Testicular Germ Cell Tumors: Methods and Protocols (Methods in Molecular Biology, 2195) 1071608592, 9781071608593

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Methods in Molecular Biology 2195

Aditya Bagrodia James F. Amatruda Editors

Testicular Germ Cell Tumors 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.

Testicular Germ Cell Tumors Methods and Protocols

Edited by

Aditya Bagrodia Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA

James F. Amatruda Cancer and Blood Disease Institute, Children’s Hospital Los Angeles, Departments of Pediatrics and Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

Editors Aditya Bagrodia Department of Urology UT Southwestern Medical Center Dallas, TX, USA

James F. Amatruda Cancer and Blood Disease Institute Children’s Hospital Los Angeles Departments of Pediatrics and Medicine Keck School of Medicine University of Southern California Los Angeles, CA, USA

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-0859-3 ISBN 978-1-0716-0860-9 (eBook) https://doi.org/10.1007/978-1-0716-0860-9 © Springer Science+Business Media, LLC, part of Springer Nature 2021, corrected publication 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 Germ cell tumors (GCTs) are among the most biologically and clinically fascinating cancers that exist. These relatively rare tumors are characterized by multiple levels of heterogeneity: they occur in both children and adults; they arise from multiple different anatomic sites of origin; and they appear in both males and females. GCTs are further characterized by histologic heterogeneity. They may manifest as pluripotent seminomas, which resemble primordial germ cells, to highly differentiated or undifferentiated nonseminomatous tumors. GCTs remain enigmatic from a tumorigenesis perspective, as we try to understand the complex interplay between genetic predisposition, in utero exposure, and environmental factors that ultimately lead to GCT development. There is an intriguing interplay between developmental and cancer biology with respect to understanding how GCTs form. Gaining this understanding is critically important for efforts to improve therapy. While most GCTs are fortunately highly susceptible to cisplatin-based chemotherapy, the mechanisms of this sensitivity are poorly understood, with the consequence that there are few viable options for patients with cisplatin-resistant tumors. Even in those patients who are cured, chemotherapy carries risks of significant adverse late effects, making it imperative to develop better models of GCT biology and treatment. In this volume, we present comprehensive chapters to understand methods for investigating GCTs, including histopathology, in vitro and in vivo techniques for studying GCT biology, novel methods for detecting minimal residual disease, as well as sequencing considerations as they apply to GCTs. We sincerely hope this work will provide a unique information tool that is useful to both beginner and seasoned GCT researchers. Dallas, TX, USA Los Angeles, CA, USA

Aditya Bagrodia James F. Amatruda

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

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1 Basic Histopathologic Assessment of Germ Cell Tumors for Clinic and Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Payal Kapur and Dinesh Rakheja 2 Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Franto Francis and Ming Zhou 3 Molecular Characterization of Testicular Germ Cell Tumors Using Tissue Microdissection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Liang Cheng, Steven A. Mann, Antonio Lopez-Beltran, Michal Chovanec, Matteo Santoni, Mingsheng Wang, Costantine Albany, Nabil Adra, Darrell D. Davidson, Alessia Cimadamore, Rodolfo Montironi, and Shaobo Zhang 4 Fluorescence In Situ Hybridization (FISH) Detection of Chromosomal 12p Anomalies in Testicular Germ Cell Tumors. . . . . . . . . . . . . 49 Liang Cheng, Darrell D. Davidson, Rodolfo Montironi, Mingsheng Wang, Antonio Lopez-Beltran, Timothy A. Masterson, Costantine Albany, and Shaobo Zhang 5 Germ Cell Tumor Cell Culture Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 John T. Lafin, James F. Amatruda, and Aditya Bagrodia 6 Three-Dimensional Cultivation of Germ Cell Cancer Cell Lines as Hanging Drops. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Margaretha A. Skowron, Meike M. Watolla, and Daniel Nettersheim 7 Cultivation of Testicular Germ Cell Cancer Cell Lines and Establishment of Gene-Edited Subclones Using CRISPR/Cas9 . . . . . . . . . . 85 Sina Jostes, Daniel Nettersheim, Simon Schneider, and Hubert Schorle 8 Evaluation of Chemotherapeutic Drugs for Treatment of (Cisplatin-Resistant) Germ Cell Cancer Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . 99 Margaretha A. Skowron, Miche`le J. Hoffmann, Meike M. Watolla, and Daniel Nettersheim 9 Assessing Homologous Recombination and Interstrand Cross-Link Repair in Embryonal Carcinoma Testicular Germ Cell Tumor Cell Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Francesca Cavallo, Cinzia Caggiano, Maria Jasin, and Marco Barchi 10 Production and Analysis of Human Primordial Germ Cell–Like Cells . . . . . . . . . 125 Shino Mitsunaga, Keiko Shioda, Jacob H. Hanna, Kurt J. Isselbacher, and Toshi Shioda

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A Genetically Engineered Mouse Model of Malignant Testicular Germ Cell Tumors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Amy M. Lyndaker, Timothy M. Pierpont, Amanda R. Loehr, and Robert S. Weiss 12 Targeted Methylation Analyses: From Bisulfite Treatment to Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ˜ o Lobo, Ad J. M. Gillis, and Leendert H. J. Looijenga Joa 13 Integrated Analysis of Germ Cell Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alok Tewari and Eliezer Van Allen 14 Use of Genomewide Association Studies to Evaluate Genetic Predisposition to Testicular Germ Cell Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anthony J. Hooten, Erica Langer, and Jenny N. Poynter 15 A Circulating MicroRNA Panel for Malignant Germ Cell Tumor Diagnosis and Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matthew J. Murray, Cinzia G. Scarpini, and Nicholas Coleman 16 Detection of Circulating Tumor Cells (CTCs) in Patients with Testicular Germ Cell Tumors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paulina Nastały, Friedemann Honecker, Klaus Pantel, and Sabine Riethdorf 17 Developing and Using a Data Commons for Understanding the Molecular Characteristics of Germ Cell Tumors . . . . . . . . . . . . . . . . . . . . . . . . . Bo Ci, Shin-Yi Lin, Bo Yao, Danni Luo, Lin Xu, Mark Krailo, Matthew J. Murray, James F. Amatruda, A. Lindsay Frazier, and Yang Xie Correction to: Germ Cell Tumor Cell Culture Techniques . . . . . . . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors NABIL ADRA • Department of Medicine, Division of Hematology and Oncology, Indiana University Simon Cancer Center, Indianapolis, IN, USA COSTANTINE ALBANY • Department of Medicine, Division of Hematology and Oncology, Indiana University Simon Cancer Center, Indianapolis, IN, USA JAMES F. AMATRUDA • Cancer and Blood Disease Institute, Children’s Hospital Los Angeles, Departments of Pediatrics and Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA ADITYA BAGRODIA • Department of Urology, UT Southwestern Medical Center, Dallas, TX, USA MARCO BARCHI • Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy CINZIA CAGGIANO • Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy FRANCESCA CAVALLO • Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA LIANG CHENG • Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA MICHAL CHOVANEC • 2nd Department of Oncology, Faculty of Medicine, Comenius University and National Cancer Institute, Bratislava, Slovakia; Division of Hematology and Oncology, Indiana University Simon Cancer Center, Indianapolis, IN, USA BO CI • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA ALESSIA CIMADAMORE • Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy NICHOLAS COLEMAN • Department of Pathology, University of Cambridge, Cambridge, UK; Department of Histopathology, Addenbrooke’s Hospital, Cambridge, UK DARRELL D. DAVIDSON • Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA FRANTO FRANCIS • Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA A. LINDSAY FRAZIER • Department of Pediatrics, Dana-Farber Cancer Institute and Boston Children’s Hospital, Boston, MA, USA AD J. M. GILLIS • Princess Ma´xima Center for Pediatric Oncology, Utrecht, The Netherlands JACOB H. HANNA • Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel MICHE`LE J. HOFFMANN • Department of Urology, Urological Research Laboratory, Translational UroOncology, University Hospital Du¨sseldorf, Du¨sseldorf, Germany FRIEDEMANN HONECKER • Department of Oncology, Hematology and Bone Marrow Transplantation with Section Pneumology, Hubertus Wald Tumorzentrum, University Medical Center, Hamburg, Germany; Tumor and Breast Cancer Center ZeTuP, St. Gallen, Switzerland

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ANTHONY J. HOOTEN • Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA KURT J. ISSELBACHER • Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA MARIA JASIN • Developmental Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA SINA JOSTES • Department of Oncological Sciences, Icahn School of Medicine, New York, NY, USA; Department of Developmental Pathology, Institute of Pathology, University Hospital Bonn, Bonn, Germany PAYAL KAPUR • Department of Pathology, UT Southwestern Medical Center, Dallas, TX, USA MARK KRAILO • Keck School of Medicine, University of Southern California, Los Angeles, CA, USA JOHN T. LAFIN • Department of Urology, University of Texas Medical Center, Dallas, TX, USA ERICA LANGER • Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA SHIN-YI LIN • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA JOA˜O LOBO • Princess Ma´xima Center for Pediatric Oncology, Utrecht, The Netherlands; Department of Pathology, Portuguese Oncology Institute of Porto (IPOP), Porto, Portugal; Cancer Biology and Epigenetics Group, Research Center of Portuguese Oncology Institute of Porto (GEBC CI-IPOP) & Porto Comprehensive Cancer Center (P.CCC), Porto, Portugal; Department of Pathology and Molecular Immunology, Institute of Biomedical Sciences Abel Salazar, University of Porto (ICBAS-UP), Porto, Portugal AMANDA R. LOEHR • Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA LEENDERT H. J. LOOIJENGA • Princess Ma´xima Center for Pediatric Oncology, Utrecht, The Netherlands; Department of Pathology, Lab. for Exp. Patho-Oncology (LEPO), Erasmus MC-University Medical Center Rotterdam, Cancer Institute, Rotterdam, The Netherlands ANTONIO LOPEZ-BELTRAN • Department of Pathology and Surgery, Faculty of Medicine, University of Cordoba, Cordoba, Spain; Pathology Service, Champalimaud Clinical Center, Lisbon, Portugal DANNI LUO • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA AMY M. LYNDAKER • Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA; Division of Mathematics and Natural Sciences, Elmira College, Elmira, NY, USA STEVEN A. MANN • Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA TIMOTHY A. MASTERSON • Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA SHINO MITSUNAGA • Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA RODOLFO MONTIRONI • Section of Pathological Anatomy, Polytechnic University of the Marche Region, School of Medicine, United Hospitals, Ancona, Italy

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MATTHEW J. MURRAY • Department of Pathology, University of Cambridge, Cambridge, UK; Department of Paediatric Haematology and Oncology, Addenbrooke’s Hospital, Cambridge, UK PAULINA NASTAŁY • Institute of Tumor Biology, University Medical Center HamburgEppendorf, Hamburg, Germany DANIEL NETTERSHEIM • Department of Urology, Urological Research Laboratory, Translational UroOncology, University Hospital Du¨sseldorf, Du¨sseldorf, Germany; Department of Developmental Pathology, Institute of Pathology, University Hospital Bonn, Bonn, Germany KLAUS PANTEL • Institute of Tumor Biology, University Medical Center HamburgEppendorf, Hamburg, Germany TIMOTHY M. PIERPONT • Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA JENNY N. POYNTER • Division of Epidemiology and Clinical Research, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA; Masonic Cancer Center, University of Minnesota, Minneapolis, MN, USA DINESH RAKHEJA • Department of Pathology, University of Texas Southwestern Medical Center and Children’s Health, Dallas, TX, USA SABINE RIETHDORF • Institute of Tumor Biology, University Medical Center HamburgEppendorf, Hamburg, Germany MATTEO SANTONI • Oncology Unit, Macerata Hospital, Macerata, Italy CINZIA G. SCARPINI • Department of Pathology, University of Cambridge, Cambridge, UK SIMON SCHNEIDER • Department of Developmental Pathology, Institute of Pathology, University Hospital Bonn, Bonn, Germany HUBERT SCHORLE • Department of Developmental Pathology, Institute of Pathology, University Hospital Bonn, Bonn, Germany KEIKO SHIODA • Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA TOSHI SHIODA • Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA MARGARETHA A. SKOWRON • Department of Urology, Urological Research Laboratory, Translational UroOncology, University Hospital Du¨sseldorf, Du¨sseldorf, Germany ALOK TEWARI • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA ELIEZER VAN ALLEN • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA MINGSHENG WANG • Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA MEIKE M. WATOLLA • Department of Urology, Urological Research Laboratory, Translational UroOncology, University Hospital Du¨sseldorf, Du¨sseldorf, Germany ROBERT S. WEISS • Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA YANG XIE • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

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LIN XU • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA BO YAO • Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA SHAOBO ZHANG • Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA MING ZHOU • Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Tufts Medical Center and Tufts School of Medicine, Boston, MA, USA

Chapter 1 Basic Histopathologic Assessment of Germ Cell Tumors for Clinic and Research Payal Kapur and Dinesh Rakheja Abstract This chapter introduces the macroscopic and light microscopic features of testicular germ cell tumors (GCT) commonly encountered in clinical practice. Accurate diagnosis of these histologically diverse neoplasms is essential not only for clinical management but also for serving as the basis for interpretation of research findings. We will focus on general histopathologic concepts and discuss the use of immunohistochemistry (IHC) as an aid to the diagnosis. Key words Germ cell tumor, Immunohistochemistry

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Introduction Pathologic evaluation of testicular germ cell tumor remains the foundation for clinical management and research. This involves macroscopic examination of the surgically removed specimen, careful dissection and representative sampling, and microscopic evaluation of the hematoxylin–eosin (H&E)-stained sections. Histomorphology reflects the underlying genetics. Therefore, histomorphologic features as discerned by expert pathologists (and soon with the aid of digital pathology and artificial intelligence) are powerful biomarkers.

1.1 Normal Macroscopy and Histology of Testis

The testis is a 15–20 g paired organ that normally resides in the scrotum and is suspended by the spermatic cord [1]. It is covered by a tunica vaginalis (flattened layer of mesothelial cells), inner tunica albuginea, and tunica vasculosa. The testicular parenchyma is divided by septa into multiple lobules each containing up to four seminiferous tubules. The seminiferous tubules are surrounded by a basement membrane and contain germ cells at various stages of maturation and Sertoli cells. The pyramid shaped Sertoli cells reside adjacent to the

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Fig. 1 Normal postpubertal testis showing spermatogenesis (H&E, 400). Black arrow—Spermatogonia; Blue arrow—Sertoli cells; Green arrow— Spermatocytes; Orange arrow—Spermatid; Black double-headed arrow— Leydig cells

basement membrane. Germ cells mature from base to center of the tubules (spermatogonia, primary spermatocytes, secondary spermatocytes, spermatids, spermatozoa). The intertubular interstitium contains Leydig cells (Fig. 1). The testis is in a static phase from birth to 4 years of age, a growth phase till 10 years, and a maturation phase till puberty. At birth, the tubules are lined by spermatogonia and Sertoli cells. The number and size of tubules increases with growth. After the appearance of gonadotropins, primary and secondary spermatocytes and Leydig cells appear. 1.2 Germ Cell Neoplasia In Situ (GCNIS)

These lesions are widely accepted as precursors of adult germ cell tumors (GCT) and are believed to originate when a primordial germ cell or gonocyte is blocked in its maturation to spermatogonium [2]. Polyploidy is an early event, especially gain of short arm of chromosome 12 (predominantly isochromosome 12), that drives sertoli cell independent growth. In the past, they have been referred to as intratubular germ cell neoplasia, unclassified type (IGCNU), and carcinoma in situ (CIS). Morphologically, GCNIS cells are seminoma-like cells with large central nuclei, clumped chromatin, prominent nucleoli, and abundant clear cytoplasm (Fig. 2). The GCNIS cells line along the basement membrane of the tubules (spermatogonial niche) and express embryonic germ cell markers like OCT 3/4 (POU5F1). They are generally seen in atrophic tubules with thick basement membranes and absent progressive spermatogenesis. The GCNIS cells are also positive for CD117 (KIT ligand), D2-40 (podoplanin), and placental alkaline

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Fig. 2 Germ cell neoplasia in situ (GCNIS) (H&E, 400). Red arrows—neoplastic germ cells

phosphatase (PLAP). When the GCNIS cells completely fill and expand the tubules, completely replacing Sertoli cells, they are classified as intratubular seminoma. Intratubular embryonal carcinoma has more pleomorphic cells and often has central necrosis. Other intratubular germ cell tumors are rare. A major change in the current 2016 classification of GCT is the distinction of GCT based on the pathogenetic model using GCNIS as a precursor lesion [2]. There are two main groups: (1) tumors derived from GCNIS and (2) tumors considered not to arise from GCNIS. 1.3 Germ Cell Tumors Derived from GCNIS 1.3.1 Tumors of a Single Histologic Type (Pure Forms) Seminoma

Seminomas are usually circumscribed, homogeneous, tan, fleshy, and lobulated that bulge on cut surface (Fig. 3). Microscopically, they are characterized by fibrous septa that divide the tumor into lobules consisting of monotonous solid sheets of tumor cells. The septa contain lymphoplasmacytic infiltrates. The seminoma cells are large polygonal cells with uniform central round nuclei, prominent nucleoli, abundant clear cytoplasm, and well-defined cytoplasmic membranes (Fig. 3). Mitoses are frequent and scattered syncytiotrophoblastic giant cells may be seen (associated with mild elevations of serum beta-human chorionic gonadotropin). Interstitial (in between the seminiferous tubules) growth pattern may be rarely seen. The amount of lymphoplasmacytic inflammation is variable and may be extensive, sometimes with formation of lymphoid follicles. Granulomatous inflammation may be seen. Seminoma cells express IHC markers seen in fetal-type gonocytes including OCT3/4 (100%; nuclear), CD117 (90–100%; cytoplasmicmembranous), D2-40 (100%; cytoplasmic-membranous), SALL4

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Fig. 3 Seminoma (typical gross, light microscopic, and immunohistochemical features; all photomicrographs at 200)

(100%; nuclear), SOX17 (95%; nuclear), and PLAP (85–95%; cytoplasmic-membranous) (Fig. 3). Nonseminomatous Germ Cell Tumors

In contract to seminomas, these tumors are more aggressive, present at an earlier age, and are not as responsive to radiotherapy.

Embryonal Carcinoma

Embryonal carcinomas (EC) are frequently poorly circumscribed, have a variegated macroscopic appearance, and frequently present as a component of a mixed GCT (Fig. 4). EC cells resemble embryonic stem cells. The classic EC cells are large with highly pleomorphic crowded nuclei, macronucleoli, moderate amount of amphophilic cytoplasm, and poorly defined cell membranes. EC can show several architectural patterns; however, cytologic features of nuclear crowding and overlap, and marked pleomorphism are helpful diagnostically (Fig. 4). Mitoses, tumor necrosis, and lymphovascular invasion are often seen. The most helpful IHC markers to distinguish EC are CD30 (85–95%; membranous) and SOX2

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Fig. 4 Embryonal carcinoma (typical gross, light microscopic, and immunohistochemical features; all photomicrographs at 200)

(96%; nuclear). They are almost always positive for Oct 3/4, SALL4, and frequently for pancytokeratin (Fig. 4). Yolk Sac Tumor, Postpubertal Type

In adults, yolk sac tumors (YST) occur almost exclusively as a component of mixed GCT. There is a strong association with elevated serum levels of alpha-fetoprotein (AFP). In children, where they occur in pure forms, YST typically have a homogeneous gelatinous cut surface (Fig. 5) [3]. Microscopically, though numerous patterns have been described, microcystic and reticular patterns are most frequent and characteristic, where one can see anastomosing thin cords forming tubules or irregular spaces that may create a sieve-like pattern (Fig. 5). The characteristics of YST are relatively uniform, cuboidal to flattened cells with usually bland nuclei and vacuolated to eosinophilic cytoplasm. There is nuclear overlapping as the cells have indistinct cytoplasmic membranes. Two features that are most helpful in the diagnosis are the presence of hyaline globules (both intracellular and extracellular) and extracellular basement membrane–like deposits called parietal differentiation. A characteristic feature is the presence of Schiller–Duval body

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Fig. 5 Yolk sac tumor (gross appearance, H&E showing hyaline globules [200], H&E showing Schiller–Duval body [400], immunohistochemistry for Glypican-3 [200])

(resembling fetal glomeruli) characterized by a central vessel, surrounded by a layer of tumor cells, a clear space, and another layer of flattened tumor cells. YST cells are positive for SALL4 (100%, nuclear), AFP (74–100%, cytoplasmic), and glypican-3 (90–100%, cytoplasmic) (Fig. 5). They can also be positive for cytokeratin and PLAP. Importantly, they are typically negative for Oct3/4 and CD30. Choriocarcinoma and Other Trophoblastic Tumors

Choriocarcinomas (CC) are aggressive GCT that show differentiation toward trophoblastic cells of the extraembryonic chorion. In adults, these occur as components of mixed GCT. Serum level of beta–human chorionic gonadotropin (hCG) is elevated, and many patients have distant hematogenous metastases, especially to the lung and sometimes without overt testicular mass. Macroscopically, these are hemorrhagic nodules with foci of solid gray-tan tumors (Fig. 6). Microscopically, they have a biphasic pattern composed of sheets of intimately admixed mononuclear cytotrophoblasts (round cells with prominent cell borders, clear cytoplasm, and bland nuclei) capped with multinucleated

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Fig. 6 Choriocarcinoma (typical gross, light microscopic, and immunohistochemical features; photomicrographs at 200/400)

syncytiotrophoblasts (large multinucleated cells with smudgy nuclei and abundant eosinophilic cytoplasm) in a hemorrhagic background reminiscent of early placental villi (Fig. 6). CC are positive for b-hCG, human placental lactogen (hPL), cytokeratin, and SALL4 (Fig. 6). Of note, the presence of isolated syncytiotrophoblastic cells in other germ cell tumor types (most commonly seminoma) is not diagnostic of CC. Teratoma, Postpubertal Type

Teratoma is composed of several different types of tissue derived from one or more germ layers. The tumors may be composed exclusively of mature tissue types or have variable amounts of immature embryonic type tissues. The words mature and immature are not used to designate testicular teratomas because that distinction has not been shown to be of prognostic value. The discrimination of pre- and postpubertal-type teratoma in the recent 2016 WHO classification is key. The postpubertal-type teratomas are associated with GCNIS, can develop metastasis (with or without other GCT elements) and therefore are considered malignant.

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Fig. 7 Teratoma in a mixed germ cell tumor (gross appearance, H&E images showing mature tissues and foci of yolk sac tumor [red arrows], photomicrographs at 100)

Macroscopically, teratomas are solid-cystic masses with variegated appearance, sometimes with visible mature cartilaginous, bone, or hair components (Fig. 7). Microscopically, they are composed of haphazardly arranged variable amounts of epithelial and mesenchymal elements derived from ectoderm, mesoderm, and endoderm (Fig. 7). These can sometimes show significant cytological atypia. Immature neuroectodermal structures resembling embryonic central nervous system are common. The immunophenotypic profile is similar to the expected specific cell/tissue type. SALL4 is positive in only 50% of the tumors (in immature neuroepithelium and immature tubuloglandular structures). Teratoma with Somatic-Type Malignancy

These are teratomas of postpubertal type, that develop somatic malignant neoplasms (either a carcinoma or sarcoma). These are frequently seen in metastatic sites after cisplatin-based chemotherapy. They typically exhibit an infiltrative or expansile growth that occupies at least one low power field (4 objective or 5 mm diameter) [2]. Sarcomas are more common and include rhabdomyosarcomas and primitive neuroectodermal tumors (PNET). These

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PNETs lack the chromosome 22 translocations seen in Ewing sarcomas. Sarcomatoid YST may mimic these tumors, and parietal differentiation and immunoreactivity for glypican-3 may be particularly helpful in making the distinction. Most carcinomas are adenocarcinomas. The prognosis is not generally affected if the somatic type malignancy is confined to the testis; however, in the metastatic setting, it increases the risk of mortality. 1.3.2 Tumors of Multiple Histologic Types (Mixed Germ Cell Tumors)

These tumors are composed of more than one GCT component and are clinically regarded as nonseminoma irrespective of the presence or absence of a seminoma component. These are the most common nonseminomatous GCT in adults. It is recommended to enumerate the percentages of all nonnecrotic GCT components as this has clinical implications. EC and teratoma are the most common components of mixed GCT. The percentage of EC is predictive of occult metastases; however, there is no consensus on the threshold. The presence of CC predicts aggressive behavior, and the absence of YST is another poor prognostic factor.

1.4 Germ Cell Tumors Unrelated to GCNIS

The 2016 WHO classification categorized spermatocytic seminomas as spermatocytic tumors to emphasize the nonaggressive behavior of these tumors and the lack of relationship with seminoma [2]. These tumors are not associated with GCNIS, do not have chromosome 12p abnormalities, occur at an older age, and are negative for Oct 3/4. They do not occur in the extragonadal sites. These tumors show paternal-only imprinting and consistent gain in chromosome 9, that corresponds to increased copies of DMRT1 (Doublesex and mab-3 related transcription factor 1) gene. They can rarely dedifferentiate into sarcoma with associated adverse prognostic impact. Microscopically, the spermatocytic tumors show multinodular architecture and are composed of three cell types: small lymphocyte-like cells, intermediate-sized cells with filamentous chromatin (spireme), and giant cells with single or multiple nuclei. Though fine fibrous septa are present, lymphocytic infiltrate is absent.

1.4.1 Spermatocytic Tumor

1.4.2 Teratoma, Prepubertal Type

Unlike postpubertal testicular teratomas, these lack association with GCNIS and lack 12p amplification. They do not show cytologic atypia and have not been described to have metastatic potential. These tumors can be found in postpubertal patients, likely because of late presentation. Dermoid and epidermoid cysts are now grouped within this entity. Well-differentiated neuroendocrine carcinomas (carcinoid tumors) have been currently placed under this category; however, these remain debated and incompletely understood.

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Payal Kapur and Dinesh Rakheja

1.4.3 Yolk Sac Tumor and Mixed Teratoma and Yolk Sac Tumors, Prepubertal Type

Though these tumors share the histologic patterns seen in YST of the postpubertal type, they are biologically different. The occur in children in pure forms and only occasionally with teratomas [3]. They are less aggressive compared to the postpubertal YST and generally are of low stage and respond very effectively to chemotherapy.

