome assay-based measures three genes, PCA3, ERG (including the TMPRSS2:ERG fusion gene), and SPDEF (sterile alpha motifpointed domain-containing Ets transcription factor) by a reverse transcription-quantitative polymerase chain reaction [29, 30]. The TMPRSS2:ERG gene fusion was first discovered in 2005 [31]. This gene fusion is one of the most frequent chromosomal rearrangements in prostate cancer. It derives from the fusion of the transmembrane protease, serine 2 (TMPRSS2) gene, and the v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) gene or other ETS (E26 transformation specific) transcription factors. This rearrangement can be measured in urine after DRE and, although it presents low sensitivity, it is highly specific for predicting clinically significant prostate cancer on biopsy [28]. Several studies aimed to combine TMPRSS2:ERG with other biomarkers, such as PCA3, to obtain a better accuracy for the prediction of prostate cancer detection and progression [32, 33] and uses as a first-line screening test in clinical practice as reported by Sanguedolce and colleagues [34]. In particular, in a prospective multicenter study performed on 516 patients, it has been demonstrated that TMPRSS2:ERG significantly correlated with the absence of malignancy, but not with the aggressiveness of prostate cancer [35]. Literature data suggested that PCA3 could be recommended for patients undergoing repeat biopsies, for whom it is a wellestablished biomarker, due to its controversial correlation with aggressiveness. Thus, it could be considered a better choice for prostate cancer screening [36]. However, up to now, there are no reports showing the direct association of PCA3 with cancer-specific survival or biochemical-free survival. 2.4

miRNAs

MicroRNAs (miRNAs) are small (20–22 nucleotides), noncoding RNAs, able to regulate gene expression at the posttranscriptional level [37]. miRNAs interact with the 3’UTR of target messenger RNAs (mRNAs) and could inhibit the translation or targets the degradation of the bound mRNAs [38]. MiRNAs can be detected in urine as a free form, in cancer cells urinary precipitates and in exosomes. Once extracted from a sample, miRNAs can be quantified in different ways. The most popular methods are microarray analysis used for multiplex analysis of already described miRNAs, RNA-seq utilizes NGS technology for

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high-throughput analysis of miRNAs and allows the screening of all miRNAs from a single sample, regardless of whether their sequence is unknown and real-time qPCR that represents a commonly used method to validate miRNA expression, northern blotting, and in situ hybridization. Compared to blood, the urinary cell-free transcriptome has been little studied, and the majority of studies have been carried out on miRNAs and, specifically, on their presence in urine extracellular vesicles (uEVs) [39, 40]. MiRNAs have been found upregulated or downregulated in most type of cancers. In bladder cancer, several miRNA family members were found to contribute to physiological and pathological processes, such as miR-144-5p [41], miR-23b/27b [42], and miR-145 [43]. miR-107 and miR-574-3p were significantly higher in urinary precipitates in patients with local and advanced prostate cancer compared to healthy controls [44], while in another study miR-205 and miR-214 were significantly decreased in urine from 32 patients with prostate cancer compared with 12 healthy controls [45]. Di Meo et al. demonstrated the potential utility of urinary miRNAs as potential diagnostic and prognostic markers for earlystage renal tumors (pT1a; 4 cm) [46]. Urine miR-96 and miR-183 overexpression has been detected particularly in urothelial carcinoma [47] while upregulation of miR-888 in urine prostatic secretions correlated with aggressiveness in prostate cancer [48]. The large amount of data obtained from literature on adopting miRNA strategy as urinary biomarkers are quite controversial and most of these data report the same miRNA differentially displayed between specimens. Despite the performance of a single miRNA biomarker was not ideal; they could potentially help assign patients to appropriate management programs.

3

Proteins Urine are generally composed of plasma proteins able to pass through the glomerular filtration barrier and proteins secreted from the kidney and urinary tract. Among the several biomarkers, the protein profile in urine may be a less invasive test and more convenient way of identifying onset and cancer progression because different proteins may be more stable in urine than in blood and their detection could be more sensitive than the same protein detected in blood. Moreover, although urine proteomics is much lower if compared to plasma proteome, it can reflect the body condition and not only urinary tract. Prostatic specific antigen (PSA) is a serine protease that physiologically dissolves seminal clots, degrading semenogelin and fibronectin. The biogenesis of PSA starts from an inactive precursor, preproPSA; then, a cleavage of 17-aa at the N-terminal end

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produces inactive pro-PSA, which is released in the prostate lumen. Proteolytic degradation of the mature enzyme and pro-PSA constitute free PSA (fPSA). The isoform [-2]pro-PSA is preferentially synthesized in malignant cells [49, 50]. Serum PSA is considered a reliable indicator in the diagnosis and management of prostate cancer, but this test presents some limitations. In 1985, it was first discovered the presence of PSA in urine [51]. Bolduc and his coworkers demonstrated the usefulness of urine PSA in the differential diagnosis of benign prostatic hyperplasia and prostate cancer, especially when the serum PSA level is between 2.5 ng/ml and 10 ng/ml [52]. Among urine biomarkers, sarcosine has a promising diagnostic potential for prostate cancer [53]. Sarcosine, also known as N-methylglycine, can be detected both in urine and in serum and it was found significantly increased during prostate cancer progression. Although serum sarcosine had a higher predictive value than total and free PSA in detecting prostate cancer in patients with total serum PSA 250 bp (“long fragments”) are analyzed in addition to one shorter sequences (125 bp, “short fragments”) (see Note 5) that is useful to check potential PCR inhibitors.

3.3

Real-Time PCR

1. Thaw primers, IQ SYBR green supermix (Bio-Rad, Milan, Italy), genomic DNA and ucfDNA diluted samples on ice. 2. Prepare a number of tubes or strips corresponding to: no. of samples * 2 + 6 standards * 2 + two negative controls.

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3. Aliquot 10 μL in duplicate for each standard and diluted sample and 10 μL of Nuclease-free water for the negative control. 4. Prepare a mix of 2 μL of each primer mix, 12.5 μL of IQ SYBR green supermix (Biorad, Milan, Italy), and 6.5 μL of nucleasefree water for each sample. When preparing the mix, consider no. of samples * 2 + 6 standards * 2 + two negative controls and an excess of 2 samples. 5. Aliquot 15 μL of the prepared mix in each tube. 6. Start the protocol with PCR conditions adequately adjusted for the optimal melting temperature of the primers (see Note 6). 3.3.1 Data Analysis and Interpretation

The ucfDNA value for each sample was obtained by the specific real-time PCR instrument software by a standard curve construction for each individual PCR gene evaluation and using standard curve interpolation. 1. Evaluate the specificity of the PCR products by Melting Analysis. 2. Evaluate the replicates: sample replicates with a difference 1 Ct must be discarded and reevaluated with a second experiment. 3. Evaluate the short sequence: if the Ct is 33 cycles, you can proceed with the analysis, otherwise, you must consider the sample as Not Evaluable (NE). 4. Calculate the median Ct of each long sequence for each sample and consider samples with a Ct value 36. 5. Evaluate the concentration of each long amplicon by an interpolation with the standard curve and obtain a concentration value (ng/μL) for each amplicon. 6. Final result is the sum of each analyzed long amplicon (see Note 7).

4

Notes 1. You can collect first morning urine or even second/third. Take into account that if you choose to collect first morning urine, this sample contains a higher number of cells and cellular debris coming from the urological tract and exfoliated in urine during the night. Therefore, you will recover a higher DNA yield. This can help you if you are studying a urological cancer (you will probably find more DNA coming from cancer cells). On the other hand, if you are approaching at other pathologies and you are searching for DNA coming from the circulation, the high amount of DNA derived from urological cells will be confounding and will probably reduce your test sensitivity.

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2. We use NanoDrop spectrophotometer as this instrument permits to use only 1 μL of isolated DNA, thus avoiding the waste of sample. 3. In our experience, 5 μM concentration resulted the best for many tested primers. However, on the basis of the chosen primers, you can adjust primer concentrations during the setup of the real-time PCR experiment. 4. These points should be considered when designing primers: (a) Primer length should be 15–30 bases. (b) Optimal G-C content should range between 40 and 60%. (c) The melting temperatures (Tm) of each primer should be similar for forward and reverse primer in order to easily select the most adequate annealing temperature. Optimal Tm for primers range between 52 and 58  C, although the range can be expanded to 45–65  C. The final Tm for both primers should differ by no more than 5  C. (d) The 30 end of primers should contain a G or C in order to clamp the primer and prevent “breathing” of ends, increasing priming efficiency. DNA “breathing” occurs when ends do not stay annealed but fray or split apart. The three hydrogen bonds in GC pairs help prevent breathing but also increase the Tm of the primers. (e) Dinucleotide repeats (e.g., ATATATATAT) or single base runs (e.g., CCCCC) should be avoided as they can cause slipping along the primed segment of DNA and or hairpin loop structures to form. If unavoidable due to nature of the DNA template, then only include repeats or single base runs with a maximum of four bases. There are many online programs to aid in designing primers, such as NCBI Primer design tool Primer3. In addition, it could be useful to run a blast on NCBI to check for the target specificity of the primers. 5. The short sequences must be identified in a region that is not amplified nor deleted in the disease you would like to study. 6. We suggest for long sequences to start with this protocol: 95  C for 3 min followed by 45 cycles: 40  C for 40 s, a temperature depending on primer set (e.g., 56  C) for 40 s, and 72  C for 1 min. For the short sequences we suggest to start with these conditions: 95  C for 90 s followed by 45 cycles at 94  C for 40 s and a temperature depending on primer set (e.g., 54  C) for 1 min. 7. In our experience, we considered the sum of each long amplicon to obtain an ucfDNA integrity value. However, you can also considered the sum of each long amplicon adjusted with the value of the short sequence to obtain an integrity index (II).

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References 1. Tsui NB, Jiang P, Chow KC et al (2012) High resolution size analysis of fetal DNA in the urine of pregnant women by paired-end massively parallel sequencing. PLoS One 7(10): e48319 2. Lu T, Li J (2017) Clinical applications of urinary cell-free DNA in cancer: current insights and promising future. Am J Cancer Res 7 (11):2318–2332 3. Lin SY, Linehan JA, Wilson TG, Hoon DSB (2017) Emerging utility of urinary cell-free nucleic acid biomarkers for prostate, bladder, and renal cancers. Eur Urol Focus 3 (2–3):265–272 4. Lopez-Beltran A, Cheng L, Gevaert T et al (2020) Current and emerging bladder cancer biomarkers with an emphasis on urine biomarkers. Expert Rev Mol Diagn 20(2):231–243 5. Salvi S, Martignano F, Molinari C et al (2016) The potential use of urine cell free DNA as a marker for cancer. Expert Rev Mol Diagn 16 (12):1283–1290

6. Casadio V, Salvi S (2019) Urinary cell-free DNA: isolation, quantification, and quality assessment. Methods Mol Biol 1909:211–221 7. Bryzgunova OE, Laktionov PP (2015) Extracellular nucleic acids in urine: sources, structure, diagnostic potential. Acta Nat 7(3):48–54 8. Jahr S, Hentze H, Englisch S, Hardt D, Fackelmayer FO, Hesch RD, Knippers R (2001) DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res 61(4):1659–1665 9. Zonta E, Nizard P, Taly V (2015) Assessment of DNA integrity, applications for cancer research. Adv Clin Chem 70:197–246 10. Casadio V, Calistri D, Tebaldi M et al (2013) Urine cell-free DNA integrity as a marker for early bladder cancer diagnosis: preliminary data. Urol Oncol 31(8):1744–1750 11. Salvi S, Gurioli G, Martignano F et al (2015) Urine cell-free dna integrity analysis for early detection of prostate cancer patients. Dis Markers 2015:57412

Chapter 3 Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer Chiara Molinari and Elisa Chiadini Abstract Urinary cell-free DNA offers an important noninvasive source of material for genomic testing also for nonurological tumors. Its clinical utility in monitoring tumor evolution and treatment failure is promising. Here we describe a method to detect cancer mutations into urine from patients affected by colorectal cancer. Key words Urinary cell-free DNA, Mutation, PCR, Cancer, Monitoring

1

Introduction Liquid biopsies analyses have been recognized as a source of prognostic, predictive, and pharmacodynamic biomarkers and their use for longitudinal monitoring of tumor evolution and appearance of treatment resistance has been increased [1]. Although the role of circulating cell-free (cf) DNA in cancer has been undoubtedly demonstrated, less is known about the role of urinary cell-free DNA (ucfDNA) and its potential use as liquid biopsy. UcfDNA takes its origin either directly from dying cells exfoliated in urine or from the circulation. Most published data on ucfDNA are focused on bladder cancer and other urological malignancies [2]. However, urine may provide information not only from kidney and urinary tracts but also from distant organs via plasma obtained through glomerular filtration. In this case, only low molecular weight DNA could pass through the glomerular filtration and could be excreted in urinary cell-free components [3]. The presence of specific cancer mutations in ucfDNA has been also demonstrated from patients affected by nonurological malignancies, such as colorectal cancer, non–small-cell lung cancer, hepatocarcinoma, Langerhans cell histiocytosis and Erdheim–Chester disease, nasopharyngeal cancer, and pancreatic cancer [4, 5].

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Notably, many studies on cfDNA have been conducted in patients treated with tyrosine kinase inhibitors since changes in the mutational status in genes encoding therapeutic targets and downstream effectors might correspond to treatment failure on anticancer therapy. Results obtained on urine samples from patients affected by non–small-cell lung cancer and colon cancer give complementary information together with those obtained on plasma samples and have good concordance with testing archival tumor tissue [6–9]. In colorectal cancer, cfDNA has been used to overcome tumor tissue heterogeneity, track clonal evolution and acquired resistance to anti-Epidermal Growth Factor Receptor (EGFR) targeted therapies, often associated with the emergence of KRAS mutations [10, 11]. In patients with advanced colorectal cancers, a mutation-enrichment next-generation sequencing approach has been shown to detect KRAS G12/G13 mutations in ucfDNA with good concordance with conventional clinical testing of archival tumor tissue [9]. In this chapter we describe an alternative and feasible protocol to detect cancer-related alterations into ucfDNA (Fig. 1), such as KRAS mutations in colorectal cancer, permitting to reveal samples

Workflow DNA extraction Sample

Reaction preparation

PCR

Data analysis

Lyse

Bind

Vacuum

Wash

Vacuum

Elute

150 min

Pure nucleic acids

150 min Fig. 1 Workflow of mutational analysis in ucfDNA

90 min

20 min

Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer KRAS wild-type sample

KRAS mutated sample 100

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Legend Control mix curve KRAS assay mix curve

Control mix curve KRAS assay mix curve

Fig. 2 Detection of KRAS mutation on ucfDNA by Easy® KRAS CE-IVD kit. Amplification curves of a sample presenting KRAS G12C mutation (left) and of a sample resulted wild-type for KRAS codon 61

mutated or wild-type at specific codons (Fig. 2). This method will enable the use of noninvasively collected urine samples for cancer monitoring and personalized treatment choice in patients affected also by nonurological tumors.

2

Materials

2.1 ucfDNA Extraction and Quality Assessment

1. Quick DNA Urine kit (Zymo Research) is used to extract ucfDNA.

2.2 Mutational Analysis from ucfDNA

1. Easy® KRAS CE-IVD kit (Diatech Pharmacogenetics) (see Note 2) is used to detect the main mutations in codons 12, 13, 61, 117, and 146 of KRAS gene (Table 1). The kit is composed of 11 different assays to detect mutations, and 1 control assay, to define the total DNA amount of every sample. At the same time, every assay detects the specific target, by the FAM-marked probe, and the internal control, by the HEX-marked probe. In particular, the internal control permits to verify the correct amplification process and the potential presence of inhibitors, that could produce false negative results. The limit of detection (LOD) of the kit is defined as the lower mutated DNA quantity in a DNA wild-type background, that

2. The Bioanalyzer instrument (Agilent Technologies) and the High Sensitivity DNA assay kit (Agilent Technologies) are used to assess the ucfDNA quality (see Note 1).

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Table 1 Mutations detected by the EasyKRAS CE-IVD kit (a) KRAS codon 12 • G12R (34G > C) • G12S (34G > A) • G12C (34G > T) • G12A (35G > C) • G12D (35G > A) • G12V (35G > T) KRAS codon 13 • G13D (38G > A) KRAS codon 59 (not distinguishable between them) • A59T (175G > A) • A59E (176C > A) • A59G (176C > G) KRAS codon 61 (not distinguishable between them) • Q61K (181C > A) • Q61L (182A > T) • Q61R (182A > G) • Q61H (183A > C) • Q61H (183A > T) KRAS codon 117 (not distinguishable between them) • K117E (349A > G) • K117R (350A > G) • K117N (351A > T) • K117N (351A > C) KRAS codon 146 (not distinguishable between them) • A146T (436G > A) • A146P (436P > C) • A146V (437C > T)

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(b) Assay

LOD C95(mean concentration of DNA input) (%)

G12A

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5–7.5

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5–7.5

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2–5

A146x

5

is able to generate a positive result in the 95% of the analyzed samples. The LOD was determined by testing samples with decreasing percentage of mutated allele for every hotspot mutation (Table 1). 2. Rotor-Gene Q (Qiagen; software v. 1.7—Build 87) or RotorGene 6000 (Corbett; software v.1.7—Build 87) are used indistinctly to run the real-time PCR (see Notes 3 and 4). For better PCR performance, it is recommended to use DNase and RNase-free, 0.1 mL thin-wall PCR tubes, suitable for RotorGene applications (see Note 5).

3

Methods

3.1 ucfDNA Extraction and Quality Assessment

1. For the detailed protocol consider the published book chapter [12] (see Notes 6 and 7).

3.2 ucfDNA Amplification

1. Thaw, for few minutes on ice, samples and the following reagents provided by the kit: Taq PreMix 920 (a solution containing hot start Taq DNA polymerase, reaction buffer, Mg2+ and a mix of dNTPs), KRAS positive control (a mixture of wild-type genomic DNA and synthetic DNA sequences, positive for every mutation detected by the kit) and negative control (DNase and RNase-free water).

2. To assess the DNA quality by Bioanalyzer consider the published book chapter [12].

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2. Prepare an appropriate number of PCR strip tubes and put them in a numbered rack. Every sample must be amplified for each mutation assay and also for the control assay. 3. Set up eleven 0.5 mL tubes to prepare a KRAS mutation mix for each target and one 0.5 mL tube for KRAS control mix (see Note 8). 4. Prepare amplification mixes with 10 μL of Taq Premix 920, 4 μL of DNase- and RNase-free water, and 1 μL of KRAS mutation mix or KRAS control mix, for a total volume of 15 μL for each sample. For each mix consider the number of samples (controls included) plus one extra sample, since an excess of volume could compensate for volume loss from pipetting. 5. Slowly vortex (or pipet up and down) and spin every mix for at least 30 s. 6. Add 15 μL of mix in each tube (see Note 9). 7. Add 5 μL of each sample, negative or positive control and pipet up and down a few times to mix the solution (see Note 10). 8. Charge the rotor support and lock the ring. It is appropriate that every first tube of the strips should be marked, in order to remember the samples layout. Moreover, if there are not enough tubes to fill the rotor support, it is possible to add empty tubes in order to balance the instrument. 9. Start the run, following the instrument setting up. 3.3 Setting Up the Instrument and Real-Time PCR

1. Switch-on the machine and launch the specific run software at least 20 or 30 min before performing the run (see Note 11). 2. Click the Rotor-Gene icon on the pc desktop to open the software. 3. In the window that appears, choose “Advanced Wizard” and then click “New Run” (see Note 12). 4. In the next window, select the rotor type from the list, flag the “Locking Ring Attached” box, and then click “Next” to proceed (see Note 13). 5. In the next window, the user’s name and notes about the run, like reaction volume, can be entered (optional). 6. The next window allows to modify the “Temperature Profile” and the “Channel Setup”. Clicking the “Edit Profile” button, another window appears, enabling changes of cycling conditions and selection of acquisition channels (see Note 14). To set up these parameters, in the “Edit Profile” window, click the “Hold” button to set up the right temperature and time; then click the “Cycling” button to set up steps at the right temperature and time. At this point it is also possible to set up the signal

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acquisition and the channel involved, clicking the “Acquisition” button: a window appears with a list of channels to choose. In particular, for the Easy® KRAS CE-IVD kit, set up the following thermal profile and fluorescence acquisition signals: hold: 95
C for 2 min; 40 cycles: 95
C for 15 s; 58
C for 1 min (acquisition signal in Green, Green 2, Yellow and Yellow 2 channels). 7. After edit the profile, click the “Gain Optimisation” button to bring up the “Gain Optimisation” window (see Note 15). In this window, it is possible to flag the right box about temperatures or acquisitions to obtain the optimal gain performance. In particular, for the Easy® KRAS CE-IVD kit, set up the following thermal profile and fluorescence acquisition signals: “Green”: source 470 nm, detector 510 nm, Gain 8; “Yellow”: source 530 nm, detector 555 nm, Gain 10; “Green 2”: source 470 nm, detector 510 nm, “Gain Optimisation” 58
C Before first acquisition, “Tube Position”:51 (KRAS A146x mix), “Target Sample Range” 20–30 Fl; “Yellow 2”: source 530 nm—detector 555 nm—“Gain Optimisation” 58
C Before first acquisition, “Tube Position”:51 (KRAS A146x mix), “Target Sample Range” 20–30 Fl. 8. After the setting is completed, the run can immediately start or the process can be saved as a template for next runs. 3.4 Data Analysis and Result Interpretation

1. At the end of the run, set up sample names, clicking “Edit samples,” in the upper taskbar. 2. Click “Analysis” in the upper taskbar and select “Quantitation” in the window that appears. Then, click “Cycling Green Channel” and “Cycling Yellow Channel” to open the two specific tables (see Note 16). 3. For each channel graphic, select “Dynamic Tube” and “Slope Correct” (see Note 17). 4. Set up the threshold at 0.04 for both channels (see Note 18). 5. First of all, check the negative control (water): it must have a Ct value greater than 35 in the green channel and greater than 32 in yellow channel, for every mix (see Note 19). 6. The second step is the positive control check for every mix: (a) For that concerning green channel, positive control Ct value of control mix must be within a range between 23.5 and 26.5; positive control Ct values for all the other mixes must be included in a range between 15 and 21; (b) For that concerning yellow channel, every Ct value must be included in a range between 19 and 24.

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7. Now, it is possible to evaluate the DNA quality, checking samples Ct values for control mix. Every Ct must be included in a range between 21 and 30, in the green channel, and between 18 and 30 in the yellow channel (see Note 20). 8. If values are ok (Fig. 2), it is possible to continue with mutation analysis, comparing ΔCt values of each sample with those reported in the results table specific for every kit (see Note 21).

4

Notes 1. To assess DNA quality we use the Bioanalyzer instrument (Agilent Technologies) and the High Sensitivity DNA assay kit (Agilent Technologies). Bioanalyzer can also give information regarding total DNA concentration. We have verified that Qubit and Bioanalyzer give concordant results. Given the high cost of Bioanalyzer, we suggest to use it only if you need a quality assessment. If a quality assessment is not necessary, it is better to use a fluorometer or proceed directly with the DNA amplification, since the Easy® KRAS CE-IVD kit includes a control assay to define the total DNA amount of each sample. 2. For an optimal PCR performance: store every reagent in the original box at 35/20
C; do not thaw the reagents more than twice, to avoid degradation; protect every probe from the light to avoid the fluorophore degradation. 3. It is mandatory that these tools have a system for fluorescence detection in FAM and HEX channels. 4. This kit is designed also for other type of real-time PCR instruments, like CFX96 (Bio-Rad; software v.3.1), ABI 7300 (Applied Biosystems; software v.1.4.1), ABI 7500, 7500 Fast (Applied Biosystems; software v. 2.0.5) and Stratagene Mx3000P, Mx3005P (Agilent Technologies; software v.4.10 Build 389). For more information about settings, thermal profiles or plastic material to use, see “Easy® KRAS CE-IVD kit User Manual” (Diatech Pharmacogenetics). 5. Strip tubes and caps, 0.1 mL (Qiagen) are suggested. However, it is possible to use also 0.2 mL thin-wall PCR tubes or tubes and plates specific for each PCR instrument, as indicated in user manuals. 6. The time of urine sampling may affect the test sensitivity. In order to be sure that the ucfDNA mainly derive from the circulation and the amount of DNA derived from urological cells is limited, avoid to collect first morning urine since it contains a higher number of cells and cellular debris coming from the urological tract and exfoliated in urine during the night.

Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer

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7. For DNA quantification, the Qubit Fluorometer and the Qubit dsDNA HS (High Sensitivity) Assay Kit (Thermofisher), as well as different fluorometric approaches can be used. For a precise quantification, a spectrophotometric approach is not recommended. However, the quantification step is not mandatory, since the fluorometer is not able to give information regarding DNA quality assessment and sizing and specific internal control are also included in the kit to assess the amount of DNA. 8. Every mix tube is numbered from 1 to 12 and has a different lid color; each assay is composed by primers and probes, specific for both the internal control and the KRAS mutation detected; the control assay is composed by primers and probes, specific for both the internal control and a KRAS gene region without any type of polymorphism or mutation; consider that every EASY kit has a different number of assay, based on the mutation to be detected. 9. Try to add the mix into the bottom of the tube, to avoid drops on the wall that are difficult to put down with the tip; anyway, the turning movement of the rotor will work like a centrifuge and helps the solution to go down in the tubes. 10. If a sample control mix is not evaluable (in other words, the amplification occurs too late and the Ct is out of the optimal range), it is possible to try to amplify the same sample using 9 μL of DNA, taking off the water by the amplification mix. Consider that the 9 μL of DNA should be used both for mutation mixes and the control mix. 11. This step is mandatory for every tool to warm up the machine lamp and to guarantee the correct PCR performance. 12. New runs can be set up using both the Quick Start wizard which appears when the software is started up, allowing the user to start the run as soon as possible with set parameters. The Advanced wizard enables more options, such as configuration of Gain Optimisation and volume settings. 13. The flag in “Locking Ring Attached” confirms that the rotor is fixed on its support and the run is ready to proceed. 14. For more information about setting up, see the “Rotor-Gene Q User manual”. According to the kit and the instrument used for the analyses, temperatures or signal setting up might be different. 15. It is recommended to use the “Gain Optimisation” function which optimizes the gain and provides the optimal range of starting fluorescence at a certain set temperature (usually the temperature at which data acquisition occurs). The “Gain Optimisation” is able to ensure that all data is collected within the dynamic range of the detector: if the gain is too low, the

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signal will be lost in background noise; if it is too high, all signal will be lost off scale (saturated). For more information about setting up, see the “Rotor-Gene Q User manual”. Gain setting up changes according to the type of the kit and the instrument used for the analyses. 16. The Green Channel (also named HEX channel) represents the mutational graph: if a sample is mutated, in this graph it will be an amplification curve; if not, the sample will be wild-type. The Yellow Channel (also named FAM channel) represents the sample internal control graph: every sample must have this amplification curve to confirm the result; if not, the sample has some amplification problems or the DNA is too fragmented. 17. The “Dynamic Tube” setting is used to determine the average background and normalize all data points; the “Slope Correct” setting is used to normalize the background noise. For more information, see “Rotor-Gene Q User Manual” (Qiagen). 18. When it is not possible doing the analysis after setting up the threshold, maybe the fluorescence is too high. In this case, it is better to perform the analysis using “Green 2 Channel” and “Yellow 2 Channel” and proceed in the same way. 19. If Ct values are smaller than those reported, a contamination might be present. In this case, it is better to replace the analysis. 20. If Ct values are smaller than those reported, the DNA concentration is too high. It is better to dilute the samples and replace the analysis; if Ct values are greater than those reported, maybe the DNA amount is too low or there are some PCR inhibitors. In the first case, it is better to replace the analysis with a greater DNA amount (it is possible to use up to 9 μL); in the second case, PCR inhibitors could be eliminated using a diluent, like water. 21. To calculate ΔCt values, apply the following formula: ΔCt ¼ Ct Green Mutation  Ct Green Control. Then compare the results with those reported in the table results on “Easy® KRAS CE-IVD kit_User Manual”. References 1. Murtaza M, Dawson SJ, Tsui DW et al (2013) Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497:108–112 2. Lin SY, Linehan JA, Wilson TG et al (2017) Emerging utility of urinary cell-free nucleic acid biomarkers for prostate, bladder, and renal cancers. Eur Urol Focus 3(2-3):265–272 3. Su Y, Wang M, Brenner D et al (2004) Human urine contains small, 150 to 250 nucleotidesized, soluble DNA derived from the

circulation and may be useful in the detection of colorectal cancer. Mol Diagn 6(2):101–107 4. Salvi S, Martignano F, Molinari C et al (2016) The potential use of urine cell free DNA as a marker for cancer. Expert Rev Mol Diagn 16 (12):1283–1290 5. Terasawa H, Kinugasa H, Ako S et al (2019) Utility of liquid biopsy using urine in patients with pancreatic ductal adenocarcinoma. Cancer Biol Ther 20(10):1348–1353

Mutational Analysis in Urinary Cell-Free DNA: KRAS in Colorectal Cancer 6. Su Y-H, Wang M, Brenner DE et al (2008) Detection of mutated K-ras DNA in urine, plasma, and serum of patients with colorectal carcinoma or adenomatous polyps. Ann N Y Acad Sci 137:197–206 7. Reckamp K, Melnikova V, Karlovich C et al (2016) A highly sensitive and quantitative test platform for detection of NSCLC EGFR mutations in urine and plasma. J Thorac Oncol 11:1690–1700 8. Chen S, Zhao J, Cui L, Liu Y (2017) Urinary circulating DNA detection for dynamic tracking of EGFR mutations for NSCLC patients treated with EGFR-TKIs. Clin Transl Oncol 19(3):332–340 9. Fujii T, Barzi A, Sartore-Bianchi A et al (2017) Mutation-enrichment next-generation

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sequencing for quantitative detection of KRAS mutations in urine cell-free DNA from patients with advanced cancers. Clin Cancer Res 23(14):3657–3666 10. Misale S, Yaeger R, Hobor S et al (2012) Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486:532–536 11. Molinari C, Marisi G, Passardi A et al (2018) Heterogeneity in colorectal cancer: a challenge for personalized medicine? Int J Mol Sci 19 (12):3733 12. Casadio V, Salvi S (2019) Urinary cell-free DNA: isolation, quantification, and quality assessment. Methods Mol Biol 1909:211–221

Chapter 4 Fluorescence In Situ Hybridization in Urine Samples (UroVysion Kit) Sara Bravaccini Abstract Cystoscopy is considered the standard approach to the diagnostic workup of urinary symptoms. It has high sensitivity and specificity for papillary tumors of the bladder but low sensitivity and specificity for flat lesions. It is also expensive and may cause discomfort and complications. Urine cytology, in contrast, has the advantage of being a noninvasive test with high specificity but suffers from low sensitivity in low-grade and early-stage tumors, possibly due to the low number of exfoliated cells in urine. Numerous new noninvasive tests have been proposed. Among these, fluorescence in situ hybridization (FISH) has been studied for long time and in 2005 UroVysion Bladder Cancer Kit (UroVysion Kit) (Abbott/Vysis) received FDA approval for initial diagnosis of bladder carcinoma in patients with hematuria and subsequent monitoring for tumor recurrence in patients previously diagnosed with bladder cancer. The UroVysion Kit is designed to detect aneuploidy for chromosomes 3, 7, 17, and loss of the 9p21 locus by FISH in urine specimens from symptomatic patients, those with hematuria suspected of having bladder cancer. Here, the approach for FISH assay by using UroVysion Bladder Cancer kit according to manufacturer’s instructions is described. Key words Bladder cancer, Voided urine, Fluorescence in situ hybridization, UroVysion, Chromosomes 3, 7, 9, and 17

1

Introduction In situ hybridization permits the visualization of specific nucleic acid sequences within cells. DNA FISH consists in annealing of a single stranded, fluorescently labeled DNA probe to complementary target sequences. Fluorescence microscopy permits to visualize by direct detection, the hybridization of the probe with the cellular DNA. Several studies have demonstrated that FISH analysis may be useful to detect aneuploidy of specific chromosomes for bladder cancer detection [1–5]. The UroVysion probes are fluorescently labeled nucleic acid probes for use in in situ hybridization assays on urine specimens fixed on slides. The UroVysion kit consists of a four-color, four-probe mixture of DNA probe sequences homologous to specific regions on chromosomes 3, 7, 9, and 17 [6]. In

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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particular, the UroVysion probe mixture consists of Chromosome Enumeration Probe (CEP) 3 SpectrumRed, CEP 7 SpectrumGreen, CEP 17 SpectrumAqua and Locus Specific Identifier (LSI) 9p21 SpectrumGold [6]. The probes are premixed and pre-denatured in hybridization buffer for ease of use. Unlabeled blocking DNA is also included with the probes to suppress sequences contained within the target loci that are common to other chromosomes. When hybridized and visualized, these probes provide information on chromosome copy number for chromosome ploidy enumeration. UroVysion kit permits the detection and quantification of chromosomes 3, 7, and 17, and the 9p21 locus in human urine specimens by FISH [6, 7]. Cells recruited from urine pellets are fixed on slides. The DNA is denatured to its single stranded form and subsequently allowed to hybridize with the UroVysion probes. Following hybridization, the unbound probe is removed by a series of washes, and the nuclei are counterstained with DAPI (4,6 diamidino-2-phenylindole), a DNA-specific stain that fluoresces blue. Hybridization of the UroVysion probes is viewed using a fluorescence microscope equipped with appropriate excitation and emission filters allowing for visualization of the intense red, green, aqua, and gold fluorescent signals. The count of CEP 3, 7, and 17, and LSI 9p21 signals is performed by microscopic examination of the nucleus by two independent observers.

2

Materials

2.1 Materials Provided

This kit contains sufficient reagents to process approximately 20 or 100 assays (depending on the chosen format). An assay is defined as one 6 mm diameter round target area. 1. UroVysion DNA Probe Mixture* Quantity: 60 μL (20 Tests); 300 μL (100 Tests). Storage: 20  C in the dark. Composition: Fluorophore-labeled DNA probes for chromosomes 3, 7, and 17, and locus 9p21 in hybridization buffer. The hybridization buffer is made up of dextran sulfate, formamide, and SSC. 2. DAPI II* Counterstain Quantity: 300 μL (20 Tests); 1000 μL (100 Tests). Storage: 20  C in the dark. Composition:125 ng/mL DAPI (4,6-diamidino-2-phenylindole) in 1,4-phenylenediamine, glycerol, and buffer. 3. NP-40* Quantity: 4 mL (2  2 mL). Storage: 20  C to 25  C. Composition: NP-40 (nonionic detergent).

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4. 20 SSC Quantity: 66 g for up to 250 mL of 20 SSC solution. Storage: 20  C to 25  C. Composition: sodium chloride and sodium citrate. *

2.2 Materials Required But Not Provided

Hazard (use personal protective equipment as required).

1. ProbeChek UroVysion Bladder Cancer Kit Control Slides. Three glass microscope slides containing both a positive control and a negative control on the same slide (i.e., 2 target areas per slide:1 negative, 1 positive). The negative control is prepared from a fixed cultured normal human male lymphoblast cell line (GM11854); the positive control is prepared from a fixed cultured human bladder carcinoma cell line (UM-UC3). Store the control slides at 20  C in a sealed container with desiccant to protect them from humidity. 2. Vysis FISH Pretreatment Reagent Kit which includes: (a) Vysis Protease (3  25 mg); pepsin Activity 1:3000 to 1:3500. (b) Vysis Pepsin Buffer (3  50 mL); 10 mM HCl. (c) Vysis PBS (2  250 mL); 1 phosphate buffered saline. (d) Vysis 100 MgCl2 (3  0.5 mL); 2 M MgCl2. (e) Vysis 20 SSC (66 g). 2. 10% neutral buffered formalin. 3. Carnoy’s fixative (3:1 methanol–glacial acetic acid). Prepare fresh daily. 4. Immersion oil for appropriate microscope objectives. 5. Ethanol (100%). 6. Concentrated (12 N) HCl. 7. 1 N NaOH. 8. Purified water (Milli-Q). 9. Rubber cement. 10. Ultra-pure, formamide. 11. A 100-W mercury lamp equipped epifluorescence microscope.

2.3 Preparation of the Solutions

1. 1% formaldehyde Solution: add together 12.5 mL 10% Neutral Buffered Formalin, 37 mL 1 PBS, 0.5 mL 100 MgCl2 (1 tube from Vysis FISH Pretreatment Reagent Kit) and lead to a final volume of 50 mL. Mix thoroughly. Pour the solution into a Coplin jar. Discard used solution after using 1 week. Store unused solution at 2–8  C for up to 6 months. 2. 20 SSC (3 M sodium chloride, 0.3 M sodium citrate, pH 5.3): add together 66 g 20 SSC, 200 mL purified water and lead to a final volume of 250 mL. Mix thoroughly. At room

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temperature, adjust pH to 5.3 with concentrated HCl. Bring the total volume to 250 mL with purified water. Filter through a 0.45 μm pore filtration unit. Store at room temperature for up to 6 months. 3. Denaturing Solution (70% formamide/2 SSC pH 7.0–8.0): not required for automated (hyBrite or thermoBrite) denaturation assay. Add together 49 mL formamide, 7 mL 20 SSC pH 5.3, and 14 mL purified water, with 70 mL as the final volume. Mix thoroughly. pH should be from 7.0 to 8.0. This solution can be used for up to 1 week. Check pH prior to each use. Store at 2–8  C in a tightly capped container when not in use. 4. Ethanol wash solutions: prepare dilutions of 70% and 85% ethanol using 100% ethanol and purified water. Dilutions may be used for 1 week unless evaporation occurs or the solution becomes diluted due to excessive use. Store at room temperature in tightly capped containers when not in use. 5. 0.4 SSC/0.3% NP-40: add together 20 mL 20 SSC pH 5.3, 877 mL purified water, 3 mL NP-40, for a final volume of 1000 mL. Mix thoroughly. At room temperature adjust pH to 7.5  0.2 with 1 N NaOH. Lead to 1 L with purified water. Filter through 0.45 μm pore filtration unit. Discard used solution at the end of each day. Store unused solution at room temperature for up to 6 months. 6. 2 SSC/0.1% NP-40: bring together 100 mL 20 SSC pH 5.3, 849 mL purified water, and 1 mL NP-40, for a final volume of 1000 mL. Mix thoroughly. At room temperature adjust pH to 7.0  0.2 with 1 N NaOH. Adjust volume to 1 L with purified water. Filter through 0.45 μm pore filtration unit. Discard used solution at the end of each day. Store unused solution at room temperature for up to 6 months. 7. Protease: warm pepsin buffer up to 37  C and immediately before its use, add 1 tube (25 mg) of powder protease (supplied by the kit).

3

Methods A troubleshooting guide is provided in Table 1.

3.1 Specimen Collection

The UroVysion Kit is designed for use on voided urine specimens. At least 33 mL of voided urine has to be collected. Mix voided urine 2:1 with preservative; Carbowax (2% polyethylene glycol in 50% ethanol) or PreservCyt preservatives are recommended. Under these conditions urine has been shown to be stable for 1 week. Transfer to a 50 mL centrifuge tube. Use of any other preservative

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Table 1 Troubleshooting guide Problem

Probable cause

No signal or weak signals

Inappropriate filter set used to view Use recommended filters slides

Low signal specificity

Solution

Microscope not functioning properly

Call microscope technical expert

Improper lamps (i.e., Xenon or Tungsten)

Use a 100-W mercury lamp

Mercury lamp too old

Replace with a new lamp

Mercury lamp misaligned

Clean and replace lens

Dirty and/or cracked collector lenses

Clean or replace mirror

Dirty or broken mirror in lamp house

Check denaturation and hybridization temperature

Hybridization conditions inappropriate

Increase hybridization time to at least 16 h

Inappropriate posthybridization wash temperature

Check temperature of 73  1  C water bath

Air bubbles trapped under coverslip and prevented probe access

Apply coverslip by first touching the surface of the hybridization mixture

Inadequate protease digestion

Check temperature of 37  1  C bath Check that pH of buffer is 2.0  0.2 increase digestion time, up to 20 min

DNA loss (poor DAPI staining)

Check fixation conditions

Probes improperly stored

Store probes at

Hybridization conditions inadequate Wash temperature too low

Check denaturation and hybridization temperatures Maintain wash temperature at 73  1  C

20  C in darkness

Noisy Inadequate wash stringency background

Check pH of wash buffers Check temperature of 73  1  C bath Provide gentle agitation during wash

Excessively bright signal

Probe concentration too high

Try to block some of the signals by placing a neutral density filter in the excitation pathway

Cells structure not intact

Sample was overdigested

Reduce protease digestion time

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must be validated by the laboratory. The preferred storage and shipping conditions are on ice packs, but specimens may be stored and shipped at temperatures up to 25  C. If urine is not shipped immediately after collection, refrigerate immediately and ship within 24 h. It is recommended processing of the specimen to the point of fixed cell pellets (see Subheading 3.2.1, step 7) within 72 h of collection. UroVysion test performance under any other conditions must be determined and validated by the user. 3.2 Specimen Processing

1. Centrifuge urine in a 50 mL centrifuge tube at 600  g for 10 min at room temperature (15–30  C).

3.2.1 Sample Processing

2. Remove the supernatant to within approximately 1–2 mL of the cell pellet. 3. Resuspend the pellet in the remaining supernatant and transfer the contents to a 15 mL centrifuge tube. Rinse the 50 mL tube with 10 mL of 1 PBS and transfer the contents to the 15 mL tube (see Note 1). 4. Centrifuge samples at 600  g for 10 min at room temperature. 5. Remove the supernatant to about 0.5 mL of the cell pellet. Go to step 6 or go to step 6A for cytospin centrifugation. 6. Resuspend pellet in the remaining supernatant. Add slowly 1–5 mL of fresh fixative (3:1, methanol–acetic acid), dropwise with frequent agitation. 7. Let fixed specimens stand at Note 2).

20  C for at least 30 min (see

8. Centrifuge sample at 600  g for 5 min at room temperature. Remove the supernatant with attention (see Note 3). 9. Wash pellet by resuspending by adding 1–5 mL fixative. 10. Centrifuge sample at 600  g for 5 min at room temperature. Repeat steps 8 and 9 twice. 11. After centrifugation of cell suspension in fixative: (a) If cell pellet is very small and imperceptible, remove as much fixative as possible, up to approximately 100 μL solution. (b) If cell pellet is easily visible, remove as much fixative as possible and add 0.5–1 mL fresh fixative to the cell pellet. Proceed immediately with the slide preparation.

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Use 12-well slides. 1. Resuspend the cell pellet and apply 3 μL, 10 μL, and 30 μL of cell suspension on three slide circles. 2. Air-dry the samples. 3. Examine the cellularity of the sample under a Phase-contrast microscope. 4. Select the hybridization area with about 100–200 visible cells in the field. The circle which best corresponds to the recommended cell density (i.e., 100–200 cells per field) should be used for UroVysion hybridization (see Note 4). Prepare at least 1 additional back up slide. Store additional slides at 20  C in a box with desiccant (see Note 5). 6A centrifuge by cytospin centrifugation 1. Centrifuge a total amount of about 150 μL of suspension of the cell pellet with 1 PBS by cytospin centrifugation using positively charged slides (1500 rpm for 10 min). 2. Fix cells in fresh fixative (3:1, methanol:acetic acid) for 20 min. 3. Dehydrate for 2 min each sample in ethanol scalar dilutions (70%, 85%, 100%), after which heat the slides at 45–50  C for 2–5 min and stored at 4  C for a maximum of 1 month.

3.2.3 Slide Pretreatment

Slides must be pretreated and fixed prior to assay with the UroVysion Kit. 1. Allow a maximum of 12 slides to completely dry at room temperature. 2. Immerse slides in 2 SSC for 2 min (2–2.5 min) at 73  1  C. 3. Immerse slides in protease solution for 10 min (1 min) at 37  1  C. 4. Wash slides in 1 PBS for 5 min (1 min) at room temperature. 5. Fix slides in 1% formaldehyde for 5 min (1 min) at room temperature. 6. Wash slides in 1 PBS for 5 min (1 min) at room temperature. 7. Dehydrate slides with a 70% ethanol solution followed by 85% and 100% ethanol solutions for at least 1 min each, at room temperature. 8. Allow slides to dry completely for at least 15 min.

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3.3 FISH Procedure: UroVysion Assay 3.3.1 Manual Assay (See Note 6) Denaturation of DNA Specimen

1. Prewarm the humidified hybridization chamber (an airtight container with a piece of damp paper towel or blotting paper put to the side of the container) to 37  1  C by placing it in the 37  1  C incubator prior to slide preparation. Moisten the paper towel or blotting paper with water before the use of the hybridization chamber. 2. Add denaturing solution to Coplin jar and place in a 73  1  C water bath for at least 30 min, or until the solution temperature reaches 73  1  C. Before use verify the solution temperature (see Note 7). 3. Denature the specimen DNA by immersing the prepared slides in the denaturing solution at 73  1  C (4 slides per jar) for 5 min (1 min). Do not denature more than four slides at one time per Coplin jar; if denaturing fewer than four slides, add blank glass slides (see Note 8). 4. Remove the slides from the denaturing solution using forceps, and immediately place into a 70% ethanol wash solution at room temperature. To remove the formamide agitate the slides. Allow the slides to stand in the ethanol wash for at least 1 min. 5. Remove the slides from 70% ethanol. Repeat step 4 with 85% ethanol and then with 100% ethanol. 6. Remove the excess of ethanol from the slide by touching the bottom edge of the slide to a blotter, and wipe the underside of the slide dry with a laboratory wipe. 7. Dry the slides on a 45–50  C slide warmer for up to 2 min.

Probe Preparation

1. Remove the UroVysion probe from 20  C and warm to room temperature and mix by vortex. Spin the tubes briefly (1–3 s) in a microcentrifuge and then gently vortex again to mix. 2. Heat UroVysion probe solution for 5 min in the 73  1  C water bath. 3. Place probe solution on a 45–50  C slide warmer.

Hybridization

1. Put 3 μL of probe solution to the target area and immediately place over a 12 mm round glass coverslip. Carefully apply light pressure to the coverslip to allow the probe solution to spread homogeneously under the coverslip. Avoid air bubbles that will interfere with hybridization. The remaining probe solution should be kept at 20  C immediately after use. 2. Draw the rubber cement into a 5 mL syringe. Eject rubber cement around the periphery of the coverslip overlapping the coverslip and the slide, forming a seal around the coverslip.

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3. Place slides in the prewarmed humidified hybridization chamber. Cover the chamber with a tight lid and incubate at 37  1  C overnight (approximately 16 h). 4. Proceed to post-hybridization washes. 3.3.2 Optional Automated (HYBrite or ThermoBrite) Codenaturation Assay Probe Preparation and Application

1. Remove the UroVysion probe from 20  C storage and warm to room temperature (15–30  C). Vortex and spin the tube briefly (1–3 s) in microcentrifuge and vortex again to mix. 2. Put 3 μL of probe solution to the slide selected target area. Immediately, place a 12 mm round glass coverslip over the probe. Carefully apply light pressure to the coverslip to allow the probe solution to homogeneously spread under the coverslip. Avoid air bubbles that will interfere with hybridization. The remaining probe solution should be returned to 20  C storage immediately after use. 3. Draw the rubber cement into a 5 mL syringe. Eject a small amount of rubber cement around the periphery of the coverslip overlapping the coverslip and the slide, forming a seal.

Denaturation of DNA Specimen and Hybridization on the HYBrite System

1. Moisten a paper towel with water and place the towel in the channels along the heating surface. 2. Turn the HYBrite instrument on. 3. Set the HYBrite program for melt temp 73  C and melt time 2 min (denaturation), and hybridization temperature 39  C and hybridization time 4–16 h. 4. Place slides on heating surface of the HYBrite instrument. Add blank glass slides, as necessary. Verify that the slides lay flat on the heating surface. 5. Close HYBrite lid and run program.

Denaturation of DNA Specimen and Hybridization on the ThermoBrite System

1. Insert two humidity cards into the slot positions of the unit lid. Moisten each card with 8–10 mL of distilled or deionized water. Refer to ThermoBrite Operator’s Manual for reuse of humidity cards in subsequent runs. 2. Turn the ThermoBrite on. 3. Set the program for denaturation temperature 76  C and denaturation time 3 min (denaturation) and hybridization temperature 39  C and hybridization time 14–18 h (hybridization). 4. Place slides on heating surface of the instrument. Ensure the slides lay flat and rest into the marked positions in the slide locator. 5. Close ThermoBrite lid and run program.

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Post-hybridization Washes (Manual and Automated Assays)

1. Fill a Coplin jar with 0.4 SSC/0.3% NP-40 and place in a 73  1  C water bath for 30 min prior to washing. Check the temperature of the solution inside the jar before adding slides for the wash procedure by using a thermometer. The temperature of the solution should be 73  1  C. 2. Fill a second jar with 2 SSC/ 0.1% NP-40 and place at room temperature. Discard both wash solutions after 1 day of use. 3. Remove the rubber cement and coverslip from the slides (see Note 9). 4. After removing the coverslip place slides immediately in the 0.4 SSC/0.3% NP-40. When all the slides are in the jar (maximum of 4) incubate for 2 min at 73  1  C. Do not wash more than four slides at a time in the same jar; add blank glass slides if necessary (see Note 10). 5. Remove the slides from the wash solution after 2 min and place the slides in the Coplin jar containing 2 SSC/0.1% NP-40 at room temperature. Incubate for 5 s to 1 min. 6. Remove the slides from the wash solution and place vertically in a dark area on a paper towel to dry completely. 7. Apply 10 μL of DAPI II and place a coverslip (18 mm square is recommended) avoiding air bubbles over the DAPI II solution. Store the slides in the dark prior to signal evaluation. Hybridized slides (with coverslips) have to be stored at 20  C in the dark. Allow slides to reach room temperature prior to viewing with fluorescence microscopy.

3.4 Analysis of the Urine Samples

UroVysion probe signals and DAPI counterstain should be viewed with the following filters: DAPI single-bandpass (cell nucleus), Aqua single-bandpass (chromosome 17), Yellow (Gold) singlebandpass (9p21 locus), Red/Green dual-bandpass (chromosomes 3 and 7). A 100-W mercury lamp equipped epifluorescence microscope is recommended. 1. Use the prescribed filters (see above) and a magnification of 400 for scanning (600–1000 for analysis, see step 5 below). 2. Adjust the depth of focus to become familiar target signals and noise. 3. Begin analysis in the upper left quadrant of the target area. There are approximately 70/80 fields of view per slide. 4. Use the following criteria (Fig. 1) to select cells suspicious for malignancy (morphologically abnormal): (a) large nuclear size, (b) irregular nuclear shape, (c) “patchy” DAPI staining, and (d) cell clusters (do not count overlapping cells in clusters) (see Note 11).

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Normal: do not count

Single cell

Overlapping cells

Suspicious for malignancy: count

Atypical nuclear Morphology

Cell cluster

Fig. 1 Cell selection criteria

5. Increase magnification to 600–1000. Focus up and down to find all of the signals present in the nucleus. 6. Determine the number of signals for all four probes in 25 morphologically abnormal cells using the filters listed above (Fig. 2). If morphologically abnormal cells are not readily apparent, the entire sample should be scanned and nuclei representing the most morphologically abnormal cells should be scored first (Fig. 1).

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Key:

CEP 3 (red)

CEP 17 (aqua)

CEP 7 (green)

LSI 9p21 (gold)

1

Chromosomally normal cell

2

Chromosomally abnormal−gains of CEP 3 and CEP 17

3

Chromosomally abnormal−homozygous loss of LSI 9p21

Fig. 2 Examples of chromosomally normal and abnormal cells

7. Record the chromosome pattern only if: (a) there is a gain (i.e., 3 or more signals) of 2 or more of chromosomes 3 (red), 7 (green), or 17 (aqua), or, (b) there is a loss of both copies of LSI 9p21 (Fig. 2). If chromosomes 3, 7, or 17 show the loss of both chromosomes, consider the cells to be uninterpretable due to hybridization failure (see Note 12). (a) Do not score morphologically “normal” cells (Fig. 1). (b) Count morphologically abnormal cells with diploid chromosome pattern in total number of cells analyzed, but do not record chromosome pattern (Fig. 1). (c) Record the chromosome pattern of morphologically abnormal cells with abnormal chromosome pattern (Fig. 2). 8. Record the total number of morphologically abnormal cells viewed (diploid and abnormal) (see Note 13). 9. If, after 25 morphologically abnormal cells have been analyzed, * any of the following criteria have been met, STOP analysis: (a) 4 of the 25 cells show gains for 2 or more chromosomes (3, 7, or 17) in the same cell, or (b) 12 of the 25 cells have zero 9p21 signals (Fig. 2).

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Otherwise, continue analysis until either four cells with gain for multiple chromosomes have been detected, or 12 cells with zero 9p21 signals have been detected, or the entire sample has been analyzed (Fig. 2). *

If morphologically abnormal cells are not readily apparent, the entire sample should be scanned and nuclei representing the most morphologically abnormal cells should be scored first.

4

Notes 1. Pellets from the same patient specimen may be combined. 2. Specimens may be stored overnight or up to 10 days. 3. If pellet is not visible or poorly visible, further washing of the pellet is not recommended in order to avoid cell loss. Instead, proceed to step 11. If sample has been stored overnight or longer, resuspend in fresh fixative prior to slide preparation. 4. If an excessive amount of debris is present, follow pretreatment procedure and then select hybridization area. 5. Fixed slides are stable at 20  C for up to 12 months. Storing any remaining cell suspension at 20  C for up to 1 month in the event preparation of additional slides is necessary. 6. The timing for preparing the probe solution (see Subheading “Probe Preparation,” steps 1–3) should be carefully coordinated with denaturing the DNA specimen (steps 1–7) so that both will be ready for the hybridization step at the same time. 7. If solution has been stored at 2–8  C, allow solution and Coplin jar to reach room temperature before placing in water bath. 8. Verify the solution temperature inside the Coplin jar before each use. 9. Remove rubber cement and coverslip from one slide at a time and place immediately into the 0.4 SSC/0.3% NP-40 Coplin jar. 10. Placing an individual slide in the jar should not require more than a few seconds; if it does, then be sure that no slide is in the wash buffer for more than 2 min. After removal of the slides, allow the temperature to return to 73  1  C before washing more slides. 11. Start with morphologically abnormal cells then the largest cells, or those with the largest nuclei. If morphologically abnormal cells are not readily apparent, the entire sample should be scanned and nuclei representing the most morphologically abnormal cells should be scored first.

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12. If surrounding cells show abnormal chromosome patterns, as described above, these cells should be recorded, even if they are not morphologically abnormal. 13. Though the individual signal counts are not recorded, cells with nondiploid counts having at least one signal for each of the four probes but not fitting the criteria specified in step 7 should be included, along with the diploid cells, in the overall total number of morphologically abnormal cells viewed. References 1. Sokolova IA, Halling KC, Jenkins RB et al (2000) The development of a multitarget multicolor fluorescence in situ hybridization assay for the detection of urothelial carcinoma in urine. J Mol Diagn 2(3):116–123 2. Halling KC, King W, Sokolova IA et al (2000) A comparison of cytology and fluorescence in situ hybridization for the detection of urothelial carcinoma. J Urol 164:1768–1775 3. Halling KC, King W, Sokolova IA et al (2002) A comparison of BTAstat, hemoglobin dipstick, telomerase and Vysis UroVysion assays for the detection of urothelial carcinoma in urine. J Urol 167(5):2001–2006 4. Sarosdy MF, Schellhammer P, Bokinsky G et al (2002) Clinical evaluation of a multi-target

fluorescent in situ hybridization assay for detection of bladder cancer. J Urol 168:1950–1954 5. Skacel M, Fahmy M, Brainard JA et al (2003) Multitarget fluorescence in situ hybridization assay detects transitional cell carcinoma in the majority of patients with bladder cancer and atypical or negative urine cytology. J Urol 169:2101–2105 6. Bravaccini S, Casadio V, Gunelli R et al (2011) Combining cytology, TRAP assay, and FISH analysis for the detection of bladder cancer in symptomatic patients. Ann Oncol 22 (10):2294–2298 7. Goodison S, Rosser CJ, Urquidi V (2013) Bladder cancer detection and monitoring: assessment of urine-and blood-based marker tests. Mol Diagn Ther 17(2):71–84

Chapter 5 Analysis of Copy Number Variation in Urine: c-Myc Evaluation Using a Real-Time PCR Approach Valentina Casadio, Filippo Martignano, Roberta Gunelli, and Samanta Salvi Abstract Urine cell-free DNA has been shown as an informative noninvasive source of biomarkers for a number of diseases, especially for urological cancers. Starting from the hypothesis that the gain of c-Myc gene is a frequent aberration in several cancer types, including prostate cancer, we analyzed c-Myc copy number variation in urine, studying a little case series of prostate cancer patients, to test its feasibility. Here we report a general protocol that may be considered to analyze gene copy number variation in the urine cell-free fraction. Key words Copy number, Urine, Prostate cancer, c-Myc

1

Introduction The amplification of 8q, in particular of the region 8q24 containing c-Myc gene, is a frequent event in many type of cancers and could be associated with a worst outcome [1–4]. A number of studies showing c-Myc copy number variation (CNV) in solid tumor such as prostate [1, 2], bladder [4], and breast [5] have been published and the role of c-Myc gene can be almost considered as a milestone in the cancer evolution and progression. While the use of circulating cell-free DNA from blood has been intensively studied [6] only few data have been published on the role of urinary cell-free DNA (ucfDNA) as a noninvasive source of biomarker for urological and nonurological tumors [8]. The probability to find molecular alterations such as CNV in ucfDNA is higher for urological cancers that are directly in contact with urine in which cells can exfoliated, releasing their nucleic acids content. Many studies have been published on bladder and prostate cancer and the role of ucfDNA alterations such as integrity, methylation, and copy number aberrations [7–10].