1.5 Regression of Germ Cell Tumor

These are also called “burnt out” GCT and typically consist of focal scaring with microlithiasis and associated reduced spermatogenesis. They are differentiated from nonspecific scarring by the presence of GCNIS and coarse intratubular calcifications [4].

1.6 Concepts for GCT Staging

Several changes have been made in the eighth edition of AJCC TNM staging for testicular tumors [5]. In seminomas, it has been recommended to distinguish between pagetoid rete testis involvement and invasion of the rete stroma. This is because correlation between rete testis stromal invasion (and not pagetoid in situ spread) and advanced clinical stage has been reported for seminoma. The presence of hilar soft tissue invasion is now categorized as pT2. Hilar soft tissue is an area of adipose tissue and vessels beyond the rete testis but before the cord and is a common site of extension for GCTs. This area lacks tunica vaginalis and in a case without lymphovascular (LVI) spread had previously represented a potentially understaged pitfall. The involvement of epididymis and penetration of the mesothelial layer of the visceral (inner) tunica vaginalis should be reported and are category pT2. Currently, distinction between lymphatic and blood vessel invasion is not required, but LVI is essential to report especially for stage I nonseminomatous GCT since that may determine if the patients receives adjuvant chemotherapy. The best place to look for LVI is at the periphery of the tumor and in the tunica albuginea. LVI in the parenchyma, cord, or tunica are all considered pT2. Direct spermatic cord stromal invasion is now considered pT3. The base of the cord is distinguished from hilar soft tissue macroscopically and a section from just above the head of epididymis may be taken to look for cord invasion. Microscopically, tumor near the vas deferens is considered involving the cord. Distant, discontinuous tumor deposit in the upper cord is now considered pM1. In retroperitoneal lymph node dissection, three factors are important to report: (1) residual, viable, nonteratomatous GCT, (2) teratoma, and (3) scar/necrosis. The presence of any amount of viable nonteratomatous GCT mandates additional systemic chemotherapy. Scar, necrosis, and teratoma (regardless of the presence of immaturity and cytologic atypia) have good prognosis. It is recommended to report the number of positive nodes and the presence of extranodal extension.

Basic Histopathologic Assessment of Germ Cell Tumors for Clinic and Research

11

References 1. Rosai J (2011) Rosai & Ackerman’s surgical pathology, vol 1, 10th edn. Mosby, Philadelphia, PA 2. WHO (2016) WHO classification of tumours of the urinary system and male genital organs, vol 8, 4th edn. IARC Press, Lyon 3. Rakheja D, Teot LA (2014) Pathology of germ cell tumors. In: Frazier A, Amatruda J (eds) Pediatric germ cell tumors. Pediatric oncology, vol 1. Springer, Berlin

4. Tickoo SK, Amin MB (eds) (2016) Diagnostic pathology: genitourinary, 2nd edn. Elsevier, Amsterdam 5. Verrill C, Yilmaz A, Srigley JR et al (2017) Reporting and staging of testicular germ cell tumors: the international society of urological pathology (ISUP) Testicular cancer consultation conference recommendations. Am J Surg Pathol 41(6):e22–e32

Chapter 2 Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men Franto Francis and Ming Zhou Abstract Germ cell tumors (GCT) in men comprise of tumor subtypes with distinct histomorphologies, genetic and genomic alterations, and clinical behavior. Immunohistochemical (IHC) markers, including many newly described nuclear transcription factors, are often applied in challenging cases to arrive at a correct diagnosis and classification, and to establish germ cell origin for metastatic tumors. However, there is no established role for IHC markers in prognosis and therapy response prediction in GCTs. This chapter briefly reviews the clinical utility of IHC in diagnosis and classification of GCTs, including technical aspects of performing IHC and clinical applications of commonly used IHC markers in the workup of common and clinically relevant diagnostic scenarios. Key words Immunohistochemistry, Germ cell tumor, Seminoma, Nonseminomatous tumor, Embryonal carcinoma, Yolk sac tumor, Choriocarcinoma, AFP, β-HCG, OCT3/4, SALL4

Abbreviations β-HCG AFP EC GCT GPC3 IHC PLAP YST

1

Beta subunit of human chorionic gonadotropin Alpha-fetoprotein Embryonal carcinoma Germ cell tumor Glypican 3 Immunohistochemistry Placental alkaline phosphatase Yolk sac tumor

Introduction Germ cell tumors (GCT) constitute approximately 95% of malignant tumors in the testis [1]. They comprise of tumor subtypes with distinct histomorphologies and genetic and genomic alterations and clinical features. For clinical management purposes, they are

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Franto Francis and Ming Zhou

classified into seminomas and nonseminomatous germ cell tumors. The latter category comprises of embryonal carcinoma, yolk sac tumor, teratoma, choriocarcinoma, and other rare tumor types. An important feature of GCTs is their morphological and genetic heterogeneity. While seminomas can often present in a “pure” histologic form, the other GCT subtypes are more commonly encountered as components of a mixed germ cell tumor, which may also include seminoma. Since clinical management of seminomas and nonseminomatous GCTs differs, accurate classification is critical [2]. Furthermore, GCTs may present first as metastasis at distant sites, most commonly in retroperitoneum, or rarely at extragonadal sites without testicular involvement [3]. The latter two scenarios often present diagnostic difficulty to clinicians and pathologists alike. While GCTs often have characteristic histomorphological features that allow correct diagnosis and classification, there are situations where use of IHC is often employed in adjunct to histopathologic assessment. IHC markers can be especially important in working up metastatic GCTs and malignant tumors of unknown primary origin. The emergence of nuclear transcriptional factors such as OCT3/4, SALL4, and SOX2 and SOX17 in recent years has added to an already existing armamentarium of wellstudied markers, usually cytoplasmic, such as AFP, PLAP, and β-HCG. In GCTs, no IHC markers are currently used as predictive markers for prognosis and response to therapy. This chapter briefly reviews technical aspects of IHC in GCTs, with emphasis on the clinical applications of commonly used markers in the workup of common and clinically relevant diagnostic issues.

2

Materials

2.1 Tissue Processing 2.2 Immunohistochemistry Slide Preparation

10% formalin, 100% ethanol, 95% ethanol, xylene, paraffin.

1. Deparaffinization fluid: 1
EZPrep (Ventana Medical Systems, Inc., Tucson, AZ, USA). EZPrep is an aqueous based detergent. 2. Cell conditioning buffer #1 (CC1) (Ventana Medical Systems, Inc., Tucson, AZ, USA). This is a slightly basic, Tris-based buffer. 3. Reaction buffer: Tris-based buffer at pH 7.6 used for rinsing slides. 4. Wash buffer: 1
SSC buffer. This is a sodium chloride–sodium citrate buffer which acts as a stringent aqueous wash buffer.

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

15

5. Rinse buffer: 1
phosphate buffer saline (PBS). 6. Liquid coverslip (LCS): a combination of low-density, paraffinic hydrocarbon and mineral oil. 2.3 Immunohistochemistry Staining Using the UltraView Universal DAB Detection Kit by Ventana

This detection system (including primary antibodies unless otherwise specified) is manufactured by Ventana Medical Systems, Inc. (Tucson, AZ, USA) used in immunohistochemistry (IHC) reactions performed on VENTANA BenchMark XT automated slide staining platforms (see Note 1). 1. Primary antibodies: SALL4 (6E3) mouse monoclonal, PLAP (NB-10) mouse monoclonal, Oct-4 (MRQ-10) mouse monoclonal, CD117 (c-kit) rabbit polyclonal, CD30 (Ber-H2) mouse monoclonal, AE1/3 (AE1/3) mouse monoclonal, GATA3 (L50-823) mouse monoclonal, SOX2 (SP76) rabbit monoclonal, alpha-fetoprotein rabbit polyclonal, and hCG rabbit polyclonal. Other antibodies are available from various manufacturers. 2. UV INHIBITOR: 3% H2O2. 3. UV HRP UNIV MULT: Cocktail of goat anti-mouse IgG/IgM and goat anti-rabbit IgG that are conjugated to horseradish peroxidase (HRP) at a concentration of ~50 μg/ mL. 4. UV DAB chromogen: 0.2% aqueous solution of 3,30 -diaminobenzidine tetrahydrochloride. 5. UV H2O2: 0.04% H2O2 in 1
phosphate buffer solution (PBS). 6. UV Copper: Aqueous copper sulfate solution at 5 g/L in acetate buffer. 7. Hematoxylin: 48% hematoxylin dye in glycol and acetic acid. 8. Bluing reagent: Contains 0.1 M lithium carbonate in 0.5 M sodium carbonate aqueous solution.

3

Methods

3.1 Tissue Processing

3.1.1 Tissue Processing for Tissue Blocks from Orchiectomies and Retroperitoneal Lymph Node Dissections

Tissue sections are processed according to standard automated staining protocols used at UT Southwestern Clements University Hospital Laboratories. 1. Two-cycle incubation in 10% formalin for 1½ h at 42  C, 15 mmHg. 2. One-cycle incubation in 60% ethanol for 1 h at 42 15 mmHg.



C,

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Franto Francis and Ming Zhou

3. Two-cycle incubation in 95% ethanol for 1 h at 42 15 mmHg.



C,

4. Three-cycle incubation in 100% ethanol for 1 h at 42  C, 15 mmHg. 5. Two-cycle incubation in Xylene for 1 h at 42  C, 15 mmHg. 6. Two-cycle incubation in Paraffin for 1½ h at 60  C, 15 mmHg. 3.1.2 Tissue Processing for Tissue Blocks from Biopsies

1. Two-cycle incubation in 10% formalin for 15 min at 42  C, 15 mmHg. 2. One-cycle incubation in 60% ethanol for 15 min at 42  C, 15 mmHg. 3. Two-cycle incubation in 95% ethanol for 15 min at 42  C, 15 mmHg. 4. Three-cycle incubation in 100% ethanol for 15 min at 42  C, 15 mmHg. 5. One-cycle incubation in xylene for 10 min at 42  C, 15 mmHg. 6. One-cycle incubation in xylene for 15 min at 42  C, 15 mmHg.

3.2 Immunohistochemistry Slide Prepping

7. One-cycle incubation in paraffin for 10 min at 42 15 mmHg.



C,

8. One-cycle incubation in paraffin for 15 min at 42 15 mmHg.



C,

1. Using a microtome, 4 μM thick tissue sections are cut from formalin-fixed paraffin-embedded (FFPE) tissue blocks, and placed on positively charged glass slides. 2. Warm slide to 75  C and incubate for 4 min. 3. Apply EZPrep and rinse with 1
PBS. Repeat twice. 4. Apply Liquid coverslip (LCS), warm slide to 76  C, and incubate for 4 min. 5. Rinse slide with 1
PBS, and apply Liquid coverslip (LCS). 6. Wash with 1
SSC wash buffer, warm slide to 95  C, and incubate for 8 min. 7. Apply cell conditioner #1 and LCS. 8. Warm slide to 100  C, and incubate for 4 min. 9. Apply LCS and cell conditioner #1. Repeat four times. 10. Apply LCS and incubate for 8 min. 11. Rinse slide with reaction buffer. 12. Apply LCS. Rinse slide with reaction buffer.

3.3 Immunohistochemistry Staining

1. Obtain prepped slide (from Subheading 3.2). 2. Warm slide to 37  C and incubate for 4 min. 3. Rinse with reaction buffer.

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

17

4. Add one drop of UV INHIBITOR, apply LCS and incubate for 4 min. 5. Rinse slide with reaction buffer, and warm slide to 37  C for 4 min. 6. Add LCS, then one drop of primary antibody and incubate for 8 min. 7. Rinse slide with reaction buffer, add LCS, and warm to 37  C for 4 min. Apply one drop of UV HRP UNIV MULT, add coverslip and incubate for 8 min. Rinse with reaction buffer. 8. Apply reaction buffer, add one drop of UV DAB and one drop of UV DAB H2O2. 9. Rinse with reaction buffer. 10. Apply one drop of UV COPPER, apply LCS, and incubate for 4 min. 11. Rinse with reaction buffer. 12. Apply one drop of HEMATOXYLIN, LCS, and incubate for 4 min. 13. Rinse with reaction buffer, and apply LCS. Repeat once. 14. Add one drop of BLUING REAGENT, apply LCS, and incubate for 4 min. 15. Rinse with reaction buffer, and then wash with 1
SSC. 16. Add one drop of mounting solution, cover the slide with a glass coverslip, and allow drying before histological examination. 3.4 Hematoxylin and Eosin (H&E) Staining

Performed according to the BenchMark XT H&E Staining Module protocol by Ventana.

3.5 Principles of Immunohistochemistry

The UltraView Universal DAB Detection Kit by Ventana is a detection system used in immunohistochemistry (IHC) reactions carried out on VENTANA BenchMark XT automated slide staining platforms. This system is based on a biotin-free method for staining antigens bound by mouse or rabbit IgG antibodies on formalinfixed, paraffin-embedded tissue sections. Together, the automated platform allows for efficient staining of multiple slides in real time with high efficiency. Tissue is fixed in different solutions (formalin, ethanol) which freeze cellular functions and preserve cellular components through crosslinking carboxy and amino groups (formalin) or by protein coagulation (ethanol). Incubation with xylene permeates cell membranes for easier staining of intracellular components while preserving cellular integrity. Incubation with paraffin allows for long term storage. However, this “fixed” state must be partially reversed for adequate staining of tissue sections. The immunostaining prepping process achieves this by using the EZPrep detergent solution, along

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with heating, to deparaffinize tissue. Cell conditioning buffer reverses covalent bonds formed during the formalin fixation process, thus renaturing proteins and unmasking epitope antigenicity for proper antibody affinity. Reaction buffer (a Tris-based buffer at an appropriate pH 7.6) supplies adequate aqueous medium for the antibodies to bind to their respective targets. Liquid coverslip (LCS; a combination of low density, paraffinic hydrocarbon and mineral oil) provides a semipermeable liquid barrier allowing reagents to contact the tissue section, but preventing excessive evaporation of water. The immunohistochemistry staining assay is based on an indirect immune complex reaction incorporating the protein target of interest, a respective primary antibody and a secondary antibody conjugated to horse-radish peroxidase (HRP) to label the protein of interest via a chromogen precipitate reaction. To reduce background signal from nonspecific reactions, endogenous tissue peroxidases are inactivated with a high dose of hydrogen peroxide (UV INHIBITOR). The tissue section is then incubated with the primary antibody (in most cases a mouse or rabbit IgG) to label the specific protein of interest. This antibody/antigen complex is then incubated with UV HRP UNIV MULT containing secondary antibodies (goat anti-mouse or goat anti-rabbit IgG) conjugated to HRP. The secondary antibody binds to the primary antibody. The HRP motif, in the presence of copper and low concentration of hydrogen peroxide, drives an oxidation reaction of the DAB chromogen to generate a brown precipitate at the site of the antibody/ antigen complex, which can be visualized on the tissue section with the cellular background highlighted by hematoxylin and bluing reagent. Using the above techniques, pathologists can test for and identify specific protein expression or lack thereof of various immunomarkers specific to a GCT tumor or to a particular GCT subtype and arrive at a correct diagnosis.

4

Immunohistochemistry in Germ Cell Tumors

4.1 Commonly Used Immunohistochemical Markers

A brief explanation of the histogenesis of testicular GCTs in relation to the normal spermatogenesis helps understand the utility of various markers in the GCT classification (Fig. 1). These tumor markers are genes expressed in primordial germ cells/gonocytes and embryonic pluripotency-related factors, such as placental-like alkaline phosphatase (PLAP), OCT3/4 (POU5F1), and NANOG, which are not expressed in mature spermatids and spermatozoa. Some markers used immunocytochemically to detect various GCT components are expressed only in GCTs but not expressed in normal spermatogenesis (Table 1). The commonly used markers are discussed below.

Embryonic Stem cells

Primordial Germ Cells

Gonocytes

5th/6th week

Spermatocytes Spermatozoa Spermatids Puberty

Spermatogonia Birth6 months

Primordial germ cells/gonocytes

Spermatogonia

Spermatocytes

Spermatids -

OCT3/4 (POU5F1)

+

-

-

NANOG

+

-

-

-

PLAP

+

-

-

-

PDPN (D2-40)

+

-

-

-

KIT (CD117)

+

-/+

-

-

SALL4

+/-

+

-

-

DMRT1

+/-

+/-

-

-

-

+

+/-

-

MAGE-A4

19

Spermatogenesis

Maturation

Differentiation Arrest

Migration to Genital Ridge

Specification

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

CD30

-

-

-

-

SOX2

-

-

-

-

SOX17

-

-

-

-

Glypican 3

-

-

-

-

AFP

-

-

-

-

Beta-HCG

-

-

-

-

Fig. 1 Expression of immunohistochemical markers during normal spermatogenesis

OCT3/4 is a nuclear transcription factor critically involved in the self-renewal of undifferentiated embryonic stem cells [4] and is expressed in embryonic stem cells, primordial germ cells and gonocytes. It positively stains seminomas and ECs, but is negative in YST, choriocarcinoma and teratomas [5]. It also stains the germ cell component of gonadoblastoma in dysgenetic gonads [5]. Dysgerminomas of the ovary are also positive for OCT3/4 [6]. In addition, immature neuroepithelium in female high grade immature teratoma is positive for OCT3/ 4 [7]. SALL4 is a zinc finger nuclear transcription factor that plays an important role in embryonic development and helps maintain pluripotency of stem cells [8]. It is expressed in almost all seminomas, EC and YST, and ~80% of choriocarcinomas and 60% of teratomas [9]. The staining in trophoblastic components of GCT varies and lager cells including the syncytiotrophoblastic cells are typically negative. The SALL4 positivity in differentiated components of teratomas also varies but in general is in low numbers. SALL4 is used as a general GCT marker for distinction from testicular sex cord stromal tumors and other

+

+

+

+

/+

+





+









variable



+

+

+

/+

+





+













PLAP

PDPN (D2–40)

KIT (CD117) +

+

NANOG

SALL4

DMRT1

MAGE-A4

CD30

SOX2

SOX17

Glypican 3

AFP

Beta-HCG

GATA3

AE1/3

EMA

+

+

OCT3/4 + (POU5F1)

+

/+ 

+/

+

/+



+

+







+

+

+



+

+

+/







+



+/



+





+

+





+

+

+

+

+



+











+







+



+

+





/+

/+

Variable

Variable







Immature Sertoli cells, glioma, mesothelial, lymphatic tumor

Many adenocarcinomas

Glioma, some carcinoma

Rare non-small cell lung, renal medullary carcinoma, lymphoma

Positive in non-GCT













Unknown

Unknown



+

+

Carcinomas

Carcinomas

Breast and urothelial carcinoma, paraganglioma

Other trophoblastic tumors

Hepatocellular carcinoma, hepatoid carcinoma

Syncytiotrophoblasts, hepatocellular carcinoma, gastric carcinoma

Endometrial and ovarian cancer

Glioma, lung, head and neck cancer, cervical cancer

Lymphomas, mesenchymal tumors

Many carcinomas, melanoma

Prostate carcinoma

Many non-GCT tumors

+ (40–50% tumors) Many mesenchymal tumors









Spermatocytic tumor

+ (60% + tumors)





Variable





Nonseminomatous GCT GCNI S Seminoma EC YST Chorio Teratoma

Table 1 Immunohistochemical markers of germ cell tumors

20 Franto Francis and Ming Zhou

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

21

metastatic carcinomas. However, SALL4 can occasionally be positive in a subset of carcinomas such as hepatoid gastric adenocarcinomas [10], malignant rhabdoid tumors [11], urothelial carcinoma, and small cell carcinoma of lung [9]. Placental alkaline phosphatase (PLAP) is a membrane bound enzyme secreted by placental syncytiotrophoblasts. In tissues, it shows membranous and occasionally cytoplasmic positivity and stain GCNIS, seminoma and EC almost universally, and to a lesser extent in YST (80%) and teratomas (50%) [12]. It can also be positive in non-GCT tumors, including gastrointestinal, gynecologic, lung, and breast tumors. CD117 is a transmembrane glycoprotein tyrosine kinase receptor critical for the survival, proliferation and migration of normal primordial germ cells and genocytes [13]. It shows membranous and/or cytoplasmic reactivity and positively stains 95–100% of seminomas and GCNIS, and some nonseminomatous GCT [12]. CD30 is a member of the tumor necrosis factor receptor family. It shows membranous and Golgi immunoreactivity (paranuclear dot-like) in 95–100% of ECs [12]. However, it is also used to confirm the diagnosis in anaplastic large-cell lymphoma (ALCL), and to identify Reed-Sternberg cells in Hodgkin lymphoma, and other lymphomas. Alpha-fetoprotein (AFP) is an important plasma protein that is also produced in the embryonic yolk sac and fetal liver cells. Up to 85% of YSTs can be positive for AFP but this often is focal [12]. AFP can be negative in up to 60% of recurrent YSTs. Glypican-3 (GPC3) is a membranous heparan sulfate proteoglycan that is positive in most YSTs, usually staining tumor cells diffusely (in contrast to focal staining with AFP) [14, 15]. Its sensitivity for YST is higher than AFP. It also stains syncytiotrophoblasts in choriocarcinoma, although much more faintly. In addition to GCTs, GPC3 is also positive in hepatocellular carcinomas and other tumors such as cholangiocarcinoma [16]. Human chorionic gonadotropin (HCG) is a glycoprotein produced by placental syncytiotrophoblasts. All choriocarcinomas stain positive for the beta subunit of HCG, predominantly in syncytiotrophoblasts and intermediate trophoblasts [12]. GATA3 is a zinc finger transcription factor required in the development of many organ systems [17]. It shows nuclear staining in 100% of choriocarcinomas and also stains syncytiotrophoblasts in other GCTs such as seminoma as well as tumors derived from intermediate trophoblast such as ETT and PSTT [18, 19]. GATA3 expression is also found in the primitive patterns of YST, including reticular and microcystic, endodermal sinus and polyvesicular, but not in differentiated somatic patterns such as glandular and hepatoid [20].

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Sox2 (Sry box 2) is a transcription factor that forms a trimeric complex with OCT3/4 and controls the expression of a number of genes involved in embryonic development and is critical for early embryogenesis and embryonic stem cell pluripotency [21]. It is present in 95–100% of ECs but not in other GCTs [15, 22]. Sox17 (Sry box 17) is another transcription factor that shows strong nuclear positivity in >90% seminomas, variable nuclear staining in majority of yolk sac tumors and low expression in some teratomas [15, 22]. Podoplanin (D2-40) is a transmembrane mucoprotein normally expressed by fetal germ cells and, like CD117, is expressed in 95–100% of seminomas and GCNIS with strong and complete membranous staining [23, 24]. The expression, usually weak and partially membranous, is also found in small percentage of embryonal carcinoma [23, 24]. Pancytokeratin AE1/3 is positive in most EC, YST, and other nonseminomas, and is mostly negative, or shows focal staining, in seminomas [12]. 4.2 Immunohistochemical Profiles in GCT Subtypes

The immunohistochemical profiles of common GCT subtypes are summarized in Table 1 and briefly discussed. Germ cell neoplasia in situ (GCNIS): Positive for CD117, OCT3/ 4, D2-40, PLAP (Fig. 1). Seminoma: Positive for SALL4, CD117, OCT3/4, D2-40, PLAP, Sox17 (Fig. 2). Embryonal carcinoma: Positive for SALL4, OCT3/4, CD30, cytokeratins, PLAP, SOX2 (Fig. 3).

Fig. 2 (a) H&E section showing germ cell neoplasia in situ (GCNIS) (left) and a benign seminiferous tubule with active spermatogenesis (right). (b) Oct3/4 IHC stain highlights GCNIS (left) but is negative in normal seminiferous tubules (right)

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

23

Fig. 3 (a) H&E section showing a classic seminoma with characteristic lymphocytic infiltration. (b) CD117 showing cytoplasmic and membranous staining of seminoma cells. (c) OCT3/4 showing nuclear staining in seminoma cells. (d) Seminoma with syncytiotrophoblasts. (e) GATA3 staining syncytiotrophoblasts

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Fig. 4 (a) H&E section showing an embryonal carcinoma. (b) CD30 staining embryonal carcinoma cells diffusely. (c) H&E section showing a mixed germ cell tumor with embryonal carcinoma (lower) and teratoma (top). (d) SALL4 is positive in embryonal carcinoma (bottom) and negative in teratoma (top)

Yolk Sac tumor: Positive for SALL4, Glypican-3, AFP (~80% of cases), pancytokeratins; negative for OCT3/4, CD30, CD117/D2-40 (Fig. 4). Choriocarcinoma: Positive for β-HCG, GATA3, SALL4 (70% of cases), pancytokeratins; negative for OCT3/4, p63, CD30, CD117/D2-40. Teratoma: These are histomorphologically distinctive tumors, composed of disorganized collections of somatic adult type tissue derived from all three embryological germ cell layers. IHC staining is variable and depends on tissue types present. Positive for SALL4 and OCT3/4 in ~50% of cases, mostly in immature, fetal type tissue (Fig. 5). Spermatocytic tumor: Positive for CD117, SALL4; negative for Oct3/4, PLAP.

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men

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Fig. 5 (a) H&E section showing a yolk sac tumor with microcystic pattern. (b) Alfa-fetoprotein (AFP) staining the tumor cells

Germ cell tumor after chemotherapy. Adjunct chemotherapy or radiation therapy to the retroperitoneal lymph nodes is prescribed for some patients with GCT. Following therapy, the tumor may exhibit significant morphological changes [25]. IHC may be required to identify residual GCT. However, some immunohistochemical markers may show decrease or loss of staining following chemotherapy. For example, expression of CD30 and Oct3/4 was reduced or lost in cases of residual EC in postchemotherapy specimens [26, 27]. 4.3 Use of Immunohistochemistry in Specific Clinical Scenarios

1. Diagnosing germ cell neoplasia in situ (GCNIS). The in situ form of testicular germ cell tumor, GCNIS, is present in about 90% of invasive GCT [28]. It can also be identified in testes that do not harbor invasive tumor and it is important to identify GCNIS in this setting as a high percentage will progress to invasive GCT [29]. While histologic features (large atypical cells with prominent nucleoli within atrophic seminiferous tubules with hyalinized basement membranes) are usually helpful, IHC can confirm the diagnosis. Most stains that are positive in seminomas also stain GCNIS, including OCT3/4, CD117, podoplanin, and PLAP. The so-called atypical germ cells, seen in the testes of patients with cryptorchidism, infertility, and sex maldevelopment, may show morphologic resemblance to GCNIS. In most instances, OCT3/4, PLAP, and D2-40 will resolve this diagnostic ambiguity as they are positive in GCNIS but do not stain enlarged, multinucleated, or otherwise atypical germ cells. An important pitfall is germ cells with delayed maturation described in patients with undervirilization syndromes may closely resemble GCNIS morphologically and immunohistochemically (positive for OCT3/4, podoplanin, PLAP).

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Diagnosis of GCNIS in this situation should be made with caution and requires a seminoma-like cytomorphology, basilar location and nondiffuse distribution of the atypical nuclei [30]. 2. Differentiating seminoma from EC and YST in orchiectomy specimens. Making this distinction is important because nonseminomatous tumors may be managed with different protocols from seminomas following orchiectomy. In most cases, seminomas are easily distinguished from EC and YST based on morphology alone. Occasionally, IHC is needed to distinguish variant morphologic patterns of seminomas from other GCT subtypes. Seminomas with atypical features with increased cellularity, atypical nuclear features and increased mitosis may be mistaken for EC. Stains specific for EC but negative in seminomas (e.g., CD30, SOX2), or vice versa (positive in seminomas but negative in EC, such as CD117 and SOX17), may be used to separate these tumors. The solid subtype of YST can also show morphological overlap with seminoma and stains including glypican 3 (positive in YST, negative in seminoma) and OCT3/4 and CD117 (positive in seminoma, negative in YST) can establish the correct diagnosis. 3. Identifying small foci of YST in mixed germ cell tumors. This is a relatively common diagnostic scenario when a patient presents with elevated serum AFP. Small or minute foci of YST can be obscured by predominant non-YST components in a mixed germ cell tumor, and use of YST-specific immunomarkers like glypican 3 and AFP may help delineate theses foci. 4. Differentiating GCT from Sertoli cell tumor. Sertoli cell tumors are biologically distinct from GCTs. The vast majority (~90%) behave in a benign fashion, and the malignant tumors are not responsive to radiation or chemotherapy and are often treated with retroperitoneal lymphadenectomy [31]. Solid variant of Sertoli cell tumor may be mistaken for seminoma or solid YST. In addition, Sertoli cell tumors growing in a corded/trabecular pattern may resemble reticular pattern of YST. In most cases, SALL4 staining will resolve the issue as it stains most GCTs but is negative in Sertoli cell tumors. Similarly, alpha-inhibin and calretinin stain Sertoli cell tumors and are nonreactive in GCTs with exception of syncytiotrophoblasts present in GCTs that can stain positively with these markers. 5. Confirming GCT at metastatic sites and distinguishing from metastatic high-grade carcinoma. While the clinical context and histomorphology are often sufficient to make the correct diagnosis of metastatic GCT at metastatic sites and in RPLND specimens, IHC may be needed for confirmation in certain challenging situations. Immunoreactivity with SALL4

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combined with OCT3/4 helps diagnose the vast majority of GCTs, and positive staining with subtype specific markers (CD117, CD30, GPC3, etc.) establishes the specific subtype. Alternatively, markers specific for the metastatic carcinoma (s) in the differential diagnosis can help rule-in and establish the diagnosis of a metastatic non-GCT tumor. An important caveat, as mentioned earlier, is that SALL4 can be occasionally positive in a subset of carcinomas such as hepatoid gastric adenocarcinomas [10], malignant rhabdoid tumors [11], urothelial carcinoma and small cell carcinoma of lung. Caution is also needed in interpreting cytokeratin cocktails such as AE1/3 and Cam 5.2 in this context, as they are also positive in EC, YST, and choriocarcinomas. For cancer of unknown primary, an IHC panel comprising SALL4, OCT3/4, CD117, CD30, GPC3, and so on can correctly diagnose the vast majority of GCT subtypes. 6. Confirming lymphovascular invasion (LVI). LVI is an important histologic feature to report in GCTs as its presence increases the likelihood of presence of metastasis. The presence of LVI in an otherwise organ-confined GCT up-stages the pathologic stage pT1 to pT2. Identification of LVI is difficult morphologically and LVI must be distinguished from tumor retraction artifact as well as from artifactual displacement of tumor cells into vascular space during processing. In the former situation, immunostains that highlight vascular endothelium such as CD31, CD34, and podoplanin can confirm the identity of a true vessel. 7. Differentiating GCTs from lymphomas. Seminomas exhibiting intertubular growth and some ECs may resemble certain lymphomas such as anaplastic large cell lymphoma (ALCL) and NK/ T-cell lymphoma. SALL4, in combination with lymphoid/lymphoma markers (CD45, CD20, CD3, CD79a, PAX5) can reliably distinguish between these tumors. A pitfall is that some lymphomas such as ALCL, lymphoblastic lymphomas and myeloid leukemia can occasionally express SALL4 [32]. 8. Differentiating spermatocytic tumor from seminoma. Spermatocytic tumor is a distinctive type of germ cell tumor usually seen in older patients than seminoma and other GCTs, and exhibits characteristic histomorphology (three cell types, nuclei with “spireme” chromatin, absent lymphocytic infiltrate, no associated GCNIS). It behaves in a benign fashion in the vast majority of cases. In difficult cases with some morphological similarity with seminomas, spermatocytic tumors can be distinguished from the latter by their negative immunoreactivity with OCT3/4 and PLAP. In contrast, seminomas are universally positive for these two markers. CD117 and SALL4 stains are not helpful as they are positive in both tumors.

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9. Identifying epithelioid trophoblastic tumor (ETT) and placental site trophoblastic tumor (PSTT) in mixed GCTs, and differentiating ETT from secondary squamous carcinoma arising in a teratoma. Nonchoriocarcinomatous trophoblastic tumors are rare in men and usually seen in the postchemotherapy setting. They include cystic trophoblastic tumor (CTT), epithelioid trophoblastic tumor, and placental site trophoblastic tumors. Because of some morphologic similarity between these entities, and sometimes with other GCTs and malignancies, IHC stains such as inhibin, human placental lactogen (hPL), and p63 are often used in their diagnostic workup. Both ETT and PSTT are positive for inhibin. ETT also shows nuclear positivity for p63 and is negative for HPL, whereas PSTT shows the reverse staining pattern and is positive for hPL and negative for p63. ETT can be differentiated from a squamous carcinoma arising secondarily in a teratoma by its positivity for inhibin in addition to specific histologic features (absent keratin and cell junctions).

5

Note 1. Immunohistochemistry slide prepping (see Subheading 3.2) and staining (see Subheading 3.3) are performed on Ventana BenchMark XT automated stainer according to the BenchMark XT IHC/ISH Staining Module protocols.

References 1. Ulbright TM, Amin MB, Balzer B, Derney DM, Epstein JI, Guo C, Idrees MT, Looijenga LHJ, Paner G, Rajpert-De Meyts E, Skakkebaek NE, Tickoo SK, Yilmaz A, Oosterhuis JW (2016) Germ cell tumours. In: Moch HP, Ulbright TM, Reuter V (eds) WHO classification of tumours of the urinary system and male genital organs. IARC Press, Lyon, pp 189–198 2. Rajpert-De Meyts E, McGlynn KA, Okamoto K, Jewett MA, Bokemeyer C (2016) Testicular germ cell tumours. Lancet 387(10029):1762–1774 3. Albany C, Einhorn LH (2013) Extragonadal germ cell tumors: clinical presentation and management. Curr Opin Oncol 25 (3):261–265 4. Nichols J, Zevnik B, Anastassiadis K, Niwa H, Klewe-Nebenius D, Chambers I et al (1998) Formation of pluripotent stem cells in the mammalian embryo depends on the POU transcription factor Oct4. Cell 95(3):379–391

5. Looijenga LH, Stoop H, de Leeuw HP, de Gouveia Brazao CA, Gillis AJ, van Roozendaal KE et al (2003) POU5F1 (OCT3/4) identifies cells with pluripotent potential in human germ cell tumors. Cancer Res 63(9):2244–2250 6. Cheng L, Thomas A, Roth LM, Zheng W, Michael H, Karim FW (2004) OCT4: a novel biomarker for dysgerminoma of the ovary. Am J Surg Pathol 28(10):1341–1346 7. Abiko K, Mandai M, Hamanishi J, Matsumura N, Baba T, Horiuchi A et al (2010) Oct4 expression in immature teratoma of the ovary: relevance to histologic grade and degree of differentiation. Am J Surg Pathol 34 (12):1842–1848 8. Yang J, Chai L, Fowles TC, Alipio Z, Xu D, Fink LM et al (2008) Genome-wide analysis reveals Sall4 to be a major regulator of pluripotency in murine-embryonic stem cells. Proc Natl Acad Sci U S A 105(50):19756–19761

Clinical Applications of Immunohistochemistry in Germ Cell Tumors in Men 9. Miettinen M, Wang Z, McCue PA, SarlomoRikala M, Rys J, Biernat W et al (2014) SALL4 expression in germ cell and non-germ cell tumors: a systematic immunohistochemical study of 3215 cases. Am J Surg Pathol 38 (3):410–420 10. Ushiku T, Shinozaki A, Shibahara J, Iwasaki Y, Tateishi Y, Funata N et al (2010) SALL4 represents fetal gut differentiation of gastric cancer, and is diagnostically useful in distinguishing hepatoid gastric carcinoma from hepatocellular carcinoma. Am J Surg Pathol 34(4):533–540 11. Venneti S, Le P, Martinez D, Xie SX, Sullivan LM, Rorke-Adams LB et al (2011) Malignant rhabdoid tumors express stem cell factors, which relate to the expression of EZH2 and Id proteins. Am J Surg Pathol 35 (10):1463–1472 12. Ulbright TM. Germ cell tumors of the gonads: a selective review emphasizing problems in differential diagnosis, newly appreciated, and controversial issues. Mod Pathol 2005;18 Suppl 2: S61–S79 13. Unni SK, Modi DN, Pathak SG, Dhabalia JV, Bhartiya D (2009) Stage-specific localization and expression of c-kit in the adult human testis. J Histochem Cytochem 57(9):861–869 14. Ota S, Hishinuma M, Yamauchi N, Goto A, Morikawa T, Fujimura T et al (2006) Oncofetal protein glypican-3 in testicular germ-cell tumor. Virchows Arch 449(3):308–314 15. Sugimura J, Foster RS, Cummings OW, Kort EJ, Takahashi M, Lavery TT et al (2004) Gene expression profiling of early- and late-relapse nonseminomatous germ cell tumor and primitive neuroectodermal tumor of the testis. Clin Cancer Res 10(7):2368–2378 16. Wang SK, Zynger DL, Hes O, Yang XJ (2014) Discovery and diagnostic value of a novel oncofetal protein: glypican 3. Adv Anat Pathol 21 (6):450–460 17. Ordonez NG (2013) Value of GATA3 immunostaining in tumor diagnosis: a review. Adv Anat Pathol 20(5):352–360 18. Miettinen M, McCue PA, Sarlomo-Rikala M, Rys J, Czapiewski P, Wazny K et al (2014) GATA3: a multispecific but potentially useful marker in surgical pathology: a systematic analysis of 2500 epithelial and nonepithelial tumors. Am J Surg Pathol 38(1):13–22 19. Mirkovic J, Elias K, Drapkin R, Barletta JA, Quade B, Hirsch MS (2015) GATA3 expression in gestational trophoblastic tissues and tumours. Histopathology 67(5):636–644 20. Schuldt M, Rubio A, Preda O, Nogales FF (2016) GATA binding protein 3 expression is present in primitive patterns of yolk sac tumours but is not expressed by differentiated variants. Histopathology 68(4):613–615

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21. Avilion AA, Nicolis SK, Pevny LH, Perez L, Vivian N, Lovell-Badge R (2003) Multipotent cell lineages in early mouse development depend on SOX2 function. Genes Dev 17 (1):126–140 22. de Jong J, Stoop H, Gillis AJ, van Gurp RJ, van de Geijn GJ, Boer M et al (2008) Differential expression of SOX17 and SOX2 in germ cells and stem cells has biological and clinical implications. J Pathol 215(1):21–30 23. Lau SK, Weiss LM, Chu PG (2007) D2-40 immunohistochemistry in the differential diagnosis of seminoma and embryonal carcinoma: a comparative immunohistochemical study with KIT (CD117) and CD30. Mod Pathol 20 (3):320–325 24. Yu H, Gibson JA, Pinkus GS, Hornick JL (2007) Podoplanin (D2-40) is a novel marker for follicular dendritic cell tumors. Am J Clin Pathol 128(5):776–782 25. Berney DM, Lu YJ, Shamash J, Idrees M (2017) Postchemotherapy changes in testicular germ cell tumours: biology and morphology. Histopathology 70(1):26–39 26. Berney DM, Shamash J, Pieroni K, Oliver RT (2001) Loss of CD30 expression in metastatic embryonal carcinoma: the effects of chemotherapy? Histopathology 39(4):382–385 27. Sung MT, Jones TD, Beck SD, Foster RS, Cheng L (2006) OCT4 is superior to CD30 in the diagnosis of metastatic embryonal carcinomas after chemotherapy. Hum Pathol 37 (6):662–667 28. Jacobsen GK, Henriksen OB, von der Maase H (1981) Carcinoma in situ of testicular tissue adjacent to malignant germ-cell tumors: a study of 105 cases. Cancer 47(11):2660–2662 29. Skakkebaek NE, Berthelsen JG, Visfeldt J (1981). Clinical aspects of testicular carcinoma-in-situ. Int J Androl 4 Suppl s4:153–160 30. Cools M, van Aerde K, Kersemaekers AM, Boter M, Drop SL, Wolffenbuttel KP et al (2005) Morphological and immunohistochemical differences between gonadal maturation delay and early germ cell neoplasia in patients with undervirilization syndromes. J Clin Endocrinol Metab 90(9):5295–5303 31. Mosharafa AA, Foster RS, Bihrle R, Koch MO, Ulbright TM, Einhorn LH et al (2003) Does retroperitoneal lymph node dissection have a curative role for patients with sex cord-stromal testicular tumors? Cancer 98(4):753–757 32. Cui W, Kong NR, Ma Y, Amin HM, Lai R, Chai L (2006) Differential expression of the novel oncogene, SALL4, in lymphoma, plasma cell myeloma, and acute lymphoblastic leukemia. Mod Pathol 19(12):1585–1592

Chapter 3 Molecular Characterization of Testicular Germ Cell Tumors Using Tissue Microdissection Liang Cheng, Steven A. Mann, Antonio Lopez-Beltran, Michal Chovanec, Matteo Santoni, Mingsheng Wang, Costantine Albany, Nabil Adra, Darrell D. Davidson, Alessia Cimadamore, Rodolfo Montironi, and Shaobo Zhang Abstract Testicular germ cell tumors are among the most common malignancies seen in children and young adults. Genomic studies have identified characteristic molecular profiles in testicular cancer, which are associated with histologic subtypes and may predict clinical behavior including treatment responses. Emerging molecular technologies analyzing tumor genomics, transcriptomics, and proteomics may now guide precision management of testicular tumors. Laser-assisted microdissection methods such as laser capture microdissection efficiently isolate selected tumor cells from routine pathology specimens, avoiding contamination from nontarget cell populations. Laser capture microdissection in combination with next generation sequencing makes precise high throughput genetic evaluation effective and efficient. The use of laser capture microdissection (LCM) for molecular testing may translate into great benefits for the clinical management of patients with testicular cancers. This review discusses application protocols for laserassisted microdissection to investigate testicular germ cell tumors. Key words Testicular cancer, Germ cell tumors, Tissue microdissection, Laser capture microdissection, Molecular genetics

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Introduction Molecular testing has become routine for the clinical workup of many different malignancies. EGFR, ALK, ROS1, and BRAF testing is now standard for metastatic non–small cell lung carcinoma [1, 2]. Assessment of the immune checkpoint pathway is incorporated in groundbreaking immunotherapies for melanoma and urinary bladder carcinomas. Next-generation sequencing technologies have revolutionized the molecular study of tumor cells and their regulatory elements. Molecular data are being mined for countlesstumor-associated alterations from a chromosomal level

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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down to protein product analysis [3, 4]. The Human Genome Project completion led to the identification of biomarkers for precise diagnosis of disease and personalized management of patients with cancer [5–8]. With so much to be gained by molecular analysis of malignant tumors, capturing adequate and appropriate molecular tumor data has become a priority. The need to employ precise acquisition techniques is further highlighted by small biopsy sizes and tumors with heterogeneous background tissue. Laser-assisted microdissection (LMD) procedures are capable of isolating pure populations by collecting small clusters or single tumor cells from tissue sections and cytology specimens [9–12]. While some LMD procedures require the use of antibodies or genetic labels for visualization, the commonly used laser capture microdissection (LCM) technique can be performed without these methods [13]. These platforms enable the direct dissection of targeted cells within a tumor section using a low-power infrared laser to melt a thermoplastic film around the cells. The film is then peeled away with the captured cells, permitting genetic (DNA), transcriptomic (RNA), and proteomic (protein) examination of enriched, selective tumor populations [14, 15] (Fig. 1). Testicular germ cell tumors are among the most common malignancies seen in children and young adults. These tumors are often composed of mixed subtypes, manifesting a wide spectrum of histologic morphology, clinical behavior, and responses to treatment [16, 17]. These clinical features suggest diverse genetic alterations and expression profiles driving such heterogeneity [18, 19]. Heterogeneity is also a common phenomenon in other solid tumors. Significant differences in phenotypic appearance, genomic expression profile, and biological behavior are found to exist among tumors of the same organ and even within the same tumor [20–22]. The heterogeneity present in solid tumors reflects diversity between cell populations at the molecular level. Tumor heterogeneity has profound biologic and therapeutic implications [23]. Mitchell et al. examined and compared normal human fetal testis cells with intratubular germ cell neoplasia cells and with seminoma cells. Intratubular germ cell neoplasia protein expression differed significantly from cells compared to normal tissue [24]. Kristensen et al. [25] used microdissection to study carcinoma in situ cells, finding deficient levels of methylcytosine compared to normal epithelium in the same tissue. This difference suggests that the hypomethylated genome contributes to early phenotypic heterogeneity and may facilitate invasive potential. In general, germ cell tumors are highly sensitive to platinumbased chemotherapy with high cure rates [16, 17]. This therapeutic sensitivity has been attributed to the intact TP53 gene, reciprocal loss of heterozygosity, and high mitochondrial priming for apoptosis. However, some germ cell tumor cases are chemoresistant, and

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Fig. 1 Laser microdissection is a powerful tool for isolating the pure cell populations from heterogeneous background. These technologies are compatible with a wide variety of specimens and the harvested cells can be used for many molecular investigations including DNA, RNA, protein, and microRNA-based analyses. LCM laser-capture microdissection

about 10% of patients with metastatic testicular germ cell tumor die of the disease. Taylor-Weiner et al. reported cases of chemoresistant metastatic testicular germ cell tumors and incurable primary mediastinal germ cell tumors characterized by loss of the pluripotency and apoptotic regulators, NANOG and POU5F1 (OCT3/4), a distinct difference from the chemosensitive cases tested [26]. After analyzing over one hundred testicular germ cell tumors that relapsed after chemotherapy, Necchi et al. found KRAS mutations in about half of the cases [27]. This trend was true for both seminomas and nonseminomas. Alterations in the RAS-RAF and cell cycle pathways were common in these relapsed germ cell tumors, but microsatellite-instability was rare [27]. Other studies have found in metastatic germ cell tumors new genomic alterations that are not present in their primary testicular tumors [28].

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Recent advances in next-generation sequencing technology have accelerated exploration of genetic mutations and gene expression alterations involved in testicular cancers. As part of ongoing Cancer Genome Atlas studies, Shen et al. found that testicular germ cell tumors have relatively low mutational burdens with few somatic mutations compared to tumors at other locations [29]. Notwithstanding, these tumors have high aneuploidy, and among germ cell subtypes there are marked differences in epigenomic profiles, DNA methylation and microRNA expression levels. Most significant for possible therapeutic advances, KIT, KRAS, and NRAS mutations are seen exclusively in testicular germ cell tumors with seminoma components [30]. Kernek et al. used LCM to demonstrate that among cases of mixed testicular germ cell tumor with a mature teratoma component, over 70% with allelic loss had identical losses in both teratomatous and immature germ cell components of the tumor [31]. Further studies are needed to address similarities and differences in the molecular profiles of seminomatous and nonseminomatous components within mixed germ cell tumors. Due to tumor heterogeneity, widespread implementation of precision medicine is challenged by the additional information needed to encompass this phenotypic diversity. Moreover, tumoral tissue must be carefully selected to avoid contamination by nontarget cell stromal and inflammatory populations. LCM is a useful tool to address heterogeneity for tumor sample molecular testing (Fig. 2). LCM has been fruitfully used in a variety of molecular studies, including the detection of oncologic driver mutations. LCM studies led to the discovery of KRAS mutations in endometriosis [32]. This review will focus on the technical aspects for applications of LCM in the study of testicular cancers.

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Laser Microdissection Technology: An Overview Genetic material from off target cells can mask pertinent findings or give conflicting data. LCM enables isolation of individual cell populations from a complex mixture of various cell types. LCM harvests the pure cell population of interest, dramatically increasing the sensitivity, and accuracy of downstream molecular assays. Currently there are two LCM methods. One uses a thermal-sensitive membrane made adherent to cells by an infrared (IR) laser (PixCellArcturus), the so-called cold laser system (Fig. 3a). The other uses a foil-cutting ultraviolet (UV) laser system (PALM [Carl Zeiss Micro Imaging GmbH, Bernried, Germany] and Leica [Leica Microsystems, Wetzlar, Germany]) (Fig. 4a) [12]. The IR cold laser system microdissects the target cells by capturing them on a thermoplastic ethylene vinyl acetate polymer film, mounted on the bottom of a plastic cap. The cap is placed in contact with the tissue, and a focused laser pulse activates the film’s adhesiveness in an area of

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Fig. 2 Laser microdissection of tumor from patients with malignant mixed germ cell tumor of the testis. Hematoxylin-eosin-stained sections showed mature teratoma (a), immature teratoma (d), embryonal carcinoma (g), yolk sac tumor (j), choriocarcinoma (m), and seminoma (p) before laser microdissection. Panels b, e, h, k, n, g, showed corresponding tumors after microdissection. Panels c, f, i, l, o, and r showed microdissected tissues ready for DNA extractions (Adapted with permission from Kernek et al. [31])

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Fig. 3 Arcturus laser-capture microdissection (infrared LCM) system (a). LCM is used to isolate cells that are distinguished by morphology, histologic stains, or labeling by fluorescent antibodies or DNA probes from a heterogeneous population of cells in a tissue section. The system places the cap with transfer film directly onto the area of interest. Near-infrared laser shot through the cap melts the film, and attaches to the cells of interest. The cells are removed from slide by lifting the cap (b)

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Fig. 4 Leica LMD6000 laser-capture microdissection (UV LCM) (a). The system uses ultraviolet laser to cut the edge around morphologically or staining distinguished target cell groups on the face down foiled slides. The laser beam is moved by optics to ensure the precision. The harvested tissue transported by gravity without contact (b)

interest. The resolution may be as low as 7.5 to 35 μm, depending on the instrument and software configuration (Fig. 3b). The UV laser cutting system uses a narrow UV laser to cut around target cell groups in tissue sections mounted on light sensitive foil-coated glass slides. (Fig. 4b) Both technologies exist in both manual and automated (robotic) platforms. One manufacturer, Arcturus, offers systems that combine both IR cold laser-capture and UV

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Fig. 5 A current microdissection system offered by Arcturus XT™ combining infrared (IR) laser-enabled lasercapture microdissection and ultraviolet (UV) laser cutting in a single platform. The IR laser captures single cells and small numbers of cells, whereas UV laser delivers unprecedented speed and precision optimal for dense tissue structures and for capturing large numbers of cells

laser-cutting in a single platform (Fig. 5). The IR cold laser is best suited to capture single cells and small groups of cells, whereas the UV laser-cutting device delivers unprecedented speed and precision for dense tissue structures and when capturing large numbers of cells. LCM is compatible with most stains and tissue preservation methods including formalin-fixed, paraffin-embedded tissue sections, frozen sections, and cytology preparations [33–47]. 2.1 LCM, Major Components

2.2 Types of Specimens Compatible with LCM (Fig. 1)

l

Light microscope.

l

Laser source and laser control unit.

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Electronically or manually operated slide stage.

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CMOS/CCD Camera to relay real-time microscopic video and to save images.