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Table 1 A summary of c-Myc CNV results

c-Myc copy number Gain Normal

Patients with urological benign pathologies n ¼ 12 0 (0%) 12 (100%)

Prostate cancer patients at first diagnosis n ¼ 31 8 (26%) 23 (74%)

The advent of the modern molecular techniques such as next generation sequencing (NGS) or Digital PCR, applied to liquid biopsies such as cell-free DNA in blood or urine [8], permits to have many information about gene CNV. However, the high costs of these approaches and, in the case of NGS, the large number of information it gives, impose to select more cheap methodologies, to be feasible and applicable in the clinical practice. Xia and coworkers [8] have recently performed a genetic profiling of ucfDNA in advanced prostate cancer using an NGS approach, demonstrating the presence of a number of genomic amplifications, including the 8q24, containing c-Myc gene. Here we describe a simple, low cost, rapid method based on a real-time PCR approach, to evaluate CNVs of ucfDNA samples. Taking into account our previous experience on ucfDNA analysis [11–13], we have chosen to evaluate CNV of c-Myc gene in urinary supernatant samples collected after radical prostatectomy in a series of patients with prostate cancer or benign urological diseases. Our results, in brief, showed that c-Myc copy number gain was detected in about 26% of prostate cancer patients and no copy number gain was detected in individuals with benign urological disease. This percentage is in line with the results published about copy number gain in prostate cancer tissues. A summary of the results is reported in Table 1. The protocol is flexible and could be applied in different case series (e.g., other type of cancer) or analyzing other genes of interest instead of c-Myc.

2

Materials

2.1 Urinary Supernatant Collection

1. 50 mL conical bottom polypropylene tubes. 2. Centrifuge. 3. Quick DNA Urine kit (Zymo Research).

Analysis of Copy Number Variation in Urine. . .

2.2

UcfDNA Isolation

51

1. Quick DNA Urine kit (Zymo Research). 2. Ethanol. 3. Beta-mercaptoethanol. 4. Prepare Genomic Lysis Buffer (Zymo Research) adding β-mercaptoethanol to a final dilution of 0.5% (V/V) under a fume-hood. 5. Prepare DNA Wash buffer (Zymo Research) adding 48 mL of ethanol. 6. Microcentrifuge. 7. Thermoblock.

2.3 UcfDNA Quantification

1. Qubit Fluorometer. 2. Qubit dsDNA HS (High Sensitivity) Assay Kit. 3. 0.5 mL Qubit assay tubes.

2.4

Real-Time PCR

1. Calibrator pool: a same quantity of three ucfDNA from healthy males over 40 is mixed to be used as a calibrator, thus a sample with no CNVs of target and reference genes. 2. TaqMan™ Universal PCR Master Mix. 3. TaqMan™ real-time assay designed for the target gene of interest (here we used c-Myc 1 assays ID: Hs01764918 and c-Myc 2 ID: HS02602824, by ThermoFisher Scientific). 4. TaqMan™ real-time assay designed for reference genes (here we chose AGO1 assay ID: Hs02320401 and TTC31 assay ID: Hs02765308, both modified with VIC-labeled probe) (ThermoFisher Scientific). 5. 96-well PCR plate. 6. Microcentrifuge for plates. 7. Applied Biosystems™ 7500Applied Biosystems™ 7500. 8. CopyCaller™ Software (free download).

3

Methods

3.1 Urinary Supernatant Collection

1. Collect at least 50 mL of first or second morning urine in a clean dry plastic cup (see Note 1). 2. Store the crude urine at 4  C for a maximum of 3 h. 3. Before centrifuge step, mix each sample by inverting and transfer into two 50 mL conical bottom polypropylene tubes. 4. Centrifuge tubes at 850  g for 10 min at room temperature. 5. Carefully aliquot about 40 mL of urinary supernatant into a clean 50 mL conical bottom polypropylene tube (see Note 2) and discard the pellet.

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6. Add 70 μL of Urine Conditioning buffer (Zymo Research) for every 1 mL of urinary supernatant. 7. Close the tube and mix well by vortexing for at least 30 s. 8. Store urinary supernatant samples at room temperature for a maximum of 1 month before proceeding with DNA isolation (see Note 3). 3.2

UcfDNA Isolation

1. Vortex for at least 1 min the Clearing Beads mixture (Zymo research), then spin briefly at low speed just to remove beads from the cap (see Note 4). 2. Vortex the urinary samples for at least 30 s. 3. Add 10 μL of Clearing Beads if processing 14 mL of urine supernatant, or 20 μL of Clearing Beads for a sample volume >14 mL. 4. Vortex well the mixture for at least 30 s. 5. Centrifuge at 3000  g for 15 min at room temperature for precipitating the DNA with beads. 6. While samples are in centrifuge, prepare the Genomic Lysis Buffer (see Note 5). 7. Carefully, discard the supernatant without disturbing the pellet (see Note 6). 8. Add an equal volume (400 μL) of Urine Pellet Digestion Buffer to the pellet (see Note 7). 9. Add 5% (V/V) of Proteinase K (40 μL) to the pellet mixture. 10. Vortex well for at least 1 min for obtaining a complete resuspension. 11. Incubate the pellet mixture at 55  C for 30 min. 12. Transfer samples in a fume-hood and add 1 volume (840 μL) of Genomic Lysis Buffer. 13. Mix samples well by vortexing. 14. Transfer 760 μL sample/bead mixture into a Zymo-Spin IC-S Columns. Centrifuge at 16,000  g for 1 min. 15. Discard the tube containing the flow through and place the column in a clean collection tube. 16. Repeat the steps 14 and 15 twice, till all the sample mixture is processed. 17. Add 200 μL of urine DNA Prep Buffer to the spin column. Centrifuge at 16,000  g for 1 min and discard the flow through. 18. Warm an aliquot of DNA Elution Buffer at 65  C into a thermoblock.

Analysis of Copy Number Variation in Urine. . .

53

19. Add 700 μL Urine DNA Wash Buffer to the column. Centrifuge at 16,000  g for 1 min and discard the flow through. 20. Repeat step 19 using 200 μL of Urine DNA Wash Buffer. 21. Transfer the spin column to a clean 1.5 mL microcentrifuge tube. Add 50 μL DNA Elution Buffer direct on the column matrix and incubate for 5 min at room temperature. 22. Centrifuge at full speed for 1 min. 23. Reload the eluted DNA and centrifuge for 1 min at full speed (see Note 8). 24. Proceed with quantification step and then store DNA samples at 20  C. 3.3 ucfDNA Quantification Using Qubit Fluorometer

1. Equilibrate at room temperature the samples and all standard HS reagents stored at 4  C. 2. Gently vortex and spin all samples and reagents. 3. Prepare the working solution as following: for each sample consider 199 μL of HS Buffer and 1 μL of HS reagent (consider the number of samples to be quantified + two HS standards + 1). 4. Vortex well and briefly spin the working solution (see Note 9). 5. Set up the required number of 0.5 mL Qubit assay tubes (see Note 10). 6. Add the correct volume of working solution (190 μL for HS standards and 198 μL for samples) to the 0.5 mL Qubit assay tubes. 7. Add 10 μL of each standard and 2 μL of each sample (see Note 11). 8. Vortex well for 2–3 s without making bubbles. 9. Incubate at room temperature for 2 min. 10. By using Qubit instrument, read the HS standard for the calibration and then all samples. 11. Read the samples twice, without performing a new calibration with standards. 12. Result will be the mean of the two readings.

3.4 Real-Time qPCR Approach

1. Equilibrate at room temperature the master mix stored at 4  C. 2. Aliquot in a 96-well PCR plate 1.5 ng of ucfDNA of samples and calibrator pool in triplicate. 3. Prepare a mix of 10 μL of TaqMan™ Universal PCR Master Mix, 1 μL of TaqMan™ real-time assay for target gene (c-Myc 1 or c-Myc 2) and 1 μL of TaqMan™ real-time assay for reference gene (AGO1 or TTC31) (see Notes 12 and 13). Consider 3 replicates of samples + 3 replicates of calibrator pool + 1 negative control (water) + 2 extra volume.

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4. Aliquot 12 μL of the mix in each well contained the samples. 5. Add water to obtain 20 μL of final reaction volume. 6. Briefly spin down the 96-well PCR plate using a microcentrifuge for plates. 7. Run using the following protocol: hold stage at 95  C for 10 min, 40 cycles at 95  C for 15 s and 60  C for 1 min (see Note 14). 3.4.1 Data Analysis and Interpretation

8. Set the Ct threshold at 0.2 for each sample and each assay analyzed. 9. Omit the replicate with high Ct difference or sample with Ct of the reference genes 35.5 (see Note 15). 10. Export the real-time PCR file in “.txt” extension. 11. Import the file .txt in CopyCaller™ Software. 12. Specify the copy number of the calibrator sample (e.g., 2 for c-Myc gene). 13. Evaluate the results by the copy number bar plot or the table (Fig. 1). 14. Define c-Myc gain when the CNV results are >2.6.

Fig. 1 An example of CNV results by CopyCaller™ Software. The bar plot represents the CNV results. We analyzed two different assays for each sample: in blue the bar plot for c-Myc 1/TTC31 assay; in light green the bar plot for c-Myc 2/AGO1 assay. We used a calibrator poll sample as a control of known normal c-Myc CNV

Analysis of Copy Number Variation in Urine. . .

4

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Notes 1. We suggest collecting the first or second-morning urine to recover a higher DNA yield. Indeed this sample is enriched by cells and cellular debris coming from the urological tract and exfoliated in urine during the night. 2. We suggest leaving at least 2 mL of the supernatant above the cell pellet to be sure to not contaminate the urinary supernatant with cells. 3. We have not found any difference in DNA quantity and quality during the time storing until 1 month at room temperature. Moreover, we have not found any difference between this sample and the urine sample stored at 80  C without adding the Urine Conditioning Buffer. Thus, if you plan to extract DNA after 1 month from urine collection, you can store the urinary supernatant at 80  C and add the Urine Conditioning Buffer immediately before the isolation procedure, after thawing and vortexing samples. 4. Proceed with a very brief and slow centrifuge to not return the beads to the tube bottom. 5. For this step, it is mandatory to use a fume-hood because β-mercaptoethanol is a toxic chemical. All buffer and samples containing β-mercaptoethanol must be opened under a fume hood. 6. To be sure do not move the pellet we suggest leaving 400 μL of the sample. 7. To maximize DNA recovery when you add the Urine Digestion Buffer do not touch the pellet and do not pipette for mixing sample. 8. We suggest reloading the eluate to maximize DNA recovery. 9. We have to be sure that the working solution is an homogeneous mixture. Thus we recommend pipetting up and down many times or vortex well the solution. 10. Use only 0.5 mL Qubit assay tubes. If using other 0.5 mL tubes you can get a not accurate quantification. 11. The Qubit HS assay permits to quantify from 1 to 20 μL of a sample. We choose to use 2 μL of the sample because is performing a reproducible quantification and permits to conserve a lot DNA volume for downstream analysis. If you find many samples with a too low concentration, you must increase the loading volume. 12. Other master mixes and probes designed for CNV analysis could be used. Note that the probe for target gene of interest

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and the reference gene must be labeled with different fluorescent dyes that permit to analyze both CNV gene status in one single run well. 13. The reference gene must be located in a chromosome region with no aberrations for the disease of interest. 14. We choose to use the Applied Biosystems™ 7500 that gives experiments results readable by CopyCaller™ Software, able to analyze specifically the CNV experiments results. 15. We choose to exclude the replicate with >1 Ct difference thus decreasing the coefficient of variation. We anyway suggest repeating the experiment. References 1. Sun J, Liu W, Adams TS et al (2007) DNA copy number alterations in prostate cancers: a combined analysis of published CGH studies. Prostate 67(7):692–700 2. Fromont G, Godet J, Peyret A, Irani J, Celhay O, Rozet F, Cathelineau X, Cussenot O (2013) 8q24 amplification is associated with Myc expression and prostate cancer progression and is an independent predictor of recurrence after radical prostatectomy. Hum Pathol 44(8):1617–1623 3. Zhou K, Xu D, Cao Y, Wang J, Yang Y, Huang M (2014) C-MYC aberrations as prognostic factors in diffuse large B-cell lymphoma: a meta-analysis of epidemiological studies. PLoS One 9(4):e95020 4. Watters AD, Latif Z, Forsyth A, Dunn I, Underwood MA, Grigor KM, Bartlett JM (2002) Genetic aberrations of c-myc and CCND1 in the development of invasive bladder cancer. Br J Cancer 87(6):654–658 5. Deming S, Nass S, Dickson R et al (2000) C-myc amplification in breast cancer: a metaanalysis of its occurrence and prognostic relevance. Br J Cancer 83:1688–1695 6. Salvi S, Gurioli G, De Giorgi U et al (2016) Cell-free DNA as a diagnostic marker for cancer: current insights. Onco Targets Ther 9:6549–6559

7. Salvi S, Gurioli G, Martignano F et al (2015) Urine cell-free DNA integrity analysis for early detection of prostate cancer patients. Dis Markers 2015:574120 8. Xia Y, Huang CC, Dittmar R et al (2016) Copy number variations in urine cell free DNA as biomarkers in advanced prostate cancer. Oncotarget 7(24):35818–35831 9. Cheng T, Jiang P, Teoh J, Heung M et al (2019) Noninvasive detection of bladder cancer by shallow-depth genome-wide bisulfite sequencing of urinary cell-free DNA for methylation and copy number profiling. Clin Chem 65(7):927–936 10. Satyal U, Srivastava A, Abbosh PH (2019) Urine biopsy-liquid gold for molecular detection and surveillance of bladder cancer. Front Oncol 9:1266 11. Casadio V, Calistri D, Tebaldi M et al (2013) Urine cell-free DNA integrity as a marker for early bladder cancer diagnosis: preliminary data. Urol Oncol 31(8):1744–1750 12. Salvi S, Casadio V (2019) Urinary cell-free DNA: potential and applications. Methods Mol Biol 1909:201–209 13. Salvi S, Martignano F, Molinari C, Gurioli G, Calistri D, De Giorgi U, Conteduca V, Casadio V (2016) The potential use of urine cell free DNA as a marker for cancer. Expert Rev Mol Diagn 16(12):1283–1290

Chapter 6 Urinary microRNA and mRNA in Tumors Erika Bandini Abstract Liquid biopsy is gaining importance in the context of analysis of circulating subcellular components, such as exosomes and nucleic acids, and the investigation of biological fluids is increasing because they express features common to the tissue of origin. Particularly, urine has become one of the most attractive biofluids in clinical practice due to its easy collection approach, its availability of large quantities, and its noninvasiveness. Furthermore, a peculiarity is that, compared to serum or plasma, urine is characterized by a simpler composition that improves isolation and identification of biomarkers. Recent studies have been associated with the investigation of mRNAs and microRNAs as potential noninvasive cancer biomarkers in urine, and to date, several approaches for isolating and measuring urinary nucleic acids have been established, despite still developing. This chapter aims at giving some main published evidences on urinary microRNAs and mRNAs, with the intent to consider their potential translational use in clinical practice. Key words Urine, mRNAs, microRNAs, Cancer, Diagnosis

1

Introduction Cancer studies describe that recent improvements in tumors management are only moderately effective in the absence of validated biomarkers for the detection, diagnosis, and monitoring of malignant disease. There are three types of urinary biomarkers: DNA, RNA, and protein-based, and they depend on their source of origin [1, 2]. Nucleic acids are constantly under investigation in order to discover and better discriminate new candidates as potential biological markers. Among them, over the last decades circulating RNAs and microRNAs (miRNAs), a family of small noncoding RNAs (19- to 24-nucleotides) that regulates gene expression, have gained great interest in biomedical research, and are considered promising biomarkers in many cancer types. However, several key issues must be taken into consideration during the development of a good biomarker. It is necessary that some main criteria are followed in terms of analytical features. To be defined a good “cancer biomarker” it needs to reflect some characteristics of the tumor in a measurable way, and it might be used for tumor

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_6, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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detection, diagnosis, prognosis, assignment of treatment and monitoring of response to anticancer therapy, and disease recurrence. In addition, some known resources such as Biomarkers, EndpointS, and other Tools (BEST) resource, also identify seven distinct types of biological indicators: susceptibility/risk, diagnosis, monitoring, prognosis, prediction, pharmacodynamics/response, and safety. The development of a new biomarker needs to take into account: the specificity to discriminate between different tumors and distinct tissues, the sensitivity to improve earlier diagnosis and detection and the proportionality that correlates with clinical features of the disease, such as tumor volume, aggressiveness, prognosis, and metastatic potential [3]. So far, human transcriptome has been estimated to include over 20,000 protein-coding RNAs and up to 9000 small RNAs, offering these molecules an excellent opportunity to be exploited as biological markers [4]. There is growing evidence toward the use of minimally invasive “liquid biopsies” to identify biomarkers in urothelial malignancy. Urine has been identified as an excellent noninvasive source of DNA, RNA, and proteins; therefore, it has been recognized as a type of liquid biopsy that could enter in routine clinical practice. Cell-free proteins and peptides, exosomes, cell-free DNA, methylated DNA and DNA mutations, circulating tumor cells, miRNAs, long noncoding RNAs (lncRNAs), and messenger RNAs (mRNAs) have been assessed in urine specimens. However, lack of large multicenter clinical studies is still a main limitation, precluding their adoption in clinical management [5]. Currently, liquid biopsy is becoming quite decisive in the context of analysis of circulating subcellular components, such as exosomes and nucleic acids, and the investigation of biological fluids is increasing because they express features common to the tissue they originate from. This is why biological fluids as urine, saliva, ascites fluids, pleural effusions are nowadays studied besides blood [6]. In particular, urine has become one of the most attractive biofluids in clinical practice: it is easy to collect also at large quantities, noninvasive and with no significant proteolytic degradation compared with other biofluids. Very important is the fact that urine, compared to serum or plasma, is characterized by a simpler composition that facilitates isolation and identification of biomarkers [7]. A number of studies have been correlated to the investigation of mRNAs and miRNAs as potential noninvasive cancer biomarkers in urine, but the diagnostic significance in the detection of RNA and especially miRNAs as respect to blood is still controversial. An early detection of various types of cancer is essential for improvement of the patient’s prognosis and general survival rates, since current diagnostic methods are still limited. This chapter aims at giving the newest directions in cancer biomarkers development, with a particular focus on latest RNAs and miRNAs discoveries in terms of techniques and features, emphasizing the possibility of using them in future clinical practice. Clinical

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application of liquid biopsy is already paving the way for precision theranostics and personalized medicine and, in the following paragraphs, we describe some published papers that reported the use of specific methods for isolating RNA and miRNAs from urine. In particular, also exosomes, small extracellular vesicles (EVs) released by both normal and cancerous cells and containing RNAs, DNAs and proteins, have been shown to be involved in tumor progression. Therefore, they represent a rich potential source of tumor biomarkers, especially for profiling analysis of their miRNAs content.

2

mRNAs In the supernatant of human urine, several species of RNAs, including mRNAs, are measurable. The level of urinary RNA in a celldepleted aliquot of urine is remarkable. The RNA detected in urine is likely protected from degradation by a mixture of carriers, including EVs (such as exosomes and microvesicles), ribonucleoproteins, and lipoproteins, although the contribution of these molecules to the total extracellular RNA content of urine is not yet been fully elucidated [8]. Circulating mRNAs were found in cancer patients, despite the majority of them are usually degraded by RNases. Anyway, the presence of nondegraded RNA outside of cells was discovered several years ago, paving the way for its possible application. Biochemically, RNA molecules are quite unstable and sensitive to several aspects such as alkaline pH, heavy metal ions, and RNA hydrolyzing enzymes and all these components that are enriched in biological fluids. Anyway, circulating RNAs are characterized by a higher half-life of the order of several minutes to hours, and this is mostly due to their association with proteins, lipoproteins and EVs, such as exosomes, microvesicles, and apoptotic bodies [3]. In sight of these aspects, the quantitative study of transcriptomes is becoming a leading area of investigation for understanding the biology of cancer and developing promising new technologies for diagnosis and treatment. In particular, many genes can generate different isoforms of protein products through alternative splicing of their mRNAs. This is considered as a major strategy for increasing the functional complexity and diversity of proteins made from the relatively small number of genes in the human genome. Alternative splicing mechanism has been observed in many diseases, first of all in cancer [9]. So mRNAs, being the protein-coding transcripts, represent good biomarkers since establish the correlation between the information contained in the DNA and proteins. In peripheral blood, urine, sputum and other biological fluids, several tumorderived mRNAs have been discovered so far and they have been correlated to cancer aggressiveness and prognosis. Anyway, although several mRNAs have been identified as potential

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Table 1 Summary of mRNAs from urine of bladder and prostate cancer patients and isolation mRNA methods mRNAs markers

Pathology Source

RNA isolation method

References

IQGAP3

Bladder cancer

Urine

QIAamp Circulating Nucleic Acid Kit (Qiagen)

[17]

SNAI2

Bladder cancer

Urine

RNeasy kit (Qiagen)

[18]

ABL1, CRH, IGF2, UPK1B, ANXA10

Bladder cancer

Urine

Xpert BC Monitor Test (Cepheid)

[19]

LASS2, GALNT1

Bladder cancer

Urine RNeasy columns (Qiagen) vesicles

[21]

CCND1, LMTK2, FN1, GSTP1, HPN, MYO6

Prostate cancer

Urine

Quick-RNA MicroPrep Kit (Zymo Research)

[31]

HOXC6, DLX1

Prostate cancer

Urine

2-Gene mRNA Urine Test

[32]

AR-V7

Prostate cancer

Urine miRNeasy kit (Qiagen) vesicles

[33]

biomarkers, further studies are needed to expand the knowledge about their application, especially because of low abundance and stability of targeted molecules and possible contamination with cellular RNA during sample preparation [10]. A list of some deregulated mRNAs concerning main urological tumors is provided in Table 1.

3

miRNAs MiRNAs are a class of short single-strand RNAs (19–24 nucleotides in length) which play a role in a variety of physiological events, such as cell proliferation, differentiation, death, stress response, and inflammatory processes. They act recognizing a “seed-region” in the target mRNA, consisting of 2–7 nucleotides, confined in the 30 -untranslated region (UTR), in the 50 -UTR or in the coding region. Their mechanism of action is performed through the inhibition of the translation of target mRNAs into proteins, but mainly because of the interaction with the 50 -UTR it has also been observed an upregulation of the encoded protein [11]. Furthermore, the role of EVs as vehicles for the transfer of nucleic acids, such as mRNAs and miRNAs, is gaining great interest, since they are present in biological fluids, becoming excellent noninvasive markers for cancer diagnosis and for the progression of the disease [12]. Numerous miRNAs have been associated with different roles in tumorigenesis, progression, and metastasis of cancer cells. In

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particular, urine is proving to be a good source for miRNAs detection for its content of cell-free nucleic acid in supernatant or sediments. Anyway, the diagnostic relevance in the investigation of miRNAs in urine as respect to blood is still quite controversial [13]. Weber et al. proved the existence of several miRNAs in 12 body fluids. miRNAs are stable in their environment and are protected against endogenous RNases in plasma by encapsulation and the binding to RNA-binding proteins. In urine, miRNA levels were observed to be unchanged after several cycles of freezing and thawing and a long storage at room temperature. Since urine can be evaluated in different fractions (noncentrifuged urine, urine sediment, as supernatant after centrifugation and as an exosome preparation from supernatant), the fact that miRNAs are detectable in all these fractions results in a great advantage [14]. A list of some deregulated miRNAs recently investigated in different tumors will be provided in Table 2.

4

Urinary Nucleic Acids in Bladder Cancer Bladder cancer (BC) is the leading cause of cancer-related morbidity and mortality among urological cancers, with an estimated 80,470 new cases and 17,670 estimated deaths in the United States in 2019 [15]. It is classified in muscle invasive (MIBC), in the 70–80% of cases, and non–muscle invasive (NMIBC), counting the 20–30% of patients. In particular, patients with diagnosis of NMIBC often recur and progress to MIBC with a worse prognosis and development of distant metastasis. Thus it is important to discover new diagnostic approaches less invasive and expensive for both diagnosis and surveillance, especially because although numerous urine-based tests commercially available, none of them has been routinely introduced in BC management. At the moment the most used methods are cytology, very specific but poorly sensitive, and cystoscopy, not only an unpleasant procedure but also characterized by complications such as urinary tract infections or hematuria, besides being an expensive procedure [13]. Currently, replacing cystoscopy with available urine markers is not recommended by international guidelines, so new studies on urinary markers may delineate a noninvasive approach for molecular characterization of BC [16]. In order to improve diagnostic accuracy in cancer diagnosis and prognosis, several studies have been carried out so far, with preliminary encouraging results of some selected studies reported below.

4.1 mRNAs in Bladder Cancer

A study examined the value of urinary cell-free nucleic acids (NAs), including RNAs, as a diagnostic marker for BC using urine from 212 patients (92 BC and 120 normal controls) using a microarray analysis. IQ motif containing GTPase activating protein

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Table 2 Summary of deregulated miRNAs and isolation miRNAs methods from urine in different cancers Deregulated microRNAs

Pathology

RNA origin RNA isolation method

References

" miRs-10b, -34b, -103 Bladder # miR-141 cancer

Urine

[23]

" miRs-146a, -205, -130b # miRs-99a, -500, -148a, -95, -362-5p, -375

Bladder cancer

Urine miRNeasy Mini Kit (Qiagen) exosomes

[24]

" miRs-135a, -135b, -345 # let-7c, miRs-148a, -204

Bladder cancer

Urine

ZR urine RNA isolation kit (Zymo Research)

[25]

miR-30a-5p, let-7c-5p, miR-486-5p

Bladder cancer

Urine

Urine microRNA Purification kit (Norgen)

[26]

miRs-107, -26b- 5p, -375-3p

Prostate cancer

Urine

Acid phenol–chloroform plus Silica columns (BioSilica Ltd.)

[35]

# let-7 family

Prostate cancer

Urine

Urine Exfoliated Cell and Bacteria RNA Purification Kit (Norgen)

[36]

" miRs-21, -574,-141

Prostate cancer

Urine Lectin-induced aggregation exosomes

[37]

# miRs-196a-5p, -501 3p

Prostate cancer

Urine Ultracentrifugation exosomes

[37]

" miR-145

Prostate cancer

Urine ExoQuickTM exosome precipitation [37] exosomes kit

miR-15a

Renal cell Urine carcinoma

# miR-30c-5p

Renal cell Urine TRIzol Plus RNA Purification Kit carcinoma exosomes (Life Technologies)

[40]

" miR-376c

Gastric cancer

Urine

TRI reagent (Applied Biosystems).