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Computer system with color monitor.

l

Formalin-fixed, paraffin-embedded specimens. – Neutral buffered formalin fixation is acceptable; however, it causes extensive cross-linking of nucleic acids and protein, which makes polymerase chain reaction (PCR) amplification more difficult. – Over-fixation degrades macromolecule quality. – Biopsies should fix 600 counts/s. 8. Make sure not to have cell clumps in the samples used for flow cytometry. In case of remaining cell clumps, use 70 μm preseparation filters. Vortex before every measurement. 9. Calibrate the flow cytometer before every measurement by using calibration beads according to the manufacturer’s protocol. 10. The above-described methods used to study cell cycle distribution and apoptosis induction include fluorochromes (PI, Annexin V-FITC), so avoid exposure to light. 11. Instead of PI, 7-AAD (7-amino-actinomycin D) can be also used to stain DNA. 12. Since the apoptosis assay uses two fluorochromes (PI, FITC), compensation according to the manufacturer’s protocol of the flow cytometry is recommended. For this purpose, use a treated positive control (e.g., cells treated with doxorubicin hydrochloride, staurosporine, or etoposide) for single staining with Annexin V-FITC or PI. Treated but unstained cells will be also necessary. References 1. Cheng L, Albers P, Berney DM, Feldman DR, Daugaard G, Gilligan T, Looijenga LHJ (2018) Testicular cancer. Nat Rev Dis Primers 4(1):29. https://doi.org/10.1038/s41572018-0029-0 2. Feldman DR (2015) Update in germ cell tumours. Curr Opin Oncol 27(3):177–184. https://doi.org/10.1097/CCO. 0000000000000179 3. Walker MC, Povey S, Parrington JM, Riddle PN, Knuechel R, Masters JR (1990) Development and characterization of cisplatin-resistant human testicular and bladder tumour cell lines. Eur J Cancer 26(6):742-747. https://doi.org/ 10.1016/0277-5379(90)90133-e 4. Jacobsen C, Honecker F (2015) Cisplatin resistance in germ cell tumours: models and mechanisms. Andrology 3(1):111–121. https://doi.org/10.1111/andr.299

5. Dasari S, Tchounwou PB (2014) Cisplatin in cancer therapy: molecular mechanisms of action. Eur J Pharmacol 740:364–378. https://doi.org/10.1016/j.ejphar.2014.07. 025 6. Piulats JM, Jimenez L, Garcia del Muro X, Villanueva A, Vinals F, Germa-Lluch JR (2009) Molecular mechanisms behind the resistance of cisplatin in germ cell tumours. Clin Transl Oncol 11(12):780–786. https:// doi.org/10.1007/s12094-009-0446-3 7. Mayer F, Honecker F, Looijenga LH, Bokemeyer C (2003) Towards an understanding of the biological basis of response to cisplatin-based chemotherapy in germ-cell tumors. Ann Oncol 14(6):825–832. https:// doi.org/10.1093/annonc/mdg242 8. Looijenga LH, Gillis AJ, Stoop H, Biermann K, Oosterhuis JW (2011) Dissecting the molecular pathways of (testicular) germ cell

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tumour pathogenesis; from initiation to treatment-resistance. Int J Androl 34(4 Pt 2): e234–e251. https://doi.org/10.1111/j. 1365-2605.2011.01157.x 9. Oronsky B, Ray CM, Spira AI, Trepel JB, Carter CA, Cottrill HM (2017) A brief review of the management of platinum-resistant-platinum-refractory ovarian cancer. Med Oncol 34 (6):103. https://doi.org/10.1007/s12032017-0960-z 10. Zhu H, Luo H, Zhang W, Shen Z, Hu X, Zhu X (2016) Molecular mechanisms of cisplatin resistance in cervical cancer. Drug Des Devel Ther 10:1885–1895. https://doi.org/10. 2147/DDDT.S106412 11. Chen X, Lu P, Wu Y, Wang DD, Zhou S, Yang SJ, Shen HY, Zhang XH, Zhao JH, Tang JH (2016) MiRNAs-mediated cisplatin resistance in breast cancer. Tumour Biol 37 (10):12905–12913. https://doi.org/10. 1007/s13277-016-5216-6 12. Chen Y, Gao Y, Zhang K, Li C, Pan Y, Chen J, Wang R, Chen L (2015) MicroRNAs as regulators of cisplatin resistance in lung cancer. Cell Physiol Biochem 37(5):1869–1880. https:// doi.org/10.1159/000438548 13. Skowron MA, Melnikova M, van Roermund JGH, Romano A, Albers P, Thomale J, Schulz WA, Niegisch G, Hoffmann MJ (2018) Multifaceted mechanisms of cisplatin resistance in long-term treated urothelial carcinoma cell lines. Int J Mol Sci 19(2):590. https://doi. org/10.3390/ijms19020590 14. Sark MW, Timmer-Bosscha H, Meijer C, Uges DR, Sluiter WJ, Peters WH, Mulder NH, de Vries EG (1995) Cellular basis for differential sensitivity to cisplatin in human germ cell tumour and colon carcinoma cell lines. Br J Cancer 71(4):684–690. https://doi.org/10. 1038/bjc.1995.173 15. Galluzzi L, Senovilla L, Vitale I, Michels J, Martins I, Kepp O, Castedo M, Kroemer G (2012) Molecular mechanisms of cisplatin resistance. Oncogene 31(15):1869–1883. https://doi.org/10.1038/Onc.2011.384 16. Chin JL, Banerjee D, Kadhim SA, Kontozoglou TE, Chauvin PJ, Cherian MG (1993) Metallothionein in testicular germ cell tumors and drug resistance: clinical correlation. Cancer 72(10):3029–3035. https://doi.org/10. 1002/1097-0142(19931115)72:103.0.CO;2-6 17. Tuzel E, Yorukoglu K, Ozkara E, Kirkali Z (2015) Association of metallothionein expression and clinical response to cisplatin based chemotherapy in testicular germ cell tumors. Cent European J Urol 68(1):45–50. https:// doi.org/10.5173/ceju.2015.01.486

18. Meijer C, Timmer A, De Vries EG, Groten JP, Knol A, Zwart N, Dam WA, Sleijfer DT, Mulder NH (2000) Role of metallothionein in cisplatin sensitivity of germ-cell tumours. Int J Cancer 85(6):777–781. https://doi. org/10.1002/(SICI)1097-0215(20000315) 85:63.0.CO;2-D 19. Reed E (1998) Platinum-DNA adduct, nucleotide excision repair and platinum based anticancer chemotherapy. Cancer Treat Rev 24 (5):331–344. https://doi.org/10.1016/ S0305-7372(98)90056-1 20. Koberle B, Grimaldi KA, Sunters A, Hartley JA, Kelland LR, Masters JR (1997) DNA repair capacity and cisplatin sensitivity of human testis tumour cells. Int J Cancer 70(5):551–555. https://doi.org/10.1002/(SICI)1097-0215( 19970304)70:53.0. CO;2-G 21. Koberle B, Masters JR, Hartley JA, Wood RD (1999) Defective repair of cisplatin-induced DNA damage caused by reduced XPA protein in testicular germ cell tumours. Curr Biol 9 (5):273–276. https://doi.org/10.1016/ S0960-9822(99)80118-3 22. Koberle B, Brenner W, Albers A, Usanova S, Thuroff JW, Kaina B (2010) ERCC1 and XPF expression in human testicular germ cell tumors. Oncol Rep 23(1):223–227. https:// doi.org/10.3892/or_00000627 23. Usanova S, Piee-Staffa A, Sied U, Thomale J, Schneider A, Kaina B, Koberle B (2010) Cisplatin sensitivity of testis tumour cells is due to deficiency in interstrand-crosslink repair and low ERCC1-XPF expression. Mol Cancer 9:248. https://doi.org/10.1186/14764598-9-248 24. Kothandapani A, Sawant A, Dangeti VS, Sobol RW, Patrick SM (2013) Epistatic role of base excision repair and mismatch repair pathways in mediating cisplatin cytotoxicity. Nucleic Acids Res 41(15):7332–7343. https://doi.org/10. 1093/nar/gkt479 25. Honecker F, Wermann H, Mayer F, Gillis AJ, Stoop H, van Gurp RJ, Oechsle K, Steyerberg E, Hartmann JT, Dinjens WN, Oosterhuis JW, Bokemeyer C, Looijenga LH (2009) Microsatellite instability, mismatch repair deficiency, and BRAF mutation in treatment-resistant germ cell tumors. J Clin Oncol 27(13):2129–2136. https://doi.org/ 10.1200/JCO.2008.18.8623 26. Rudolph C, Melau C, Nielsen JE, Vile Jensen K, Liu D, Pena-Diaz J, Rajpert-De Meyts E, Rasmussen LJ, Jorgensen A (2017) Involvement of the DNA mismatch repair system in cisplatin sensitivity of testicular germ cell tumours. Cell Oncol (Dordr) 40(4):341–355.

Drug Testing to Treat Cisplatin-Resistant GCC Cell Lines https://doi.org/10.1007/s13402-017-03268 27. Spierings DC, de Vries EG, Vellenga E, de Jong S (2003) Loss of drug-induced activation of the CD95 apoptotic pathway in a cisplatinresistant testicular germ cell tumor cell line. Cell Death Differ 10(7):808–822. https:// doi.org/10.1038/sj.cdd.4401248 28. Spierings DC, de Vries EG, Stel AJ, te Rietstap N, Vellenga E, de Jong S (2004) Low p21Waf1/Cip1 protein level sensitizes testicular germ cell tumor cells to Fas-mediated apoptosis. Oncogene 23 (28):4862–4872. https://doi.org/10.1038/ sj.onc.1207617 29. Scudiero DA, Shoemaker RH, Paull KD, Monks A, Tierney S, Nofziger TH, Currens MJ, Seniff D, Boyd MR (1988) Evaluation of a soluble tetrazolium/formazan assay for cell growth and drug sensitivity in culture using human and other tumor cell lines. Cancer Res 48(17):4827–4833 30. Nicoletti I, Migliorati G, Pagliacci MC, Grignani F, Riccardi C (1991) A rapid and simple method for measuring thymocyte apoptosis by propidium iodide staining and flow cytometry. J Immunol Methods 139 (2):271–279. https://doi.org/10.1016/ 0022-1759(91)90198-O 31. Vermes I, Haanen C, Steffens-Nakken H, Reutelingsperger C (1995) A novel assay for apoptosis. Flow cytometric detection of phosphatidylserine expression on early

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apoptotic cells using fluorescein labelled Annexin V. J Immunol Methods 184 (1):39–51. https://doi.org/10.1016/00221759(95)00072-I 32. van Engeland M, Ramaekers FC, Schutte B, Reutelingsperger CP (1996) A novel assay to measure loss of plasma membrane asymmetry during apoptosis of adherent cells in culture. Cytometry 24(2):131–139. https://doi.org/ 10.1002/(SICI)1097-0320(19960601) 24:23.0.CO;2-M 33. Schutte B, Nuydens R, Geerts H, Ramaekers F (1998) Annexin V binding assay as a tool to measure apoptosis in differentiated neuronal cells. J Neurosci Methods 86(1):63–69. https://doi.org/10.1016/S0165-0270(98) 00147-2 34. van Engeland M, Nieland LJ, Ramaekers FC, Schutte B, Reutelingsperger CP (1998) Annexin V-affinity assay: a review on an apoptosis detection system based on phosphatidylserine exposure. Cytometry 31(1):1–9. https://doi.org/10.1002/(SICI)1097-0320( 19980101)31:13.0.CO;2R 35. Hoffmann MJ, Koutsogiannouli E, Skowron MA, Pinkerneil M, Niegisch G, Brandt A, Stepanow S, Rieder H, Schulz WA (2016) The new immortalized uroepithelial cell line HBLAK contains defined genetic aberrations typical of early stage urothelial tumors. Bladder Cancer 2(4):449–463. https://doi.org/10. 3233/BLC-160065

Chapter 9 Assessing Homologous Recombination and Interstrand Cross-Link Repair in Embryonal Carcinoma Testicular Germ Cell Tumor Cell Lines Francesca Cavallo, Cinzia Caggiano, Maria Jasin, and Marco Barchi Abstract Testicular germ cell tumors (TGCTs) are typically exquisitely sensitive to DNA interstrand cross-link (ICLs) agents. ICLs covalently link both strands of the DNA duplex, impeding fundamental cellular processes like DNA replication to cause cell death. A leading drug used for the treatment of TGCTs is cisplatin, which introduces ICLs and leads to formation of double strand breaks (DSBs), a DNA lesion that can be repaired in the S/G2 phases of the cell cycle by homologous recombination (HR, also termed homology-direct repair). Although most TGCTs respond to cisplatin-induced ICLs, a fraction is resistant to treatment. One proposed mechanism of TGCT resistance to cisplatin is an enhanced ability to repair DSBs by HR. Other than HR, repair of the ICL-lesions requires additional DNA repair mechanisms, whose action might also be implemented in therapy-resistant cells. This chapter describes GFP assays to measure (a) HR proficiency following formation of a DSB by the endonuclease I-SceI, and (b) HR repair induced by site-specific ICL formation involving psoralen. These experimental approaches can be used to determine the proficiency of TGCT cell lines in DSB repair by HR in comparison to HR repair of ICLs, providing tools to better characterize their recombination profile. Protocols of these assays have been adapted for use in Embryonal Carcinoma (EC) TGCT cell lines. Assays only require transient introduction of plasmids within cells, affording the advantage of testing multiple cell lines in a relatively short time. Key words TGCTs, Embryonal carcinoma (EC), Homologous recombination, Interstrand cross-link (ICL) repair, GFP reporters (DR-GFP, Tr-OriP-GFP), Psoralen

1

Introduction TGCTs are a curable cancer in a high percentage of patients with a standard treatment regimen. However, up to 30% of patients with a subtype of metastatic disease (i.e., type II nonseminomas) do not achieve durable or complete remission after initial chemotherapy as a result of either innate resistance or resistance acquired during treatment [1–3]. High-dose chemotherapy and surgery can overcome chemotherapy resistance in some patients; however, about 50% will ultimately succumb to the disease [3]. Multiple studies

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_9, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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have identified the presence of vascular invasion and the concomitant presence of an Embryonal Carcinoma (EC) cell component within the primary tumor as risk factors for tumor recurrence in stage-I nonseminoma TGCTs [4–6]. Therefore, understanding mechanisms behind EC responsiveness to chemotherapy is highly relevant in the clinic. The most effective drug used in the treatment of TGCTs is cisplatin [7], which causes the formation of interstrand cross-links (ICLs). ICLs are toxic to proliferating cells because they impede DNA replication and other vital cellular processes such as transcription. Moreover, ICLs causes gross chromosomal rearrangements, compromising genomic integrity. Repair of ICLs requires the coordinate action of several DNA repair pathways that recognize, process, and resolve the ICL adduct, including homologous recombination (HR), to allow replication fork restart and proliferation [8]. In replicating cells, a key intermediate in this process is a double-strand break (DSB), a lesion typically repaired in the S/G2 stages of the cell cycle by HR [8]. To facilitate the study of DSB repair events in mammalian cells, a number of reporters have been developed. The DR-GFP reporter, in which a DSB is introduced by an endonuclease (I-SceI, [9]) (Fig. 1), is perhaps the most widely used. Using this reporter, we previously demonstrated that EC cell lines that were more sensitive to cisplatin were also less proficient in repairing DSBs by HR [10]. This suggests that modulation of HR repair proficiency likely impacts the resistance of TGCTs to therapy. Notably, the genetic requirements for repairing frank DSBs induced by I-SceI do not fully overlap with that needed to repair DSBs that originate from ICL damage. Specifically, somatic cells from Fanconi anemia (FA) patients have only mild defects in the repair of I-SceI-induced DSBs even though they are extremely sensitive to ICL-inducing agents [11]. This was confirmed in a side-by-side comparison of I-SceI-induced HR using the DR-GFP assay with ICL-induced HR, measured using a derivative reporter, TR-OriP-GFP [12]. The precursor reporter, TR-GFP (Fig. 2ai), is a modified form of DR-GFP where the I-SceI site was replaced with a sequence that can bind a triplex-forming oligonucleotide (TFO) conjugated with psoralen (pso) at its 50 -end (pso-TFO) [12, 13]. ICL-induced HR repair was found to be enhanced by replication when TR-GFP was additionally modified to contain an Epstein Barr origin of replication (OriP), forming TR-OriP-GFP [12] (Fig. 2ai). In this case, the reporter replicates when EBNA1 is expressed (see Note 1). Following triplex formation between pso-TFO and TR-OriP-GFP (pso-TFO:TR-OriP-GFP), intercalation of psoralen into duplex DNA, and exposure to UVA light causes the formation of a site-specific ICL between psoralen and TR-OriP-GFP that can be repaired following DSB formation, by HR, leading to the generation of GFP+ cells [12, 13] (Fig. 2aii).

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p10% cell density. 3.1.2 Passaging Primed Pluripotency hiPSCs

1. Passage primed pluripotency hiPSCs when colonies occupy ~80% of the effective growth area. Correct passaging procedures and densities is important for experimental reproducibility. 2. Prewarm 10 mL PBS( ) in a 15-mL centrifuge tube for >5 min at 37  C in a water bath. Take 4 mL Accutase in a 15-mL centrifuge tube and equilibrate to room temperature for 5 min (do not prewarm Accutase in a 37  C water bath or a CO2 incubator). Add 2 μL of 50 mM Y27632 (ROCK inhibitor) to 10 mL mTeSR1 medium in a 15-mL centrifuge tube (see Note 3). In another 15-mL centrifuge tube, take 5 mL mTeSR1 without adding Y27632. Prewarm mTeSR1  Y27632 in a 37  C water bath for 5 min. 3. Aspirate old medium from a 10-cm dish cell culture and rinse cells once with prewarmed 10 mL PBS( ). Discard PBS( ) and add 4 mL Accutase to the dish. Place the dish in a CO2 incubator (tri-gas incubator works but not necessary) for ~5 min until cells detach from the bottom. Gently pipet cells up and down to make single-cell suspension (see Note 6). Transfer hiPSC single-cell suspension to the prewarmed tube containing 10 mL mTeSR1 + Y27632. Centrifuge cell suspension at room temperature, 300  g for 8 min. 4. Discard supernatant and resuspend cell pellet with 10 mL of the prewarmed mTeSR1 medium containing Y27632. Remove cell aggregates using a 40-μm cell strainer and determine cell density using a Coulter counter. Dilute cell suspension with prewarmed mTeSR1 + Y27632 to achieve 3.0  106 cells in 10 mL to inoculate a Matrigel-coated 10-cm dish. Place the inoculated dish in a tri-gas CO2 incubator (37  C, 6.5% O2, 5% CO2). Change medium (mTeSR1 without Y27632) every day.

3.2 Production of hPGCLCs

Initiate the following steps when hiPSC cells in a 10 cm dish (mTeSR1 medium, Matrigel-coated) reaches to ~80% confluency (approximately 107 cells). This is a 13-day protocol, and best yields of hPGCLCs can be achieved at Day 12 to Day 14.

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3.2.1 Day 1: Inoculation of Primed Pluripotency hiPSCs into MatrigelCoated 6-Well Cluster Plates

1. Thaw one aliquot of Matrigel on ice (see Note 1) and dilute in 25 mL of ice-cold DMEM/F12. Dispense diluted Matrigel to 6-well plates (1 mL/well). An aliquot of Matrigel is sufficient to coat 24 wells (four 6-well plates). Incubate plates at room temperature for at least 1 h (see Note 2). 2. Take 4 mL Accutase in a 15-mL centrifuge tube and equilibrate to room temperature for 5 min (do not prewarm Accutase in a 37  C water bath or a CO2 incubator). Prewarm 10 mL PBS ( ) in a 15-mL centrifuge tube for >5 min at 37  C in a water bath. Add 7 μL of 50 mM Y27632 (ROCK inhibitor) to 35 mL mTeSR1 medium in a 50-mL centrifuge tube (see Note 3). Make two tubes for total 70 mL mTeSR1 + Y27632 and prewarm the medium in a 37  C water bath for 15 min. 3. Aspirate old medium from a 10-cm dish cell culture and rinse cells once with prewarmed 10 mL PBS( ). Discard PBS( ) and add 4 mL Accutase to the dish. Place the dish in a CO2 incubator (tri-gas incubator works but not necessary) for ~4 min until cells detach from the bottom. Gently pipet cells up and down to make single-cell suspension (see Note 6). Transfer hiPSC single-cell suspension to the prewarmed tube containing 10 mL mTeSR1 + Y27632. Centrifuge cell suspension at room temperature, 300  g for 8 min. Discard supernatant and resuspend cell pellet with 10 mL prewarmed mTeSR1 + Y27632. 4. Filter cell suspension through a cell strainer (40 μm pore size) to remove cell aggregates and count cells using a Coulter counter (see Note 6). Dilute hiPSC single-cell suspension to 5.0  106 cells in 50 mL prewarmed mTeSR1 + Y27632. 5. Inoculate 2 mL cell suspension (2.0  105 cells) in each well of Matrigel-coated 6-well plates (4 plates, 24 wells). Make sure that cells are evenly distributed in each well (see Note 7). Place cell culture plates in a tri-gas CO2 incubator (37  C, 6.5% O2, 5% CO2).

3.2.2 Day 2: Medium Change

1. Change medium with 2 mL/well mTeSR1 (without Y27632) at 24 h after inoculation. Cells should be 20–30% confluent at this stage (Fig. 1).

3.2.3 Day 3 to Day 4: Conversion of the Primed Pluripotency State of mTeSR1-Maintained hiPSCs to the 4i-Naive (ERK-Independent) Pluripotent State

1. Prepare 4i complete naive pluripotency medium in a 50-mL centrifuge tube (see Subheading 2 and Note 8): 2. Warm 50 mL 4i complete medium in a 37  C water batch for 15 min. Change medium with prewarmed 4i complete medium (2 mL/well) at 48 and 72 h after inoculation (see Note 9). Cells will be ~50% and 100% confluent at Day 3 and Day 4, respectively (Fig. 1).

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Fig. 1 Cell densities of hiPSCs. Phase-contrast microscopy images of hiPSCs grown on Matrigel-coated 6-well plates are shown. Note that Day 2 hiPSCs (in mTeSR1 medium) show elongated shapes due to the presence of ROCK inhibitor. Cells rapidly proliferate in the 4i reprogramming medium and reach to confluency at Day 4. At Day 5, the culture of 4i-reprogrammed naive hiPSCs is overconfluent and ready for inoculation into AggreWell 400 plates 3.2.4 Day 5: Spin-EB Formation Using AggreWell 400 Microwell Plates

1. Prewarm 35 mL DMEM/F12 in a 50-mL centrifuge tube in a 37  C water bath for ~15 min before use. 2. Prepare an AggreWell 400 plate as follows. Add 5% (w/v) filtersterilized Pluronic F-127 detergent into eight active wells of an AggreWell400 plate (0.5 mL/well) (see Note 10). Centrifuge the plate at room temperature, 1000  g for 5 min. Inspect microwells with an inverted microscope to ensure the absence of air bubbles. Leave the plate for 30 min at room temperature and discard Pluronic F-127 solution from wells by pipetting. Then add prewarmed DMEM/F12 (2 mL/well) and centrifuge the plate at room temperature, 1000  g for 5 min. Inspect microwells to ensure the absence of air bubbles. Discard DMEM/F12 from wells of by pipetting and wash wells with prewarmed DMEM/F12 (2 mL/well) one more time. After removal of DMEM/F12, add 4i complete medium into wells (1 mL/well). Keep the remaining 4i complete medium in a 37  C water bath. Centrifuge the plate at room temperature, 1000  g for 5 min. Inspect microwells to ensure the absence of air bubbles. Place the detergent-coated AggreWell 400 plates with 1 mL/well 4i complete medium in a tri-gas incubator (37  C, 6.5% O2, 5% CO2) until inoculation of 4i naive hiPSCs. 3. This step and step 4 describe inoculation of 4i naive hiPSCs to AggreWell 400 plates. Take 13 mL Accutase in a 15-mL centrifuge tube and equilibrate to room temperature for 5 min (do not prewarm Accutase at 37  C). Prewarm 50 mL PBS( ) in a 50-mL centrifuge tube for >5 min at 37  C in a water bath. 4. Cells should be over-confluent on Day 5 (Fig. 1). Remove medium from naive hiPSC cell culture and rinse cells once with prewarmed 2 mL/well PBS( ). Discard PBS( ) and add 0.5 mL/well Accutase. Place cell culture plates in a CO2 incubator for ~4 min until cells detach from the bottom. Gently pipet cells up and down to make single-cell suspension (see Note 6).

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5. Transfer hiPSC single-cell suspension to prewarmed 20 mL 4i complete medium. Centrifuge cell suspension at room temperature, 300  g for 8 min. Discard supernatant and resuspend cell pellet with 5 mL prewarmed 4i complete medium. Filter cell suspension through a cell strainer (40 μm pore size) to remove cell aggregates. Wash cell strainer 3 times with 1 mL prewarmed 4i complete medium. Count cells using a Coulter counter. 6. Dilute single-cell suspension of 4i naive hiPSCs to 27.0–32.4  106 cells in 9 mL prewarmed 4i complete medium. Note that 4i complete medium already contains Y27632. 7. Take the AggreWell 400 plate containing 1 mL of 4i complete medium in each of the 8 active 8 wells from a tri-gas incubator (prepared in step 2). Inoculate 1 mL naive hiPSC suspension (3.0–3.6  106 cells/well) into each well. This cell density is critical (see Note 11). Make sure that cells are evenly distributed in each microwell by gently pipetting. 8. Centrifuge AggreWell 400 plates at room temperature, 100  g for 3 min. Then place the plates in a tri-gas CO2 incubator (37  C, 6.5% O2, 5% CO2). Do not disturb cells pelleted in microwells. Incubate cells for 24–30 h (see Note 12). 3.2.5 Day 6: Transfer of EBs to Low Attachment Plates for Rocking Culture

1. During Day 6 to Day 13, freshly prepare hPGCLC complete medium each day in a 50 mL centrifuge tube (see Subheading 2 and Note 13). 2. Confirm formation of EBs by phase contrast microscopy. Round contour of EBs should be visible when EBs are ready for harvesting (Fig. 2a, b). 3. Prewarm 5 mL hPGCLC basal medium and 20 mL hPGCLC complete medium for >5 min at 37  C in a water bath. 4. Carefully place a cell strainer upside down atop a 50 mL centrifuge tube (Fig. 2c). Pipet each well of the AggreWell 400 plates containing EBs very gently to detach EBs from microwells, and filter all contents of the plates through the cell strainer to remove cells that are not incorporated in EBs. Wash EBs retained on the cell strainer with 1 mL of prewarmed hPGCLC basal medium. Gently repeat wash 5 times. Place a fresh, empty 50 mL centrifuge tube onto the cell strainer and quickly invert the cell strainer with the empty centrifuge tube. Collect EBs to the centrifuge tube by adding 18 mL prewarmed hPGCLC complete medium. 5. Inoculate suspension of EBs into wells of a low-attachment 6-well plate (3 mL/well). Place the low-attachment plate on a seesaw-move rocker (such as Vari-Mix Rocker) in a trigas incubator (37  C, 6.5% O2, 5% CO2). Set the rocking speed at

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Fig. 2 EB formation in microwells of AggreWell 400 plates. (a, b) Round contour of EBs becomes visible with phase contrast microscopy 24–30 h after inoculation of 4i naive hiPSCs to AggreWell 400 plates in the 4i reprogramming medium. Panel (b) is an enlarged view of a part of panel (a). Cells not incorporated in EBs surround EBs to contrast the contour. Recover EBs from the plates after the contour become visible. (c) Setup of inverted cell strainer over a centrifuge tube. Load EBs on top of the inverted cell strainer and wash with hPGCLC medium to remove unincorporated cells. Recover EBs on the inverted cell strainer by putting a fresh centrifuge tube on the strainer (i.e., the normal position) and quickly inverse the strainer with the new tube

~20 turns/min (see Note 14). EBs should show spherical shape in homogeneous size (300–350 μm in diameter) (Fig. 3). 3.2.6 Day 7 to Day 14: Generation of hPGCLCs on the Surface of EBs

1. Freshly prepare 20 mL hPGCLC complete medium each day. Without rocking, EBs will sink down to the bottom of wells in ~1 min. Remove old medium without drying EBs (~0.2 mL/ well old medium may remain) and add fresh hPGCLC complete medium (3 mL/well). EBs increase their size from Day 6 until Day 12; thereafter, EBs tend to be disintegrated without increase size (Fig. 3). hPGCLCs will emerge as OCT4+/ CD38+ cells and detectable by FACS or immunohistochemical staining from Day 10. Robust production of hPGCLCs is typically achieved on Day 12 to Day 14. Extended EB culture in hPGCLC complete medium beyond Day 14 may cause rapid dismantling of EBs without further increasing hPGCLC yield.