[42]

" miR-21-5p

Gastric cancer

Urine

TRIZOL reagent (Invitrogen)

[43]

" miRs-6807-5p, -6856-5p

Gastric cancer

Urine

miRNeasy Serum/Plasma kit (Qiagen)

[44]

TRIzol LS total nucleic acid isolation solution (Invitrogen)

mirVana™ miRNA Isolation Kit (Applied Biosystems)

[38]

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3 (IQGAP3) was found significantly higher in urine samples of BC patients, at all tumor grades, than in normal controls, proving that it could be considered a valuable diagnostic marker for BC. Urinary cell-free RNAs (ucfRNAs) were extracted starting from frozen urine samples using the QIAamp Circulating Nucleic Acid Kit (Qiagen GmbH). Then, ucfRNAs expression was measured by real-time PCR and the Quant-iT RiboGreen RNA Reagent and Kit (Thermo Fisher Scientific) was used as a reference to measure the concentration of total cell-free NAs purified from urine samples [17]. Another group, starting from the collection of 30–50 mL of urine samples from 107 controls and 89 with a diagnosed primary urothelial carcinoma, investigated 44 mRNA transcripts in urine samples through a customized quantitative PCR platform, for the development of accurate assays for the noninvasive detection and monitoring of BC. Urothelial cells were pelleted by centrifugation (600  g, 4  C, 5 min) from the total urine sample, then total RNA was purified using Qiagen RNeasy kit (Qiagen) followed by Qiagen DNase treatment (Qiagen). RNA samples were estimated quantitatively and qualitatively through an Agilent Bioanalyzer 2000 (Agilent) and subsequently, to reach high-throughput analyses, custom TaqMan low density arrays (TLDAs) were constructed by Applied Biosystems to exploit a 384-well system that uses standard TaqMan assays with automated loading. Among targets investigated, there were endogenous controls PPIA, GAPDH, UBC, PGK1, that were previously identified using pooled urine samples on a TaqMan Human Endogenous Control Array (Applied Biosystems), along with several panels of mRNA biomarkers previously correlated to the presence of BC (called Florida, Australasian and Barcelona Panels). Complementary DNA was synthesized from 20 to 500 ng of total RNA, using the High Capacity cDNA Reverse Transcriptase Kit (Applied Biosystems). A multiplex RT-PCR preamplification reaction was performed. Results confirmed that 75% of candidate biomarkers were highly associated with BC and multivariate models determined a good 18-gene diagnostic signature able to predict the presence of BC with a sensitivity of 85% and a specificity of 88%. Among deregulated biomarkers, SNAI2 expression was typical of patients but absent in the controls. This study is an additional step toward the beginning of a precise RNA-based diagnostic test for BC detection [18]. In the follow-up of 140 cases of NMIBC, Pichler et al. evaluated the diagnostic accuracy of the Xpert BC Monitor test (Cepheid, Sunnyvale), a mRNA-based urinary marker test for BC surveillance, for the purpose of comparison with cystoscopy and cytology. They analyzed five target mRNAs (ABL1, CRH, IGF2, UPK1B, ANXA10) using real-time PCR, reporting that the overall sensitivity and negative predictive value (NPV) of the Xpert BC Monitor were significantly higher than those of bladder cytology. The advantage of Xpert BC Monitor is that it automates and integrates sample processing (including

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capturing cells on a filter, lysis by sonication, addition of released nucleic acid to dry RT-PCR reagents), nucleic acid amplification and the detection of target sequences. Within 1 h of collection, urine samples are supplemented with the Xpert Urine Transport Reagent Kit (Cepheid) by transferring 4.5 mL of voided urine to the urine transport reagent tube and, subsequently, 4 mL of pretreated urine was transferred to the disposable cartridge, requiring less than 2 min for preparation. In addition, ABL1 was used as a sample adequacy control [19]. On this trail, another group aimed to validate the Xpert test in patients undergoing surveillance for NMIBC, and reported higher sensitivity and NPV compared with cytology and UroVysion, confirming that it could be a promising tool for monitoring BC [20]. Perez et al. conducted a pilot study in order to generate an array-based catalog of mRNA associated with urinary vesicles. After an analysis of transcripts in BC EVs, a list of genes was selected for further validation using PCR technique and they reported four genes differentially expressed in cancer samples: LASS2 and GALNT1 were expressed in cancer patients, while ARHGEF39 and FOXO3 were present only in noncancer urinary vesicles. To isolate EVs from urine, samples were thawed, centrifuged at 2000  g for 10 min, and ultracentrifuged at 100,000  g for 75 min. Whole-genome expression characterization was performed using Human HT12 v4 BeadChips (Illumina) and cRNA synthesis was obtained with TargetAmp Nano-g Biotin-aRNA Labeling Kit for the Illumina System (Illumina). Raw data were extracted with GenomeStudio analysis software (Illumina) [21]. 4.2 miRNAs in Bladder Cancer

miRNA expression was analyzed in 37 urinary extracellular vesicles for discriminating MIBC from NMIBC, through microarray analysis and further validated by qRT-PCR. A different expression was reported for miR-146b-5p and miR-155-5p, which exhibited a significant upregulation in urinary EVs from patients with MIBC, paving the way to a new diagnostic tool that could facilitate in the future individual therapeutic decisions to select patients for early cystectomy [22]. In a study conducted by Nekoohesh et al., a diagnostic panel of miRNAs was investigated to discriminate patients with BC compared to healthy individuals. The study was performed on urine samples of 119 male subjects, including 66 with BC and 53 controls. Total RNA was extracted from 1.5 mL of urine sample through TRIzol LS Total nucleic acid isolation solution (Invitrogen). To evaluate the expression profile of miRNAs, Mir-Q assay was exploited, where a miRNA-specific oligonucleotide with 50 overhang (RT-6) is used to synthesize cDNA from miRNA molecules. Results revealed that 3 miRNAs (miR-10b, miR-34b, and miR-103) were upregulated, while miR-141 was downregulated in urine samples of patients compared with the control group, opening the possibility of their application as biomarkers for an early diagnosis of BC [23]. Andreu et al. analyzed 43 urine samples, among which 34 BCs that previously

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underwent cystoscopy and 9 healthy subjects. First morning 200 mL urine was collected and centrifuged at 3500  g for 25 min at 4  C, and the supernatant filtered through a 220 nm filter (Millipore) to remove cells and cellular debris, in order to isolate EVs by serial ultracentrifugation. Then 200 mL of sample were transferred to 35 mL open top Ultra-Clear™ centrifuge tubes (Beckman Coulter, Brea, CA) and ultracentrifuged for 1 h at 100,000  g at 4  C in a Beckman Coulter Avanti J-30 centrifuge (Js-24,38 rotor, Beckman Coulter). A microarray platform containing probes for 851 human miRNAs (Human miRNA 8x15K v3, Agilent Technologies), revealed the deregulation of 26 miRNAs in high-grade BC urine EVs: 23 downregulated (among which mostly miRs-99a, -500, -148a, -95, -362-5p, -375) and 3 upregulated (miR-146a, miR-205, and miR-130b). Reverse transcription was performed for EV-miRNAs using miRCURY LNA Universal RT miRNA PCR Kits (Applied Biosystems) and RT-qPCR reactions for selected urinary miRNAs were performed. RNU1A1 and RNU5G small RNAs were used as endogenous controls. Results showed how miR-375 expression is typical of high-grade BC while miR146a could discriminate low-grade patients. The authors concluded that miR-146a, particularly, could represent an useful biomarker for recurrence and differentiation between high and low-grade BC patients [24]. Through another study a miRNA signature was identified to predict accurately the presence of BC, and validated it in a large prospective cohort. After enrolling 133 patients and 112 healthy volunteers, they extracted RNA from fresh urine samples using the ZR urine RNA isolation kit (Zymo Research). To detect potential urinary biomarkers, 364 miRNAs and 20 endogenous controls were analyzed after preamplification of RT product through a MegaplexTM PreAmp Primer and TaqmanTM PreAmp Master Mix (Applied Biosystems). A signature of 6 miRNAs discriminating BC versus controls was identified through a multivariate logistic diagnostic model adjusted for gender and age: miRNAs- 135a, -135b, -345 upregulated and let-7c, miR-148a, and miR-204 downregulated in urine from BC patients [25]. Pardini et al. investigated urinary miRNA profiles in correlation to BC and different clinicopathological subtypes by next-generation sequencing (NGS), on urine samples from 66 BC and 48 controls. miR-30a-5p, let-7c-5p, and miR-486-5p were found altered in all BC subtypes, showing a significant accuracy increased in the discrimination of cases and controls. Small RNA transcripts were modified into barcoded cDNA libraries with the NEBNext Multiplex Small RNA Library Prep Set (Illumina). Selected miRNA biomarkers were validated in independent urine samples using the miRCURY LNA™ Universal RT microRNA PCR system (Exiqon) and Reverse transcription (RT) was performed using the Universal cDNA synthesis kit II (Exiqon) with the use of one spike-in (UniSp6) to the RT reaction [26].

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Urinary Nucleic Acids in Prostate Cancer Prostate Cancer (PC) is the most commonly diagnosed noncutaneous malignancy and the second leading cause of cancer-related death affecting men in the United States. American Cancer Society estimated 174,650 new cases and 31,620 deaths in the United States in 2019. As for other types of cancers, lack of early symptomatic manifestation is a major contributor to the late detection of PC. Current PC diagnostics are based on the detection of prostate specific antigen (PSA) in blood, digital rectal examination (DRE) and transrectal ultrasonography (TRUS). Other tumor markers have been reported to be associated with the outcome of PC, such as Bcl-2, Bax, Ki67, p53 mutation or expression, p27, E-cadherin, DNA ploidy, p16, but none of these has been actually validated, and they are not a part of the routinely clinical practice [27]. PSA is not disease specific, and elevated PSA level is commonly detected in patients with noncancerous conditions such as benign prostatic hyperplasia (BPH) and prostatitis. The poor specificity of PSA test results in a large amount of unnecessary biopsies: only 25% of patients were found to have PC in the subsequent biopsy using the empirically determined threshold of 4 ng/mL [28]. Moreover, androgens play a key role in PC growth and development, becoming androgen-deprivation therapy the main approach in the different stages of disease. Anyway, hormone therapies represent only a transitory solution due to a decrease in testosterone and dihydrotestosterone synthesis, and in most cases after treatments there is a progression to a castration-resistant PC (CRPC) status [29]. Recently, methods of molecular biology have been extensively adopted for use in diagnostics of PC, including the analysis of cell-free nucleic acids in plasma and urine.

5.1 mRNAs in Prostate Cancer

In addition to PCA3 and TMPRSS2:ERG, many urine RNA-based markers are currently being investigated. Several genes significantly overexpressed in PC are considered potential RNA urinary biomarkers in early tumor detection. Monitoring RNA transcripts, such as α-methylacyl-coenzyme-A racemase (AMACR), Golgi membrane protein 1 (GOLM1), human telomerase reverse transcriptase (hTERT), and prostate-specific membrane antigen (PSMA), has led to an increase of PSA specificity for PC diagnosis. However, due to the multifactorial features of PC, many studies often used more RNA transcripts rather than a single biomarker [30]. Guo et al. identified six biomarkers with various gene expression in 154 urine samples from patients with PC and benign disease, discriminating PC with higher specificity and efficiency than PSA: CCND1, LMTK2, FN1, GSTP1, HPN and MYO6. In particular, a volume of 10–45 mL urine sample was collected and added to a DNA/RNA preservative AssayAssure (Thermo Fisher Scientific). qRT-PCR was performed using β-actin as normalizer of biomarkers

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gene expression [31]. Haese et al. collected urine samples from 1955 men from prior to an initial prostate biopsy and tried to optimize and validate a 2-Gene mRNA urine test for detection of clinically significant PC before initial prostate biopsy. They quantified urinary HOXC6 and DLX1 mRNA levels, then RNA results were associated to other risk factors in a clinical model optimized that includes patient age, DRE and PSA. In patients with PSA less than 10 ng/mL, the test demonstrated high sensitivity and negative predictive value to detect clinically significant PC [32]. Also Woo et al. proposed a practical and noninvasive liquid biopsy method for the analysis of androgen-receptor splice variant 7 (AR-V7), in order to discover new RNA biomarkers in urinederived EVs. Urine-derived EVs were extracted by an injectionmolded and ultrasonic bonded disc of polystyrene, integrated with AAO filter units with 20 nm pore size (Exo-Hexa) favoring simultaneous processing of six single samples. From 4 mL urine sample a rapid enrichment of EVs ( 4.0 ng/ml are recommended to undergo a biopsy for a definitive diagnosis, but only 25–40% of them have actually PC, whereas 65–75% of men with PSA between 4.0 and 10.0 ng/ml (also referred to as “gray zone”) have a negative prostate biopsy [91]. Hence, there is an urgent need to develop novel biomarkers for PC diagnosis at earlier stage, and to avoid unnecessary biopsies and overtreatment of indolent disease [92]. In this context, lncRNAs have shown some promise for the urinary detection of prostate cancer.

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PCA3

3.2 MALAT1 and FR0348383

The prostate cancer gene 3 (PCA3) (aka DD3, PCAT 3) was the first PC-associated lncRNA to be discovered in 1999 [93]. PCA3 is a prostate-specific lncRNA that is highly overexpressed in PC compared to nonmalignant prostate tissue [94]. Regarding its role in PC biology, PCA3 is involved in the control of the PC cell survival through modulation of several key cancer-related genes, including androgen receptor cofactors and EMT markers [95, 96] (Table 2). The feasibility that high levels of PCA3 can be detected in urine [94, 97] has led to the development of the FDA-approved PROGENSA® PCA3 assay in the first catch urine sample following a digital rectal examination (DRE) [98]. This urine test is based on an in vitro nucleic acid amplification that, from the measurement of both PCA3 and PSA RNA molecules, yields a ratio of the two RNA, referred to as PCA3 score. The diagnostic value of PCA3 was initially evaluated in several clinical studies with patients having either a first or repeat biopsy. Results demonstrated that although the sensitivity of PCA3 test was less than that of serum PSA, its specificity, and positive and negative predictive values appeared to be better, especially in patients with a previous negative biopsy [99], thus suggesting that PCA3 test might aid in guiding repeat biopsy decisions. Despite these findings, one of the major concerns about PCA3 application relies on the optimal cut-off value to identify patients with or without PC [100]. In this setting, a number of studies indicated that a PCA3 cutoff of 35 provides an optimal balance between specificity (from 54% to 58%) and sensitivity (from 72% to 74%) in PC detection [101–103], and could reduce the number of biopsies by 77% [98]. On the other hand, Wu et al. reported that a PCA3 cutoff of 25 presented a better optimal balance than 35, with an enhanced negative predictive value [104]. At present, the PCA3 role in clinical practice has not been truly validated; in addition, the different studies are often contradictory in their results, and limited by several factors (i.e., lack of multiinstitution accrual, small sample sizes, and potential selection bias) [105]. However, recent studies have been made searching for other possible urinary lncRNAs-based biomarkers. In a study of Ren S. et al., RNA-seq profiling between 14 prostate cancer tissues and their matched normal tissues identified 406 lncRNA differentially expressed, including MALAT1 and FR0348383, which were the most top differentially expressed transcripts [106]. In particular, MALAT1 and FR0348383 were overexpressed in 82.5% and 80% of PC, respectively, and FR0348383 expression level could significantly differentiate PC from benign prostatic hyperplasia [106]. In a subsequent analysis, the same research group also revealed that MALAT1 up-regulation correlated with aggressive characteristics of PC. Consistent with these data, MALAT1 silencing was shown to inhibit PC cell growth, invasion and migration, and to induce cell cycle arrest both in vitro and in vivo [107]. Furthermore, MALAT1 was

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demonstrated to recruit EZH2 to its target genes in order to enhance EZH2-mediated histone 3 lysine 27 trimethylation, and to suppress the transcription of genes involved in PC cell proliferation and invasion [108] (Table 2). Differently, the biological role of FR0348383 remains to be elucidated. Following the evaluation of the performance of MALAT1 as a blood-based biomarker [109], the diagnostic power of urinary MALAT1 was measured in patients scheduled for prostate biopsy because of elevated PSA levels (PSA > 4.0 ng/ml) and/or suspicious DRE. Results indicated urinary MALAT1 as a potential noninvasive biomarker for detecting PC, principally in patients in PSA gray zone [110]. Similarly, urinary FR0348383 improved diagnostic accuracy in patients undergoing prostate biopsy [111]. In fact, the urinary FR0348383 score, defined as the ratio of PSA mRNA and FR0348383 level, showed a significantly better clinical performance for predicting PC compared with PSA, especially in the gray zone cohort [111]. Altogether, these data indicate that MALAT1 and FR0348383 are potential diagnostic biomarker for PC detection and warrants further validation in larger cohorts. 3.3

4

LincRNA-p21

In a single study, Isis et al. quantified the levels of two urine-derived exosomes lncRNAs, the long intergenic noncoding RNA (LincRNA)-p21 and GAS5, trying to find their potential as PC diagnostic biomarkers. Authors observed that urine-derived exosomes collected from PC patients were enriched in lncRNA-p21 respect to patients with benign prostatic hyperplasia, thus identifying lincRNA-p21 as a potential urine biomarker for the detection and stratification of PC. In addition, the specificity of lincRNA-p21 for predicting PC increased from 63% to 94% when combined with PSA [112]. On the molecular level, lincRNA-p21 might act as a tumor-suppressor molecule in PC by regulating p53 and its downstream genes [113], and by decreasing pyruvate kinase M2 activity which promotes the Warburg effect [114] (Table 2).

Conclusions At present, several reports have been published indicating the potential of urinary lncRNAs to be translated into clinical applications for diagnosis of BC and PC. However, urinary lncRNAs research and their clinical evaluation are still in their infancy, and most of the studies are based on small cohorts of patients. Therefore, the applicability of urinary lncRNAs as diagnostic biomarkers will require additional studies in larger sample size. Furthermore, more efforts are needed to profile and validate urinary lncRNAs as diagnostic biomarkers in kidney cancer. In fact, although lncRNA RP11-354P17.15-001 has been recently identified as a novel noninvasive urinary biomarker for acute rejection after renal transplantation [115], the diagnostic value of lncRNAs in urine from renal cancer patients is still unexplored.

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Chapter 8 Urinary Nucleic Acid in Tumor: Bioinformatics Approaches Davide Angeli Abstract Application of next generation sequencing techniques in the field of liquid biopsy, in particular urine, requires specific bioinformatics methods in order to deal with its peculiarity. Many aspects of cancer can be explored starting from nucleic acids, especially from cell-free DNA and circulating tumor DNA in order to characterize cancer. It is possible to detect small mutations, as single nucleotide variants, small insertions and deletions, copy-number alterations, and epigenetic profiles. Due to the low fraction of circulating tumor DNA over the whole cell-free DNA, some methods have been exploited. One of them is the application of unique barcodes to each DNA fragment in order to lower the limit of detection of cancerrelated variants. Some bioinformatics workflows and tools are the same of a classic analysis of tumor tissue, but there are some steps in which specific algorithms have to be introduced. Key words cfDNA, ctDNA, SNV, CNV, Methylation, Urine, Bioinformatics, Cancer

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Introduction Liquid biopsy is a noninvasive method used to detect molecular biomarkers in accessible body fluids with the great advantage of an intrinsic minimum invasiveness. Common targets of this technique are cell-free nucleic acids, in particular cell-free DNA (cfDNA). In urine, cfDNA can derive from different sources, mainly from DNA of the urinary tract cells or from transrenal DNA [1]. Since urinebased methods are noninvasive, they give opportunity to investigate diseases derived by urinary and not-urinary tract [2]. cfDNA is released from cells in small fragments with size of 150–200 base pairs (bp), proportional to DNA length contained in a nucleosome [3, 4]. It can derive either from apoptotic or necrotic cells (normal or tumor) [5], or from viable cells [6]. Moreover, the levels of cfDNA is lower in healthy people with respect to individuals with diseases [7]. One of the best methods for cfDNA analysis is Next Generation Sequencing (NGS). Despite its intrinsic high cost per sample and the lower power of detection if compared with other techniques

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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(e.g., droplet digital PCR techniques which can reach a limit of detection of 0.0005% for known mutations and even lower under specific conditions [8]), NGS has the great advantage of detecting simultaneously a wide range of known and novel mutations and alterations in a variable number of genes and samples. By applying NGS techniques for the study of urinary cfDNA, we are able to obtain information on different aspects of cancer, which will be discussed in the following sections: discovery of single nucleotide variants (SNV) or small insertions or deletions (INDEL), copy-number variations (CNV), and changes in methylation profile. Analysis of NGS data derived from cfDNA requires specific bioinformatics approaches, being aware of the peculiarity of the input material we are dealing with. The main problem is that the proportion of circulating tumor DNA (ctDNA), which is released only by tumor cells, with respect to cfDNA is very low, especially in early-stage cancer.

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Analysis of Small Variants from ctDNA Analysis of ctDNA small variants in urine needs to take into consideration some aspects which we are going to introduce in the next section. Some steps of the process (Table 1) are the same of an analysis from tissue DNA but there are some specific methods that are used to face ctDNA issues. For instance, in case of sequencing with Illumina platforms, even if FastQC [9] can be a valid software for the quality control and filtering of reads, there are some dedicated tools for working on FASTQ files derived from ctDNA sequencing, such as AfterQC [10]. This tool exploits the fact that ctDNA median fragment length is lower than 200 bp and, using a 2  150 bp sequencing, there are a lot of nucleotides which are sequenced starting by both the ends. The software checks the presence of inconsistent nucleotides in the overlapping reads and computes a sequencing error rate which is used for error base

Table 1 Tools for each activity required for calling small variants from ctDNA Activity

Tool

References

FASTQ quality control

AfterQC

[10]

Alignment

BWA

[11]

SAM to BAM conversion

Samtools

[12]

Deduplication

CAPP-seq

[13]

Variant calling

VarScan2

[15]

Variant annotation

ANNOVAR

[16]

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correction in the case that one of the two overlapping reads has a low quality score. For the alignment against a reference genome, the classic algorithms are used (e.g., BWA [11]) and the SAM files can be converted in BAM files, sorted and indexed with Samtools [12], as in common workflows. Due to the presence of short fragments with a peak of length distribution at 167 bp, it is likely that some cfDNA fragments are identical, making impossible to avoid software to discard those fragments which are recognized as duplicates. If we do not want to lose the information derived by identical fragments, the deduplication step should be performed with dedicated tools, as CAPPseq [13], which overpasses this problem performing deduplication after the variant calling. This kind of algorithm merges the reads which map to the same genomic coordinates in a unique reads and assigns them a reference count and an alternative count on the basis of the reads supporting the two conditions. Since they are characterized by low frequency, ctDNA variants require a small limit of detection. Some of the commonly variant callers do not fit well for ctDNA, being suited to call only polymorphisms or variants with higher frequency [14]. On the contrary, VarScan2 [15] is very sensitive to detect low frequency variants but raises a large number of false positives and for this reason it should be used with a stringent filtering, for the generation of a VCF file containing SNVs and INDELs which can be annotated with ANNOVAR [16] in order to detect putative cancer-related variants. One of the applications of small variants analysis from urine ctDNA is the detection of EGFR T790M mutation in non–smallcell lung cancer. Such mutation is responsible of the mechanism of resistance in the 60% of treated patients [17]. For the subset of patients with this mutation, the third generation tyrosine kinase inhibitors are given, with an increase in the targeting of therapy and, consequently, of its efficacy. In lung cancer patients, in which there is often a lack of tissue availability, the urine assay allows the detection of EGFR T790M mutation and increases the progression-free survival and the overall survival.

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cfDNA Molecule Tagging Usually, the minimum threshold for variant allele frequency is 5% that can be reduced, in targeted panels with a high depth of coverage, to 1–2% at the expense of an increase risk of getting false positives. Since the very low fraction of ctDNA and the possible presence of different clones in the urine, it is necessary to improve the limit of detection, that is, the minimum allele frequency which can be detected, by highly increasing the depth of coverage which can reach even 10,000 or more. In this way, low frequency variants can be discovered even if PCR amplification

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errors as well as sequencing errors generate noise and the possibility to call artifact variants if the allele frequency threshold is set too low. One of the most common methods for reducing the number of false positives derived by PCR amplification errors and to improve the sequencing accuracy is the introduction of tags on DNA molecules as unique molecular identifiers (UMIs) before PCR amplification. For what concerns the bioinformatics analysis of data, there are different tools designed for this purpose: MAGERI [18], UMI-tools [19], umi-consolidate (for Illumina sequencing, https://github.com/aryeelab/umi), Connor (https://github. com/umich-brcf-bioinf/Connor), and others. Generally, in these tools the first step is the identification of UMIs from the reads. Since UMIs are generated by DNA synthesis, the presence of some erroneous nucleotides is possible. For this reason, the algorithm which splits the UMIs from DNA molecules allows one mismatch. The insertion of molecular tags on the indexes for demultiplexing simplifies the UMIs step extraction since it can be done directly from the sample indices on the FASTQ files. Then, the reads which derive from the same original DNA molecule have to be clustered together. Since the possibility of PCR or sequencing artifacts, one, or in some cases even two, mismatches among the reads are allowed. Finally, the consensus sequence of the original DNA molecule has to be generated, starting from a multiple alignment of the reads which were clustered together in the previous step. For each position, all the reads are scanned and the consensus nucleotide will be the one present in the majority of the reads, but taking into consideration also the quality score. After building consensus reads, the variant calling can be performed as discussed in the previous section.

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Copy-Number Variants from cfDNA CNVs are genomic abnormalities which have been found to be sensitive cancer biomarkers [20–22] and their detection from urine ctDNA is a noninvasive opportunity. Usually, low coverage (about 0.1 or slightly higher) Whole-genome sequencing (WGS) is used for the detection of CNVs. Most of the algorithms for calling CNVs (Table 2) are based on the following features: detection of copy number breakpoint locations, depth of coverage, enhancing of accuracy with the inclusion of paired-end read information, and reduction of biases mainly due to GC content. The comparison of samples against their own matched normal or against a baseline of normal samples is usually the best practice and in many algorithms it is mandatory, but often it is not possible to obtain one of them. Generally, the first step is the identification and removal of regions with low mappability from the reference: in this way, all the reads are aligned to one single genomic location

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Table 2 Tools for each activity required in CNV calling from ctDNA Activity

Tool

References

FASTQ quality control

FastQC

[9]

Alignment

BWA

[10]

SAM to BAM conversion

Samtools

[11]

Genome Mappability

GEM

[23]

Genome subdivision and read count

QDNAseq

[24]

Circulary binary segmentation

DNAcopy

[26]

Copy number calling

CGHcall

[27]

with low uncertainty. One of the most used algorithms which evaluates mappability along the whole genome is GEnome Multitool (GEM) [23]. Since coverage depth of WGS for CNVs discovery is usually very low, not all the regions are covered by NGS reads. Moreover, the covered regions have only one or few reads. For this reasons, the following step is to subdivide the genome in windows (bins) of a fixed size, in which reads are merged and aggregated. The size of the bins for each sample depends on read depth in order to make sure that each bin contains a significative number of reads. Then, counts are normalized on total read counts and further corrected on the GC content using smoothing algorithms. The software called QDNAseq [24] performs all of the previous steps. The matched normal control or a reference baseline, if available, are used to correct cfDNA read counts, obtaining log2 ratio values. Otherwise, in case there is not a reference, log2 ratio values derive from median normalization among all the bins. The inference of CNVs is performed with segmentation: it means that regions with the same putative copy number value are merged in single segments. Some of the most popular methods are hidden Markov model (HMM) [25] and circular binary segmentation (CBS) as DNAcopy [26]. After the segmentation step, CGHcall [27] infers regions with a copy number different by 2 and classifies them as aberrant assigning a Z-score to each segment. For inferring CNVs, the percentage of tumor cells has to be established, since many CNV calling methods are very sensitive to that parameter. One of the possible solutions to estimate the ratio of ctDNA and cfDNA is based on the insert size given that ctDNA is shorter than the cell-free DNA from normal cells [28, 29]. The fraction of ctDNA can be inferred as the proportion of fragments of cfDNA with length lower than 150 bp and the total cfDNA. Analysis of urine ctDNA CNVs has been performed on patients with prostate cancer [30]. In this, study, it has been observed that all the 19 patients had some alterations. In particular, androgen

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receptor (AR) region is amplified in 5/10 castration-resistant prostate cancer (CRPC) patients but never in the nine cases of hormone-sensitive prostate cancer (HSPC). Moreover, in 4/10 CRPC patients there is a PTEN deletion, while is 1/9 in cases with HSPC. Finally, CNV changes in nine genes occurred on preand posttreated patients and represent possible predictive biomarkers. Among these genes, there are RNF43, ZNRF3, and MYC. RNF43 and ZNRF3 genes could be involved in Wnt signaling [31] while MYC is a protooncogene which is deregulated in many prostate cancers [32]. In conclusion, CNV test on urine ctDNA provides the potential for the computation of scores in order to classify patients on the basis of the treatment response in prostate cancer patients.

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Analysis of Epigenetic Profile in cfDNA DNA methylation is one of the epigenetic processes which modulates chromatin and it is involved in the regulation of transcription, being able to switch genes on and off. For that reason, it can be involved in cancer development [33]. Since each tissue contains a specific methylation profiling, from urinary cfDNA it is possible to detect the presence of cancer cells and their origin [34, 35]. Bioinformatics analysis of DNA methylation data from NGS methods derived from sodium bisulfite sequencing (Table 3) starts from a standard quality control of reads contained in FASTQ files. It is important to take into account that there are an undersized number of cytosines (Cs) and an oversized number of thymines (Ts) due to the deamination of unmethylated Cs in uracils, and then to Ts, in the first steps of the process after the treatment of DNA with sodium bisulfite [36]. For this reason, in the quality control steps, specific tools for BS-seq data should be used (BseQC [37] or MethyQA [38]). Since the presence of Ts in place of Cs, the reads do not correspond exactly to a reference genome and specialized alignment tools are required: one of them is Bismark [39] which overpasses this issue by replacing all the Cs in Ts in the FASTQ

Table 3 Tools for each activity required in epigenetic profiling of ctDNA Activity

Tool

References

FASTQ quality control

BseQC

[37]

Alignment

Bismark

[39]

Methylation profile analysis

Methylkit

[40]

ctDNA fraction estimation

CancerLocator

[43]

Cancer identification

CancerLocator

[43]

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reads and in the reference. After the alignment, Bismark computes aggregated cytosine methylation scores and region methylation scores in order to detect differences in the cytosine/region patterns of methylation against the control. Then, Fisher’s exact test is applied to get statistical significance of the differentially methylated cytosines and regions with Methylkit [40]. The identification of unique pattern of CpG methylation in a specific tissue, gives a potential information of the tissue origin of the investigated cfDNA. By employing deconvolution algorithms [41], it is possible to estimate ctDNA fraction and identify healthy individuals and patients with some cancer types [42]. Moreover, there are some probabilistic models, such as CancerLocator [43] and Cancer Detector [44], which are able to identify cancer types and ctDNA fraction starting from ctDNA methylation data. Song et al. [45] studied methylation status of VIM gene in the urine of 20 colorectal cancer patients and 20 cancer-free subjects. Hypermethylation of VIM was found in 75% of cancer patients and only in 10% of healthy subjects, demonstrating significant correlation of hypermethylation of VIM in colorectal cancer, with a possible implication in screening and diagnosis of this disease.

6

Conclusions Urinary nucleic acids analysis is a promising noninvasive technique for the detection of molecular cancer biomarkers. Even if other liquid biopsy assays are able to give similar results, it has been demonstrated that, in patients with renal cell carcinoma, bladder and prostate cancers, cfDNA is more detectable in urine than in plasma/serum, respectively in over the 70% and the 50% of the analyzed samples [46]. In general, for the investigation of tumors located in sites which are in intimate contact with the urinary tract, the urine assays are obviously more appropriate than other liquid biopsies [47], but they can be used even for other tumor types such as for non–small-cell lung cancer. NGS techniques are frequently used for the analysis of nucleic acid from urine, while the investigation of other biological materials in urine, such as specific proteins related to a given cancer type, demonstrated low specificity and sensitivity, for instance in the discovery of bladder cancer [48]. Otherwise, NGS has shown a great sensitivity detecting even at low levels of cfDNA and is able to identify a wide variety of known and novel somatic mutations, CNVs and DNA methylation. Although other techniques (e.g., digital PCR) can be more sensitive than NGS in the detection of ultralow frequency variants, the latter one has the great advantage of being highly multiplexed, meaning that it can analyze more samples within the same experiment. Finally, the introduction of UMIs in the experimental procedure for the discovery of small variants decreases the limit of detection of NGS analysis.

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Bioinformatics tools are fundamental to extract useful information from sequenced data. Due the peculiarity of the material, some specific bioinformatics tools have been developed for some specific analyses, for example, mutation calling, CNV inferring, and epigenetic profiling, but for the next future there will be the need of building specific automated workflows fit for each specific kind of experiment, taking FASTQ file as input and giving a ready-to-use output with a minimum intervention of the operator in order to standardize analysis. References 1. Su YH, Song J, Wang Z et al (2008) Removal of high-molecular-weight DNA by carboxylated magnetic beads enhances the detection of mutated K-ras DNA in urine. Ann N Y Acad Sci 1137:82 2. Bryzgunova OE, Skvortsova TE, Kolesnikova E et al (2006) Isolation and comparative study of cell-free nucleic acids from human urine. Ann N Y Acad Sci 1075(1):334–340 3. Fleischhacker M, Schmidt B (2007) Circulating nucleic acids (CNAs) and cancer—a survey. Biochim Biophys Acta 1775(1):181–232 4. Su YH, Wang M, Brenner DE et al (2004) Human urine contains small, 150 to 250 nucleotide-sized, soluble DNA derived from the circulation and may be useful in the detection of colorectal cancer. J Mol Diagn 6(2):101–107 5. Jahr S, Hentze H, Englisch S et al (2001) DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res 61(4):1659–1665 6. Alix-Panabie`res C, Pantel K (2014) Challenges in circulating tumour cell research. Nat Rev Cancer 14(9):623–631 7. Koffler D, Agnello V, Winchester R et al (1973) The occurrence of single-stranded DNA in the serum of patients with systemic lupus erythematosus and other diseases. J Clin Invest 52(1):198–204 8. Milbury CA, Zhong Q, Lin J et al (2014) Determining lower limits of detection of digital PCR assays for cancer-related gene mutations. Biomol Detect Quantif 1(1):8–22 9. Andrews S (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics. babraham.ac.uk/projects/fastqc 10. Chen S, Huang T, Zhou Y et al (2017) AfterQC: automatic filtering, trimming, error removing and quality control for fastq data. BMC Bioinformatics 18(3):80

11. Li H, Durbin R (2010) Fast and accurate longread alignment with burrows–wheeler transform. Bioinformatics 26(5):589–595 12. Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25(16):2078–2079 13. Newman AM, Bratman SV, To J et al (2014) An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med 20(5):548 14. Chen S, Liu M, Zhou Y (2018) Bioinformatics analysis for cell-free tumor DNA sequencing data. In: Computational systems biology. Humana Press, New York, NY, pp 67–95 15. Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res 22 (3):568–576 16. Wang K, Li M, Hakonarson H (2010) ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38(16):e164 17. Sands J, Li Q, Hornberger J (2017) Urine circulating-tumor DNA (ctDNA) detection of acquired EGFR T790M mutation in nonsmall-cell lung cancer: an outcomes and total cost-of-care analysis. Lung Cancer 110:19–25 18. Shugay M, Zaretsky AR, Shagin DA et al (2017) MAGERI: computational pipeline for molecular-barcoded targeted resequencing. PLoS Comput Biol 13(5):e1005480 19. Smith T, Heger A, Sudbery I (2017) UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res 27 (3):491–499 20. Dawson SJ, Tsui DW, Murtaza M et al (2013) Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 368 (13):1199–1209

Urinary Nucleic Acid in Tumor: Bioinformatics Approaches 21. Heitzer E, Ulz P, Belic J et al (2013) Tumorassociated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing. Genome Med 5(4):30 22. Leary RJ, Sausen M, Kinde I et al (2012) Detection of chromosomal alterations in the circulation of cancer patients with wholegenome sequencing. Sci Transl Med 4 (162):162ra154 23. Derrien T, Estelle´ J, Sola SM et al (2012) Fast computation and applications of genome mappability. PLoS One 7(1):e30377 24. Scheinin I, Sie D, Bengtsson H et al (2014) DNA copy number analysis of fresh and formalin-fixed specimens by shallow wholegenome sequencing with identification and exclusion of problematic regions in the genome assembly. Genome Res 24(12):2022–2032 25. Lai D, Ha G, Shah S (2020). HMMcopy: copy number prediction with correction for GC and mappability bias for HTS data. R package 26. Seshan VE, Olshen A (2019). DNAcopy: DNA copy number data analysis. R package 27. van de Wiel M, Vosse S (2019). CGHcall: calling aberrations for array CGH tumor profiles. R package 28. Underhill HR, Kitzman JO, Hellwig S et al (2016) Fragment length of circulating tumor DNA. PLoS Genet 12(7):e1006162 29. Yang N, Li Y, Liu Z et al (2018) The characteristics of ctDNA reveal the high complexity in matching the corresponding tumor tissues. BMC Cancer 18(1):319 30. Xia Y, Huang CC, Dittmar R, Du M, Wang Y et al (2016) Copy number variations in urine cell free DNA as biomarkers in advanced prostate cancer. Oncotarget 7(24):35818 31. Zebisch M, Jones EY (2015) ZNRF3/ RNF43–a direct linkage of extracellular recognition and E3 ligase activity to modulate cell surface signalling. Prog Biophys Mol Biol 118 (3):112–118 32. Hsieh AL, Walton ZE, Altman BJ, Stine ZE, Dang CV (2015) MYC and metabolism on the path to cancer. In: Seminars in cell & developmental biology, vol 43. Academic Press, Cambridge, pp 11–21 33. Baylin SB, Jones PA (2011) A decade of exploring the cancer epigenome—biological and translational implications. Nat Rev Cancer 11 (10):726–734 34. Warton K, Samimi G (2015) Methylation of cell-free circulating DNA in the diagnosis of cancer. Front Mol Biosci 2:13

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35. Lehmann-Werman R, Neiman D, Zemmour H et al (2016) Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A 113(13): E1826–E1834 36. Frommer M, McDonald LE, Millar DS et al (1992) A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A 89(5):1827–1831 37. Lin X, Sun D, Rodriguez B et al (2013) BSeQC: quality control of bisulfite sequencing experiments. Bioinformatics 29 (24):3227–3229 38. Sun S, Noviski A, Yu X (2013) MethyQA: a pipeline for bisulfite-treated methylation sequencing quality assessment. BMC Bioinformatics 14(1):259 39. Krueger F, Andrews SR (2011) Bismark: a flexible aligner and methylation caller for bisulfiteSeq applications. Bioinformatics 27 (11):1571–1572 40. Akalin A, Kormaksson M, Li S, GarrettBakelman FE, Figueroa ME, Melnick A, Mason CE (2012) methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol 13 (10):R87 41. Teschendorff AE, Breeze CE, Zheng SC et al (2017) A comparison of reference-based algorithms for correcting cell-type heterogeneity in epigenome-wide association studies. BMC Bioinformatics 18(1):105 42. Guo S, Diep D, Plongthongkum N et al (2017) Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet 49 (4):635 43. Kang S, Li Q, Chen Q et al (2017) CancerLocator: non-invasive cancer diagnosis and tissueof-origin prediction using methylation profiles of cell-free DNA. Genome Biol 18(1):1–12 44. Li W, Li Q, Kang S et al (2018) CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res 46(15):e89–e89 45. Song BP, Jain S, Lin SY et al (2012) Detection of hypermethylated vimentin in urine of patients with colorectal cancer. J Mol Diagn 14(2):112–119 46. Goessl C, Mu¨ller M, Straub B et al (2002) DNA alterations in body fluids as molecular

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Chapter 9 PCA3 in Prostate Cancer Roberta Gunelli, Eugenia Fragala`, and Massimo Fiori Abstract Prostate cancer antigen 3 (PCA3) is a urinary biomarker for prostate cancer and has demonstrated a good specificity and sensitivity representing a minimally invasive test. PCA3 assay could be useful in combination with PSA to suggest an eventual rebiopsy in men who have had one or more previous negative prostate biopsies. Combination of multiple tumor biomarkers will be the trend in the near future to achieve the goal of evaluate the aggressiveness of cancer and at the same time reducing the number of unnecessary biopsies. Key words PCA3, DD3, Prostate Cancer, Biomarker, Prostate Biopsy

1

Introduction PCA3 (Prostate Cancer gene 3) also known as DD3 (Differential Display code 3), is a prostate-specific noncoding mRNA. The first study was conducted by Bussemakers in 1999 [1], then PCA3 has continued to attract researchers’ attention by confirming fairly high specificity and sensitivity and becoming, after prostate specific antigen (PSA), the most promising biomarker in the 2000s, for the early diagnosis of prostate cancer (PCa). The combined use of PCA3 and PSA could have a better specificity for prostate cancer diagnosis and, in conjunction with other patient information, can help in the decision for repeat biopsy in men 50 years who have had one or more previous negative prostate biopsies and for whom a repeated biopsy would be recommended by an urologist based on the current standard of care.

2

PCA3 PCA3 is a long noncoding RNA (lncRNA) and the gene encoding PCA3 is located on chromosome 9q21-22.

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_9, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Bussmakers and coworkers [1] tested in 1999 several human PCa cell lines (ALVA-31, DU-145, JCA-1, LNCaP, PC-3, PPC-1, and TSU-pr1) for PCA3 expression and it was only detected in LNCaP cell line; no PCA3-related product was detected in any other normal human tissue and its analysis in 56 specimens of PCa tissues were found to contain 10 to 100-fold greater levels of PCA3 than in adjacent nonmalignant prostate tissues [1, 2]. The PCA3 gene consists of four exons and contains several transcripts. Multiple transcripts arise both from alternative splicing and from alternative polyadenylation [3]. In 2009 Clarke et al. [4] detected four new transcription start sites as well as two additional exon 2 sequences, identifying two transcriptional variants: PCA3-TS4 and PCA3-TS5. Salagierski et al. [5] in 2010 showed that the previous described major PCA3 isoform constitutes the best target for diagnostic purpose. Moreover, the expression of PCA3 gene is androgen-regulated [6]. As PCA3 gene does not encode a protein, the only molecule that can be tested is the mRNA, and its expression is mainly restricted to the nuclear and microsomal compartments [7].

3

PCA3 as a Biomarker In 1999, Bussemaker et al. [1] described the presence of PCA3 in PCa, but only in 2003 Hessels and coworkers [2] described its use as a biomarker and the laboratory methods [8, 9]. PCA3 has been investigated due to its major overexpression in PCa cells; its expression does not appear to be greatly influenced by patient age, inflammation, trauma, use of 5 ζ-reductase inhibitors or prostate volume [10]. Recent studies highlight a possible genetic and race dependency of urinary PCA3 and its relations with Gleason Score [11–13]. In 2006, Groskopf et al. [14] presented APTIMA PCA3 molecular urine test, a prototype quantitative test with interesting clinical performances compared to that of PSA (specificity 79% vs. 28%) and the results for prebiopsy samples from men, with PSA value between 2.5 and 10 ng/mL, was similar to that obtained for the entire prebiopsy group (sensitivity 69%, specificity 83%). Groskopf et al. [14] highlighted the importance of prostate manipulation (three strokes per prostate lobe) to get a good informative sample. In 2011 a test with the potential for general use in clinical settings was developed. Durand and coworkers [15] retrace the path of PCA3 and highlight how the Progensa™ (Gen-Probe Inc., San Diego, CA, USA) PCA3 urine test has proven its clinical relevance and predictive abilities for biopsy outcome. This test is

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commercially available in Europe and provides the PCA3 score from male urine by using transcription-mediated amplification (TMA™) technology and Hybridization Protection (HP) for quantitation. TMA™ is an isothermal nucleic acid-based method that can amplify RNA or DNA targets by 1 billion-fold in less than 1 h. The methodology has been applied to the measure PCA3 in first-catch urine samples collected following prostate manipulation. To promote accurate measurement, a normalizing factor was required to adjust for the number of prostatic cells released into urine following prostate manipulation. Since kallikrein 3 (KLK3), the gene encoding PSA, has not been shown to be upregulated in cancer [16], it was selected as the normalizing factor. The stability of the PCA3 and PSA mRNAs in urine specimens was demonstrated to be at least 5 days at 2–8  C [17], and the manufacturer developed an improved collection medium that allows urine specimens to be shipped to the testing laboratory under ambient conditions [15]. In 2012, the Food and Drug Administration (FDA) approved the Progensa™ PCA3 assay for use in conjunction with other patient information to aid in the decision for repeat biopsy in men 50 years who have had one or more previous negative prostate biopsies and for whom a repeat biopsy would be recommended by a urologist based on the current standard of care. In one of the largest reports to evaluate the diagnostic potential of PCA3 for PCa, Cui et al. [18] combined the data from 46 studies involving 12,295 men investigated for PCa. Pooled sensitivity, specificity, positive likelihood ratio (+LR), negative likelihood ratio ( LR), diagnostic odds ratio (DOR) and AUC (Area Under the Receiver Operating Characteristic Curve) for PCa were 0.65 (95% confidence interval [CI]: 0.63–0.66), 0.73 (95% CI: 0.72–0.74), 2.23 (95% CI: 1.91–2.62), 0.48 (95% CI: 0.44–0.52), 5.31 (95% CI: 4.19–6.73), and 0.75 (95% CI: 0.74–0.77), respectively. An important issue is that there is no consensus about the most appropriate thresholds to be used for the PCA3 test. The US FDA recommends a PCA3 score threshold of 25, but a threshold of 35 is commonly used [19–21]. Some studies indicated that a cutoff score of 25 provides a good balance between sensitivity and specificity [22–25] but others have supported the use of different thresholds such as 51 [26] and 43 [27]. Merdan and coworkers [28] considered different sets of PCA3 threshold (25, 35 and 100) and performed an head to head comparison of PCA3 and TMPRSS2-ERG marker with the purpose to ameliorate the decision-making process. As PSA alone is ineffective for recommending patients to undergo repeat biopsy after previous negative biopsy results, the addition of PCA3 or TMPRSS2-ERG tests can reduce the number of unnecessary biopsies (54.4% and 63.2%, respectively) with only some reduction in 10-year overall

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survival and 15-year cancer-specific survival [28]. TMPRSS2-ERG is a biomarker associated with PCa that seems to be useful at discriminating between low-grade and high-grade cancers [29]. As demonstrated by Truong and coworkers, PCA3 specificity decreases with a lower PCA3 score [10]. In 2019 Munoz Rodriguez published a systematic review and meta-analysis emphasizing the best results with a cutoff value of 35 (sensitivity 0.69 and specificity 0.65 with an overall DOR 4.244 and the AUC of 0.734) and highlighted the possibility to avoid unnecessary prostate biopsy [30].

4

The FDA-Approved Progensa™ PCA3 Assay The feasibility of a PCA3 gene-based molecular assay for the detection of PCa cells in urine, contribute to develop a quantitative PCA3 urine test with the potential for general use in clinical settings, the Progensa™ (Gen-Probe Inc., San Diego, CA, USA) PCA3 urine test that is an in vitro nucleic acid amplification test. The assay measures the concentration of PCA3 and PSA mRNA and calculates the ratio of them multiplied for 1000 (PCA3 score) in at least 2.5 mL of the initial urine stream collected following a digital rectal examination (DRE) consisting of three strokes per lobe. A PCA3 score 500 cells/slide) and it is approved only as an adjunct to cystoscopy [8]. The 2006 study by Mian and colleagues, in which 942 patients with a history of TCC were enrolled, found that ImmunoCyt/uCyt + had an increased sensitivity for low-grade tumors (G1), with a sensitivity of 8.3% for cytology alone compared to 79.3% for the combination of ImmunoCyt/uCyt+ and cytology. Sensitivity was also improved for high grade tumors (G3), with a sensitivity of 75.3% for cytology alone and 98.9% for the combination of cytology and ImmunoCyt/uCyt+ [22]. In general, the ImmunoCyt/uCyt+ and cytology combination has a specificity like other commercially available tests but it is more sensitive, especially for the detection of low grade/low stage tumors. These performance characteristics make it attractive when combined with cytology to help to extend the interval between cystoscopies in patients followed for bladder cancer [22].

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3 Commercially Available (Not FDA-Approved) Urinary Biomarkers for Bladder Cancer 3.1

Cxbladder

Cxbladder (Pacific Edge Diagnostics, Dunedin, New Zealand) employs the reverse transcriptase polymerase chain reaction (RT-PCR) detection of five mRNA (IGFBP5, HOXA13, MDK, CDK1, and CXCR2) that are expressed in urine of bladder cancer patients. The sensitivity of this assay is 83%, greater than NMP22 and cytology sensitivity, and the specificity is 85%. Interestingly, the specificity for high-grade tumors was 97% while the specificity for low-grade tumors was 69%. The study of Kavalieris et al. [23] assessed the expression of IGFBP5, HOXA13, MDK, CDK1, and CXCR2 genes in a voided urine sample (genotypic) and age, gender, frequency of macrohematuria and smoking history (phenotypic) data were collected from 587 patients with macrohematuria. A combined genotypic–phenotypic model (G + P INDEX) was compared with genotypic (G INDEX) and phenotypic (P INDEX) models [23]. The G + P INDEX offered a bias-corrected Area Under the Curve (AUC) of 0.86 compared with 0.61 and 0.83, for the P and G INDEXs, respectively. When the test-negative rate was 0.4, the G + P INDEX (sensitivity ¼ 0.95; NPV ¼ 0.98) offered improved performance compared with the G INDEX (sensitivity ¼ 0.86; NPV ¼ 0.96). Eighty percent of patients with microhematuria who did not have UC were correctly triaged out using the G + P INDEX, therefore not requiring a full urological workup.

3.2

AssureMDx

AssureMDx (MDx Health, Irvine, California, USA) is a DNA-based urine assay that detects mutations in three genes (FGFR3, TERT , and HRAS ) in combination with methylation analysis of other three genes (OTX1, ONECUT2, and TWIST1) [24]. A multicenter cohort study validated the sensitivity and specificity of this method in patients with hematuria [25]. The AssureMDx assay combined with age predicted the presence of bladder cancer with 97% sensitivity, 83% specificity and an AUC of 0.93. Another international multicenter cohort study demonstrated 93% sensitivity and 86% specificity, with AUC of 0.96 (95% confidence interval (CI): 0.92–0.99) [24]. This assay may therefore hold promise as a predictive urine test in patients with hematuria and avoid cystoscopy in those who are not likely to have bladder cancer.

3.3

XPert BC

XPert BC (Cepheid, Sunnyvale, California, USA) is a mRNA-based assay that measures the expression of five genes in the urine (UPK1B, IGF2, CRH, ANXA10, and ABL1) in a model to predict the presence of bladder cancer [26]. A recent study compared the sensitivity and negative predictive value (NPV) of XPert BC with

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urine cytology to detect recurrence of bladder cancer. In this study of 230 NMIBC patients, the sensitivity of Xpert BC for detection of bladder cancer was 46.2% and the NPV was 83%. The test had 84% sensitivity and a 93% NPV, whereas cytology had a 33% sensitivity and 76% NPVp < 0.001) [27]. The relatively low specificity of Xpert BC also undermines the potential role as a replacement for urine cytology in NMIBC surveillance. 3.4 Urinary Bladder Cancer Rapid Test

Urinary bladder cancer (UBC) Rapid test is a urine-based assay that probes the qualitative or quantitative expression of cytokeratin 8 and 18. Hakenberg et al. [28] studied 112 patients before transurethral resection of bladder tumor, 40 patients before secondary surgical treatment, and 2 groups of control study participants comprising 29 healthy study participants and 10 women with acute urinary tract infection with UBC Rapid test, UBC II ELISA (point of care) test, and urine cytology for the diagnosis of bladder cancer. The UBC Rapid test, UBC II ELISA, and urine cytology confirmed sensitivity of 64.4, 46.6, and 70.5 and and specificity of 63.6, 86.3, and 79.5%, respectively [29].

3.5

Telomeres are repeated noncoding DNA sequences at the 30 end of eukaryotic chromosomes which prevents the loss of crucial genetic information at the end of each DNA replication cycle. Loss of telomeres leads to chromosomal instability and cellular senescence. Telomerase is a ribonuclease protein complex responsible for telomere maintenance and lengthening. Telomerase is expressed in germ cells, proliferating cells such as leukocytes, and tumor cells; normal somatic cells do not express telomerase. The presence of telomerase in the urine is a sensitive marker for urologic neoplasms [30]. The telomeric repeat amplification protocol (TRAP) is a specialized assay that detects the presence of telomerase in exfoliated cells. Another assay measures the messenger RNA levels of human telomerase reverse transcriptase (hTERT), the catalytic subunit of telomerase, by RT-PCR. hTERT itself has been shown to be significantly associated with risk of tumor recurrence [2]. Both assays are significantly affected by sample collection and processing, which limits their clinical application. Degradation of telomerase and hTERT by urine can decrease assay sensitivity dramatically [31, 32].

4

Telomerase

Conclusion Actually, a number always increasing of urine-based assays for bladder cancer detection and surveillance is available, but it is important to evaluate a combination of sensitivity and specificity to justify their cost and routine use. Most of them show a low specificity

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with a higher sensitivity than cytology for low-grade cancers. Diagnostic accuracy may be slightly higher for initial diagnosis of bladder cancer in patients with signs and symptoms than surveillance and accuracy is poor for early stage and low grade tumors. Combination of genes and epigenetic markers has given promising results. However, the appropriate population for a rationale and clinical endpoints is essential for the trials regarding urine biomarkers, to justify their cost and incorporation into practice. Urinary biomarkers in combination with cytological evaluation are more precise than biomarkers alone; future research is needed to understand how the use of these biomarkers in conjunction with other diagnostic tests affects the use of cystoscopy and clinical outcomes. References 1. American Urological Association. (2016). Standard guideline statement emphasizing the role of urine cytology as biomarker. https:// www.auanet.org/guidelines/bladder-cancernonmuscle-invasive 2. Lotan Y, Roehrborn CG (2003) Sensitivity and specificity of commonly available bladder tumor markers versus cytology: results of a comprehensive literature review and metaanalyses. Urology 61:109–118, discussion 118 3. Riesz P, Lotz G, Pa´ska C et al (2007) Detection of bladder cancer from the urine using fluorescence in situ hybridization technique. Pathol Oncol Res 13:187–194 4. Sassa N, Iwata H, Kato M et al (2019) Diagnostic utility of urovysion combined with conventional urinary cytology for urothelial carcinoma of the upper urinary tract. Am J Clin Pathol 151(5):469–478 5. Nagai T, Okamura T, Yanase T et al (2019) Examination of diagnostic accuracy of UroVysion fluorescence in situ hybridization for Bladder Cancer in a Single Community of Japanese Hospital Patients. Asian Pac J Cancer Prev 20 (4):1271–1127 6. Kocsma´r I, Pajor G, Gyo¨ngyo¨si B et al (2020) Development and initial testing of a modified UroVysion-based fluorescence in situ hybridization score for prediction of progression in bladder cancer. Am J Clin Pathol 153 (2):274–284 7. Hajdinjak T (2008) UroVysion FISH test for detecting urothelial cancers: meta analysis of diagnostic accuracy and comparison with urinary cytology testing. Urol Oncol Semin Orig Investig 26:646–651 8. He H, Han C, Hao L et al (2016) ImmunoCyt test compared to cytology in the diagnosis of

bladder cancer: a meta-analysis. Oncol Lett 12:83–88 9. Seideman C, Canter D, Kim P et al (2015) Multicenter evaluation of the role of UroVysion FISH assay in surveillance of patients with bladder cancer: does FISH positivity anticipate recurrence? World J Urol 33:1309–1313 10. Kim PH, Sukhu R, Cordon BH et al (2014) Reflex fluorescence in situ hybridization assay for suspicious urinary cytology in bladder cancer patients with negative surveillance cystoscopy. BJU Int 114:354–359 11. Savic S, Zlobec I, Thalmann GN et al (2009) The prognostic value of cytology and fluorescence in situ hybridization in the follow-up of nonmuscle-invasive bladder cancer after intravesical Bacillus Calmette-Guerin therapy. Int J Cancer 124:2899–2904 12. Miyake M, Goodison S, Giacoia EG et al (2012) Influencing factors on the NMP-22 urine assay: an experimental model. BMC Urol 12:23 13. Jamshidian H, Kor K, Djalali M (2008) Urine concentration of nuclear matrix protein 22 for diagnosis of transitional cell carcinoma of bladder. Urol J 5:243–247 14. Mowatt G, Zhu S, Kilonzo M et al (2010) Systematic review of the clinical effectiveness and cost-effectiveness of photodynamic diagnosis and urine biomarkers (FISH, ImmunoCyt, NMP22) and cytology for the detection and follow-up of bladder cancer. Health Technol Assess Winch Engl 14:1–331 15. Behrens T, Stenzl A, Bru¨ning T (2014) Factors influencing false-positive results for nuclear matrix protein 22. Eur Urol 66:970–972 16. Hatzichristodoulou G, Ku¨bler H, Schwaibold H et al (2012) Nuclear matrix protein 22 for

Urinary Biomarkers In Bladder Cancer bladder cancer detection: comparative analysis of the BladderChek® and ELISA. Anticancer Res 32(11):5093–5097 17. Kumar A, Kumar R, Gupta NP (2006) Comparison of NMP22 BladderChek test and urine cytology for the detection of recurrent bladder cancer. Jpn J Clin Oncol 36(3):172–175 18. Grossman HB, Soloway M, Messing E et al (2006) Surveillance for recurrent bladder cancer using a point-of-care proteomic assay. JAMA 295:299–305 19. Malkowicz SB (2000) The application of human complement factor h-related protein (BTA TRAK) in monitoring patients with bladder cancer. Urol Clin North Am 27:63–73 20. Ellis WS, Blumenstein BA, Lm I et al (1997) Clinical evaluation of the BTA TRAK assay and comparison to voided urine cytology and the Bard BTA test in patients with recurrent bladder tumors. The Multi Center Study Group. Urology 50(6):882–887 21. Cha EK, Tirsar L-A, Schwentner C et al (2012) Immunocytology is strong predictor of bladder cancer presence in patients with painless hematuria: a multicentre study. Eur Urol 61:185–192 22. Mian C, Maier K, Comploj E et al (2006) uCyt +/ImmunoCyt in the detection of recurrent urothelial carcinoma. Cancer 108(1):60–65 23. Kavalieris L, O’Sullivan P, Frampton C et al (2017) Performance characteristics of a multigene urine biomarker test for monitoring for recurrent urothelial carcinoma in a multicenter study. J Urol 197:1419–1426 24. Van Kessel KEM, Van Neste L, Lurkin I et al (2016) Evaluation of an epigenetic profile for

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the detection of bladder cancer in patients with hematuria. J Urol 195:601–607 25. Van Kessel KEM, Beukers W, Lurkin I et al (2017) Validation of a DNA methylation mutation urine assay to select patients with hematuria for cystoscopy. J Urol 197:590–595 26. D Elia C, Pycha A, Folchini DM et al (2019) Diagnostic predictive value of Xpert Bladder Cancer Monitor in the follow-up of patients affected by non-muscle invasive bladder cancer. J Clin Pathol 72:140–144 27. Pichler R, Fritz J, Tulchiner G et al (2018) Increased accuracy of a novel mRNA based urine test for bladder cancer surveillance. BJU Int 121:29–37 28. Hakenberg OW, Fuessel S, Richter K et al (2004) Qualitative and quantitative assessment of urinary cytokeratin 8 and 18 fragments compared with voided urine cytology in diagnosis of bladder carcinoma. Urology 64:1121–1126 29. Ritter R, Hennenlotter J, Ku¨hs U et al (2014) Evaluation of a new quantitative point of-care test platform for urine-based detection of bladder cancer. Urol Oncol 32:337–344 30. Lamarca A, Barriuso J (2012) Urine telomerase for diagnosis and surveillance of bladder cancer. Adv Urol 2012:693631 31. Kim NW, Piatyszek MA, Prowse KR et al (2011) Specific association of human telomerase activity with immortal cells and cancer. Science 1994:266 32. Mucciardi G, Gali’ A, Barresi V et al (2014) Telomere instability in papillary bladder urothelial carcinomas: Comparison with grading and risk of recurrence. Indian J Urol 30:245

Chapter 12 Telomerase Activity Analysis In Urine Sediment for Bladder Cancer Valentina Casadio and Sara Bravaccini Abstract Bladder cancer with an incidence of 15 cases per 100,000 persons in the global population is the most common tumor of the urinary tract. Imaging techniques, cytoscopy, and cytology are not sufficiently accurate to detect early stage tumors, and the need for new diagnostic markers is still an urgency. Among the biomarkers most recently proposed to improve diagnostic accuracy and especially sensitivity, increasing attention has been focused on the role of the ribonucleoprotein, telomerase. Previous studies have shown that the quantitative telomerase repeat amplification protocol (TRAP) assay performed in voided urine is an important noninvasive tool for the diagnosis of bladder tumors since it has very high sensitivity and specificity, even for early stage and low-grade tumors. Telomerase activity in urine determined by TRAP seems to be marker of great potential, even more advantageous in cost–benefit terms when used in selected symptomatic patients or professionally high-risk subgroups. Here we report the real-time PCR protocol to detect telomerase activity in urine sediment for bladder cancer. Key words Urine sediment, Telomerase activity, TRAP, Bladder cancer

1

Introduction For bladder cancer, there are numerous studies aimed at identifying new molecular markers in urine of diagnostic relevance, but, so far, none of these have an optimal accuracy (both in terms of sensitivity and specificity). Cytology is considered the gold standard but has low sensitivity especially for low grade and stage tumors. Among the markers most recently proposed to improve bladder cancer diagnostic accuracy, especially in terms of sensitivity, increasing attention has been focused on the role of the ribonucleoprotein, telomerase. This enzyme consists of three subunits: an RNA component (hTR), which acts as a template for DNA replication [1], a telomerase associated protein (TP1) [2] of as yet unknown function, and the telomerase reverse transcriptase (hTERT), which is responsible for catalytic activity [3]. Telomerase activity (TA) has been detected in almost all malignant cells and tissues, and only

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_12, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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very occasionally in normal somatic cells [4–6]. The telomeric repeat amplification protocol assay (TRAP), a polymerase chain reaction (PCR) based method for detection of TA, has been available since 1994 [4]. The introduction of this method is an important milestone in telomerase research and has become the standard method for studying the diagnostic relevance of this enzyme [5–9]. TA has also been determined qualitatively and quantitatively using modified TRAP assays, for example TRAP scintillation proximity assay, TRAP-ELISA, fluorescent TRAP assay, TRAP hybridization assay, and bioluminescence linked with TRAP. Other methods have focused on the detection of the telomerase subunits, hTR and hTERT, using the reverse transcriptase polymerase chain reaction (RT-PCR). Real-time PCR methods have also permitted a quantitative and reproducible determination of these subunits. Expression of the hTERT protein has also been analyzed by immunocytochemistry using anti-hTERT monoclonal [10, 11] and polyclonal antibodies. The detection of TA in bladder washing and voided urine has been investigated for its diagnostic potential. Since this technique detects TA, and not only the presence of the enzyme, viable cells are a fundamental. In fact, a possible limitation of the TRAP assay is the potential vulnerability and inactivation of the enzyme by external factors. In native urine, suspended tumour cells are exposed to destructive substances such as proteases, urea, salts and, usually, acid pH, for variable times. All of these factors may lead to early inactivation or degradation of the enzyme that could explain the lack of reproducibility of results among the different studies. The first reported TRAP assay studies were based on qualitative, and thereafter with semiquantitative TA determinations [12]. To obtain more accurate and reliable results, a quantitative TRAP assay was developed in bladder washings and voided urine, based on exponential amplification of the primer-telomeric repeats generated in the telomerase reaction [7, 13–16]. Important results on the diagnostic relevance of urine telomerase have been published by our group in pilot and confirmatory case–control studies [6–8]. We demonstrated that this test is more accurate in males than females [7, 17, 18], with a higher specificity in younger than older individuals [8]. In other studies we suggested that the lower accuracy in females could be due to the presence of inflammatory cells, which are almost always positive to telomerase [7, 17, 18]. The major benefit of using TRAP assay at the best cut off value of 50 arbitrary enzymatic units (AEU) for bladder cancer could be obtained in symptomatic and high risk patients [19– 21]. We thus design a mixed procedures including cytology in parallel with the in series combination of TRAP assay and FISH analysis which provided the best trade-off between increase in sensitivity and loss in specificity [19].