3.3 Immunohistochemical Staining of EBs

1. To identify hPGCLCs as OCT4+ cells by FFPE immunohistochemistry, harvest EBs in a 1.5 mL low-bind microcentrifuge tube. Let EBs sink to the bottom or briefly centrifuge (~3 s) at room temperature and discard medium. Rinse EBs with ice-cold PBS( ).

3.3.1 Embedding EBs in Matrigel for FFPE Immunohistochemistry

2. Remove PBS( ) and incubate EBs in 1 mL of ice-cold 1% (w/v) sodium azide in PBS( ) for 5 min on ice (see Note 15). Let EBs sink to the bottom or briefly centrifuge (2 s) at room temperature. Discard supernatant, and rinse EBs with ice-cold PBS( ).

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Fig. 3 Phase contrast microscopic images of EBs: Time course. Bars indicate 400 μm. EBs increase their size from Day 6 until Day 12 while keeping the spherical shape. EBs at Day 13 and Day 14 may show distorted shapes and crack into smaller pieces

3. Thaw ~200 μL Matrigel on ice and transfer directly to the microcentrifuge tube containing EBs. Leave the tube at room temperature for ~10 min until Matrigel is solidified. 4. Fix EBs and Matrigel by adding 1 mL of 4% formaldehyde in PBS( ) and incubate at room temperature for 15 min with gentle rocking. Discard formaldehyde, and rinse EBs once with ice-cold PBS( ). Add 1 mL of 70% ethanol to each tube. Matrigel-embedded EBs can be stored in 70% ethanol at 4  C for 1 week.

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Fig. 4 Detection of hPGCLCs as OCT4+ cells by immunostaining (a–d) or as CD38+ cells by FACS (e, f). (a, b) FFPE immunohistochemistry of (a) Day 11 and

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5. Process the Matrigel-embedded EBs with the standard FFPE protocol. Prepare 5-μm thickness FFPE slides. Let slides dry overnight and store them at room temperature until immunostaining. 6. For immunostaining, completely dry slides in 56  C oven for at least 30 min. Deparaffinize and hydrate slides with xylenes and graded alcohol series. Then wash slides in tap water for 5 min. To inactivate endogenous peroxidases, incubate rehydrated slides with BLOXALL Blocking Solution at room temperature for 10 min. Then wash slides with PBS for 5 min. Block slides with Normal Horse Serum (or normal sera of other species generated secondary antibodies) at room temperature for 20 min. Incubate slides with an anti-OCT-3/4 goat primary antibody (see Note 16) diluted with 0.1% BSA in PBS( ) at 4  C overnight (optimize dilution and incubation conditions for each primary antibody). 7. After incubation with the primary antibody, wash slides with PBS( ) 3 times at room temperature, 5 min each. Incubate slides with the ImmPRESS Reagent Anti-Goat IgG (horseradish peroxidase–conjugated secondary antibody generated by horse) at room temperature for 30 min. Then wash slides with PBS( ) 3 times at room temperature, 5 min each. 8. Mix necessary amounts the ImmPACT DAB staining substrates immediately before use, and incubate slides in complete DAB staining solution at room temperature for 5 min. Monitor DAB staining using an inverted microscope and stop the reaction when OCT4+ cells are visualized at the outermost surface layer of EBs by quickly rinsing slides in flowing tap water. Dehydrate the slides with graded alcohol and xylene series. The slides are ready for laser-capture microdissection. Prepare ä

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Fig. 4 (continued) (b) Day 14 EBs. OCT4+ nuclei are stained with dark brown DAB dye. Note the location of hPGCLCs at the outermost surface layer of EBs. (c, d) Whole-mount EB staining of Day 14 EBs. Note that EBs have intrinsic background color (reddish brown), which appears without immunostaining. OCT4+ nuclei, stained with dark brown DAB dye, are visualized on the surface of EBs mostly as scattered dots. Occasionally, OCT4+ nuclei are highly packed to form protruding structures on the surface of EBs (arrow). (e, f) FACS enrichment of CD38+ hPGCLCs and CD38 EB cells. Dissociated EB cells were subjected to FACS with (e) or without (f) staining for plasma membrane CD38 using an APC-conjugated antibody. FITC filter was used to evaluate autofluorescence. A clear population of CD38+ hPGCLCs is visible. In this example, a cell population strongly expressing CD38 is collected as hPGCLCs

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permanent FFPE slides using Permount for high-resolution microscopic observations. Typical results are shown in Fig. 4a, b. 3.3.2 Detecting hPGCLCs on the Surface of EBs by Whole-Mount Immunohistochemistry

1. Harvest EBs from hPGCLC production culture into a 2 mL low-bind, round-bottom Eppendorf tube. Remove medium and briefly rinse EBs with 1 mL ice-cold PBS( )  1. Centrifuge ~3 s to pellet EBs. Carefully remove supernatant as much as possible using small-volume pipet tips. 2. Fix EBs in 4% formaldehyde (electron microscopy grade) in PBS( ) for 1 h at 4  C (1 mL per tube) with gently mixing. Wash EBs with Wash Buffer (ice-cold; 1 mL  3, 5 min each; see Note 17). EBs can be stored in 70% ethanol at 4  C. 3. Equilibrate EBs to RT for 5 min. If stored in 70% ethanol, wash EBs with Wash Buffer (RT; 1 mL  3, 5 min each). Permeabilize EBs in Permeabilization Buffer for 10 min at RT. Wash EBs with Wash Buffer 3 times (RT; 1 mL  3, 5 min each). 4. Block endogenous peroxidase by soaking EBs in BLOXALL (Vector SP-6000) (RT; 0.5 mL, 15 min). Wash EBs with Wash Buffer 3 times (RT; 1 mL  3, 5 min each). Then block EBs with normal horse serum (Vector MP-7405) (RT; 0.5 mL, 1 h). 5. Dilute 1 μL anti-Oct4 antibody (Santa Cruz sc-8629, Goat) in 0.5 mL Wash Buffer. Incubate EBs in the diluted anti-Oct4 antibody or Wash Buffer overnight at 4  C (~250 μL per tube with gentle mixing). 6. Wash EBs with Wash Buffer (RT; 1 mL  3, 5 min each). Take EBs in a fresh, low-bind 2 mL tube and incubate them in the ImmPRESS HRP-conjugated secondary antibody (horse antigoat IgG; Vector MP-7405) for 30 min at RT. Wash EBs with Wash Buffer 4 times (RT, 1 mL each; 5 min  3, 15 min  1). Transfer EBs to wells of a 48-WC plate containing 200 μL Wash Buffer per well. 7. Mix Vector ImmPACT DAB chromogen (50 μL) in DAB diluent (1 mL) on ice. Centrifuge the diluted DAB for 5 min maximum speed at 4  C. Carefully take supernatant for staining; discard brown pellet, if present. Remove Wash Buffer from wells and stain EBs in the diluted DAB chromogen (~250 μL/ well) at RT. Monitor staining under an inverted microscope for up to 5 min. Stop DAB staining by washing EBs with Wash Buffer (RT, 1 mL each; 5 min  3). 8. Observe EBs using an inverted microscope or a confocal microscope (see Note 18). Typical results are shown in Fig. 4c, d. Stained EBs can be stored in 70% ethanol at 4  C. hPGCLCs will be visualized as OCT4+ cells on the surface of EBs. Occasionally, the OCT4+ hPGCLCs may form dense aggregations,

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which could represent hPGCLC “niches” of proliferation (Fig. 4c, arrow). 3.4 FACS Enrichment of hPGCLCs from EBs as CD38+ Cells

1. Prepare enzyme mixes of Miltenyi Biotec Embryoid Body Dissociation Kit as follows. Enzyme Mix 1: add 50 μL Enzyme P to 1900 μL of Buffer X and vortex. Prewarm Enzyme Mix 1 in a 37  C water bath for 15 min. Enzyme Mix 2: add 10 μL Enzyme A to 20 μL Buffer Y. Mix Enzyme Mixes 1 and 2 to prepare Complete EB Dissociation Enzyme Mix. 2. Harvest EBs from hPGCLC production culture into a 2-mL microcentrifuge tube. Centrifuge the tubes at room temperature (300  g for 2 min) and discard supernatant. Resuspend EBs with 2 mL Wash Buffer and pellet again. 3. Discard supernatant and resuspend EBs with Complete EB Dissociation Enzyme Mix prepared in step 1. Incubate EB suspension in a 37  C water bath for 15–20 min until EBs are dissociated. During incubation, gently pipet EBs at every 3–5 min to facilitate dissociation. Alternatively, use a Miltenyi gentleMACS dissociator with Embryoid Body Dissociation Program. 4. Take dissociated EB cells in a 15-mL centrifuge tube containing 8 mL ice-cold PBS( ). Filter cell suspension through a 40-μm cell strainer. Wash cell strainer with 1 mL ice-cold PBS ( ) 3 times. Count cells in filtered cell suspension using a Coulter counter. 5. Centrifuge cell suspension at 4  C, 300  g for 8 min and discard supernatant. Resuspend cell pellet with ice-cold 10 mL PBS( ). 6. Divide cell suspension into two tubes, one is for no-staining control (~105 cells) and the other is for anti-CD38 staining (all remaining cells). Centrifuge the two tubes at 4  C, 300  g for 8 min and discard supernatant. Place the tubes containing cell pellets on ice. 7. Resuspend cell pellet for no-staining control (step 6) with 200 μL ice-cold 3% (w/v) BSA and 1% (w/v) sodium azide in PBS( ) (see Note 15). Place cells on ice until flow cytometry. 8. Resuspend cell pellet for anti-CD38 staining (step 6) with ice-cold 3% (w/v) BSA and 1% (w/v) sodium azide in PBS ( ) to 1  106 cells/100 μL. Add 10 μL per 1  106 cells of a FACS-grade, APC-conjugated anti-CD38 antibody. Cover the tube with aluminum foil to avoid exposure to light. Incubate at 4  C for 45 min with gentle rocking. Add 5 mL ice-cold PBS ( ) and centrifuge at 4  C, 300  g for 8 min. Discard supernatant and resuspend cell pellet with 5 mL ice-cold PBS( ). Repeat wash with PBS( ) 3 times in total. Resuspend cell pellet

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with 500 μL ice-cold 3% (w/v) BSA and 1% (w/v) sodium azide in PBS( ) to a cell density adequate for FACS. 9. Enrich hPGCLCs as CD38+ cells by FACS. CD38+ cells typically form clearly distinguished cell population from CD38 EB cells (Fig. 4e). Use the no-staining control to ensure identification of CD38+ cells (Fig. 4f). Typical yield of hPGCLCs are 1–5% of all FACS-examined single cells from Day 10 EBs and 20–40% from Day 13 to Day 14 EBs (see Note 19).

4

Notes 1. Thaw Matrigel in a glass vial on ice in a cold room or refrigerator overnight. Aliquot ~200 μL Matrigel (the volume of an aliquot is determined by manufacturer for each batch and indicated on the label of vial) into sterile cell cryopreservation tubes on ice. Do not thaw Matrigel at room temperature or using a warm water bath. It is important to keep Matrigel ice-cold during dispensing to avoid solidification. Store Matrigel aliquots at 80  C. 2. Matrigel-coated dishes containing the DMEM/F12 medium can be sealed using Parafilm and stored at room temperature for up to 1 week. Thereafter, the capacity of the coating to support human iPSC growth deteriorates slowly. Do not store Matrigel-coated dishes in a freezer, refrigerator, or CO2 incubator. In contrast to coating with collagen or several other extracellular matrix proteins, Matrigel coating does not work well when it is dried up. Aspirate Matrigel from dishes immediately before inoculation of primed pluripotency hiPSCs. It is not necessary to wash Matrigel-coated dishes with hiPSC medium or PBS( ). 3. Use Y27632-supplemented mTeSR1 in the same day. Do not store Y27632-supplemented mTeSR1 medium at 4  C longer than ~6 h. Minimize the time of prewarming medium containing Y27632. Effective suppression of ROCK1 by Y27632 is critical for survival of hiPSCs immediately after subculture or initiation of fresh culture from a frozen stock. Y27632 can be removed from the medium after cells start to aggregate with >10% confluency. 4. Add 2 μL of 50 mM Y27632 to 10 mL hiPSC cell culture medium (mTeSR1) in a 10-cm dish 2–3 h before collecting cells for frozen stock. Remove the medium, wash briefly with PBS( ) at room temperature to remove dead cells, and add 4 mL Accutase to dissociate cells from the dish. Incubate in a CO2 incubator for ~5 min and add ice-cold 10 mL DMEM/ F12 medium. Suspend cells well by pipetting and count cells.

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Centrifuge in a 15-mL centrifuge tube at 4  C (300  g for 8 min) and discard supernatant on ice. Suspend cell pellet in cryopreservation medium such as CryoStor CS10 to achieve 3.0  106/mL density. Freeze cells slowly in a Styrofoam box in a 80  C freezer overnight and store them at 80  C (~1 month) or in liquid nitrogen (>1 month). 5. We recommend culture of hiPSCs and EBs under a low oxygen pressure (5–6.5% O2). Although we do not have data about quantities and qualities of hPGCLCs generated under the ambient (~21%) and low O2 pressures, hiPSCs and EBs show distinct morphological characteristics under these conditions. Under the physiological condition in human early-stage embryos, it is likely that PGC specification occurs under a low O2 pressure, and a nonphysiological high O2 pressure may cause unwanted production of reactive oxygen species. A conventional CO2 incubator can be readily converted to a tri-gas incubator to achieve a low and well-controlled O2 pressure using a commercially available, affordable nitrogen gas injector equipped with O2 pressure monitor (e.g., BioSpherix ProOx 110; https://www.biospherix.com/). 6. Accurate counting of hiPSCs is critical for inoculation of cells at exact density. Prepare single-cell suspension by gently but repeatedly pipetting the cells with frequent microscopic examinations. The ratio of small aggregates consisting of two or three cells should be reduced to 75% and efficiency of conversion is >99%, as

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reported by the manufacturer. If planning to convert a lot of DNA samples, consider acquiring a kit with 96-deepwell format (e.g., EZ-96 DNA Methylation-Gold™ Kit), for time saving purposes. 5. To avoid contaminations and better protect your primers it is good practice to make working solutions from your stock primers. Consider eluting primers in TE buffer (10 mM Tris–HCl: 0.1 mM EDTA; pH ¼ 8.0) as water can be acidic and cause your primers to degrade. Elute each primer in its own stock solution, in the volume indicated by the manufacturer. Make working solutions of 10 μM by diluting each solution in water. 6. In every PCR-based methylation analysis use an internal reference for normalizing the reaction, as an input-control. One of the most used and reported in literature is a housekeeping gene such as ACTB, using a sequence that does not contain CpG islands. Appropriate primers (without CpG—methylationindependent primers) should be used and are widely reported in literature [20]. These primers are also useful for assuring the quality of the DNA sample (e.g., no amplification of the mentioned ACTB sequence frequently means a problem in the template, for instance bad quality DNA that is highly fragmented, often from FFPE samples). If DNA is high quality, the problem might have occurred in the bisulfite treatment, which was not well performed (since these primers are bisulfitespecific, containing unmethylated non-CpG cytosines). On the other hand, if clear-cut amplification is obtained with this primer set, the issue should be related to optimization of the PCR reaction itself. 7. Quantification in qMSP relies on detection of a fluorescent signal that is proportional to the amount of DNA produced during PCR amplification. Two main methodologies can be pursued for detecting this: DNA-intercalating dyes, such as SYBR Green or MeltDoctor (which we present in this chapter) and oligonucleotide fluorescent probes. The former bind to double-stranded DNA being formed by DNA polymerase and emit a fluorescent signal; its main advantages are low price and easy-to-pursue protocol, relying mainly on primer design. However, it binds to all double-stranded DNA fragments, and hence may produce false-positive signals due to unspecific binding or primer dimer. Melting curve analysis is needed to exclude this. Probes are significantly more specific since they act as a third primer for the specific region of interest, they can be labeled with different reporter dyes allowing detection of distinct sequences in the same reaction, and obviate the need for melting curve analysis. The main disadvantage relates to the need for specific synthesis of the probe, which is more delayed and costly.

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8. If planning to run a large number of samples consider using PCR plates (96- or 384-well formats) and run in each plate all the appropriate controls to assure inter-plate comparisons. 9. For detailed optimization, the buffers, magnesium, dNTP’s and specific dye are available separately, and quantities can be adjusted for achieving optimal results, especially in difficult templates. 10. Each tube of CT conversion reagent is desired for ten bisulfite treatment reactions (ten samples) and, because this reagent is sensitive to light, it should be used immediately after preparation, which is the optimal setting; consider setting your experiment to convert ten (or multiples of 10) samples. If needed, the reagent can be stored: overnight at room temperature, 1 week at 4  C, or up to 1 month at 20  C. When using a previously stored CT Conversion Reagent it must be previously warmed to 37  C and vortexed. 11. The amount of DNA input is critical for the final outcome of the experiment. In this protocol, the optimal amount of input DNA should be from 200 to 500 ng (which assures a >99% conversion efficiency—>99% unmethylated cytosines are converted to uracils and >99% methylated cytosines remain cytosines). 12. Make sure to have the most appropriate thermocycler for placing and fitting the samples, and choose PCR tubes or a plate in function of that. The sample container (either individual tubes or plate) should completely fit into the thermocycler all the way. This assures that the whole sample is always at the same temperature (instead of the top part of the solution being at a different temperature compared to the bottom part). In our experience this has been most likely associated with the end-result of the methylation analyses. Also, because the sample undergoes high temperatures, make sure either the tubes or plate are well sealed, otherwise the sample will partially evaporate. If using PCR tubes mark the name of the sample on the top and on the side of the tube, since with heat it might get partially erased. 13. In every step, avoid touching the filter at the bottom of the column with the pipet tip, to avoid damaging it. 14. In this step the lids of the 1.5 mL tubes may often break during centrifuging. Have extra tubes nearby to label and replace damaged ones. The end-volume in which you elute your DNA in this step can also be optimized. Depending on the final purpose, repeating the last step and eluting in an end-volume of 20 μL may also be possible. Also, re-eluting the same 10 μL through the same column may be tried. Make sure the whole volume went through the filter and was collected in the tube.

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15. Primer design is often the major challenge in PCR-based methylation analyses. By converting all unmethylated cytosines to thymines a bias is created in the composition of bisulfiteconverted DNA: the original strands are A-rich (mainly composed of A, T, G), while the reverse strand is T-rich (mainly composed of T, A and C). This results in multiple annealing possibilities and more frequent unspecific bindings/primer dimer. The best option is to first search the literature for primers already reported for the region of interest. Additionally, consider using the Methyl Primer Express® Software (https://www.thermofisher.com/order/catalog/product/ 4376041#/4376041). This software is user-friendly and allows designing both bisulfite sequencing and methylationspecific primers. By uploading the genomic DNA region of interest (which you can find in public databases such as UCSC Genome Browser https://genome.ucsc.edu/), it automatically determines presence of CpG islands and simulates the final outcome of a bisulfite conversion on DNA. Be sure to include about 2000 bp upstream the transcription starting site but also around 200 bp following it, as CpG islands often extend downstream. Importantly, it also designs primers and allows the user to tune the primer characteristics. For better results in qMSP, primer design should as much as possible follow these indications: (a) Consider the size of the amplicon, which should be around 150–300 bp. Take also into account the quality of your starting material and DNA sample (e.g., for FFPE samples, DNA is often fragment, so lower amplicon sizes are preferable [T/ G>A mispairing during PCR due to DNA damage from the FFPE process (FFPE filter); and (3) common germline and technical artifacts from the sample set (Panel of Normals filter). For further information see “Broad Mutation Calling Filtering Workflow” at https://gatkforums.broadinstitute.org/firecloud/discussion/ 7512/broad-mutation-calling-best-practice-workflows. Mutations that are significantly enriched across a cohort are called using the MutSigCV algorithm [24]. 3.5 Copy Number Analysis

We utilize the ReCapSeg tool to estimate copy number ratios (https://gatkforums.broadinstitute.org/gatk/discussion/5640/ recapseg-overview). This analysis does not provide information on the absolute copy number changes in the tumor cells alone because the DNA extracted for the tumor sample invariably contains some component of normal cells. We therefore input the estimated copy number ratios from ReCapSeg as well as the variant calls into an algorithm called ABSOLUTE [25]. ABSOLUTE mathematically determines the purity and ploidy of the tumor DNA and provides an estimate of total allele copy numbers as well as the fraction of cancer cells containing the detected variants. Further copy number analysis to detect allele-specific changes such as reciprocal loss of heterozygosity is described in our publication from 2016 [15].

Acknowledgments We thank the members of the Van Allen lab for helpful discussion, especially Brendan Reardon and Eric Kofman. References 1. Segal R (2006) Surveillance programs for stage I nonseminomatous germ cell tumors of the testis. Urol Oncol Semin Orig Investig 24:68–74. https://doi.org/10.1016/j. urolonc.2005.07.006 2. Bahrami A, Ro JY, Ayala AG (2007) An overview of testicular germ cell tumors. Arch Pathol Lab Med 131(8):1267. https://doi.org/10. 1043/1543-2165(2007)131[1267: AOOTGC]2.0.CO;2

3. American Cancer Society (2018) Cancer facts & figures 2018. https://www.cancer.org/con tent/dam/cancer-org/research/cancer-factsand-statistics/annual-cancer-facts-andfigures/2018/cancer-facts-and-figures-2018. pdf 4. Li C, Ekwueme DU, Rim SH, Tangka FK (2010) Years of potential life lost and productivity losses from male urogenital cancer deaths-United States, 2004. Urology

Integrated Analysis of Germ Cell Tumors 76:528–535. https://doi.org/10.1016/j.urol ogy.2010.04.030 5. Kemmer K, Corless CL, Fletcher JA et al (2004) KIT mutations are common in testicular seminomas. Am J Pathol 164:305–313. https://doi.org/10.1016/S0002-9440(10) 63120-3 6. Honecker F, Wermann H, Mayer F et al (2009) Microsatellite instability, mismatch repair deficiency, and BRAF mutation in treatmentresistant germ cell tumors. J Clin Oncol 27:2129–2136. https://doi.org/10.1200/ JCO.2008.18.8623 7. Feldman DR, Iyer G, Van Alstine L et al (2014) Presence of somatic mutations within PIK3CA, AKT, RAS, and FGFR3 but not BRAF in cisplatin-resistant germ cell tumors. Clin Cancer Res 20:3712–3720. https://doi.org/10. 1158/1078-0432.CCR-13-2868 8. Yang X, Fraser M, Moll UM et al (2006) Akt-mediated cisplatin resistance in ovarian cancer: modulation of p53 action on caspasedependent mitochondrial death pathway. Cancer Res 66:3126–3136. https://doi.org/10. 1158/0008-5472.CAN-05-0425 9. Koster R, De Jong S (2010) Cytoplasmic p21 expression levels determine cisplatin resistance in human testicular cancer. J Clin Invest 120:3594–3605. https://doi.org/10.1172/ JCI41939DS1 10. Wang L, Yamaguchi S, Burstein MD et al (2014) Novel somatic and germline mutations in intracranial germ cell tumours. Nature 511:241–245. https://doi.org/10.1038/ nature13296 11. Litchfield K, Summersgill B, Yost S et al (2015) Whole-exome sequencing reveals the mutational spectrum of testicular germ cell tumours. Nat Commun 6:1–8. https://doi.org/10. 1038/ncomms6973 12. Van Allen EM, Mouw KW, Kim P et al (2014) Somatic ERCC2 mutations correlate with cisplatin sensitivity in muscle-invasive urothelial carcinoma. Cancer Discov 4:1140–1153. https://doi.org/10.1158/2159-8290.CD14-0623 13. Liu D, Plimack ER, Hoffman-Censits J et al (2016) Clinical validation of chemotherapy response biomarker ERCC2 in muscle-invasive urothelial bladder carcinoma. JAMA Oncol 4:1140–1153. https://doi.org/10.1001/ jamaoncol.2016.1056 14. Bagrodia A, Lee BH, Lee W et al (2016) Genetic determinants of cisplatin resistance in patients with advanced germ cell tumors. J Clin

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Oncol 34:4000–4007. https://doi.org/10. 1200/JCO.2016.68.7798 15. Taylor-Weiner A, Zack T, O’Donnell E et al (2016) Genomic evolution and chemoresistance in germ-cell tumours. Nature 540:114–118. https://doi.org/10.1038/ nature20596 16. Shen H, Shih J, Hollern DP et al (2018) Integrated molecular characterization of testicular germ cell tumors. Cell Rep 23:3392–3406. https://doi.org/10.1016/j. celrep.2018.05.039 17. Van Allen EM, Wagle N, Stojanov P et al (2014) Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffinembedded tumor samples to guide precision cancer medicine. Nat Med 20:682–688. https://doi.org/10.1038/nm.3559 18. Fisher S, Barry A, Abreu J et al (2011) A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol 12:1–15. https:// doi.org/10.1186/gb-2011-12-1-r1 19. Li H (2013) Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv 20. Cibulskis K, McKenna A, Fennell T et al (2011) ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics 27:2601–2602. https:// doi.org/10.1093/bioinformatics/btr446 21. Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31:213–219. https:// doi.org/10.1038/nbt.2514 22. Saunders CT, Wong WSW, Swamy S et al (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28:1811–1817. https:// doi.org/10.1093/bioinformatics/bts271 23. Ramos AH, Lichtenstein L, Gupta M et al (2015) Oncotator: cancer variant annotation tool. Hum Mutat 36:E2423–E2429. https:// doi.org/10.1002/humu.22771 24. Lawrence MS, Stojanov P, Polak P et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218. https://doi.org/10.1038/ nature12213 25. Carter SL, Cibulskis K, Helman E et al (2012) Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol 30:413–421. https://doi.org/10.1038/nbt. 2203

Chapter 14 Use of Genomewide Association Studies to Evaluate Genetic Predisposition to Testicular Germ Cell Tumors Anthony J. Hooten, Erica Langer, and Jenny N. Poynter Abstract Genomewide association studies (GWAS) have been widely used in recent years to identify common variants that are associated with multiple types of cancer, including testicular germ cell tumors. These studies require no a priori hypotheses and have advantages, including the ability to highlight new pathways relevant to the biology of common diseases. GWAS require collection of germline DNA from individuals with and without the disease of interest. Following DNA extraction and quantification, a variety of array based platforms are available to evaluate common and moderately rare germline variation throughout the genome in an agnostic fashion. Here, we describe DNA extraction methods from samples typically used in the evaluation of germline genetic variation (blood and saliva). We also describe assays used to assess DNA quality and quantity. Finally, we include methods describing array based genotyping using the Illumina platform and validation of relevant variants using the iPLEX Agena Multiplexed Genotyping (formerly Sequenom). Key words Genetic susceptibility, Genomewide association study, Testicular, Epidemiology

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Introduction Germline genetic susceptibility has long been hypothesized to play a role in the etiology of testicular germ cell tumors due to the high heritability of disease [1]. Identification of variants that increased risk of TGCT proved difficult [2] until the advent of the genomewide association study (GWAS) [3]. To date, studies of TGCT have identified 40 susceptibility alleles [4–13], some of which we have confirmed as risk factors for pediatric GCT [14]. Genetic studies of adult TGCT explain a larger percentage of heritability using a smaller number of SNPs compared with other cancers [13], and the majority of the 40 SNPs identified for adult TGCT have revealed a central role for germ cell biology genes [13]. Notably, a recently published sequencing study in adult TGCT did not find evidence for rare mutations with large genetic effects in TGCT [15]. Thus, available data support polygenic inheritance with a substantial contribution from common variants. Array-based

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_14, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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genotyping platforms, including the Illumina Infinium BeadChips, permit identification of susceptibility loci across the genome. Evaluating genetic susceptibility to disease in epidemiologic studies requires the collection of biological specimens. Blood samples provide a large quantity of high quality DNA; however, collection of blood is invasive, expensive and typically not feasible in studies covering a large geographic area. Importantly, the use of noninvasive DNA collection methods has been shown to increase study participation rates [16]. Buccal and saliva samples are a noninvasive and inexpensive way to collect DNA in populationbased studies, which, in turn, have been successfully evaluated using high-throughput genotyping methods [17–19]. Saliva DNA collection using Oragene sample collection kits (DNA Genotek, Ottawa, CA) provides a solution for collection of high quality DNA from a geographically diverse population using minimally invasive techniques. Collection kits are available for self-collection and also in an assisted collection kit that permits inclusion of small children [20]. We describe DNA extraction methods for blood samples and saliva samples using both the self-collected and assisted collection kits. Prior to genotyping, assessment of DNA quality and quantity is essential. DNA quality is typically assessed using gel electrophoresis. DNA quantity is typically measured using two main techniques, spectrophotometry and fluorescent dye tagging. Spectrophotometry is easy and inexpensive but does not distinguish between human and bacterial DNA, which can be an issue in DNA extracted from saliva. UV fluorescence dye tagging is a more sensitive method and also allows the specific quantification of human DNA [21]. We describe the Quant-iT PicoGreen dsDNA Assay (Thermo Fisher Scientific, Waltham, MA), which is a fluorescent dye tagging approach. Genomewide genotyping arrays are available from two main companies, Illumina (San Diego, CA) and Affymetrix (Thermo Fisher Scientific, Waltham, MA). Here, we describe the use of Illumina Infinium BeadChip arrays [22, 23], which include SNPs densities ranging from 300,000 to nearly 1.2 million markers selected to provide dense coverage, few large gaps, and cuttingedge content. These arrays include common variants (minor allele frequency >0.05) and rare variants identified through exome sequencing with the possibility to add custom content. Specifically, we describe procedures used to run the Illumina HumanCoreExome array. This array includes the ~240,000 variants from the Exome array and the Core GWAS backbone (~300,000 variants), which has been shown to tag nearly all common variation in individuals of European and Asian ancestry at (r2 > 0.50). Many methods have been developed for the quality control and statistical analysis of GWAS data. These methods are beyond the scope of this

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chapter. A full volume of Methods in Molecular Biology has been dedicated to the analysis of GWAS data [24]. We refer the reader to this volume for guidance on analysis. Following the identification of variants that reach genomewide statistical significance, variants should be confirmed in an independent set of samples to confirm true associations. The iPLEX Agena Multiplexed Genotyping (formerly Sequenom) is well suited for this purpose, as custom plexes of up to 40 SNPs per reaction can be designed. This assay is based on distinguishing allele-specific primer extension products by mass spectrometry (MALDI-TOF). We describe the use of this assay and also refer the reader to a previous chapter describing this technology [25].

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Materials All solutions, except ethanol solutions, are ready-made stock solutions to minimize contamination and keep chemistry consistent. Autoclaved Milli-Q water is used to make ethanol solutions. Sterile filter tipped pipettes and tips and autoclaved or sterile tubes or microplates are essential to avoid contamination. Gloves are to be worn throughout all protocols. Blood processed in a biosafety cabinet hood.

2.1 DNA Extraction from Saliva Collection Kit

1. OragenelDISCOVER OGR-500 (DNA Genotek). 2. Sterile 15 mL conical tubes. 3. 5 mL filtered polystyrene disposable serological pipette. 4. 1 mL, 200 μL, and 10 μL filtered pipette tips. 5. Autoclaved 1.5 mL tubes (three per Oragene kit). 6. 1
Dulbecco’s phosphate buffered saline. 7. Cell lysis solution. 8. 4 mg/mL RNase A solution. 9. Protein precipitation solution. 10. 20 mg/mL glycogen solution. 11. 100% isopropanol. 12. 70% ethanol. 13. 10 mM Tris–HCl, 0.1 mM EDTA DNA Suspension Buffer. 14. Ice. 15. Centrifuge capable of holding 15 mL conical tubes, equipped with swing-out buckets, and reaching a force of 4000
g. 16. Orbital shaker. 17. Vortex mixer.

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18. Water bath. 19. AccuWipe wipers. 20. Autoclaved Milli-Q water. 2.2 DNA Extraction from Assisted Saliva Collection Kit

1. OragenelDISCOVER Assisted Collection OGR-575 (DNA Genotek). 2. Sterile 15 mL conical tubes. 3. 5 mL filtered polystyrene disposable serological pipette. 4. 1 mL, 200 μL, and 10 μL filtered pipette tips. 5. Autoclaved 1.5 mL tubes (two per Oragene kit). 6. Cell lysis solution. 7. PrepIT-L2P Solution (DNA Genotek). 8. 95–100% ethanol. 9. 70% ethanol. 10. 10 mM Tris–HCl, 0.1 mM EDTA DNA Suspension Buffer. 11. Autoclaved Milli-Q water. 12. Ice. 13. Water bath. 14. Vortex. 15. Centrifuge capable of holding 15 mL conical tubes, equipped with swing-out buckets, and reaching a minimum force of 3500
g. A force of 5000
g is preferable.

2.3 Blood Processing Using Ficoll-Paque Plus

1. Ficoll-Paque Plus (GE Healthcare Life Sciences). 2. 15 mL conical tubes. 3. 5 mL filtered polystyrene disposable serological pipette. 4. Autoclaved 1.5 mL tubes. 5. Dry ice. 6. 95% ethanol. 7. Centrifuge capable of holding 15 mL tubes, equipped with swing-out buckets, and reaching a speed of 835
g. 8. Autoclaved Milli-Q water.

2.4 DNA Integrity Analysis by Gel Electrophoresis

1. LE Agarose. 2. Tris–acetate–EDTA (TAE) buffer, 10
: Dissolve 48.4 g Tris base, 11.4 mL 17.4 M glacial acetic acid, and 3.7 g EDTA disodium salt in 800 mL of deionized water. Dilute buffer to 1.0 L and store at room temperature. Dilute to 1
for electrophoresis use.

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3. 0.25% bromophenol blue DNA loading dye: Dilute 7.5 g Ficoll in 50 mL deionized water. Mix in 0.125 g bromophenol blue and 0.125 g xylene cyanol FF. Store at room temperature. 4. TrackIt 50 base pair DNA Ladder (Invitrogen). 5. Microwave. 6. 100 mL Erlenmeyer flask. 7. Gel electrophoresis power supply. 8. Gel electrophoresis gel box. 9. Ultraviolet (UV) light box for imaging gel. 10. 10 μL sterile pipette tips. 2.5 DNA Quantification

1. Quant-IT PicoGreen dsDNA reagent (Molecular Probes). 2. 1
TE (prepared from 20
TE: 200 mM Tris–HCl, 20 mM EDTA, pH 7.5). 3. Lambda DNA standard: 100 μg/mL in TE. 4. Sterile, distilled, DNase-free water. 5. 1, 10, and 25 mL filtered polystyrene disposable serological pipettes. 6. 15 and 50 mL conical tubes. 7. 1 mL, 200 μL, and 10 μL filtered pipette tips. 8. Tin foil. 9. Autoclaved 1.5 mL tubes. 10. 96-well assay microplate—black, flat bottom, polystyrene. 11. Fluorescence microplate reader capable of reading fluorescein wavelengths at excitation 480 nm and emission 520 nm.

2.6 DNA Dilutions and Plating

1. 1 mL, 200 μL, and 10 μL filtered pipette tips. 2. Autoclaved Milli-Q water. 3. Autoclaved 0.5 and 1.5 mL tubes. 4. 96 well microplate. 5. Adhesive plate seal. 6. Plate sealer tool. 7. Vortex.

2.7 Illumina Infinium HumanCoreExome Array

1. MA1. 2. MA2. 3. MSM (multisample amplification master mix). 4. FMS. 5. PM1. 6. RA1.

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7. PB2. 8. XC4. 9. PB1. 10. RA1. 11. XC1. 12. XC2. 13. TEM. 14. XC3. 15. STM. 16. ATM. 17. PB1. 18. XC4. 19. Alconox powder detergent. 20. 95% formamide–1 mM EDTA. 21. 0.1N NaOH. 22. 100% isopropanol. 23. 100% ethanol. 24. dH2O. 25. 24
1 HD BeadChips. 26. Hyb chambers. 27. Hyb chamber gaskets. 28. Hyb chamber inserts. 29. Multi-Sample BeadChip Alignment Fixture. 30. Te-Flow Flow-Through Chambers (with black frames, spacers, glass back plates, and clamps). 31. Wash dishes. 32. Wash racks. 33. Air gun or Whoosh-Duster to remove accumulated dust. 34. Small cleaning brush. 35. 96-well 0.8 mL microplates. 36. 10 μL, 200 μL, 1 mL filtered pipette tips. 37. 50 mL filtered polystyrene disposable serological pipette. 38. 8 or 12 channel multichannel pipette. 39. 96-well cap mat. 40. Foil seal for plates. 41. Vortex. 42. Centrifuge for spinning microplates.

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43. Hybridization Oven (Illumina). 44. Heat block. 45. Heat sealer. 46. Plate rocker. 47. Water circulator connected to chamber rack on robot bed (Tecan). 48. Temperature Probe (Illumina). 49. BeadArray Reader or iScan System (Illumina). 50. Staining rack (Illumina). 51. Vacuum desiccator. 52. Tube racks. 53. Self-locking forceps. 54. Large Kimwipes. 55. Vacuum hose. 2.8 iPLEX Agena Multiplexed Genotyping (Formerly Sequenom)

1. HotStar Taq polymerase (5 U/μL) (Qiagen). 2. 25 mM MgCl2. 3. 10
PCR Buffer: 500 mM KCl, 100 mM Tris–HCl (pH 8.3), 15 mM MgCl2. 4. 500 nM Primer mix. 5. 25 mM dNTP mix. 6. 1.7 U/μL shrimp alkaline phosphatase enzyme (SAP). 7. 10
SAP Buffer. 8. iPLEX Gold Reagent Kit (containing 10
iPLEX Buffer Plus, iPLEX termination mix, and iPLEX enzyme). 9. 7 μM:14 μM primer mix for single base extension (SBE) primers. 10. iPLEX mass modified nucleotides. 11. Water (deionized: HPLC grade), nanopure water. 12. 50% ethanol. 13. 70% reagent-grade isopropanol. 14. SpectroCHIP arrays. 15. Clean resin kit. 16. 384-well microplate. 17. 96-well polystyrene microplate (v-bottom). 18. 6 mg dimple plate. 19. MassARRAY Liquid-Handler (Matrix). 20. Liquid-handler controller PC. 21. Liquid-handler plate flatteners.

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22. Supply and waste reservoirs. 23. Clean resin plates, spoon, and scraper. 24. MassARRAY Samsung Nanodispenser. 25. MassARRAY Analyzer Compact. 26. TYPER software. 27. Thermal cycler capable of cycling 384-well microplates. 28. Microplate rotator. 29. Centrifuge for spinning microplates. 30. Tube centrifuge. 31. Multichannel pipette. 32. Sterile reagent reservoirs. 33. Repeater (multistep pipette). 34. 0.5–30.0 μL 96-channel tips for liquid-handler (Matrix). 35. Disposable plate sealing film for plates. 36. Adhesive sealing foil for plates. 37. Sterile tubes 1.5 and 5.0 mL.

3

Methods

3.1 DNA Extraction from Saliva Collection Kit

1. Pipet buccal cell lysate sample in Oragene OGR-500 collection container into 15 mL conical tube (see Note 1). 2. Add 1
Dulbecco’s phosphate buffered saline to bring sample volume up to 5.0 mL. 3. Add 1.2 mL cell lysis solution and 30 μL 4 mg/mL RNase A solution (see Note 2). 4. Pulse-vortex for 10 s to mix sample. 5. Incubate tube at room temperature for 10 min. 6. Add 2.0 mL protein precipitation solution. 7. Pulse-vortex for 10 s to mix sample. 8. Incubate tube on ice for 10 min. 9. Centrifuge tube at 3000
g for 2 min. 10. Pour supernatant into new 15 mL conical tube (see Note 3). 11. Add 5.0 mL 100% isopropanol and 40 μL 20 mg/mL glycogen solution. 12. Invert tube 50 times. 13. Centrifuge tube at 4000
g for 5 min. 14. Discard supernatant to waste container (see Note 4).

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15. Invert tube on AccuWipe for 1 min to evaporate any remaining 100% isopropanol. 16. Add 5.0 mL 70% ethanol. 17. Centrifuge tube at 4000
g for 1 min. 18. Discard supernatant to waste container. 19. Invert tube on AccuWipe for 1 min to evaporate any remaining 70% Ethanol. 20. Add 200 μL DNA Suspension Buffer (see Note 5). 21. Incubate tube at 65  C for 1 h. 22. Transfer tube to orbital shaker and shake overnight at 500 rpm. 23. Centrifuge tube at 2000
g for 1 min. 24. Pipet to mix and aliquot 200 μL rehydrated DNA across three 1.5 mL micro centrifuge tubes, 66.6 μL per tube (see Note 6). 25. Store rehydrated DNA at 20  C for short-term storage or 80  C for long-term storage. 3.2 DNA Extraction from Assisted Saliva Collection Kit

1. Mix OragenelDISCOVER Assisted Swab OGR-575 sample tube by inversion for 10 s. 2. Incubate sample tubes in water bath for 1 h at 56  C (see Note 7). 3. Pipet buccal cell lysate into 15 mL conical tube. 4. Add 1 mL cell lysis solution to 15 mL conical tube. 5. Pulse-vortex 10 s to mix sample. 6. Note volume of sample in tube for each sample and add 1/25th volume of prepIT-L2P Solution to each sample (see Note 8). 7. Pulse-vortex tube 10 s and put tube in ice bath for 10 min. 8. Centrifuge tube at 3500
g for 10 min. 9. Pipet supernatant containing DNA into a new 15 mL conical tube. 10. Add 95–100% ethanol to the sample tube to reach 6 mL volume. 11. Invert tube 10 times to mix. 12. Incubate tube for 10 min at room temperature. 13. Centrifuge tube at 5000
g for 10 min (a minimum speed of 3500
g is required). 14. Pour supernatant into waste container. 15. Slowly add 1 mL of 70% ethanol without disturbing the pellet and gently swirl tube. 16. Incubate for 1 min at room temperature.

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17. Use a P1000 pipette, without disturbing the pellet, carefully pipet and discard the ethanol. 18. Let tube air-dry with cap off for 1–2 min to evaporate any remaining ethanol. 19. Add 100 μL of DNA Suspension Buffer. 20. Pulse-vortex tube for 30 s. 21. Incubate tube at 50  C water bath for 1 h. 22. After the 1 h incubation, incubate tube at room temperature overnight. 23. Centrifuge tube at 2000
g for 1 min. 24. Pipet to mix and aliquot 100 μL rehydrated DNA across two 1.5 mL micro centrifuge tubes, 50 μL per tube. 25. Store rehydrated DNA at 20  C for short-term storage or 80  C for long-term storage. 3.3 Blood Processing Using Ficoll-Paque Plus

1. Pipet 4.0 mL blood into 15 mL conical tube. 2. Add equal volume 1
Dulbecco’s Phosphate Buffered Saline to tube. Mix by pipetting. 3. Pipet 3.0 mL Ficoll-Paque Plus to two new 15 mL conical tubes. 4. Pipet 4.0 mL blood and 1
DPBS mixture slowly on top of Ficoll-Paque Plus. Repeat for second tube containing FicollPaque Plus (see Note 9) (Fig. 1). 5. Centrifuge tubes at 470
g for 30 min at room temperature. Blood will separate into four layers, from top to bottom: Plasma, Lymphocytes, Ficoll-Paque Plus, and Erythrocytes (Fig. 1). 6. Transfer plasma using 5 mL pipette across four 1.5 mL tubes, 1.0 mL per tube (see Note 10). 7. Store 1.5 mL tubes containing plasma at 80  C immediately. 8. Using a 5 mL pipette, draw up lymphocyte layer. Transfer to a new 15 mL conical tube. 9. Add 1
DPBS to bring volume up to 10 mL. 10. Gently invert 10 times to mix. 11. Centrifuge tube at 835
g for 10 min to pellet cells. 12. Discard supernatant to waste container. 13. Add 3.0 mL 1
DPBS to cell pellet. Pipet to mix. 14. Transfer lymphocytes across three 1.5 mL tubes, 1.0 mL per tube. 15. Discard Ficoll-Paque Plus layer into waste container. 16. Transfer erythrocytes across three 1.5 mL tubes.

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Fig. 1 Ficoll-Paque Plus blood separation (a) 4 mL blood and 1
DPBS layered on top of Ficoll-Paque Plus precentrifugation (b) Plasma, lymphocyte, Ficoll-Paque Plus, and erythrocyte layers postcentrifugation

17. Snap-freeze lymphocytes and erythrocytes by placing 1.5 mL tubes into rack in metal bowl. Add dry ice and 10 mL 95% ethanol. Freeze at room temperature for 10 min (see Note 11). 18. Store 1.5 mL tubes containing frozen lymphocytes and erythrocytes to 80  C. 3.4 DNA Integrity Analysis Using Gel Electrophoresis

1. Cast 1% agarose gel with 1
TAE: Add 1.0 g LE Agarose and 100 mL 1
TAE buffer to Erlenmeyer flask. 2. Microwave 90 s to melt agarose (see Note 12). 3. Pour mixture into gel mold containing desired well comb. 4. Let stand for 15 min to solidify. 5. Dilute 50 ng total DNA samples using water to 6.25 ng/μL in 8 μL. 6. Add 2 μL DNA loading dye to 8 μL DNA at 6.25 ng/μL. 7. Transfer solidified gel to gel box filled with 1
TAE buffer. 8. Load 10 μL DNA sample mixed with DNA loading dye to remaining wells. 9. Load 5 μL DNA TrackIt ladder to last well. 10. Run gel for 90 min at 80 V. 11. Remove gel from gel box and image on UV light box (see Note 13) (Fig. 2).

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Fig. 2 DNA integrity analysis by gel electrophoresis. DNA samples in lanes 1–4 show high molecular weight DNA that is intact. DNA samples in lanes 5–8 show lower molecular weight DNA that is fragmented, indicating lower quality DNA for downstream applications. This is visualized as streaks throughout the lane. Intact, high molecular weight DNA appears as crisp bands with very little streaking. A 50-base pair DNA ladder is used to determine molecular weight 3.5 DNA Quantification by Quant-iT PicoGreen dsDNA Assay

1. To prepare 1
TE assay buffer working solution aliquot 1 mL of 20
TE assay buffer from kit into a 50 mL conical tube and add 19 mL of sterile, distilled, DNase-free water. 2. On the day of the experiment, make a 200-fold dilution working stock of PicoGreen reagent by adding 50 μL Quant-iT dsDNA PicoGreen reagent into a 15 mL conical tube and adding 9.95 mL 1
TE assay buffer (see Note 14). 3. Cover PicoGreen working stock with tin foil as the reagent is susceptible to light degradation. 4. Prepare a working solution of the provided 100 μg/mL standard stock Lambda DNA. (a) To dilute Lambda DNA to 2 μg/mL, in a 1.5 mL tube add 294 μL 1
TE assay buffer and add 6 μL of 100 μg/ mL stock Lambda DNA. Briefly Pulse-vortex to mix. (b) A low-range standard is also required and needs an additional dilution. For this, in a 1.5 mL tube add 312 μL 1
TE assay buffer and add 8 μL of 2 μg/mL stock Lambda DNA. Briefly Pulse-vortex to mix. (c) Cover both Lambda DNA dilutions with tin foil. 5. Prepare high and low standard curve wells to a total volume of 100 μL per well by adding appropriate volume of 1
TE assay buffer and standard Lambda DNA to each standard well of a

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96 well plate. Perform each standard in triplicate to accurately determine the standard curve (see Note 15). 6. For each unknown sample well, add 99 μL 1
TE. Then briefly Pulse-vortex unknown DNA tubes and add 1 μL unknown DNA to respective unknown DNA wells for a total of 100 μL sample per well. Pipet up and down to mix (see Note 16). 7. Add 100 μL PicoGreen working solution to each well, pipetting up and down several times to mix. 8. Cover plate with tin foil to protect from light and incubate at room temperature for 5 min. 9. Remove tin foil and read plate on fluorescence microplate reader using the fluorescein wavelengths excitation 480 nm and emission 520 nm (see Note 17). 10. Subtract the reagent’s blank fluorescence reading for each sample. 11. Plot the standard curve of dsDNA concentration against fluorescence (RFU). 12. Using the standard curve, determine the dsDNA concentration of each unknown sample. 3.6 DNA Dilutions and DNA Plating

1. Depending on your application, calculate the final concentration needed for your DNA sample for a normalized concentration and volume using the C1V1 ¼ C2V2 calculation where C1 ¼ starting concentration or amount, V1 ¼ starting volume, C2 ¼ final concentration or amount, and V2 ¼ final volume. 2. Aliquot the calculated volume of autoclaved Milli-Q water to a sterile 0.5 mL tube, 1.5 mL tube, or 96 well plate. 3. Briefly Pulse-vortex your stock DNA tube to mix so it is a homogeneous sample and aliquot the calculated volume of DNA into the respective tube or plate well. 4. Briefly Pulse-vortex your DNA dilution tube to mix so it is a homogeneous dilution. 5. If it is a dilution made into a 96-well plate, do not vortex, instead mix dilution by pipetting up and down each well. Seal plates tightly with an adhesive plate seal and sealer tool. 6. Alternatively, instead of the C1V1 ¼ C2V2 formula if you would like a normalized mass of DNA in tubes or across a 96-well plate (i.e., 200 ng DNA) calculate the total mass of DNA needed divided by the DNA concentration of the sample and aliquot the calculated volume of DNA into a tube or plate. 7. For many downstream assays, 5% replicate samples are routinely performed for confirmation genotyping. In some cases with multiple assay plates, it is necessary to have both intraplate and interplate replicate wells.

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8. For most plated assays, two or more control blank wells (negative controls) and two or more positive control wells are performed per plate. 3.7 Illumina Infinium HumanCoreExome Array

1. Add 10 μL DNA normalized to 50 ng/μL per well in a new 96-well microplate. This is your DNA plate. Apply a barcode to the DNA plate.

3.7.1 Preparation of DNA Plate and MSA3 Plate for Amplification

2. In a new plate called the MSA3 plate, pipet 20 μL MA1 reagent into each MSA3 plate well. Apply a barcode to the MSA3 plate (see Note 18). 3. Using a multichannel pipette, transfer 4 μL of each DNA sample from the DNA plate to the respective well of the MSA3 plate. 4. Record the sample ID for each well of the MSA3 plate. 5. Using a multichannel pipette, add 4 μL of 0.1N NaOH to each well of the MSA3 plate and seal the plate using the 96-well cap mat. Make sure that the plate is sealed tightly. 6. Vortex plate at 1600 rpm for 1 min to mix. 7. Centrifuge the plate at 280
g for 1 min. 8. Remove the cap mat carefully. 9. Using a multichannel pipette, add 34 μL MA2 reagent to each well of the MSA3 plate. 10. Using a multichannel pipette, add 38 μL MSM reagent to each well of the MSA3 plate. 11. Seal the plate using the cap mat. 12. Vortex plate at 1600 rpm for 1 min to mix. 13. Centrifuge the plate at 280
g for 1 min. 14. Incubate the MSA3 plate in a preheated hybridization oven for at least 20 h but not more than 24 h at 37  C. Record time incubation began.

3.7.2 Fragmentation of the MSA3 Plate

1. Remove the plate from the hybridization oven and record time at end of incubation. 2. Centrifuge plate at 50
g for 1 min. 3. Remove the cap mat carefully (see Note 19). 4. Using a multichannel pipette dispense 25 μL FMS reagent to each well. 5. Seal the MSA3 plate with the cap mat. 6. Vortex the plate at 1600 rpm for 1 min. 7. Centrifuge plate at 50
g for 1 min at 22  C. 8. Put the sealed plate on a preheated 37  C heat block for 1 h. Record incubation start and stop times (see Note 20).