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However, before introducing this test in routine clinical practice, in combination with, or as an alternative to invasive cystoscopy, its potential, in terms of sensitivity and specificity, must be further investigated and defined in a consecutive series of symptomatic individuals or workers at high risk for bladder cancer (for example workers in rubber tyres industry) [20]. Here we report the Real Time PCR protocol used by our research group for a semiquantitative detection of TA. Briefly, real-time PCR for the semiquantitative analysis of TA is performed using the Rotor Gene 6000 tool and provides two consecutive steps. During the first incubation phase at 30
C telomerase adds TTAGGG at the end of a synthetic primer with homologous sequence to the telomeric one (called TS primer). At the end of the incubation telomerase is inactivated by heat (94
C). In the second step, the oligonucleotide thus extended is amplified through a reverse-primer (called CX) which is complementary to the repetition sequence. In this phase, serial dilutions of an internal control (called ITAS), an oligonucleotide of about 150 bp which is amplified by the same primers CX * and TS, are also amplified (Fig. 1).

Fig. 1 A summary of the TRAP assay is reported with the three main steps

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Materials Protein Isolation

1. PBS. Phospate-buffered saline (pH 7.4) 1, used to wash urine pelleted cells. PBS could be prepared or it could be purchased ready to use. 2. Lysis Buffer (maintained at 4
C till covered by light as it is photolabile). Tris–HCl pH 7.5 (10 mM), MgCl (1 mM), EGTA (ethylene glycol-bis(β-aminoethyl ether)-N,N,N0 ,N0 -tetraacetic acid) (1 mM), β-mercaptoethanol (5 mM) (see Note 1), CHAPS 3-[(3-colammidopropil) dimetilammino]-1-propansulfonate (0.5%), Glicerol (10%), PMSF (phenylmethylsulfonyl fluoride) (0.1 mM) (see Note 2). 3. BCA Protein Assay Kit (Pierce). We recommended the use of this kit to quantify protein in urine sediments but also other colorimetric approaches might be used. 4. A spectrophotometer to read samples at 750 nm.

2.2 TRAP ASSAY with Real Time PCR

1. A Real Time PCR instrument: we used a Rotor Gene Thermocycler but also another similar instrument can be used. 2. An internal telomerase assay standard (ITAS) as an internal control: a 150 bp sequence, with two ends recognized by the primers forward and reverse, used for the TRAP assay (see Note 3). 3. Two primers prepared at a final concentration of 0.1 μg/μL with the following sequences: Primer TS: 50 -AAT CCG TCG AGC AGA GTT-3. Primer CX: 50 -CCC TTA CCC TTA CCC TTA CCC TTA-30 4. T4 gene 32 protein (Roche), an essential protein useful to stabilize the region of single strand DNA.

3 3.1

Method Urine Collection

1. Obtain clean-catch first or second morning urine sample (see Note 4) in a clean dry plastic cup. Collect at least 5 mL of urine sample. 2. Maintain urine at 4
C for a maximum of 3 h and send to the laboratory at the same temperature. 3. Mix each sample by inverting it twice immediately upon arrival in the laboratory and transfer into two 2 mL conical bottom polypropylene tubes.

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4. Centrifuge tubes at 850  g for 10 min at 4
C (see Note 5). 5. Carefully discard the supernatant, the upper part of the urine of the two tubes. 6. Wash pelleted cells in one tube with 1 mL of PBS 1 buffer, resuspend by pipetting. 7. Carefully transfer all the washed cells resuspended in PBS 1 into the tube and resuspend also the second pellet such to unify the cells. 8. Centrifuge the tube at 850  g for 10 min at 4
C. 9. Without disturbing the pellet, slowly, pipette out the supernatant and store the pellet at 80
C. 3.2 Proteins Isolation and Quantification

1. Pelleted cells must be resuspended in 200 μL of Lysis Reagent. 2. Incubate the pellet mixture on ice for 30 min. 3. Centrifuge at 10,000  g for 20 min at 4
C. 4. Without disturbing the pellet, slowly, pipette out the supernatant and transfer the supernatant in a new 1.5 mL conical tube and store it at 20
C till use.

3.3 Protein Quantification

1. Prepare bovine serum albumin (BSA) standards starting from BSA ampule provided in BCA protein assay kit and using lysis buffer (see Note 6) as indicated in Table 1. 2. Prepare a number of spectrophotometer cuvettes considering (number of samples + 8 standards + two blank samples only containing the lysis buffer). 3. Thaw samples on ice. 4. Add 50 μL of the standards and samples to each cuvette and 50 μL of lysis buffer for the two blank samples.

Table 1 Standards for protein quantification Name of standard

Sample

Lysis buffer

Bovine serum albumin concentration

STOCK

300 μL of STOCK

–

2000 μg/μL

A

375 μL of STOCK

125 μL

1500 μg/μL

B

325 μL of STOCK

325 μL

1000 μg/μL

C

175 μL of A

175 μL

0.750 μg/μL

D

325 μL of B

325 μL

0.500 μg/μL

E

325 μL of D

325 μL

0.250 μg/μL

F

325 μL of E

325 μL

0.125 μg/μL

G

100 μL of F

400 μL

0.025 μg/μL

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5. Immediately store samples at 20
C. 6. Add 250 μL of reagent A and 5 μL of reagent S to each cuvette and carefully resuspend by pipetting. 7. Add 2 mL of reagent B to each cuvette and gently resuspend. 8. Incubate the cuvettes at room temperature away from light for 15 min (see Note 7). 9. Set up the spectrophotometer for 750 nm. 10. Read blanks, standards, and samples absorbance twice to the spectrophotometer and discard the cuvettes. 11. The standards absorbances values must be used to construct a reference curve (absorbance vs concentration) (see Note 8). 12. On the basis of the standard curve, calculate the sample concentration and multiply by 5. 13. Thaw samples on ice and dilute them at 0.1 μg/μL (see Note 9). 14. Store samples at 80
C. 3.4 TRAP Assay with Real Time PCR

1. Thaw diluted samples (0.1 μg/μL) and ITAS on ice (see Note 10). 2. Dilute ITAS, internal control, to obtain different concentrations, corresponding to different arbitrary enzymatic units: 25  1021 (1AEU), 25  1020 (10AEU), 25  1019 (100AEU), 25  1018 (1000AEU), 25  1017 (10,000AEU). 3. Place two 10 μL aliquots of each sample and ITAS standards into wells. 4. Prepare two wells of 10 μL nuclease-free water as negative controls. 5. For each filled well, prepare a mix of 1 μL of each primer, 12.5 μL of green super mix, 0.25 μL of T4 gene protein, and 0.25 1 μL of water. Prepare the PCR mix considering two extra samples. 6. Transfer 15 μL of the mix into each filled well, pipetting slowly and placing the pipette tip at an angle (10
to 45
) against the inside wall of the receiving well/tube. 7. Perform the PCR with a Real-Time instrument according to the manufacturer’s specifications. See Table 2 for PCR conditions to perform TRAP assay. Analysis of the results. 8. Using the instrument software, check if any replicates have a cycling threshold (Ct) value difference of one or more. Discard those samples from the analysis and evaluate the mean Ct for the remaining samples (see Note 11).

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Table 2 PCR conditions to perform TRAP assay Time

Temperature

Telomerase activity

Hold Hold

20 min 15 min

30
C 95
C

Real-time PCR (40 cycles)

Denaturation Annealing Extension

20 s 30 s 60 s

94
C 56
C 72
C

9. Various amounts of DNA and consequently AEU from the ITAS standards are analyzed to draw a standard curve. Then, use the standard curve to determine AEU activity for each sample by interpolation (see Note 12).

4

Notes 1. Beta-mercaptoethanol (βME) is a toxic chemical. A fume hood is necessary to handle this solvent and it is recommended to discard separately any pipette tips that touch βME and always any protective gloves used while handling this chemical directly after use. All buffer and samples containing βME must be opened under a fume hood. 2. PMSF reagent must be added immediately before starting the procedure with a dilution 1:1000. It has a role as a protease inhibitor. 3. We obtained ITAS by isolating a sequence by myogenin rat gene. Two sequences recognized by primers forward and reverse were inserted by cloning ITAS in a PCR Script Stratagene plasmid, then the plasmid was extracted. You can also buy the sequence or product it as you want. 4. You can collect first morning urine or even second/third etc. Take into account that if you choose to collect first morning urine, this sample contains a higher number of cells and cellular debris coming from the urological tract and exfoliated in urine during the night. Therefore, you will recover a higher protein yield. This can help you as you are studying an urological cancer (you will probably find more DNA coming from cancer cells). 5. It is mandatory to maintain urine at 4
C to save the telomerase enzyme activity. 6. It is mandatory to use for standard dilutions the same lysis buffer used for protein isolation, such to avoid differences between samples and standards in the spectrophotometric reading.

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7. The procedures for protein quantification might be done away from light as it is a colorimetric approach that could be affected by direct light exposure for long time. 8. We suggest to use the average value between the two absorbance lectures to construct the standard reference curve. You can eliminate a standard value if the value is out of the curve. 9. Samples with a concentration below 0.1 μg/μL are not evaluable for TRAP assay. 10. It is mandatory to maintain samples on ice to save the enzymatic activity, till the start of the real-time PCR protocol. 11. The threshold for Ct determination should be set up as close as possible to the base of the exponential phase, when all reagents are still in excess, the low amount of product will not compete with the primers’ annealing capabilities, making data more accurate. It is recommended to set the same Ct for the different PCR experiments. 12. Use the instrument software to calculate the R2 value, to test the linearity of the standard curve. It should be as close as possible to 1. References 1. Feng J, Funk WD, Wang SS, Weinrich SL et al (1995) The RNA component of human telomerase. Science 269:1236–1241 2. Harrington L, McPhail T, Mar V et al (1997) A mammalian telomerase-associated protein. Science 275:973–977 3. Nakamura TM, Morin GB, Chapman KB, Weinrich SL, Andrews WH, Lingner J (1997) Telomerase catalytic subunit homologs from fission yeast and human. Science 277:955–959 4. Kim NW, Piatyszek MA, Prowse KR et al (1994) Specific association of human telomerase activity with immortal cells and cancer. Science 266:2011–2015 5. Shay JW, Bacchetti S (1997) A survey of telomerase activity in human cancer. Eur J Cancer 33:787–791 6. Fedriga R, Gunelli R, Nanni O, Bacci F, Amadori D, Calistri D (2001) Telomerase activity detected by quantitative assay in bladder carcinoma and exfoliated cells in urine. Neoplasia 3:446–450 7. Sanchini MA, Bravaccini S, Medri L et al (2004) Urine telomerase: an important marker in the diagnosis of bladder cancer. Neoplasia 6:234–239 8. Sanchini MA, Gunelli R, Nanni O et al (2005) Relevance of urine telomerase in the diagnosis of bladder cancer. JAMA 294:2052–2056

9. Weikert S, Krause H, Wolff I et al (2005) Quantitative evaluation of telomerase subunits in urine as biomarkers for noninvasive detection of bladder cancer. Int J Cancer 117:274–280 10. Soldateschi D, Bravaccini S, Berti B et al (2005) Development and characterization of a monoclonal antibody directed against human telomerase reverse transcriptase (hTERT). J Biotechnol 118:370–378 11. Volpi A, Bravaccini S, Medri L, Cerasoli S, Gaudio M, Amadori D (2005) Usefulness of immunological detection of the human telomerase reverse transcriptase. Cell Oncol 27:347–353 12. Yokota K, Kanda K, Inoue Y, Kanayama H, Kagawa S (1998) Semi-quantitative analysis of telomerase activity in exfoliated human urothelial cells and bladder transitional cell carcinoma. Br J Urol 82:727–732 13. Wright WE, Shay JW, Piatyszek MA (1995) Modifications of a telomeric repeat amplification protocol (TRAP) result in increased reliability, linearity and sensitivity. Nucleic Acids Res 23:3794–3795 14. Kim NW, Wu F (1997) Advances in quantification and characterization of telomerase activity by the telomeric repeat amplification protocol (TRAP). Nucleic Acids Res 25:2595–2597

Telomerase Activity Analysis In Urine Sediment for Bladder Cancer 15. Kavaler E, Landman J, Chang Y, Droller MJ, Liu BC (1998) Detecting human bladder carcinoma cells in voided urine samples by assaying for the presence of telomerase activity. Cancer 82:708–714 16. Gelmini S, Crisci A, Salvadori B, Pazzagli M, Selli C, Orlando C (2000) Comparison of telomerase activity in bladder carcinoma and exfoliated cells collected in urine and bladder washings, using a quantitative assay. Clin Cancer Res 6:2771–2776 17. Bravaccini S, Sanchini MA, Granato AM et al (2007) Urine telomerase activity for the detection of bladder cancer in females. J Urol 178 (1):57–61 18. Bravaccini S, Casadio V, Amadori D, Calistri D, Silvestrini R (2009) The current role of telomerase in the diagnosis of bladder cancer.

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Indian J Urol 25(1):40–46. https://doi.org/ 10.4103/0970-1591.45535. 19. Bravaccini S, Casadio V, Gunelli R et al (2011) Combining cytology, TRAP assay, and FISH analysis for the detection of bladder cancer in symptomatic patients. Ann Oncol 22 (10):2294–2298 20. Cavallo D, Casadio V, Bravaccini S et al (2014) Assessment of DNA damage and telomerase activity in exfoliated urinary cells as sensitive and noninvasive biomarkers for early diagnosis of bladder cancer in ex-workers of a rubber tyres industry. Biomed Res Int 2014:370907 21. Casadio V, Bravaccini S, Gunelli R et al (2009) Accuracy of urine telomerase activity to detect bladder cancer in symptomatic patients. Int J Biol Markers 24(4):253–257

Chapter 13 Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarkers of Schistosoma-Associated Bladder Cancer Tariq Ganief, Bridget Calder, and Jonathan M. Blackburn Abstract Advances in mass spectrometry instrumentation have revolutionized analytical capability in clinical proteomics. In parallel, various sample preparation methods have been developed to try to address the inherent complexity and dynamic range of clinical samples, typically involving a combination of depletion of abundant proteins followed by extensive prefractionation. However, the depth of coverage routinely achieved in discovery proteomics experiments on peripheral fluids such as serum, still leaves something to be desired, especially if no depletion or prefractionation is done in order to increase the throughput of clinical samples. Remarkably, despite being an easily accessible, typically sterile and diagnostically rich clinical sample, urine is often overlooked and as such has received less development effort. As an ultrafiltrate of blood, urine contains proteins and protein fragments originating from all parts of the body which may have diagnostic or prognostic potential if accurately and reproducibly quantified. Here, we describe an efficient and simple method for the concentration of urine samples by methanol–chloroform precipitation and subsequent in-solution tryptic digestion prior to discovery or targeted mass spectrometry analysis. We exemplify this method by reference to the discovery of novel candidate urinary biomarkers of schistosomiasis. Importantly, the methods described here have been used to identify >1900 protein groups in human urine by label-free discovery proteomics, without requiring any prior depletion or prefractionation, making this approach amenable to high throughput clinical biomarker studies in many diseases. Key words Urine, Proteomics, Mass spectrometry, Biofluid, Biomarkers

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Introduction With an estimated 732 million persons being vulnerable to infection, urinary schistosomiasis caused by the parasite Schistosoma haematobium is of great public health significance in tropical and sub-tropical areas. In 2008, 17.5 million people were treated globally for schistosomiasis, 11.7 million of those from sub-Saharan Africa [1]. Chronic infection with S. haematobium has been reported as a possible risk factor in the etiology of bladder cancer. Further, S. haematobium has been associated with a two- to tenfold

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increase in the risk of bladder squamous cell carcinoma, as well as being a potential cause of kidney damage [2]. Hence, the parasite is considered as a group 1 carcinogen [3]. In S. haematobiumendemic regions, infected persons are unlikely to receive timely cancer diagnoses which may lead to poor disease prognoses and debilitating or fatal outcomes. Thus, the identification of markers for the early detection of bladder cancer in resource poor areas is critically important. Intuitively, samples from the site of disease (bladder biopsy) may offer the greatest potential for biomarker discovery, however, sample acquisition may often require advanced clinical training or surgery which are not always feasible and requires early patient identification. Therefore, more accessible body fluids such as blood/plasma/serum are attractive. While these are theoretically an excellent source of biomarkers due to relatively noninvasive sampling requirements and its access to secreted proteins from all tissues, there are significant caveats. Mass spectrometry-based plasma proteomics suffers due to the extraordinarily high dynamic range (ca. ten orders of magnitude) which typically requires efficient depletion of high abundance proteins and fractionation to acquire deep identification. Urine, however, requires minimal training for sample collection in resource poor settings, is available in large quantities from patients, is typically considered sterile and, as an ultrafiltrate of blood, also contains proteins or protein fragments secreted from all tissues and organs in the body. Urinary proteins tend to be relatively heat stable (having been incubated at 37  C in the body for significant periods before sample collection) and have likely already been exposed to endogenous proteolytic degradation; thus, storage and transport of clinical urine samples is relatively simple. Urine is particularly attractive in bladder cancer due to proximity to the site of disease but holds high promise as a biomarker resource for many other diseases as well. Urine, therefore, may be valuable as a source of biomarkers for diverse diseases and is suitable for mass spectrometry-based proteomic measurement [4]. Previously, sample preparation of urine for mass spectrometry has been carried out using molecular-weight cutoff filters for buffer exchange [5]. This could introduce bias by favoring larger proteins and depleting shorter fragments. Previous methods have also involved preparing urine samples for in-gel tryptic digest, which is no longer considered to be reproducible and high throughput [6]. The method presented herein aims to standardize the handling of urine samples for mass spectrometry proteomic analysis and to minimize technical error, while at the same time streamlining and simplifying the workflow to enable high-throughput processing. Using this method we analyzed 49 patients in four distinct clinical groups (SH- S. haematobium infected groups, PT- bladder pathology group, PS- group with combination of pathology and

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Fig. 1 Individual urinary sample comparison between schistosomiasis, bladder cancer, and controls. Similar patterns with minor overlap were seen by multivariate testing using principal component analysis (PCA). The analysis showed different sample groups: S. haematobium infected group (red); bladder pathology group (black); group with combination of pathology and S. haematobium infection (green); No Pathology or Schistosomiasis (blue)

S. haematobium infection and NPS- no pathology and schistosomiasis (control group)) with the aim of identifying novel candidate protein biomarkers for schistosomiasis [7]. We identified 9680 unique peptides corresponding to 1289 proteins groups including many unique Schistosoma proteins. Importantly, the analysis depth and reproducibility allowed for distinct clustering of each pathology by principal component analysis (PCA) analysis (Fig. 1). Further, we identified discriminatory proteins able to separate schistosomiasis samples from bladder cancer samples and which are therefore suitable for use in followup biomarker validation studies [7]. Following this work, we have successfully applied this workflow to the high throughput analysis of up to 120 human urine samples per study in other infectious and noncommunicable diseases, enabling identification and quantitation of over 1900 protein groups without requiring any laborious and costly depletion or prefractionation of the samples before mass spectrometry analysis.

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Materials

2.1 Urine Sample Storage

1. 50 mL conical tubes.

2.2 Methanol/ Chloroform Precipitation

1. 12 mL amber glass vials with polypropylene caps. 2. Denaturation buffer (6 M urea, 2 M thiourea, 10 mM Tris buffer, pH 8.0). 3. Vortex. 4. Centrifuge fitted with rotor for 50 mL tubes.

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2.3 Estimation of Protein Concentration

1. Bradford reagent.

2.4 In-Solution Tryptic Digest

1. 50 mM ammonium bicarbonate (ABC). 2. 1 M dithiothreitol (DTT) in 50 mM ABC. 3. 0.5 M stock solution iodoacetamide (IAA) in 50 mM ABC. 4. MS-grade modified trypsin in 50 mM ABC.

2.5 Stage-Tip Desalting

1. C18 discs. 2. Solution A: 2% ACN in MQ-H2O with 0.1% FA. 3. Solution B: 100% methanol. 4. Solution C: 80% ACN in MQ-H2O with 0.1% FA.

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3.1 Urine Sample Handling

1. Collect first pass, mid-stream urine in an mass spectrometry– appropriate plastic container (e.g., a 50 mL conical tube) and store at 80  C. 2. Thaw urine samples on ice immediately prior to processing.

3.2 Methanol– Chloroform Precipitation

1. Vortex urine sample briefly to mix and remove 4 mL to glass vial with PTFE lid for processing. Avoid additional freeze–thaw cycles (see Note 1). 2. Add 4 mL of 100% chloroform and 3 mL of 100% methanol to the sample vial and vortex (see Note 2). 3. Centrifuge at 4000  g for 15 min at 25  C. 4. Carefully remove the vial from the centrifuge without disturbing the phases. 5. Remove the top phase from the vial without disturbing the interphase disc which contains protein. 6. Add 3 mL of 100% methanol to the sample and mix vigorously. 7. Centrifuge at 4000  g for 15 min at 25  C. 8. Remove the vial from the centrifuge without disturbing the pellet. 9. Aspirate or pour off the supernatant without disturbing the pellet and turn tubes upside down on tissue for 5 min. 10. Air-dry the pellet in a fume hood for approximately 15 min, or until pellet is no longer glazed and appears dull. 11. Resuspend the pellet in 500μL denaturation buffer, triturating and shaking gently. Add additional denaturation buffer as needed (see Note 3).

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12. Remove the resuspended sample to a clean 2 mL Eppendorf tube. 13. Perform protein quantification using the Bradford assay, following manufacturer instructions (see Note 4). 3.3 In-Solution Tryptic Digest

1. Aliquot an equivalent mass of each protein sample (10–20μg) into a fresh Eppendorf tube (see Note 5). 2. Add DTT to a final concentration of 3 mM and incubate at 25  C for 30 min. 3. Add iodoacetamide to a final concentration of 10 mM and incubate in the dark for 30 min at 25  C. 4. Dilute the samples by adding 5 volumes of 50 mM ABC (see Notes 6 and 7). 5. Add trypsin to a final sample:enzyme ratio of 50:1 and mix briefly. 6. Incubate for 16 h at 30  C (see Note 8). 7. Acidify the tryptic peptide solution by adding 0.1% formic acid to a pH 10,000  g). 2. Vortex mixer. 3. Tabletop centrifuge. 4. Magnetic stand. 5. Rotator or tube mixer. 6. Micropipette. 7. Pipette tips. 8. 1.5 mL tube 9. 50 mL tube 10. The MagCapture Exosome Isolation Kit PS (Wako, Japan). 11. Washing buffer: 20 mM Tris–HCl, pH 7.4, 150 mM NaCl, 0.0005% Tween 20. 12. Exosome Elution Buffer: 20 mM Tris–HCl, pH 7.4, 150 mM NaCl, 2 mM EDTA.

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3.1 Collection and Preparation Steps for Urine Samples

1. Upon informed consent, collect voided urine samples (over 38.5 mL) in specimen cups and keep them at 4  C for up to 6 h prior to processing. 2. Transfer collected urine samples into 50 mL tubes and centrifuge them at 2000  g for 30 min to remove cellular debris. 3. Transfer supernatants to new 50 mL tubes. These samples can either be processed immediately to the next step or stored at 80  C until subsequent analysis. When frozen samples are used, they should be placed in a 37  C water bath until fully thawed before proceeding to the next step (see Note 1). 4. Centrifuge samples at 10,000  g for 30 min to remove cell debris and large EVs (see Note 2). 5. Transfer supernatants to new 50-mL tubes (see Note 3). 6. Pass the supernatant through a 0.22-μm filter to remove contaminating proteins and large EVs (see Note 4). 7. Store prepared urine samples at 80  C until EV isolation procedures.

3.2 Ultracentrifugation

1. Transfer prepared samples into ultracentrifuge tubes and balance weights of tubes with a balance scale (see Note 5). 2. Place ultracentrifuge tubes in buckets and set them on the rotor (see Note 6). 3. Ultracentrifuge samples at 100,000  g for 90 min (see Note 7). 4. Lift ultracentrifuge tubes carefully from buckets with Kocher forceps. 5. Discard supernatants by decantation. 6. Suspend precipitates in 500 μL PBS (see Note 8).

3.3 Sucrose–D2O Cushion Ultracentrifugation

1. Add 2 mL of PBS (chilled) to new ultracentrifuge tubes (4.5 mL). 2. With a 1 mL syringe fitted with a 21-G needle, load 450 μL of the 30% sucrose–D2O to the bottom of the ultracentrifuge tubes to make a cushion. 3. Slowly pipet the resuspended precipitates from Subheading 3.2, step 6. on the sucrose–D2O cushion without disturbing the interface. 4. Balance the weight of tubes with PBS so that the total volume of each tube is 4 mL or more. 5. Ultracentrifuge at 100,000  g for 90 min.

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6. Remove all but 1 cm of the supernatants with a micropipette, including the sucrose–D2O layer, which contains EVs (see Note 9). 7. Retrieve 400 μL of the sucrose–D2O layer with a 1 mL syringe fitted with a 21G needle (see Note 10). 8. Transfer EVs to new ultracentrifuge tubes and dilute EVs to 4 mL with PBS. 9. Ultracentrifuge the diluted interface at 100,000  g for 90 min. 10. Discard supernatants by decantation and wash the precipitates twice with PBS, followed by an additional ultracentrifugation step at 100,000  g for 90 min each time. 11. Discard the final supernatants by decantation (see Note 11). 12. Suspend the final precipitate in 50–100 μL PBS and transfer to 1.5 mL tubes (Low Protein Binding) and store at 80  C for subsequent analyses (see Note 12). 3.4 PS Affinity Method for Isolation of EVs from Urine

The MagCapture Exosome Isolation Kit PS (Wako, Japan) was used according to the manufacturer’s instructions.

3.5 Immobilization of the Biotinylated Tim4 to Streptavidin Magnetic Beads

1. Transfer 60 μL of streptavidin magnetic beads into a 1.5 mL tube (included in the kit). 2. Add 500 μL of the Exosome Capture Immobilizing Buffer into the 1.5 mL tube and suspend it by vortexing. 3. Spin down magnetic beads from step 2 and place it on the magnetic stand for 1 min. 4. Remove the supernatant after separation of magnetic beads and supernatant. 5. Add 500 μL of the Exosome Capture Immobilizing Buffer and 10 μL of the Biotin-labeled Exosome Capture (Tim4) into a 1.5 mL tube and mix it by vortexing. 6. Mix for 10 min at 4  C with a rotator. 7. Spin down magnetic beads from step 6 and place it on the magnetic stand for 1 min. 8. Remove the supernatant after separation of magnetic beads and supernatant. 9. Add 500 μL of the Exosome Capture Immobilizing Buffer into the 1.5 mL tube from step and mix by vortexing. 10. Repeat steps 8 and 9 twice, to obtain the Tim4-conjugated magnetic beads.