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1. Carefully remove the cap mat. 2. Using a multichannel pipette dispense 50 μL FMS reagent to each well. 3. Seal the MSA3 plate with the cap mat. 4. Vortex the plate at 1600 rpm for 1 min. 5. Put the sealed plate on a preheated 37  C heat block for 5 min. 6. Centrifuge plate at 50
g for 1 min at 22  C. 7. Carefully remove the cap mat and discard cap mat. 8. Using a multichannel pipette dispense 155 μL of 100% 2-propanol to each well. 9. Seal the MSA3 plate with a new, dry cap mat. Make sure not to shake the plate during this step. 10. After tightly sealing plate, invert plate 10 times to mix. 11. Incubate MSA3 plate for 30 min at 4  C. 12. Place the MSA3 plate in a centrifuge. Get a matched weight plate to balance the centrifuge and spin at 3000
g for 20 min at 4  C. 13. Immediately after centrifuging, remove the cap mat and discard cap mat (see Note 21). 14. Quickly invert the MSA3 plate and smack it down on an absorbent sheet to decant the supernatant. 15. Keep plate inverted and tap firmly on absorbent sheet multiple times for 1 min until all wells are empty of any liquid. Ensure that there is no cross-contamination of supernatant between wells. 16. Allow the uncovered, inverted plate to air-dry on the tube rack for 1 h to dry the pellets. After 1 h, blue pellets should be present at the bottoms of the wells. Record the start and end times for drying (see Note 22).

3.7.4 Resuspension of the MSA3 Plate

1. Using a multichannel pipette, dispense 23 μL RA1 reagent to each well (see Note 23). 2. Adhere a foil seal to the plate by holding the heat sealer block down for 5 s. 3. Put the sealed plate in a preheated 48  C hybridization oven for 1 h. Record start and stop times. 4. Vortex the plate at 1800 rpm for 1 min to mix. 5. Centrifuge the plate to 280
g. Ensure the pellets are completely resuspended (see Note 24).

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3.7.5 Hybridization to Multi BeadChips

1. Assemble the Hyb Chambers for the 24
1 HD BeadChip per the manufacturer instructions (see Note 25). 2. Add 400 μL PB2 reagent into the humidifying buffer reservoirs of the Hyb Chambers. 3. Close and lock the Hyb Chamber lid of the BeadChip per the manufacturer’s instructions. 4. Let the closed Hyb Chambers sit at room temperature until the BeadChips have had the DNA samples added. 5. Remove the BeadChips from 4 packaged.



C storage. Leave them

6. Put the MSA3 plate that has already been resuspended onto the preheated 95  C heat block for 20 min. This step denatures the samples. 7. Remove the plate from the heat block and let sit at room temperature for 30 min to cool. 8. Centrifuge the plate to 280
g. 9. Remove the foil seal. 3.7.6 Loading the BeadChips

1. Immediately before loading DNA samples, remove the BeadChips from packaging (see Note 26). 2. Put each BeadChip in a Hyb Chamber insert so that the barcode end is matched to the barcode symbol on the insert. 3. Using a multichannel pipette, add 12 μL of each DNA sample onto the appropriate 24
1 BeadChip section according to your sample ID form (see Note 27). 4. From the MSA3 plate load samples A1-F1 into the left side of the BeadChip inlet ports A1-F1. This is every other inlet port on the left side of the BeadChip. 5. From the MSA3 plate load samples G1 and H1 into the left side of the BeadChip inlet ports G1 and H1. 6. From the MSA3 plate load samples A2-D2 into the left side of the BeadChip inlet ports A2-D2. 7. From the MSA3 plate load samples E2-H2 into the right side of the BeadChip inlet ports E2-H2. 8. From the MSA3 plate load samples A3 and B3 into the right side of the BeadChip inlet ports A3 and B3. 9. From the MSA3 plate load samples C3-H3 into the right side of the BeadChip inlet ports C3-H3. This is every other port on the right side of the BeadChip. 10. Continue loading samples from the MSA3 plate to additional BeadChips using the method above for subsequent sample wells until all remaining samples are loaded onto BeadChips (see Note 28). 11. For each group of samples, record the BeadChip barcode.

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1. After inspection of the BeadChips, immediately remove the Hyb Chamber lid and load the Hyb Chamber Inserts containing the BeadChips into the Hyb Chamber (see Note 29). 2. Ensure that the barcode end is over the ridges shown on the Hyb Chamber. 3. Put the back side of the lid onto the Hyb Chamber. Carefully close the front end so as not to disturb the inserts. 4. Shut the clamps on each side of the chamber (see Note 30). 5. Put the Hyb Chamber into the preheated 48  C hybridization oven so that the clamps are facing the sides of the oven and the Illumina label on the chamber is facing you. Incubate chambers in the oven for at least 16 h but not more than 24 h. Record incubation start and stop times (see Note 31). 6. The MSA3 sample plate is no longer needed and may be discarded.

3.7.8 Preparation of Reagent for XStain HD BeadChip Process and Washing the BeadChips

1. Dispense 330 mL of 100% ethanol to the XC4 reagent bottle so that the total volume of bottle is 350 mL. 2. Vigorously shake for 15 s. 3. Set bottle on bench at room temperature overnight (see Note 32). 4. Remove the Hyb Chamber from the oven and let it cool on the bench for 25 min before opening. 5. Fill two wash dishes with 200 mL PB1 reagent each and label dishes. 6. Fill a Multi-Sample BeadChip Alignment Fixture with 150 mL PB1 reagent. 7. Ensure that the glass back plates have been cleaned per the manufacturer’s instructions. Separate the spacers for ease of use. 8. Put the wire handle on the wash rack and place the rack into a dish that contains PB1 reagent. 9. Take the Hyb Chamber inserts out of the Hyb Chamber. 10. Take the BeadChips out of the inserts individually. 11. Over and absorbent towel, carefully remove the IntelliHyb Seal from each of the BeadChips per the manufacturer’s instructions. Discard the seal. 12. Carefully and immediately, only touching edges of BeadChips, place each BeadChip into the wash rack. Ensure that each BeadChip is completely covered by the PB1 reagent in the dish (see Note 33). 13. Grab the wire handle and gently move the rack up and down to break the surface and agitate for 1 min.

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14. When washing is complete, remove the rack from the dish and put the rack immediately into the second wash dish that contains PB1. Again, make sure that the BeadChips are completely covered by PB1. 15. Again, grab the wire handle and gently move the rack up and down to break the surface and agitate for 1 min. 3.7.9 Assembling the Flow-Through Chamber

1. For each BeadChip to be processed, put a black frame into the Multi-Sample BeadChip Alignment Fixture. 2. Put each BeadChip into a black frame so that the barcode is aligned with the ridges on the fixture. Make sure each BeadChip is completely submerged in PB1 in the fixture. 3. On top of each BeadChip, place a clear spacer using the fixture grooves to place the spacers into position. 4. Put the Alignment bar onto the fixture, fitting it over the metal tab on the fixture. 5. Use a laboratory air gun to blow air over glass black plates to remove dust. 6. Put a glass black plate onto the clear spacer that is on each BeadChip. The reservoir of the plate should be at the barcode end of the BeadChip so it faces inward to make a reservoir against the surface of the BeadChip. 7. Attach the clamps onto the Flow-Through Chamber per the manufacturer’s instructions. 8. Use scissors to trim the spacer at the nonbarcode end and at the barcode end. 9. Wash the Hyb Chamber reservoirs immediately with dH2O and scrub with small cleaning brush. Make sure that no PB1 remains (see Note 34).

3.7.10 Single Base Extension and Staining of the BeadChip

1. After the Chamber Rack has reached 44  C and the temperature has been checked, quickly put each Flow-Through Chamber into the Chamber Rack. Place the four BeadChips into the Chamber Rack every other position beginning at 1 in the first row of rack and record positions of BeadChips (see Note 35). 2. Vigorously shake XC4 bottle in preparation for next steps. Into the reservoir of each Flow-Through Chamber: 3. Pipet 150 μL RA1. Incubate for 30 s. Repeat this step 5 times (see Note 36). 4. Pipet 450 μL XC1. Incubate for 10 min. 5. Pipet 450 μL XC2. Incubate for 10 min. 6. Pipet 200 μL TEM. Incubate for 15 min.

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7. Pipet 450 μL 95% formamide/1 mM EDTA. Incubate for 1 min. Repeat this step one more time. 8. Incubate for 5 min. 9. Ramp the Chamber Rack down to the temperature on the STM tube. If no temperature is listed on the STM tube, ramp down to 37  C. 10. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step one more time. 11. When the Chamber Rack has reached 37  C, staining of the BeadChip can begin. 12. Pipet 250 μL STM. Incubate for 10 min (see Note 37). 13. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step once and incubate for 5 min. 14. Pipet 250 μL ATM. Incubate for 10 min. 15. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step once and incubate for 5 min. 16. Pipet 250 μL STM. Incubate for 10 min. 17. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step once and incubate for 5 min. 18. Pipet 250 μL ATM. Incubate for 10 min. 19. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step once and incubate for 5 min. 20. Pipet 250 μL STM. Incubate for 10 min. 21. Pipet 450 μL XC3. Incubate for 1 min. Repeat this step once and incubate for 5 min. 22. Remove the Flow-Through Chambers immediately from the Chamber Rack and put on the bench horizontally at room temperature. 3.7.11 Washing and Coating of BeadChips

1. On bench top, set up two wash dishes, one labeled PB1 and one labeled XC4 (see Note 38). 2. Pour 310 mL water into each wash dish and with a marker, mark the water level on side of each dish. 3. Pour out the water so that dishes are now empty. This step is to ensure that reagents may be poured directly into the dishes at the correct level. 4. Pour 310 mL PB1 to the labeled dish. 5. Put the empty staining rack into the PB1 dish so that the locking arms and tab are facing toward you per the manufacturer’s instructions. 6. Handling only the edges, take apart each Flow-Through Chamber per the manufacturer’s instructions.

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7. Put the BeadChips into the staining rack while rack is in PB1. Barcodes will be facing away from you. Make sure BeadChips are completely covered by PB1. 8. Agitate staining rack by moving up and down 10 times. Incubate for 5 min. Do not allow to incubate longer than 30 min. 9. Pour 310 mL XC4 into the labeled dish and cover dish to keep dust out (see Note 39). 10. Remove the staining rack from PB1 dish and put into XC4 dish with barcodes facing away from you. 11. Agitate staining rack by moving up and down 10 times. Incubate for 5 min. 12. Remove staining rack from XC4 quickly but smoothly and put on clean tube rack on Kimwipes. Ensure that barcodes are facing up. 13. To coat the BeadChips, working with the top four BeadChips from top to bottom, using a self-locking forceps carefully grab each BeadChip by the barcode end and place each BeadChip on a tube rack with barcode facing up and toward you. 14. Remove the staining rack handle per the manufacturer’s instructions and remove the remainder of the BeadChips to the tube rack as you did with the first. Ensure six of the BeadChips are placed on the top of tube rack and two BeadChips on the bottom. 15. Put the rack containing the BeadChips into the vacuum desiccator and cover with desiccator lid. 16. Remove the plug from the valve before application of vacuum pressure. Start the vacuum using a minimum of 508 mmHg or 0.68 bar. Try lifting the lid off desiccator. You should not be able to lift it if proper vacuum. 17. Leave BeadChips in vacuum desiccator for 50–55 min. Upon completion, slowly release the valve. 18. Touch the edges of the BeadChips, not the stripes, to make sure BeadChips are dry. 19. If the underside of the BeadChips are sticky to touch, particularly the bottom two BeadChips, manually clean the underside per the manufacturer’s instructions. 20. Clean the glass black plates and Hyb Chambers per the manufacturer’s instructions. 3.7.12 Imaging of BeadChips Using the iScan System

1. Download the decode content of the DVD provided for each BeadChip. Label the folder as the BeadChip barcode number (see Note 40). 2. Start the iScan software to initialize the iScan reader. Red dot will turn green when ready.

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3. From the drop-down menu, click LIMS server and enter username and password. Select Start. 4. Put the BeadChips into the carrier and place the carrier into the iScan reader tray. Select Next. The reader will scan the barcodes. Ensure that the correct scan setting displays, in this case, Infinium HD-HT (for 24
1 HD BeadChip) (see Note 41). 5. Choose Infinium HD-HT.scst and select Open. 6. Click the Menu, Tools, Options, TIFF .tif for the image format (see Note 42). 7. Select the Scan Settings tab and on left side select Infinium HD-HT. 8. On right side select Image Format, Tiff, click OK. Doublecheck that the input and output paths are correct. 9. Click Scan to start scanning BeadChips. 10. When scanning is complete, a window will pop up which shows the intensity values, registration, and focus metrics for each BeadChip stripe. High focus scores indicate successful images with a high bead intensity value. 11. If any stripes failed to scan correctly, click Rescan. 12. When data viewing is complete, click Submit to LIMS to return to the Start window (see Note 43). 13. Data analysis is performed with Illumina software for automated genotype calls. 3.8 iPLEX Agena Genotyping Using iPLEX Gold Method (Formerly Sequenom) 3.8.1 PCR Amplification of DNA

1. For the multiplexed PCR cocktail, per each 5 μL sample reaction (for a 384-well microplate) the following reagents are needed: 1.85 μL HPLC grade water, 0.625 μL 10
PCR Buffer with 15 mM MgCl2, 0.325 μL 25 mM MgCl2, 0.1 μL 25 mM dNTP mix for each nucleotide, 1.0 μL 500 nM Primer mix for each primer, and 0.1 μL 5 U/μL HotStar Taq. Multiply each component by 384 for accommodating enough cocktail volume for a 384 well plate (see Note 44). 2. In a 5 mL tube, add the calculated volume of reagents for a 384-well plate volume in the order in which listed above and mix well for a homogeneous cocktail solution. 3. Using a P1000 pipette, transfer the cocktail mix to a sterile reagent reservoir. 4. To each well of the 384-well plate, add 1 μL of 5–10 ng/μL gDNA per sample. 5. Using a multichannel pipette, dispense 4 μL of the PCR cocktail to each well for a total of 5 μL in each well. (The plate is now considered the source plate.)

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6. Apply adhesive seal to plate and tighten seal on plate. Make sure edges are well sealed with plate sealing tool. 7. Centrifuge the plate at 168
g for 1 min. 8. Gently vortex the plate and spin down again before thermocycling. 9. Thermal cycler conditions include enzyme activation at 94  C for 15 min, amplified for 45 cycles of 94  C
20 s, 56  C
30 s, 72  C
1 min, followed by a 3 min extension at 72  C, and a final 4  C hold. 3.8.2 Preparation and Addition of SAP

1. For doing iPLEX gold reactions for a low plex (1 plex–18 plex) for one destination plate, begin by preparing the Shrimp Alkaline Phosphatase enzyme (SAP) enzyme solution. This step is to remove unincorporated dNTPs. Per each 5 μL sample reaction (for a 384-well plate) the following reagents are needed: 1.53 μL HPLC grade water, 0.17 μL 10
SAP Buffer, 0.3 μL 1.7 U/μL SAP enzyme. Multiply each component by 384 for accommodating enough SAP enzyme solution volume for a 384-well plate. 2. In a 1.5 mL tube, add the calculated volume of reagents for the SAP enzyme solution for a 384-well plate volume in the order in which listed above. 3. Vortex the 1.5 mL tube containing the SAP enzyme solution mixture for 5 s to mix. 4. Centrifuge the solution at 5251
g for 10 s. 5. In a new 96-well plate (v-bottom), pipet 85 μL of SAP enzyme solution to each well of row H. The wells of row H will be used as reservoirs from which you will distribute SAP enzyme solution to the rest of the wells in the microplate. 6. Using a multichannel pipette, draw from wells in row H and distribute 10 μL of SAP enzyme solution to each well in rows A-G. When pipetting is complete, rows A-G should have 10 μL and row H should have 15 μL (see Note 45). 7. Place the 96-well plate of the SAP solution on position 1 of the liquid-handler deck. 8. Carefully remove the seal from the 384-well plate source plate. 9. Centrifuge the 384-well plate at 168
g for 1 min. 10. Place the 384-well plate on a plate flattener. Then place it on position 3 of the liquid-handler deck. 11. Use the liquid-handler controller PC for the methods for transferring 2 μL of SAP enzyme solution from the 96-well plate to each well of the 384-well plate. When completed, remove and discard 96-well plate. 12. Remove the 384-well plate and flattener from position 3.

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13. Seal the 384-well plate with plate sealing film. Remove the plate from the plate flattener. Make sure edges of the plate sealing film are well sealed. 14. Centrifuge the 384-well plate at 168 x g for 1 min. 15. In a thermal cycler incubate the plate for 37  C for 40 min, followed by 80  C for 5 min, followed by a final 4  C hold. 3.8.3 Adjustment, Preparation, and Addition of Low-Plex iPLEX Gold Reaction Cocktail

1. During the plate incubation, but before making the low plex iPLEX Gold reaction cocktail, adjust the single base extension primers for your primer mix for the cocktail by dividing the primers into a low mass group and a high mass group. For an 18 plex, the nine lowest mass primers will be at a final concentration of 0.625 μM and the nine highest mass primers will be at a final concentration of 1.25 μM in the final 9 μL reaction. Make this primer mix in a 1.5 mL tube. 2. For the iPLEX Gold reaction cocktail, per each sample reaction the following reagents are needed: 0.7395 μL HPLC grade water, 0.2 μL 10
iPLEX Buffer Plus, 0.1 μL iPLEX Termination mix, 0.94 μL Single Base Extension (SBE) primers, 0.0205 μL iPLEX enzyme. Multiply each component by 384 for accommodating enough iPLEX cocktail volume for a 384 well plate (see Note 46). 3. In a 1.5 mL tube, add the calculated volume of reagents for the iPLEX Gold reaction cocktail for a 384-well plate volume in the order in which listed above and mix well. 4. Into a new 96-well plate (v-bottom), dispense 85 μL of low plex iPLEX Gold reaction cocktail into each well of row H. The wells in row H will be used as reservoirs from which you will distribute low plex iPLEX gold reaction cocktail into the rest of the wells. 5. Using a multichannel pipette, draw from wells in row H and distribute 10 μL of iPLEX Gold reaction cocktail to each well in rows A-G. When pipetting is complete, rows A-G should have 10 μL and row H should have 15 μL. 6. Centrifuge the cocktail microplate at 430
g for 1 min. 7. Place the 96-well cocktail plate onto position 1 of the liquidhandler deck. 8. Remove the 384-well sample plate from the thermal cycler and centrifuge at 168
g for 1 min. When complete, remove the plate sealing film. 9. Place the 384-well sample plate on a plate flattener and then place it on position 3 on deck. 10. Use the liquid-handler controller PC for the methods for transferring 2 μL of low plex iPLEX Gold reaction cocktail

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from the 96-well plate to each well of the 384-well plate. When completed, remove and discard 96-well plate. 11. Remove the 384-well plate and flattener from position 3. 12. Seal the 384-well plate with plate sealing film. Remove the plate from the plate flattener. Make sure edges of the plate sealing film are well sealed. 13. Centrifuge the 384-well plate at 168
g for 1 min and then place in the thermal cycler. 14. Thermal cycler conditions include enzyme activation at 94  C for 30 s, amplified with 1 cycle of 94  C
5 s, 52  C
5 s, 80  C
5 s, followed by 4 cycles of 52  C
5 s and 80  C
5 s, followed by 39 cycles of 94  C
5 s, 52  C
5 s, 80  C
5 s, and a 3 min extension at 72  C, and a final 4  C hold. 3.8.4 Cleaning the Low Plex iPLEX Gold Reaction Products

1. After thermocycling, in order to clean up the iPLEX Gold reaction products you must prepare a plate of resin. Lay down a clean plastic sheet so that the excess resin from the dimple plate will fall to plastic sheet. Using the long handled spoon, transfer Clean Resin from the container onto the 6 mg resin 384-well dimple plate (see Note 47). 2. Using the scraper, spread resin into wells of the dimple plate by sweeping the scraper back and forth across the plate. Make sure there is resin in each well. Scrape excess resin off the dimple plate using the scraper and return the excess resin to the resin container. 3. Leave resin the sit in the dimple plate for at least 20 min before continuing. 4. While waiting for dimple plate resin to set, begin adding water to the 384-well sample plate. Put a reservoir of 80 mL of nanopure water on position 1 of the liquid-handler deck. 5. Centrifuge the 384-well sample plate at 168
g for 1 min. 6. After centrifugation, remove the plate sealing film from the 384-well sample plate. 7. Place the 384-well sample plate on a plate flattener and then place it in position 3 of the liquid-handler deck. 8. On the controller PC, use the methods for adding 16 μL of water to each well of the 384-well plate. 9. Remove the 384-well plate and flattener from position 3. 10. Seal the 384-well plate with sealing film and then remove the flattener from plate. Make sure the edges are well sealed. 11. Centrifuge the 384-well plate at 430
g for 30 s or longer if needed until the air bubbles are gone.

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12. Now that the resin is set, gently place the 384-well sample plate upside-down onto the dimple plate. Make sure that the sample plate rests against the small rounded post on the dimple plate in order to align the wells. 13. Holding the plates together, gently flip them over so the resin from the dimple plate falls into the sample wells. Tap the dimple plate so the resin falls into the sample wells making sure that all of the resin falls into the sample plate. 14. Place the 384-well sample plate on a rotator so that the plate is rotated 360 on the long axis for 5 min at room temperature (see Note 48). 15. Centrifuge the 384-well sample plate at 3200
g for 5 min. The samples are ready to be transferred to the SpectoCHIP using the MassARRAY Nanodispenser (see Note 49). 3.8.5 Transfer of iPLEX Reaction to a SpectroChip

1. Make sure the distilled water tank and 50% ethanol tank reservoirs are filled and the waste tank is empty. 2. Precondition the spotting pins (see Note 50). 3. On the nanodispensing Conditioning tab.

software,

select

the

Pin

4. Fill the sonicator with 100% ethanol. 5. Begin pin conditioning of the main head (24 pins) and run for 30 min. 6. Repeat the pin conditioning now for a single head (one pin). 7. Drain the sonicator by selecting drain sonicator. 8. Refill sonicator with 50% ethanol by selecting Fill Sonicator. 9. Put the 384-well plate and an old test SpectroChip on the deck of the Nanodispenser. 10. Go to Load Method, select System, change the file type from *. tmf to *.vmf, and select the file Volume384.vmf, which is in the Volume folder. 11. Run a volume check to adjust the dispense speed. 12. Under the Run Setup tab, load the iPLEX file and change the dispense speed to the best results seen in the volume check. 13. Replace the test SpectroChip with a new one. 14. Under the Status tab pick Start. During the step, approximately 15 nanoliters (nL) of samples are transferred onto the SpectroCHIP. 15. Add 70 μL calibrant into the calibrant reservoir on the Nanodispenser. 16. Under Run Setup tab, load the Calibrant dispense file and in the Status tab, pick the start button.

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17. Open the MassARRAY TYPER software and under plate editor, select plate. 18. Create a new project and add a new plate to the project. 19. Add a new sample group under Sample. 20. Give the sample group a name. Under open folder, choose your sample file. 21. Under MassARRAY TYPER window, select Assay editor. 22. Create a new file with your assay information from your excel output file. 23. Under plate editor, pick your plate and select all wells in the plate. 24. Pick the sample group you created and apply samples. 25. Apply assays by choosing the wells for each assay and make sure that all of the samples and assays are correct on the plate. 26. Before beginning the mass spectrometer, make sure that the FLEXcontrol and ServerControl programs are running. 27. For this application, ensure that the method iPLEX.par and the sample carrier SequenomChip384C are properly loaded in FLEX control. 28. Ensure that MassARRAY CALLER is running. 29. Under Plate Editor, create a new chip run with our assays and samples. 30. In the TYPER folder, choose ChipLinker. 31. Connect to the database so that there is access to both the assay design and plate design. 32. Choose the plate to analyze and the Experiment Name and Chip Barcode. 33. In Dispenser, choose Nanodispenser S. 34. For Process Method, pick Genotype. 35. Enter the barcode of the Chip. 36. Click Add, Create, Done and then exit ChipLinker. 37. Go to SpectroAQUIRE. Under Auto Run Set Up in Barcodes, enter name of SpectroCHIP and ensure that the Parameter file iPLEX is loaded. 38. Place the Chip on the chip holder. 39. Use the MassARRAY® READER and the In/Out function on the Autoflex instrument to introduce target. 40. Under SpectroAQUIRE in the Auto Run Set Up tab to turn on high voltage. 41. On the Auto Run tab under Run, choose Start Auto Run. This will allow the results to be automatically loaded into the database upon completion of the run.

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1. To analyze results, open the MassARRAY® TYPER 4.0 program and select Typer Analyzer section. 2. Load the data into Typer Analyzer. 3. Proceed to the view option and create a set of results visualization panes (see Note 51). 4. Choose experiments for analysis from the Project Explorer. 5. View the Traffic Light window to determine the success of your experiment (see Note 52). 6. View the Automated Data Error window to check for incompatibility with repeats or water samples. 7. Under Histogram Plot look at the individual marker success. 8. View the Call Cluster Plot. The plot shows the clustering of individual genotypes based on calls for each marker (see Note 53). 9. Look at the Details window to check detection peaks for each sample (see Note 54). 10. Save changes upon completion of editing. 11. Export the information and save all wells to file to create an . xml file.

4

Notes 1. Oragene kits are stable for 5 years at room temperature. Volume of collection kit with saliva normally ranges between 2.0 and 5.0 mL. If volume is 5.0 mL, do not add any 1
DPBS. 2. Samples can be stored overnight at 4  C in cell lysis solution if necessary. Add RNase A solution the following day. 3. Slowly pour supernatant in one continuous motion to not disturb the pelleted proteins. 4. Slowly pour supernatant into waste container in one continuous motion to not disturb pelleted DNA. 5. Use low-EDTA DNA suspension buffer to prevent amplification inhibition in downstream experiments. 6. DNA is aliquoted across three 1.5 mL tubes in case of sample contamination or damage in future uses. 7. Tubes will float in the water bath. Ensure that the samplecontaining portion of the tube remains immersed in water. This is accomplished by putting tubes in a rack and placing an Eppendorf rack over the tubes to hold the tubes down in water. Do not allow the water to come into contact with the tube caps to guard against contamination.