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Affinity Reaction

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1. Add the Tim4-conjugated magnetic beads from step 10 to prepare urine samples supplemented with a 1:500 volume of Exosome Binding Enhancer (500) (see Note 13). Mix it by vortexing. 2. Rotate the mixture overnight at 4  C (see Note 14). 3. Centrifuge the mixture at 300  g for 1 min and remove all but 1 cm of the supernatants, including the Tim4-conjugated magnetic beads (¼EVs binding beads) that capture the EVs. 4. Transfer the remaining supernatant and beads bound to the EVs into a 1.5 mL tube (included in kit) with a micropipette.

3.7 Washing of the EV-Binding Beads

1. Add 1:500 volume of the Exosomes Binding Enhancer (500) to 3 mL of the washing buffer and mix it by vortexing. 2. Add 1 mL of the washing buffer (+Exosome Binding Enhancer (500)) to a 1.5 mL tube containing the EV-binding beads from Subheading 3.6, step 3 and mix by vortexing. 3. Spin down the EV-binding beads and place it on the magnetic stand for 1 min. 4. Remove the supernatant after separation of the EV-binding beads and supernatant. 5. Repeat steps from 2 to 4 twice. 6. Spin down again the 1.5 mL tube and place it on the magnetic stand for 1 min. 7. Remove the supernatant completely after separation of the EV-binding beads and supernatant (see Note 15).

3.8 Elution of the EVs

1. Add 50 μL of the Exosome Elution Buffer to a 1.5 mL tube containing the washed EVs-binding beads, and mix it by vortexing, then spin down. 2. Place it at room temperature for 10 min. After the reaction, suspend EVs by vortexing and by a spin down. 3. Place the 1.5 mL tube on the magnetic stand for 1 min. 4. Transfer the supernatant into a new 1.5 mL tube (low protein binding) (not included in the kit) after separation of the magnetic beads and supernatant (see Note 16). 5. Repeat steps 1–4 to obtain a total of 100 μL of the eluted EVs. Store EVs at 80  C for subsequent analyses.

4

Notes 1. When frozen urine samples are used, salt crystals may form in the 50 mL tubes. Therefore, thaw the urine samples in a 37  C water bath and dissolve thoroughly before use. In addition, vigorous vortexing during thawing will maximize the yield of EVs [11].

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2. When centrifuging at 4  C, precipitation of salt crystals may be observed. In this case, adjust the temperature accordingly. 3. When large EVs are needed, retain precipitates after 10,000  g centrifuging. 4. Since filtration before 10,000  g centrifuging reduces the yield of recovered EVs, it is better to filter after the step involving centrifugation at 10,000  g. The filter should be polyethersulfone (PES) instead of polyvinylidene fluoride (PVDF). 5. Ultracentrifuge tubes are capable of withstanding spinning forces up to 100,000  g, which are compatible with the selected rotor and sample input volume. 6. Use a swing rotor instead of an angle rotor. An angle rotor is not suitable for sucrose–D2O cushion ultracentrifugation. 7. Since abnormalities such as imbalance are likely to occur during acceleration, take precautions to continue the ultracentrifugation step after confirming that the maximum speed has been reached. 8. EV precipitates at this stage will most likely not be visible, so you do not have to worry if you do not see them. 9. The sucrose–D2O layer disappears. 10. Do not retrieve all of the sucrose–D2O layer (450 μL), as there may be contaminating proteins at the interface. 11. Tilt the tube and wait 30 s and aspirate completely the final liquid clings to the sides of the tube. This liquid does not contain any EVs. 12. The final particle number of EVs varies depending on individual differences, such as the concentration of urine samples. When an analysis using urinary EVs is performed, it is necessary to adjust the particle number of EVs. The particle number of urinary EVs correlates with urinary creatinine levels and can be corrected by dividing the particle number with the urinary creatinine levels. 13. The final Ca2+ concentration should be around 2 mM. Tim4 cannot capture EVs without Ca2+. 14. When the magnetic beads become saturated, the recovery amount of EVs does not increase further. 15. Remove the washing buffer (Exosome Binding Enhancer (500)) in the tube completely, as the remaining buffer may cause a reduction in the elution efficiency of the captured EVs. 16. EVs are released from the Tim4-binding magnet beads by elution buffer containing the Ca2+ chelating agent, EDTA.

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References 1. Yanez-Mo M, Siljander PR, Andreu Z et al (2015) Biological properties of extracellular vesicles and their physiological functions. J Extracell Vesicles 4:27066 2. Raposo G, Stoorvogel W (2013) Extracellular vesicles: exosomes, microvesicles, and friends. J Cell Biol 200(4):373–383 3. Jia S, Zocco D, Samuels ML et al (2014) Emerging technologies in extracellular vesiclebased molecular diagnostics. Expert Rev Mol Diagn 14(3):307–321 4. Matsuzaki K, Fujita K, Jingushi K et al (2017) MiR-21-5p in urinary extracellular vesicles is a novel biomarker of urothelial carcinoma. Oncotarget 8(15):24668–24678 5. Pisitkun T, Shen RF, Knepper MA (2004) Identification and proteomic profiling of exosomes in human urine. Proc Natl Acad Sci U S A 101(36):13368–13373 6. Livshits MA, Khomyakova E, Evtushenko EG et al (2015) Isolation of exosomes by differential centrifugation: theoretical analysis of a commonly used protocol. Sci Rep 5:17319

7. Rood IM, Deegens JK, Merchant ML et al (2010) Comparison of three methods for isolation of urinary microvesicles to identify biomarkers of nephrotic syndrome. Kidney Int 78 (8):810–816 8. Raj DA, Fiume I, Capasso G, Pocsfalvi G (2012) A multiplex quantitative proteomics strategy for protein biomarker studies in urinary exosomes. Kidney Int 81(12):1263–1272 9. The´ry C, Amigorena S, Raposo G, Clayton A (2006) Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr Protoc Cell Biol Chapter 3:Unit 3.22 10. Nakai W, Yoshida T, Diez D et al (2016) A novel affinity-based method for the isolation of highly purified extracellular vesicles. Sci Rep 6:33935 11. Zhou H, Yuen PS, Pisitkun T et al (2006) Collection, storage, preservation, and normalization of human urinary exosomes for biomarker discovery. Kidney Int 69 (8):1471–1476

Part III Physical Activity and Urinary Markers

Chapter 16 Urinary Catecholamines as Markers in Overtraining Syndrome Marina Casadio Abstract In this study, potential urinary markers that show the presence of overtraining syndrome (OTS) were investigated. After a hard training period without an optimal recovery, OTS could appear in athletes. This syndrome could result in a decreasing of performance, a state of chronic fatigue and a not well-being state. The search for markers that demonstrate the presence of OTS could prevent the physiological and psychological health of the athletes, improving the performance. In this chapter, we will analyze some studies that have examined biochemical, physiological, and immunological markers of overtraining in urine and the variation of the catecholamines in a situation of stressed training. Key words Urine, Catecholamine, Overtraining, Overreaching, Markers

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Introduction The regular practicing of physical activity increases health state and well-being, improves the cardiovascular and respiratory systems, increases muscles and make stronger bones, manages the weight better and feel better—with more energy, a well mood, a more relaxed feeling, and an healthier sleep [1]. This is the reason why physical exercise is considered a protection against some pathologies, such as obesity, insulin resistance, and atherosclerosis [2]. In order to optimize performance improvement, athletes and nonprofessional subjects must maintain a balance between highintensity training and recovery [2]. When this balance is altered, the overtraining syndrome (OTS) appears and the performance decreases, along with other training, physiological, and biochemical factors. Body is always seeking to maintain a state of homeostasis so it will constantly adapt to the stress from environment: training is the manipulation of the application of stress and the body’s subsequent adaptation to that stress to maintain homeostasis. The positive

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Fig. 1 A graphical representation of the effect of training on fitness level

adaptive response to the planned stress is called “supercompensation” [3]. Supercompensation is divided in a training or loading stress and the body’s subsequent reaction to this training stress, such as fatigue or tiring, and in recovery phase, when the energy stores and performance will return to the baseline. After those phases, there is a rebound response because the body is essentially recovering from the low point of the greatest fatigue (Fig. 1). The supercompensation could be declined or improved. If no training stress is applied, there will also be a decline, while if a new training stress is practiced after the peak of supercompensation, there will also be an upgrading. However, when the phases of training and recovery are not balanced and there are high levels of sport specific stress in combination with too little regeneration, the supercompensation becomes negative and it is called overreaching or overtraining [4]. An excessive exercise training, a smallest recovery and other type of stress (work, family, environment, etc.) could cause overtraining in high performance athletes: the state of performance decrements, fatigue, disturbed sleep, alterations in mood state, and other possible symptoms appear [5]. It is possible to individuate different types of OTS: functional overreaching (FO) and nonfunctional overreaching (NFO) [3]. In FO, performance decrements and fatigue are reversed within a preplanned recovery period, so it might even have a positive consequence. When performance does not improve, the training sessions are more difficult and the feelings of fatigue do not disappear after the recovery period, overreaching is not functional, so it is called NFO. In that condition, athletes improve their performance with a short-term recovering (from few days to 2 weeks). OTS only affects in the most severe cases, when the consequences and the recovery have a long term (weeks or months).

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OTS also results in physiological features, such as persistent fatigue, not training improvement, lower performance, high lactate (4 mMl); organic modifications, illness and muscular pain; psychology changes, alterations in mood state, disturbed sleep and hunger; and a variation of biochemical factors [5]. The consequences of overtraining range from muscle function to motivation and decreased training performance. The pathophysiology of OTS can include the following: – Negative performance: performance, strength and power decrease, while fatigue increasing and the recovery is longer. – Heart rate change: resting and sleeping heart rate growth, but after the training session heart rate is higher than in normal conditions. – Physical and psychological effects: weight and muscle tone decrease, illness is frequent, sleep is disturbed, appetite decreases, and mood is altered. – Immunological and biochemical response: cytokine actions, hormone and hematological changes, different number of circulating white blood cells are some of the signals that demonstrate the presence of OTS [6]. However, a single parameter is not sufficient to demonstrate the state of overtraining, that can be identified only by a combination of biochemical and immunological markers and performing tests. In particular, several weeks or more of heavy training could change the response of some hormones and immune system: adrenocorticotropic hormone (ACTH), growth hormone (GH), follicle-stimulating hormone (FSH) decrease and cortisol increases as a consequence of stress. Therefore, circulating numbers and functional capacities of leukocytes and glutamine decline and plasma concentrations of inflammatory cytokines rise [7]. An appropriate battery of markers can be useful to prevent the development of an OTS in athletes. Some markers could be measured in laboratory and offered to athletes as part of their training support, in order to consider appropriate intervention and to prevent athletes from progressing to a more serious stage of the OTS [7]. In this review, we will focus on urinary markers, in particular the catecholamines, and their potential role in the early identification of overtraining.

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Blood Biochemical Markers: An Overview Biochemical markers are commonly used in practiced field and laboratory tests to allow early prediction and treatment of the syndrome [8]. Potential biochemical markers of overtraining

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effects are: plasma glutamine, plasma creatine kinase activity, plasma urea, plasma hormones, blood lactate profile, and urinary hormonal secretion. 2.1 Plasma Glutamine

The concentration of plasma glutamine has been suggested as a possible indicator of excessive endurance training stress. Indeed, the plasma glutamine concentration falls after a prolonged exercise, but not after short-term exercise [9].

2.2 Blood Lactate Profile

Some studies have reported lower blood lactate responses during submaximal exercise tests in overtrained athletes and this has been explained on the basis of low muscle glycogen levels, a decrease of catecholamine response to exercise or a decreased muscle tissue responsiveness to the effect of catecholamines [7].

2.3 Plasma Hormones

Creatine kinase activity increases, as a response to muscle damage and it identifies a state of temporary overreaching, while plasma urea may provide a measure of muscle protein breakdown and hence may be an overtraining syndrome’s marker because in association with a catabolic state [7]. The cortisol–testosterone ratio could be an OTS index because, respectively, catabolic and anabolic plasma hormones that do not show a significant change during progressive, increase in the training loads. Many studies have also evidenced of adrenocortical deficiency in athletes suffering from OTS (with a deregulation of growth hormone, prolactin, and ACTH), but this would not be useful as a part of a routine test battery to detect impending overtraining [7].

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Urinary Markers: The Catecholamines Urinary catecholamine excretion may reflect an important index to demonstrate OTS in daily tests in laboratory. As an urinary marker, it is noninvasive, easy to be recruited even many times in a week with a great patient compliance. Catecholamines act both as neurotransmitters and hormones vital to the maintenance of homeostasis through the autonomic nervous system. They are produced by adrenal medulla and the principal ones are: dopamine, norepinephrine, and epinephrine. Secretion from the adrenal medulla proceeding the activation of the sympathetic nervous system functions to regulate blood pressure by contracting the smooth muscle in the vasculature. The adrenergic receptors linked to blood vessels have an especially high affinity for norepinephrine relative to the other amines. Further musculoskeletal actions of catecholamines include enhanced contractility of cardiac muscle, contraction of the pupillary dilator

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and relaxation of smooth muscle in the gastrointestinal tract, urinary tract, and bronchioles. Both epinephrine and norepinephrine modulate metabolism to increase blood glucose levels by stimulating glycogenolysis in the liver, increased glucagon secretion and decreased insulin secretion from the pancreas, and lipolysis in adipose tissue. Epinephrine also inhibits release of mediators from mast cells and basophils in type I hypersensitivity reactions [10]. Normally, the catecholamines and their metabolites are present in the urine in very low-concentrations, that increase/decrease during and after the stress situation, as a heavy training session [11]. Nocturnal urinary catecholamine excretion appears to be lower than normal in overtrained athletes, so it could offer an index that shows the OTS. 3.1 Urinary Catecholamines in Overtraining Syndrome

The possibility to have some urinary markers for OTS has some advantages: the non invasiveness, the possibility to repeat the collection of the samples many times in a day with a good athletes compliance. Despite these good characteristics nowadays there are only few studies on urine catecholamine, all with promising results in different sport categories. Twenty years ago some authors [12] showed the relationship between OTS and the occurrence of catecholamine in urine in the elite swimming athletes. Mackinnon et al. investigated the role of catecholamine in OTS with the aim to compare the responses of selected hormonal, immunological, and hematological variables in athletes showing symptoms of overreaching with these variables in well-trained athletes during intensified training [12]. The study compared some variables in a group of highly trained swimmers (8 male, 16 female): training volume was progressively increased over 4 weeks and symptoms of overreaching were identified in 8 swimmers based on decrements in swim performance, persistent high ratings of fatigue, and comments in log books indicating poor adaptation to the increased training. There were no significant differences between overreaching (OR) and well-trained swimmers (WT) for some variables including: concentrations of plasma norepinephrine, cortisol, and testosterone, and the testosterone–cortisol ratio; peripheral blood leukocyte and differential counts, neutrophil–lymphocyte ratio; serum ferritin and blood hemoglobin concentrations, erythrocyte number, hematocrit, and mean red cell volume, but the data shows that urinary excretion of norepinephrine was significantly lower (P < 0.05) in OR compared with WT swimmers throughout a determinate period. Indeed, low urinary norepinephrine excretion was observed from 2 to 4 weeks before the appearance of symptoms of OR, suggesting the possibility that neuroendocrine changes may precede and potentially contribute to the development of the overreaching/overtraining syndromes. To perform this study, an overnight urine sample was obtained from each swimmer, who collected all urine after 10 p.m. the previous

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night up to and including the first void in the morning. Urine volume was measured and urine was aliquoted into freezer vials and then stored at
70  C until the norepinephrine detection. A performance measure (200 m time trial), a urine sample and venous blood were obtained at three times during the study: before the start of the 4-week period (time 1), after 2 weeks (time 2), and at the end of the 4 weeks (time 3). The main finding of this study was that of the 16 hormonal, immunological, and hematological variables studied, the urinary norepinephrine excretion appeared to be the strongest discriminator between OR and WT swimmers. No consistent differences between OR and WT swimmers were observed for other variables. Higher resting norepinephrine concentration was also noted in swimmers diagnosed as overtrained compared with those considered well-trained. The lower plasma norepinephrine concentration observed during maximal exercise in some studies has been attributed to adrenal exhaustion, or the so-called “parasympathetic” form of overtraining, in which catecholamine secretion is impaired following prolonged periods of intense training resulting in many of the symptoms of overtraining such as chronic fatigue, decreased maximal heart rate, and impaired performance. Excretion of catecholamines in the urine has been suggested to be an integrative indicator of total production and excretion of catecholamines over 24 h. Thus, a decline in urinary excretion of norepinephrine is consistent with the concept of adrenal exhaustion. Lehmann and coworkers in a study conducted on cyclism demonstrated a progressively decrease of urinary norepinephrine excretion in high performance training, in order to recognize overtraining promptly [13]. The study examined the relevance of nocturnal “basal” urinary excretion of free catecholamines with respect to its practical application: (1) during a pilot study (training of road and track cyclists before the 1988 Olympic Games in Seoul), (2) through a 4-week prospective, experimental study in 1989 and 1990 (middle- and long-distance runners), (3) during the competitive season and winter break of a soccer team between August 1990 and April 1991. The results suggested an overtraining or exhaustion syndrome in athletes may usually be accompanied by at least a 50% decrease in basal dopamine, noradrenaline, and adrenaline excretion. When training is effective or the athletes are not exhausted, the decrease of the excretion rate (noradrenaline and adrenaline with the exception of dopamine) is more likely to be lower. Generalization of these results requires further experimental studies. More recently, in 2006 Atlaoui and coworkers [14] investigate the effect of training variations on 24 h urinary noradrenaline (NA) and adrenaline (AD) levels and AD/NA ratio to search for a possible relationship between catecholamine excretion, training,

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and performance in highly swimmers. Fourteen swimmers (5 female and 9 male) were tested after 4 weeks of intense training (IT), 3 weeks of reduced training (RT), and 5 weeks of low training (LT). At the end of each period, the swimmers performed their best event at an official competition. The changes in NA levels after 4 weeks of IT were negatively related to the changes in training volume and total training load while the NA levels measured at the end of IT were positively related to the changes in performance after 3 weeks of RT. The changes in performance between RT and LT were related to NA levels at the end of RT. AD–NA ratio and AD were related to performance (P < 0.01). 24-h NA and the AD– NA ratio were related to both training variations and performance. Thus, 24-h NA levels and AD–NA ratio may provide useful markers for monitoring training stress in elite swimmers. In the last years a very interesting project called the EROS study (Endocrine and Metabolic Responses on Overtraining Syndrome) [15, 16] has been proposed. A consistent part of this protocol is focused on the evaluation f OTS biomarkers in blood, saliva and urine. Cadegiani and coworkers [16] showed in 12 athletes affected by OTS that nocturnal urinary catecholamines were significantly reduced (P ¼ 0.043), demonstrating another time the potential role of this biomarker.

4

Conclusions Biomarkers of overtraining or overreaching could be important and may offer to athletes an useful medical support and thus hlep consider appropriate intervention to prevent the performer’s progression to a more advanced stage of OTS. According to the studies cited above, urinary catecholamines could be good markers to show the presence of OTS, easy to monitor and to be examined in laboratory. Several studies confirmed that urinary catecholamine decreases in case of overtraining, because of adrenal exhaustion after a long stress training. This situation results in chronic fatigue, decreased maximal heart rate, and impaired performance. A controlled well-being state of elite athletes permits to increase the performance and prevent the OTS effects. Excessive training without an optimal recovery can lead to a debilitating syndrome. A regular monitoring of the heart rate, urinary catecholamine and blood lactate, and other markers, could provide an objective and reliable method of identifying athletes at risk of developing OTS. In conclusion, despite the encouraging results obtained, only few studies have been conducted on urine catecholamines and the number of cases analyzed is too low to make definitive statistical considerations. However, with the proceeding of the EROS study, maybe more data will be available in a short time on this topic.

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References 1. Beech DJ (2018) Endothelial Piezo1 channels as sensors of exercise. J Physiol 596 (6):979–984. https://doi.org/10.1113/ JP274396 2. Vittori LN, Tarozzi A, Latessa PM (2019) Circulating cell-free DNA in physical activities. Methods Mol Biol 1909:183–197. https:// doi.org/10.1007/978-1-4939-8973-7_14 3. Gambetta V (2007) Athletic development: the art & science of functional sports condition in English edition. Human Kinetics, Champaign, IL 4. Nederhof E, Lemmink KA, Visscher C, Meeusen R, Mulder T (2006) Psychomotor speed: possibly a new marker for overtraining syndrome. Sports Med 36(10):817–828. https://doi.org/10.2165/00007256200636100-00001 5. MacKinnon LT (2000) Special feature for the Olympics: effects of exercise on the immune system: overtraining effects on immunity and performance in athletes. Immunol Cell Biol 78 (5):502–509. https://doi.org/10.1111/j. 1440-1711.2000.t01-76. Hartmann U, Mester J (2000) Training and overtraining markers in selected sport events. Med Sci Sports Exerc 32(1):209–215 7. Tyler-McGowan CM, Golland LC, Evans DL, Hodgson DR, Rose RJ (1999) Haematological and biochemical responses to training and overtraining. Equine Veterinary J Supplement (30):621–625. https://doi.org/10.1111/j. 2042-3306.1999.tb05297.x 8. Gleeson M (2002) Biochemical and immunological markers of over-training. J Sports Sci Med 1(2):31–41 9. Rowbottom DG, Keast D, Goodman C, Morton AR (1995) The haematological, biochemical and immunological profile of athletes suffering from the overtraining syndrome.

Eur J Appl Physiol Occup Physiol 70 (6):502–509. https://doi.org/10.1007/ BF00634379 10. Jeukendrup AE, Hesselink MK, Snyder AC, Kuipers H, Keizer HA (1992) Physiological changes in male competitive cyclists after two weeks of intensified training. Int J Sports Med 13(7):534–541. https://doi.org/10.1055/s2007-1021312 11. Paravati S, Rosani A, Warrington SJ (2019) Physiology, catecholamines. StatPearls Publishing, Treasure Island, FL 12. Mackinnon LT, Hooper SL, Jones S, Gordo RD, Bachmann AW (1997) Hormonal, immunological, and hematological responses to intensified training in elite swimmers. Med Sci Sports Exerc 29(12):1637–1645 13. Lehmann M, Schnee W, Scheu R, Stockhausen W, Bachl N (1992) Decreased nocturnal catecholamine excretion: parameter for an overtraining syndrome in athletes? Int J Sports Med 13(3):236–242. https://doi.org/ 10.1055/s-2007-1021260 14. Atlaoui D, Duclos M, Gouarne C, Lacoste L, Barale F, Chatard JC (2006) 24-hr urinary catecholamine excretion, training and performance in elite swimmers. Int J Sports Med 27 (4):314–321. https://doi.org/10.1055/s2005-865669 15. Cadegiani FA, Kater CE (2019) Novel insights of overtraining syndrome discovered from the EROS study. BMJ Open Sport Exerc Med 5 (1):e000542. https://doi.org/10.1136/ bmjsem-2019-000542 16. Cadegiani F, Kater C, Abrao T, Silva P (2020) Novel hormonal and metabolic markers of recovery from overtraining syndrome unveiled by the longitudinal ARM of the Eros study the Eros-longitudinal study. J Endocr Soc 4: April-May 2020, SAT

Chapter 17 Urinary Markers and Chronic Effect of Physical Exercise Leydi Natalia Vittori, Jenny Romasco, Andrea Tarozzi, and Pasqualino Maietta Latessa Abstract Chronic kidney disease (CKD) is a type of kidney disease in which there is gradual loss of kidney function over a period of months to years. The clinical protocol of CKD forecasts that markers such as serum creatinine, the estimate of the glomerular filtration rate value, microalbuminuria, cystatin c are evaluated as routine markers. In recent years, new studies have identified new markers to diagnose and assess the level of kidney damage. The prevalence of CKD increases in subjects suffering from cardiovascular and metabolic diseases. The highest risk of cardiovascular risk in the CKD patient compared to the general population is related to risk factors such as hypertension, obesity, and specific renal disease factors such as albuminuria. Physical exercise, especially aerobic, has been seen through the analysis of urinary markers, able to mitigate the adverse effect of sedentary, hypertension and interstitial damage in patients with CKD and decrease the urinary levels liver-type fatty acid binding protein (L-FABP) and lower urinary albumin excretion. Key words Chronic kidney disease, Physical exercise, Glomerular filtration, Urinary markers, Chronic exercise

1

Introduction The kidney is an organ with a very important function such as filter waste and excess fluid from the blood but also secrete erythropoietin (that promotes the maturation of red blood cells) and renina (which has an important role in regulating blood pressure). Chronic kidney disease (CKD) is a condition in which the kidneys are damaged and cannot filter blood as well as they should. This alteration of physiological function may cause other health problems. CKD represents an important mortality and morbidity risk factor [1]. It has been found, in general population and in studies conducted by nephrological units, that the presence of an estimated glomerular filtration rate (eGFR) 30 mg/24 h or an albumin-creatinine ratio (ACR) of 30–300 mg/g (0.3–3 mg/mmol). Higher values indicate macroalbuminuria, also called clinical nephropathy. ACR > 30 mg/g has been shown to be a risk factor for cardiovascular death and all-cause mortality, progression of kidney disease, acute kidney injury, and kidney failure [16]. The presence of MA has been shown to predict the development of full-blown renal failure and cardiovascular events in type 1 and type 2 diabetic patients. In assessing kidney damage, combining MA with eGFR improves the prediction of CKD progression. In a 2010 cohort prospective study [17] the authors evaluated the combination of eGFR, creatinine, CysC urine, ACR. The study highlighted how increased albuminuria was an independent risk factor for all-cause mortality, and how decreased eGFR was associated with increased mortality risk in those with high-normal and high ACRs. The mortality rate was low in the normal-ACR group and increased in the very-high-ACR group, but did not vary with eGFR. In this cohort the adjusted mortality risk was sixfold higher in patients with CKD identified by all three markers and was also threefold higher in patients with CKD defined by both eGFR and CysC, compared to those with CKD defined by eGFR and creatinine alone. Albumin in urine is therefore a very important marker in the assessment of kidney damage but the diagnosis can be even more precise if the values of CysC and serum creatinine level are also associated with it.

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In the evaluation of kidney damage, other markers which could be considered kidney injury molecule (KIM-1), monocyte chemoattractant protein 1 (MCP-1) and urine retinol-binding protein 4 have also been considered in recent years. KIM-1 is a type 1 transmembrane protein whose expression has been upregulated after kidney injury [18]. MCP-1 belongs to the group of inflammatory chemokines. Expression of MCP-1 is upregulated in kidney diseases that have a sustained inflammatory response, such as in diabetic nephropathy and lupus nephritis [19]. Studies have demonstrated glomerular and tubular kidney cells release MCP-1 in response to high glucose levels and urine levels of MCP-1 are increased in diabetic nephropathy [20]. Finally, retinol binding protein 4 (RBP4) is a 21 KDa protein derived of plasma and it is filtered at the glomerulus and completely reabsorbed in proximal tubule. Above all, it is used in several disease related with proximal tubule dysfunction, either hereditary such Dent type 1 syndrome and Lowe syndrome. Sensitivity for urinary RBP4, however, decreases in patients with kidney diseases due to false positive results [21].

3

Physical Exercise and Urinary Biomarkers Urinary markers are used in clinical research to early diagnose cardiovascular disease, cancer, rejection following kidney transplantation. Some urinary markers could be useful to assess physical activity and its relationship with some diseases. In this field, the analysis of urine, however, is complicated by the need to interpret the increase or reduction of potentially thousands of molecules. Moreover, some markers are influenced by the frequency, duration and intensity of the physical exercise. In a prospective study, the authors [22] verified if there was a relationship between physical activity level (total, high, and low), time spent sitting, sedentary behavior with eGFR, and albuminuria. In this study, 2258 individuals between the ages of 40 and 75 years were enrolled and it was evaluated the level of physical activity and the eGFR values. The total amount of physical activity was based on the stepping posture and calculated as the mean time spent in stepping during waking time per day. The level of physical activity (high intensity physical activity (HPA) or low intensity (LPA)) was calculated: HPA minutes with step frequency > 110 step/min during walking time while LPA minutes with step frequency < 110 step/min during walking time. The total amount of sedentary time was based on the sedentary posture (sitting/lying) and calculated as the mean time spent in a sedentary position during waking time per day. Three constructs of sedentary behaviour patterns were identified: number of sedentary breaks, number prolonged sedentary bouts, and average sedentary bout duration.

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GFR was estimated with the CKD-EPI equation based on the combination of serum creatinine and serum cystatin C (eGFRcrcys). More daily HPA was also associated with higher eGFRcrcys after adjustment for confounders and sedetary time [B¼0.53 (0.21;0.85) mL/min/1.73 for 10 min daily] HPA), while more daily sedentary time was associated with a lower eGFR crcys [B¼0.71 (1.08;0.35) mL/min/1.73 m2 per h/daily sedentary time]. Having more daily prolonged sedentary bouts and having a longer average sedentary bout duration were both associated with a lower eGFRcrcys. Regarding albuminuria and daily activity, more daily total physical activity was associated with lower ORs (odds ratio) for higher urinary albumin excretion (UAE), although this was not evident for all quartiles. Prolonged sedentary bouts and having a longer average sedentary bout duration were only associated with a higher odd for a UAE of 15- 30 mg/L) was significantly lower compared to the inactive group (54% vs 61%, P < 0.0001). This association was observed for all classes of systolic blood pressure and heart rate as well as in nondiabetic and in diabetic patients. For this reason, the researchers highlighted that the exercise-induced reduction on blood pressure and heart rate could contribute to the reduction of MA by reducing intraglomerular pressure. A sedentary lifestyle was associated with an increased risk for MA. The lower UAE levels found in active patients compared to nonactive ones imply how regular physical activity reduces renal and organ damage. In a multivariate analysis, risk reduction associated with strenuous exercise (OR 0.66; 95% CI 0.47–0.95; P < 0.05) was higher than that associated with moderate physical exercise

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(OR 0.76; 95% CI 0.68–0.85; P < 0.0001). These results highlight how the benefits can be related to the dose of exercise and how physical activity reduces cardiovascular risk, considering MA as an independent cardiovascular risk marker. The relationships found between high levels of physical activity and low levels of urinary albumin excretion were published in a cross-sectional study that included type 1 diabetic patients. Patients with MA were those who practiced low levels of physical activity compared with those with a normal albuminuria (P < 0.05) [24]. While in another study, the microalbuminuria levels were observed in a cohort of 30 type 2 diabetic patients and after 6 months of aerobic exercise MA level were observed in only 1 patient [25]. The relationship between exercise and albuminuria is still controversial; it could be assumed that this is due to the effects on the vascular endothelium mediated by nitric oxide. In fact, in some studies, an increase in urinary albumin values in endothelial damage has been observed [26, 27]. In men, aerobic exercise has been observed to increase nitric o may induce the acceleration of vasodilation in the kidney [28]. In a study conducted by Kosaki et al. [29] the authors verified the relationship between physical exercise and urinary L-FABP in middle ages and older adults and to determine the effect to aerobic exercise training on urinary L-FABP levels. Urinary L-FABP has been validated as novel tubular biomarker and it has been shown to be related to the progression of CKD. In this study, L-FABP was evaluated as the ratio of urinary L-FABP (mcg) to urinary creatinine level (in grams). In the intervention group, it was observed how dose and response effect to exercise resulted in a significant decrease in weight and body mass index (BMI) and improvement in aerobic fitness (VO2 peak). Blood pressure data, both systolic and diastolic, in the intervention group recorded original differences. The renal parameters eGFRct (serum creatinine level), eGFRcys (cystatin C level) and urinary albumin level were not statistically different between the two groups, showing no dependent correlation with physical activity. After 12 weeks of aerobic exercise L-FABP levels in the intervention group decreased significantly (P < 0.05); in addition, L-FABP levels were inversely correlated with the improvement of aerobic fitness (r ¼ 0.374, P ¼ 0.038) and positively correlated with relative changes in mean arterial pressure (r ¼ 0.530; P ¼ 0.002). These values can be related to the increase in endothelium-derived nitric oxide. This hypothesis is supported by the reduction of blood pressure and intra renal resistive index by aerobic exercise training. Furthermore, the levels of L-FABP were higher in the group that practiced higher physical activity than in the lower physical activity group. This highlights the strong relationship between the level of physical activity and degrees of various stresses on renal proximal tubule.

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From a clinical point of view, the L-FABP levels were 3.6 μg/ g creatinine in middle aged and older healthy adults, while the value increased in patients with hypertension (5.2 μg/ g creatinine) and diabetes mellitus (5.2 μg/ g creatinine). The authors thus demonstrated that this value increased further in subjects with comorbidities, such as hypertension and diabetes mellitus (risk factors for onset CKD). Interestingly, L-FABP decreased by 1.27 μg/g creatine after the 12-week aerobic exercise in the intervention group.

4

Conclusions Recent studies in molecular biology have highlighted how GFR is the most used parameter in the clinic and remains the ideal marker of renal function; however, albumin remains the most used marker in the assessment of organ damage as it gives an excellent estimate of the development of the disease. Furthermore, new biomarkers such as KIM-1, MCP-1, and urinary RBP4, have emerged in publications in recent years regarding kidney damage. However, no accurate data are still available to use these biomarkers in clinical practice. Urinary markers, in recent years, have also been studies to evaluate the effects of physical activity in subjects with CKD. Most of these are studied analyzing the combination of the level of physical activity and other markers such as eGFR, albuminine, and L-FABP. In the meantime, current biomarkers in CKD should be implemented with caution recognizing their strengths and limitations.

References 1. Coresh J, Selvin E, Stevens LA et al (2007) Prevalence of chronic kidney disease in the United States. JAMA 298:2038–2047 2. Gansevoort RT, Matsushita K, Van der Velde et al (2011) Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int 80(1):93–104 3. Diaz KM, Shimbo D (2013) Physical activity and the prevention of hypertension. Curr Hypertens Rep 15(6):659–668 4. Winzer EB, Woitek F, Linke A (2018) Physical activity in the prevention and treatment of coronary artery disease. J Am Heart Assoc 7(4): e007725; Published 2018 Feb 8 5. Stevens LA, Coresh J, Greene T et al (2006) Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med 354:2473–2483

6. Perrone RD, Madias NE, Levey AS (1992) Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem 38 (10):1933–1953 7. Patel SS, Kalantar-Zadeh K, Molnar MZ et al (2013) Serum creatinine as a marker of muscle mass in chronic kidney disease: results of a cross-sectional study and review of literature. J Cachexia Sarcopenia Muscle 4(1):19–29 8. Baxmann AC, Ahmed MS, Marques NC et al (2008) Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 3 (2):348–354 9. Jablonski KL, Chonchol M (2013) Cystatin-Cbased eGFR: what is it telling us? Nat Rev Nephrol 9(6):318–319 ¨ rnlo¨v J et al 10. Shlipak MG, Matsushita K, A (2013) Cystatin C versus creatinine in

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determining risk based on kidney function. N Engl J Med 369(10):932–943 11. Keevil BG, Kilpatrick ES, Nichols SP et al (1998) Biological variation of cystatin C: implications for the assessment of glomerular filtration rate. Clin Chem 44:1535–1539 12. Levey AS, Bosch JP, Lewis JB et al (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of diet in renal disease study group. Ann Intern Med 130:461–470 13. Levey AS, Stevens LA, Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612 14. Inker LA, Schmid CH, Tighiouart H et al (2012) Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 367:20–29 15. Dharnidharka VR, Kwon C, Stevens G (2002) Serum cystatin C is superior to serum creatinine as a marker of kidney function: a metaanalysis. Am J Kidney Dis 40:221–226 16. Cockcroft DW, Gault MH (1976) Prediction of creatinine clearance from serum creatinine. Nephron 16:31–41 17. Bianchi C, Donadio C, Tramonti G et al (2001) Reappraisal of serum beta2microglobulin as marker of GFR. Ren Fail 23:419–429 18. Svensson MK, Cederholm J, Eliasson B et al (2013) Albuminuria and renal function as predictors of cardiovascular events and mortality in a general population of patients with type 2 diabetes: a nationwide observational study from the Swedish National Diabetes Register. Diab Vasc Dis Res 10(6):520–529 19. Wada T, Furuichi K, Sakai N et al (2000) Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney Int 58:1492–1499 20. Van Coillie E, Van Damme J, Opdenakker G (1999) The MCP/eotaxin subfamily of CC

chemokines. Cytokine Growth Factor Rev 10:61–8615 21. Norden AG, Scheinman SJ, DeschodtLanckman MM et al (2000) Tubular proteinuria defined by a study of Dent’s (CLCN5 mutation) and other tubular diseases. Kidney Int 57:240–249 22. Robinson-Cohen C, Katz R, Mozaffarian D et al (2009) Physical activity and rapid decline in kidney function among older adults. Arch Intern Med 169(22):2116–2123 23. Po¨ss J, Ukena C, Mahfoud F et al (2012) Physical activity is inversely associated with microalbuminuria in hypertensive patients at high cardiovascular risk: data from I-SEARCH. Eur J Prev Cardiol 19(5):1066–1073 24. Waden J, Forsblom C, Thorn LM et al (2008) Physical activity and diabetes complications in patients with type 1 diabetes: the Finnish diabetic nephropathy (FinnDiane) study. Diabetes Care 31(2):230–232 25. Lazarevic G, Antic S, Vlahovic P et al (2007) Effects of aerobic exercise on microalbuminuria and enzymuria in type 2 diabetic patients. Ren Fail 29(2):199–205 26. Ochodnicky P, Henning RH, van Dokkum RP et al (2006) Microalbuminuria and endothelial dysfunction: emerging targets for primary prevention of end-organ damage. J Cardiovasc Pharmacol 47(suppl 2):S151–S162; discussion S172 – S176 27. Erdely A, Freshour G, Tain YL et al (2007) DOCA/NaCl-induced chronic kidney disease: a comparison of renal nitric oxide production in resistant and susceptible rat strains. Am J Physiol Renal Physiol 292(1):F192–F196 28. Green DJ, Maiorana A, O’Driscoll G et al (2004) Effect of exercise training on endothelium-derived nitric oxide function in humans. J Physiol 561(Pt 1):1–25 29. Kosaki K, Kamijo-Ikemori A, Sugaya T et al (2018) Effect of habitual exercise on urinary liver-type fatty acid-binding protein levels in middle-aged and older adults. Scand J Med Sci Sports 28(1):152–160

Part IV Urinary Metabolic Markers

Chapter 18 Urinary Metabolic Biomarkers in Cancer Patients: An Overview Serena De Matteis, Massimiliano Bonafe`, and Anna Maria Giudetti Abstract The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice. Key words Urinary metabolomics, Cancer biomarkers, Metabolic pathways, Diagnosis, Prognosis

1

Introduction Metabolomics is an “omic” science that attempts to capture global changes and overall physiological status in biochemical networks and pathways. One area of considerable interest in the field of metabolomics is the detection of potential biomarkers associated with diseases, and the metabolic profiling may provide global changes of endogenous metabolites of patients. Metabolic profiling of urine is particularly attractive because urine represents a convenient source of biomarkers for cancer diagnosis, prognosis, and treatment monitoring. The advantages of urine include its noninvasive collection, availability and its richness in metabolites. Monitoring certain metabolite levels in urine has become an important way to detect early stages of various diseases or to differentiate cancer patients from healthy subjects or benign from malignant tumors.

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The most common approaches in metabolomics involve gas chromatography-mass spectrometry (GC-MS) [1], liquid chromatography–mass spectrometry (LC-MS) [2], or nuclear magnetic resonance spectroscopy (NMR) [3]. Genes linked to altered cancer metabolism contribute to production and secretion of the cancer-specific metabolites into biofluids [4]. We examined the literature on urinary metabolic biomarkers in cancer patients to provide an overview of the most promising aspects in the field of urinary metabolomics and its application to cancer biomarker discovery (Table 1). Given the breadth of this topic, our chapter focused on the most widely studied cancers related to hepatopancreatic and urinary tract.

2

Hepatopancreatic Tract Cancers

2.1 Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. The disease displays a complex molecular landscape that hampers the patient’s prognosis and therapy [5]. HCC commonly arises in people with underlying chronic inflammatory liver diseases associated with viral infections (chronic hepatitis B and C), toxic (alcohol and aflatoxin), metabolic (diabetes, hemochromatosis, and nonalcoholic fatty liver disease), and immune (autoimmune hepatitis and primary biliary) factors [5]. Although many therapeutic approaches have so far been established for HCC treatment, the unavailability of adequate biomarkers of early HCC diagnosis causes a poor prognosis. A study performed by liquid chromatography–hybrid triple quadrupole linear ion trap mass spectrometry (LC-QTRAP MS) reported elevated urinary 5-deoxy-5-methylthioadenosine and 6-methyladenosine in patients with cirrhosis and HCC compared with healthy controls, thus indicating an abnormal methylation activity in these patients [6]. The authors also showed elevated level of glutamine and short- and medium-chain acylcarnitines in HCC patients than those with cirrhosis [6]. Interestingly, carnitine C4:0 and hydantoin-5-propionic acid were identified as a combined marker for distinguishing HCC from cirrhotic patients [6], highlighting a level of sensitivity and specificity of these markers better than the alpha-fetoprotein in HCC. Wu et al. [7] conducted a study by GC/MS method on urine samples from 20 HCC patients and 20 healthy donors, observing metabolic changes in several metabolic pathways. The authors observed an increased level of glycine suggesting an involvement of this amino acid in the metabolic changes associated with HCC. In addition, the change of xylitol metabolism was likely due to the up-regulation of glycolysis and the disruption of TCA cycle [7]. Xylitol is a precursor of xylulose 5-phosphate, an intermediate

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Table 1 Metabolomic analysis of urine samples from cancer patients

Ref.

Population and sample

Method

Significantly changed metabolites or pathways

[6]

HCC, urine

LC-QTRAP MS

Nucleosides, bile acids, citric acid, several amino acids, cyclic adenosine monophosphate, glutamine, and short- and medium-chain acylcarnitines. Purine, energy, and amino acid metabolism

[7]

HCC, urine

GC/MS

Glycine, hypoxanthine, xylitol. Glycine and xylitol metabolism

[9]

HCC, sera and urine

Bile acids, histidine, and inosine. Bile acids, free fatty GC-TOF acids, glycolysis, urea cycle, and methionine MS + UPLC-Qmetabolism TOF MS

[10]

HCC, urine

1

H-NMR

Glycine, trimethylamine-N-oxide, hippurate, citrate, creatinine, creatine, and carnitine

[11]

HCC, urine

1

H-NMR

Acetate, creatine, creatinine, dimethylamine, formate, glycine, hippurate, and trimethylamine-N-oxide

[15]

PDAC, urine

1

H-NMR

Acetoacetate, leucine, glucose, 2-phenylacetamide, some acetylated compounds, citrate, creatinine, glycine, hippurate, 3-hydroxyisovalerate, and trigonelline

[16]

PDAC, urine

1

H-NMR

Increased amount of urinary acetate, acetoacetate, and glucose in PDAC patients than healthy individuals

[17]

RCC, urine

LC-MS

N0 -formylkynurenine. Folate, tryptophan, and biopterin metabolism

[19]

RCC, urine

LC-MS

Lysine and phenylalanine metabolism

[20]

RCC, urine

UHLC/MS/ MS2 + GC/MS

Several species of acylcarnitines

[26]

PC, urinary extracellular vesicles

UHLC/MS

Phosphathidylcholines, acyl carnitines, citrate and kynurenine. Intermediary metabolites of androgen synthesis

[27]

PC, tissues and cell lines

ESI-MS/MS

Shift from lipid uptake toward de novo lipogenesis

[29]

PC, urinary extracellular vesicles

UPLC-MS-MS

Metabolic shifting toward β-oxidation of fatty acids

[32]

BCA, urine

LC/MS

Carnitine C9:1

[33]

BCA, urine

HPLC-QTOFMS

Acetyl-CoA, carnitine and derivatives

[34]

BCA, urine

LC/MS + CE-MS Tryptophan metabolism

[35]

BCA, urine

GC  GC-TOFMS Leucine, isoleucine and valine, palmitoyl sphingomyelin, phosphocholine and arachidonate. Amino acid metabolism (continued)

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Table 1 (continued)

Ref.

Population and sample

[37, BCA, blood and 38] urine

Method

Significantly changed metabolites or pathways

LC/MS

Amino acid metabolism

HCC ¼ hepatocarcinoma; PDAC ¼ pancreatic ductal adenocarcinoma; RCC ¼ renal cell carcinoma; PC ¼ prostate cancer; BCA ¼ bladder cancer LC-QTRAP MS ¼ liquid chromatography–hybrid triple quadrupole linear ion trap mass spectrometry; GC/MS ¼ gas chromatography–mass spectrometry; GC-TOF MS + UPLC-Q-TOF MS ¼ gas chromatography–time-of-flight mass spectrometry and ultraperformance liquid chromatography–quadrupole time of flight mass spectrometry; 1 H-NMR ¼ proton nuclear magnetic resonance spectroscopy; LC-MS ¼ liquid chromatography–mass spectrometry; UHLC/MS/MS2 ¼ ultrahigh-performance liquid chromatography–tandem mass spectrometry; ESI-MS/MS ¼ electrospray ionization tandem mass spectrometry; UPLC-MS-MS ¼ ultraperformance liquid chromatography–tandem mass spectrometry; HPLC-QTOFMS ¼ high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry; CE-MS ¼ capillary electrophoresis–mass spectrometry; GCGC-TOFMS ¼ two-dimensional gas chromatography–time-of-flight mass spectrometry

of the pentose phosphate and glycolytic pathways, that is inhibited by okadaic acid, an inhibitor of protein phosphatases [8]. The authors speculated that the alteration of xylitol in urine samples of HCC patients may be related to large energy requirement by aggressive proliferating tumor cells. By using a combination of GC time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF MS), a total of 31 urinary metabolites involved in several key metabolic pathways such as bile acids, free fatty acids, glycolysis, urea cycle, and methionine metabolism, were found to be differentially expressed in a cohort of 82 HCC patients, compared to 71 healthy controls [9]. In particular, the altered level of glycochenodeoxycholic acid, glycocholic acid, taurocholic acid, and chenodeoxycholic acid were associated with liver cirrhosis and hepatitis of ongoing HCC patients. Various studies conducted by using 1H NMR comparing urinary metabolic samples from HCC patients with cirrhosis or chronic hepatitis B and healthy subjects, reported an increased level of carnitine and decreased level of creatinine, hippurate, and Trimethylamine N-oxide (TMAO) in HCC than the other groups [10, 11]. This metabolic profile could be indicative of tumor effects on physiology, energy production, and aberrant chromosomal methylation. All these studies highlighted the clinical utility of various noninvasive techniques of identifying HCC biomarkers. 2.2 Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide, with 1- and 5-year survival rates of 29% and 7% for all stages [12]. The structure of

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pancreatic tissue is complex, with exocrine and endocrine compartments that regulate the secretion of a variety of molecules including metabolites, hormones, and so on [13]. As previously stated, the poor prognosis of PDAC is associated with its early dissemination and metastatic abilities. The poor prognosis is related in part to the lack of early detection and screening methods. Contrary to what might be expected, recent studies have described certain proteins in urine that could have a role as potential biomarkers for PDAC [14]. Given the complex and heterogeneous nature of pancreatic cancer, unbiased analytical methods such as metabolomics by NMR spectroscopy show promise to identify tumor-associated perturbations in cellular metabolism. A study conducted by using 1 H-NMR aimed to analyze the urine metabolome of 33 PDAC patients, in comparison with 54 healthy matched controls, reported that the level of acetoacetate, leucine, glucose, 2-phenylacetamide, and some acetylated compounds appeared elevated, and those of citrate, creatinine, glycine, hippurate, 3-hydroxyisovalerate, trigonelline, were lower in patients compared to controls [15]. In line with this study, other researchers used the same methodology for the analysis of urine samples, reporting an increased amount of urinary ketone bodies such as acetate and acetoacetate, and glucose in PDAC patients than healthy individuals [16]. NMR spectroscopy analysis of urinary metabolic profiles proved successful in identifying a complex molecular signature of PDAC. Furthermore, results of a descriptive-level analysis show the possibility to follow disease evolution and to carry out tumor site mapping. Given the high reproducibility and the noninvasive nature of the analytical procedure, the described method bears potential to impact large-scale screening programs.

3 3.1

Urinary Tract Cancers Renal Cancer

Renal cell carcinoma (RCC) is the second most lethal urinary cancer, frequently asymptomatic and already metastatic at diagnosis [17]. RCC is well-suited to metabolomic analysis. Understanding and measuring metabolic status variations accompanying disease progression would represent a useful strategy for the discovery of potential new diagnostic biomarkers. Urinary metabolomic analysis is an ideal noninvasive means to explore this disease [18]. Xiaoyan Liu et al. performed a metabolomic analysis on urine samples from RCC patients, healthy controls, and benign kidney tumor patients [18]. They reported that several pathways, including folate, tryptophan, and biopterin metabolism, were significantly enriched in RCC group. Interestingly, one metabolite known as N0 -formylkynurenine discriminated RCC from healthy controls and benign tumors patients. The authors suggested the feasibility

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of utilizing urine metabolites for clinical diagnosis, but they highlighted the influence of diet on urine metabolomics proposing a diet standardization design for future studies. A recent study performed on 39 RCC, 22 benign renal tumors and 68 healthy controls described other altered metabolic pathways, including lysine and phenylalanine metabolism, in the RCC patients [19]. Moreover, metabolomics analysis reported significant differences in a metabolite panel consisting of cortolone, testosterone, and L-2-aminoadipate adenylate of hormone biosynthesis between the benign renal tumors group and RCC group. Interestingly, in the urine of RCC patients a higher quantity of several species of acylcarnitines, which mediate fatty acid metabolism in mitochondria, were observed, in comparison to a set of matched control patients [20]. In addition, the authors performed in vivo and in vitro experiments showing cytotoxicity and immune modulatory properties of acylcarnitines. Acylcarnitines in the urine of kidney cancer patients could reflect alterations in metabolism, cell component synthesis and/or immune surveillance, contributing to explain the profound chemotherapy resistance seen with this cancer. All these studies highlight the value of a novel class of metabolites, which may lead to new therapeutic approaches for cancer and may prove useful in cancer biomarker studies. Moreover, these findings open up a new field of investigation into the metabolic basis of kidney cancer. 3.2

Prostate Cancer

Prostate cancer (PC) is among the most frequently diagnosed and deadly types of cancer in men in Western countries [21]. For the lack of sensitive and specific diagnostic tools, there is a high demand to discover more sensitive and specific biomarkers to improve PC diagnosis and prognosis. To date, prostate-specific antigen (PSA) blood screening tests, together with clinical T-stage and Gleason score are the standard tests to discriminate PC patients with low, intermediate, or high risk. There are significant advantages to using biofluids including urine as sources of biomarkers, due to its easy availability [22]. Prostate carcinogenesis involves metabolic reprogramming to provide essential cellular components such as lipids and nucleotides to support the anabolic needs of rapidly proliferating tumor cells. Beyond the Warburg effect, the tricarboxylic acid (TCA) cycle and oxidative phosphorylation are known to play important roles in PC [23]. Several metabolomics studies [24, 25] described alterations in metabolic pathways including glycine synthesis and degradation, and carbohydrate and energy metabolisms in PC patients. Recent urine metabolomics profile data reported significantly higher levels of metabolites involved in TCA cycle and glutamate pathway in PC urine samples in respect with urine samples from

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healthy subjects with or without inflammatory prostates. These pathways are critical for energy generation and carbon and nitrogen metabolism for biomass accumulation, especially in rapidly dividing cells such as cancer cells [25]. Another key aspect is related to androgen signaling that is among the predominant stimuli supporting PC growth. Interestingly, urinary extracellular vesicles (EVs) could be used to monitoring androgen metabolism in a noninvasive manner. Marc ClosGarcia et al. [26] showed that intermediary metabolites of androgen synthesis were among the most elevated in PC urine EVs. Moreover, the abundance of these steroids, together with cAMP, was significantly associated to perineural invasion. Recently, other researchers analyzed the molecular and metabolic profiling of the urinary EVs from PC patients reporting the upregulation of metabolites involved in de novo lipid biosynthesis [27] and fatty acid β-oxidation [28]. In this study, they found increased levels of acylcarnitines in the urinary EVs from PC patients, highlighting the association of differential levels of carnitines in PC EVs with a metabolic shifting toward β-oxidation of fatty acids, as also discussed by Puhka et al. [29]. All studies discussed above highlight that the identification of urinary biomarkers through a noninvasive approach can help in the decision to carry out prostate biopsy or in the design of a therapeutic strategy. 3.3

Bladder Cancer

Bladder cancer (BCA) is the seventh most common cancer worldwide and the fourth most common in developed countries [30]. The current standard of care for detecting and monitoring bladder tumors is cystoscopy, voided urine cytology and imaging. However, cystoscopy is invasive, painful and costly [31]. Recent studies reported that metabolites uniquely detected in urine samples from patients with BCA could be useful for disease diagnosis and prognosis [32]. Huang et al. demonstrated that the level of urinary carnitine C9:1 was significantly decreased in patients with BCA, particularly in those at early stage [32]. In line with this study, Jin et al. performed urine metabolomics in 138 BCA patients and 121 healthy subjects, identifying 12 differential metabolites including acetyl-CoA, carnitine and derivatives, as potential diagnostic biomarkers for BCA [33]. Carnitine is essential for the entry of fatty acid into the mitochondria for oxidation, and acetyl-CoA is the final product of this oxidation event. The authors suggest that fatty acid oxidation could be an important factor in determining the cancer status. Although these evidences, the contribution and regulatory mechanism of the carnitine metabolic pathway to BCA remains unclear. Several studies highlighted the clinical value of metabolites such as histidine, phenylalanine, tyrosine and tryptophan in bladder tumor patients [34–36]. The results are also consistent with

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previous observations that showed that an amino acid rich metabolome is an essential hallmark of bladder tumor development [37, 38]. Tryptophan has previously been described as a biomarker of BCA [34]. Alberice et al. [34] reported that tryptophan was shown to be particularly significant in low risk patients affected by BCA, supporting the idea that this metabolite can be used as a biomarker for patients at early stage. Another research group described an altered panel of metabolites including leucine, isoleucine and valine involved in branched chain amino acids catabolism in urine of BCA subjects, suggesting an increased mobilization of amino acids to support the TCA cycle through anaplerotic reactions [35]. In addition to those above discussed, the same authors identified three metabolites associated with lipid metabolism, such as palmitoyl sphingomyelin, phosphocholine and arachidonate in urine samples [35]. This was somewhat surprising since, in general, lipids are not abundantly secreted in the urine. A possible explanation could be increased lipid membrane remodeling associated to a higher cell proliferation, increased liberation of free fatty acids from phospholipids either in the tumor or in adjacent tissue. This study demonstrates the possibility of employing urine metabolites as noninvasive biomarkers to complement existing diagnostic methods and provide improvements to bladder cancer patient monitoring and care [35].

4

Future Directions Future quantitative targeted assays based on the identified biomarker candidates will be required to validate the predictive value of these metabolites. The studies discussed above highlight the potential of utilizing urine metabolites as a noninvasive test for several types of cancer. Moreover, the description of putative alterations characterizing the metabolic profiling in a pathologic condition can offer the possibility to detect and manage the recurrent disease and also help to design new therapeutic strategies.

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INDEX B Bioinformatics approaches......................................95–102 Bladder cancers (BC) ................................. 5, 6, 9, 18, 23, 35, 37, 60–65, 75–85, 101, 121–130, 133–140, 143–145, 209, 210 Bladder tumor antigen (BTA).....................122–124, 127

Metabolic pathways............................................. 204, 206, 208, 209 Methylation ............................................4, 5, 49, 96, 100, 101, 128, 204, 206 MicroRNA (miRNA) ............................... 8, 9, 57–71, 82, 155, 173 Mutational analysis....................................................24, 25

C

N

Chronic effects of physical exercise..................... 193–199 c-MYC................................................................. 18, 49–56 Colorectal cancers ........................................................5, 7, 23–32, 101 Copy number variation (CNV) ...............................49–56, 96, 99, 100, 102

Next generation sequencing (NGS) ...........................6, 8, 50, 65, 68, 95, 96, 99–101, 160, 162 NMP-22 ........................................................................ 122 Nonurological cancers .................................................... 17

O

D

Overtraining syndrome (OTS)............................ 185–192

Density gradient separation ........................ 159, 162, 165

P

E

Prostate cancer antigen 3 (PCA3) ..............................5, 6, 8, 10, 11, 66, 67, 77–79, 81, 86, 105–111 Prostate cancers (PC)................................................... 4–6, 8–11, 18, 49, 50, 60, 62, 66–68, 75, 76, 79–81, 85–87, 99–101, 105–111, 115–120, 208, 209 Proteomics..............................................9, 144, 154, 155, 158, 160–162

Early diagnosis...................................................3, 4, 6, 63, 69, 82, 105

F FISH analysis ................................................................. 134

I ImmunoCyt................................................. 122, 123, 127

K Kidney diseases .............................................154, 193–196 KRAS mutations..................................................... 24, 25, 28, 31

L Long non-coding RNA ......................................... 81, 105

M

S Schistosoma-associated bladder cancer............... 143–150 Size-exclusion chromatography (SEC) .......................157, 159–160, 165, 166

T Tamm–Horsfall protein (THP).................................... 174 Telomerase activity............................................... 133, 139 Telomerase repeats amplification protocol (TRAP) .......................... 129, 134–136, 138–140, 204, 206

MACSPlex kit....................................................... 117, 119 Mass spectrometry analysis .................................. 145, 148

Samanta Salvi and Valentina Casadio (eds.), Urinary Biomarkers: Methods and Protocols, Methods in Molecular Biology, vol. 2292, https://doi.org/10.1007/978-1-0716-1354-2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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AND

PROTOCOLS

U Ultracentrifugation ...........................................62, 65, 68, 154–160, 162, 164–167, 173–181 Urinary catecholamines ....................................... 188–191 Urinary exosomes ............................................... 8, 11, 68, 75, 115, 162 Urinary extracellular vesicles ........................63, 153–169, 205, 209

Urinary metabolomics ......................................... 204, 207 Urinary miRNAs ............................................9, 65, 69, 70 Urine cell-free DNA ..................................................... 5–7 Urine cell-free DNA integrity ........................... 17–19, 21 Urine sediments .................................................... 4, 6, 61, 82, 83, 136 Urological cancers................................................... 20, 49, 61, 139 Urovysion kit.................................................... 35–48, 126