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8. For example, if sample volume is 2.0 mL, then add 80 μL Oragene prepIT-L2P Solution to each sample. It is helpful to have a table listed in the protocol with different sample volumes and associated volume of prepIT-L2P Solution that is needed. 9. Tilt tube containing Ficoll-Paque Plus at 45 angle and slowly pipet blood along the side of the tube. Blood should sit nicely on top of Ficoll-Paque Plus. Do not jostle tube once blood is layered on Ficoll-Paque Plus, pre- and postcentrifugation. Layers will mix and become indistinguishable. 10. Be careful not to disturb lymphocyte layer when drawing up plasma layer. The lymphocyte layer is very thin and easy to disturb. It is best to leave a small amount of plasma that is directly above the lymphocytes instead of trying to get every last drop of plasma. 11. Wipe off tubes of residual ethanol before storage. If using permanent marker to label tubes, make sure ethanol did not smear writing on tube. If using printed labels, wipe off because ethanol can damage label adhesive and printed ink over time. 12. Place a water-soaked paper towel into Erlenmeyer flask mouth to prevent melted agarose from spilling in microwave. 13. High quality DNA will be very high molecular weight band (20–25 kb). Fragmented DNA will appear as streaks at lower molecular weights. 14. Allow PicoGreen stock solution to come to room temperature before opening tube. Prepare the PicoGreen working stock solution in a plastic tube, not glass, as the reagent may adsorb to glass surfaces. You will need 100 μL PicoGreen working stock per sample. For best results, use the PicoGreen working solution within a few hours of preparation. Turn the fluorescent plate reader on to allow instrument to warm up before beginning plating. 15. Volumes of TE and Lambda DNA per well to make the highrange standard and low-range standards for the standard curve. High-range standard curve Volume of 1
TE (μL)

Volume of 2 μg/mL Lambda DNA (μL)

Total DNA in well (ng)

Standard H200

0

100

200

Standard H20

90

10

20

Standard H2

99

1

2

Standard H0.2

99.9

0.1

0.2

Blank

100

0

0

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Low-range standard curve Volume of Volume of 50 ng/mL 1
TE (μL) Lambda DNA (μL) Total DNA in well (ng) Standard L5

0

100

5

Standard L0.5

90

10

0.5

Standard L0.05

99

1

0.05

Standard L0.005 99.9

0.1

0.005

Blank

0

0

100

16. Instead of using 1 μL of stock DNA, a 1:10 DNA dilution per sample can be made in sterile autoclaved water (1 μL DNA: 9 μL water). One microliter of the 1:10 dilution may be used for quantification. This may be done to preserve precious DNA or if your sample is highly concentrated and will fall above the highest standard. If a 1:10 dilution is made make sure to multiply the measured PicoGreen concentration DNA yield by 10. 17. The PicoGreen plate can be read up to 1 h after adding the PicoGreen reagent to plate. PicoGreen quantification of saliva DNA samples is a more accurate method to measure dsDNA than optical density by a spectrophotometer. Spectrophotometer measurement will give an inflated yield as it absorbs anything at the 260 nm wavelength including ssDNA, dsDNA, RNA, proteins, and other contaminants. 18. For this and all future reagents: thaw all reagents completely at room temperature. Once thawed, invert each reagent tube gently several times to mix. Gently centrifuge to collect contents to bottom of tube. If DNA plate was prepared previously, thaw it to room temperature. When using multichannel pipette, place tips to upper sides of well for optimal pipetting results. 19. When removing cap mat, make sure to set it upside down in a safe place so that it may be used later in protocol. When placing the cap mat back on, make sure to match to the original orientation of the plate. 20. After the 1 h incubation, you can store the sealed MSA3 plate at 20  C or you can proceed to the Precipitation of the MSA3 plate step. 21. Do not delay in the step so that the blue pellet remains in place. If the blue pellet does not remain in place, repeat the centrifugation step before proceeding.

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22. Make sure not to over dry the pellet as the will be hard to resuspend later. After the 1 h air drying step, you can seal MSA3 plate and store at 20  C or you can proceed to the Resuspension of the MSA3 plate step. 23. Keep any remaining RA1 reagent for XStain HD BeadChip. 24. If the pellets were stored for a long period of time after the precipitation step, you may have to repeat steps 3–5 under the Resuspension of the MSA3 plate section until the pellets are completely resuspended. After resuspension, you can proceed on to the Hybridization Multi BeadChip step or you can store the sealed plate at 20  C. Store at 80  C for storing more than 24 h. Store RA1 at 20  C. 25. 12
1 HD BeadChips are also available. Methods vary slightly for 12
1 HD BeadChips, reference manufacturer’s guide. 26. Only handle the unpackaged BeadChips by the edges. Do not touch the beadstripe area or the sample inlets. 27. When loading the BeadChip, place pipette tip directly to the array surface. Do not hold tip at an angle. After loading, go immediately to the next step in the BeadChip process. 28. Inspect all loaded BeadChip sections visually to make sure DNA samples completely cover each beadstripe. Note any sections that are not completely covered. 29. Keep a steady hand when moving the BeadChips. Do not shake. Keep parallel to bench. Do not touch the sample inlets. 30. Keep a steady hand when moving the Hyb Chamber. Do not shake. Keep parallel to bench. 31. Up to 3 Hyb Chambers may be stacked in a row in the oven as long as the feet of the top chamber fit into the indents on top of the next chamber. A maximum of six chambers per oven. 32. Make sure XC4 reagent is completely dissolved. If pellet or coating is still visible after overnight on bench, vortex at 1625 rpm until completely resuspended. XC4 reagent is used at room temperature. 33. Each wash rack can hold a maximum of eight BeadChips. 34. Make sure to wash reservoirs immediately so that no PB1 remains or it may affect results of future assays. 35. Before beginning, put all single base extension and staining reagents in a rack on bench and keep them in order throughout procedure. If reagent came frozen, thaw to room temperature and centrifuge at 3000
g for 3 min. Gently mix RA1 to dissolve crystals after thawing. Mix XC4 bottle by shaking vigorously. If not dissolved completely, vortex XC4. Make sure the water level on circulator reservoir is filled to correct level. Ensure Chamber Rack is at 44  C and has been tested in multiple locations with temperature probe. Ensure bubbles

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have been removed in the Chamber Rack each time per Tecan manufacturer’s instructions for Te-Flow. Make sure each FlowThrough Chamber is set in rack accurately to allow for correct heat exchange. 36. For the single base extension and XStain pipetting steps, do not touch the pipette tip to the BeadChip surface. Touch instead the reservoir of the glass black plate. 37. If you will image the BeadChip right after the staining procedure, then at this point turn on the BeadArray Reader or iScan to let lasers stabilize. 38. Before starting the washing and coating steps, keep covers on wash dishes to minimize dust and clean them out with low-pressure air. Wash tube racks and dishes before and after each use and rinse with deionized water. Lay out a few layers of Kimwipes on bench to put staining rack on after XC4 wash. You will need an additional clean tube rack that fits the vacuum desiccator. 39. Make sure to use the XC4 in the dish within 10 min of pouring into dish. 40. Before scanning, switch on the iScan Control Software and PC and start the iScan application. Let scanner warm up at least 5 min. See the manufacturer’s instructions for using a non-Illumina LIMS system. Alternatively, instead of iScan, Illumina’s BeadArray Reader may be used to image BeadChips. 41. If the software does not display the correct scan setting, click Settings and follow the manufacturer’s instructions to change. An incorrect scan setting for the BeadChip type may cause problems with the images. 42. JPEG (.jpg) files will let you view the imaged array sections but you cannot extract bead intensity data. TIFF (.tif) files will let you view the imaged array sections and extract bead intensity data but are much larger files than .jpg files. 43. Once returning to the start window, images can no longer be viewed in the iScan software. However, a different software such as GenomeStudio (Illumina) may be used to view scanned images. 44. All primers for the iPLEX assay are designed using Sequenom Assay Design Software per the manufacturer’s instructions. 45. The SAP enzyme solution is somewhat viscous. Pipet slowly to minimize loss due to pipetting. Make sure to pipet into centers of microplate wells. Droplets must not be placed on the sides of wells so that they adhere to well walls. Make sure that there are no air bubbles in the wells. If there are bubbles, make sure to centrifuge the sealed plate for 1 min to remove air bubbles and collect liquid into center of wells. This same method applies for

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transferring the iPLEX Gold reaction cocktail. These volumes are approximate and actual volumes may be slightly less due to pipetting loss during transfer. 46. 7 and 14 μM is for the doubled concentration of the high mass primers. Low mass primers will be at 0.625 μM and high mass primers will be at 1.25 μM in the final 9 μL reaction. This method is for preparing low plex iPLEX Gold reaction cocktail for the same multiplexed assays, different DNAs for 96 to 384 well dispensing. Slightly different methods apply for instead preparing low plex iPLEX Gold reaction cocktail for same DNA, different multiplexed assays, more than one destination plate, or when performing a higher plex number (19 plexes–36+ plexes). 47. After thermocycling, allow sample plate to come to room temperature before performing clean resin step. You must wear gloves and safety glasses when handling all equipment, parts, and reagents like Clean Resin. Excess resin that fell onto the plastic sheet can be put back into the container for future use. Make sure to keep the Clean Resin tightly closed when not in use so that it does not dry out. If you are not ready to perform the resin step, you may store the sample plate by sealing the plate with plate-sealing film and storing at 20  C. 48. Make sure that there are no air bubbles in the wells before putting the 384-well sample plate on the rotator. If there are air bubbles, centrifuge the plate briefly and then rotate it. 49. If you are not ready to transfer samples to the SpectroCHIP, you may seal the plate with adhesive sealing foil and store the sample plate at 20  C. Make sure the edges of foil are well sealed. Do not store the plate for more than 2 weeks. When you are ready to use it, thaw, rotate and then centrifuge the plate at 3200
g for 5 min before transferring samples to the SpectroCHIP. 50. Reference the MassARRAY Nanodispenser Guide for complete details on preconditioning the spotting pins. 51. Various layouts may be created. 52. Overall success is measure by a color rating of green for good, yellow for moderate, or red for bad quality for the tested markers for each well. 53. Individual calls for the three different genotypes can be changed to assign discarded data or to remove data. Use a set of predetermined criteria to determine these calls. 54. This allows you to look at assay efficiency and to determine other problems that may have occurred such as a failure of the iPLEX reaction.

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10. Chung CC, Kanetsky PA, Wang Z, Hildebrandt MA, Koster R, Skotheim RI, Kratz CP, Turnbull C, Cortessis VK, Bakken AC, Bishop DT, Cook MB, Erickson RL, Fossa SD, Jacobs KB, Korde LA, Kraggerud SM, Lothe RA, Loud JT, Rahman N, Skinner EC, Thomas DC, Wu X, Yeager M, Schumacher FR, Greene MH, Schwartz SM, McGlynn KA, Chanock SJ, Nathanson KL (2013) Meta-analysis identifies four new loci associated with testicular germ cell tumor. Nat Genet 45(6):680–685. https://doi.org/10.1038/ng.2634 11. Litchfield K, Sultana R, Renwick A, Dudakia D, Seal S, Ramsay E, Powell S, Elliott A, Warren-Perry M, Eeles R, Peto J, Kote-Jarai Z, Muir K, Nsengimana J, UKTCC, Stratton MR, Easton DF, Bishop DT, Huddart RA, Rahman N, Turnbull C, UKTCC (2015) Multi-stage genome-wide association study identifies new susceptibility locus for testicular germ cell tumour on chromosome 3q25. Hum Mol Genet 24 (4):1169–1176. https://doi.org/10.1093/ hmg/ddu511 12. Litchfield K, Holroyd A, Lloyd A, Broderick P, Nsengimana J, Eeles R, Easton DF, Dudakia D, Bishop DT, Reid A, Huddart RA, Grotmol T, Wiklund F, Shipley J, Houlston RS, Turnbull C (2015) Identification of four new susceptibility loci for testicular germ cell tumour. Nat Commun 6:8690. https://doi. org/10.1038/ncomms9690 13. Wang Z, McGlynn KA, Rajpert-De Meyts E, Bishop DT, Chung CC, Dalgaard MD, Greene MH, Gupta R, Grotmol T, Haugen TB, Karlsson R, Litchfield K, Mitra N, Nielsen K, Pyle LC, Schwartz SM, Thorsson V, Vardhanabhuti S, Wiklund F, Turnbull C, Chanock SJ, Kanetsky PA, Nathanson KL, Testicular Cancer C (2017) Meta-analysis of five genome-wide association studies identifies multiple new loci associated with testicular germ cell tumor. Nat Genet 49 (7):1141–1147. https://doi.org/10.1038/ ng.3879 14. Marcotte EL, Pankratz N, Amatruda JF, Frazier AL, Krailo M, Davies S, Starr JR, Lau CC, Roesler M, Langer E, Hallstrom C, Hooten AJ, Poynter JN (2017) Variants in BAK1, SPRY4, and GAB2 are associated with pediatric germ cell tumors: a report from the children’s oncology group. Genes Chromosom Cancer 56 (7):548–558. https://doi.org/10.1002/gcc. 22457 15. Litchfield K, Loveday C, Levy M, Dudakia D, Rapley E, Nsengimana J, Bishop DT, Reid A, Huddart R, Broderick P, Houlston RS, Turnbull C (2018) Large-scale sequencing of

Genetic Predisposition to TGCT testicular germ cell tumour (TGCT) cases excludes major TGCT predisposition gene. Eur Urol 73:828. https://doi.org/10.1016/ j.eururo.2018.01.021 16. Hansen TV, Simonsen MK, Nielsen FC, Hundrup YA (2007) Collection of blood, saliva, and buccal cell samples in a pilot study on the Danish nurse cohort: comparison of the response rate and quality of genomic DNA. Cancer Epidemiol Biomark Prev 16 (10):2072–2076. https://doi.org/10.1158/ 1055-9965.EPI-07-0611. 16/10/2072 [pii] 17. Woo JG, Sun G, Haverbusch M, Indugula S, Martin LJ, Broderick JP, Deka R, Woo D (2007) Quality assessment of buccal versus blood genomic DNA using the Affymetrix 500 K GeneChip. BMC Genet 8:79. https:// doi.org/10.1186/1471-2156-8-79. 14712156-8-79 [pii] 18. Feigelson HS, Rodriguez C, Welch R, Hutchinson A, Shao W, Jacobs K, Diver WR, Calle EE, Thun MJ, Hunter DJ, Thomas G, Chanock SJ (2007) Successful genome-wide scan in paired blood and buccal samples. Cancer Epidemiol Biomark Prev 16 (5):1023–1025. https://doi.org/10.1158/ 1055-9965.EPI-06-0859. 16/5/1023 [pii] 19. Bahlo M, Stankovich J, Danoy P, Hickey PF, Taylor BV, Browning SR, Brown MA, Rubio JP (2010) Saliva-derived DNA performs well in large-scale, high-density single-nucleotide polymorphism microarray studies. Cancer Epidemiol Biomark Prev 19(3):794–798. https://

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doi.org/10.1158/1055-9965.EPI-09-0812. 1055-9965.EPI-09-0812 [pii] 20. Koni AC, Scott RA, Wang G, Bailey ME, Peplies J, Bammann K, Pitsiladis YP, Consortium I (2011) DNA yield and quality of saliva samples and suitability for large-scale epidemiological studies in children. Int J Obes 35 (Suppl 1):S113–S118. https://doi.org/10. 1038/ijo.2011.43 21. Chadwick RB, Conrad MP, McGinnis MD, Johnston-Dow L, Spurgeon SL, Kronick MN (1996) Heterozygote and mutation detection by direct automated fluorescent DNA sequencing using a mutant Taq DNA polymerase. Biotechniques 20(4):676–683 22. Gunderson KL, Steemers FJ, Lee G, Mendoza LG, Chee MS (2005) A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet 37(5):549–554 23. Steemers FJ, Chang W, Lee G, Barker DL, Shen R, Gunderson KL (2006) Whole-genome genotyping with the single-base extension assay. Nat Methods 3(1):31–33 24. Genome-wide association studies and genomic prediction (2013) Methods in Molecular Biology, vol 1019, 1st edn. Humana Press, Totowa, NJ 25. Bradic M, Costa J, Chelo IM (2011) Genotyping with Sequenom. Methods Mol Biol 772:193–210. https://doi.org/10.1007/ 978-1-61779-228-1_11

Chapter 15 A Circulating MicroRNA Panel for Malignant Germ Cell Tumor Diagnosis and Monitoring Matthew J. Murray, Cinzia G. Scarpini, and Nicholas Coleman Abstract Reverse transcription (RT) based quantitative PCR (qPCR) for quantifying microRNAs (miRNAs) in in the circulation presents specific challenges. Here, we describe an optimized research protocol to assess serum sample quality and quantify levels of a panel of four test miRNAs (miR-371a-3p, miR-372-3p, miR-3733p, and miR-367-3p) that enables highly sensitive and specific malignant germ cell tumor (GCT) diagnosis and monitoring. This protocol utilizes a multiplex RT step using Taqman miRNA stem-loop primers. A multiplexed preamplification stage is then employed to increase the sensitivity of the final quantification step, which is performed using standard singleplex Taqman qPCR methodology. Key words Circulating, microRNA, Serum, Preamplification, RT-qPCR

1

Introduction MicroRNAs (miRNAs) are short, non–protein-coding RNAs that regulate the expression of protein-coding genes. They are dysregulated in a wide range of cancers [1]. As miRNAs are released into the bloodstream from cancer cells, measurement of circulating miRNAs offers substantial promise for cancer diagnosis and monitoring [2]. However, robust quantification of circulating cancerspecific miRNAs in body fluids is challenging, due to their relatively low abundance and technical variations caused by preanalytical parameters (e.g., storage and processing differences) [3]. Previously, we reported a generic pipeline that can be utilized for quantification of circulating cancer biomarker miRNAs [4]. The pipeline included quality control (QC) steps that are performed following RNA isolation from circulating biospecimens, and prior to the formal final quantitative PCR (qPCR) step, to minimize technical variation [4]. These steps included the following: l

assessment of hemolysis, which can affect circulating miRNA profiles [5–7];

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_15, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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use of a nonhuman exogenous spike-in miRNA (cel-miR-39-3p) for QC purposes, to identify technical differences in RNA recovery and PCR efficiency between samples [8];

l

use of a stable endogenous miRNA (miR-30b-5p) for normalization within a sample, to allow for biological differences in endogenous miRNA levels between samples [8];

Here, we describe in detail the refined and optimized protocol we use for quantifying a specific panel of miRNAs for potential diagnosis and disease monitoring in malignant germ cell tumors (GCTs). Our approach includes the QC steps described above. The rationale for selection of the miRNA panel used is that in malignant GCT tissues, over-expression of two specific miRNA clusters, namely miR-371–373 (at chromosome 19q13.41) and miR-302–367 (miR-302a-d plus miR-367; at 4q25) is seen regardless of patient age (pediatric or adult), tumor histologic subtype (yolk sac tumor, seminoma, or embryonal carcinoma) or anatomical site (gonadal or extragonadal) [9]. Such changes represent the first universal molecular abnormality detected in this tumor type [10]. Importantly, these miRNAs are not coordinately overexpressed in any other cancer or disease state, adding to their potential as specific biomarkers, particularly if used in combination [9]. These data suggested that miRNAs from the miR-371–373 and miR-302–367 clusters would be highly sensitive and specific universal biomarkers of all malignant GCTs [11]. This is important, as levels of the conventional protein markers alpha-fetoprotein (AFP) and human chorionic gonadotrophin (HCG) are only elevated in ~60% of malignant GCT patients at diagnosis [12]. The development of serum biomarkers that offer greater sensitivity and specificity for diagnosing and monitoring malignant GCTs would therefore be of considerable clinical value [12]. Indeed, since our first report in 2011 demonstrating that circulating levels of members of the miR-371–373 and miR-302–367 clusters were elevated in the serum at the time of malignant GCT diagnosis and fell with treatment [13], multiple other studies have confirmed these observations [8, 12, 14–24], predominantly in malignant testicular GCTs. A panel of just four circulating miRNAs (namely miR-371a-3p, miR-372-3p, miR-373-3p, and miR-367-3p) is highly sensitive and specific for malignant GCT diagnosis [8, 19, 22]. Circulating miRNA levels from the panel fall after definitive surgery and/or chemotherapy treatment [8, 13, 18, 19, 22], and are also highly sensitive for detecting relapse [8]. Recently, circulating miR-371a-3p levels were shown to be more sensitive than AFP and HCG for detection of residual malignant disease/relapse [23], while elevated levels can precede radiological or clinical manifestations of disease by many months [14]. Of the panel of four microRNAs, miR-371a-3p is the most individually predictive [16, 17, 24–27]. The translational promise of this circulating miRNA panel

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has been independently highlighted. Comments include that published data “provide convincing evidence for the use of miRNAs as biomarkers for (germ cell) testicular cancer” and that the “introduction of miRNA measurements is anticipated for the clinical management of patients with testicular germ cell tumors in the near future” [28]. In general, protocols currently utilized by different research groups are broadly based on our original, highly sensitive preamplified reverse transcription (RT) based qPCR methodology (RT-qPCR) [13] and there is now convergence in use of endogenous microRNA normalization. However, there remain some minor variations, for example, in RNA extraction method and QC approaches. For example, one study did not use an exogenous nonhuman spike-in miRNA and relied on endogenous miR-93-5p for normalization [17]. Recent large-scale studies however have adopted miR-30b-5p as the endogenous housekeeping gene [16, 24], after it was demonstrated to be highly stable and appropriate for such purposes [8]. Interestingly, this was because miR-93-5p was found to be unsuitable for normalization, due to a significant difference in levels between the malignant GCT and control cohorts [24]. The protocol described here includes a formal hemolysis quantification step and the assessment of exogenously spiked-in nonhuman (cel-miR-39-3p) to control for RNA extraction efficiency, as well as normalization of test miRNA levels to the endogenous human (miR-30b-5p) housekeeping miRNA [11]. We therefore believe that this protocol maximizes sensitivity and specificity for malignant GCT diagnosis and monitoring. The thorough and scalable RT-qPCR approach for malignant GCT diagnosis and monitoring described here allows for the sensitive quantification of a panel of four malignant GCT-specific miRNAs, including miR-371a-3p. With this approach, sample quality can be verified before proceeding to quantify the panel of malignant GCT-specific miRNAs. Only a very small quantity of input RNA from circulating samples is required, due to the use of a multiplexed RT and preamplification step [4]. This approach maximizes test sensitivity, reduces the quantity of RNA and reagents required, and therefore lowers costs [4]. This is achieved by combining multiple different stem-loop primers in the RT step and subsequently preamplifying the copy DNA (cDNA) products prior to the final singleplex qPCR step [4]. In principle, the methods described here can be used for quantifying the test miRNA panel in other body fluids, for example cerebrospinal fluid (CSF) [8]. However, it should be noted that the QC steps and housekeeping miRNAs detailed in this protocol have been specifically developed and validated for use on serum samples [4]. Housekeeping miRNAs should be validated for each body fluid being tested, so we recommend optimizing QC screening and housekeeping miRNA(s) for each sample type [4].

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Materials All reagents and plasticware must be sterile and nuclease-free; nuclease-free working practices should be implemented [4]. Preparation of solutions should be carried out at room temperature unless otherwise stated. Frozen reagents should be thawed on ice. Reagents should be kept on ice, where appropriate.

2.1 Serum Isolation from Whole Blood

1. Appropriate volume tube with clotting activator and gel for serum separation (e.g., Sarstedt S-monovette Z-gel tube). 2. 2 mL nuclease-free cryovials.

2.2

RNA Extraction

1. Qiagen miRNeasy serum/plasma kit (Cat No. 217184): QIAzol lysis reagent, RNeasy MinElute spin columns in 2 mL collection tubes, buffer RWT, buffer RPE, 2 mL collection tubes, nuclease-free water (NFW). Prior to first use, dilute buffer RWT and RPE with 2 and 4 volumes of 96–100% ethanol, respectively, as per the manufacturer’s recommendations. 2. Cel-miR-39-3p (nonhuman exogenous spike-in miRNA) stock solution: Qiagen miRNeasy serum/plasma spike-in control (Cat No. 219610, 10 pmol), NFW. Centrifuge the lyophilized miRNeasy serum/plasma spike-in control for 30 s at 12,000
g, add 300 μL NFW then vortex to give a 2
1010 copy/μL solution (see Note 1). 3. Cel-miR-39-3p working solution: Add 4 μL of the cel-miR-393p stock solution (2
1010 copy/μL) to 16 μL of the NFW then briefly vortex. Transfer 2 μL of this mix to a further 48 μL of NFW then briefly vortex, giving a 1.6
108 copies/μL working solution sufficient to process 12 samples (see Note 1). 4. QIAzol-MS2-cel-miR-39-3p mix: Roche MS2 bacteriophage RNA (Cat No. 10165948001), NFW, Qiagen QIAzol lysis reagent (from kit), cel-miR-39-3p working solution (see above). Prepare a solution of 0.8 μg/μL MS2 bacteriophage RNA in NFW. Make a stock solution consisting of 1000 μL QIAzol lysis reagent, 1.6 μL* of 0.8 μg/μL MS2 bacteriophage RNA and 3.5 μL cel-miR-39-3p working solution per sample to be tested [giving a fixed quantity of 5.6
108 cel-miR-393p copies per sample; [4, 8]]. Invert to mix. *Note the very small volume for a single reaction. To minimize any potential technical variation, it is therefore suggested to process samples in batches of at least 12. 5. Chloroform. 6. 100% ethanol. 7. 80% ethanol made with NFW.

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2.3 Quality Control (QC) Reverse Transcription Step

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1. NFW. 2. ThermoFisher Scientific TaqMan MicroRNA Reverse Transcription Kit (Cat No. 4366597): 10
RT buffer, dNTP mix w/dTTp (100 M), RNase inhibitor (20 U/μL), MultiScribe RT enzyme (50 U/μL). 3. ThermoFisher Scientific TaqMan microRNA Assay primers 5
. The primers are part of the ThermoFisher Scientific TaqMan microRNA Assays, Cat No. 4427975. The primers used are cel-miR-39-3p (assay ID 000200), miR-30b-5p (000602), miR-23a-3p (000399) and miR-451a (001141) as the QC miRNAs. For details see Table 1. 4. RNA sample. 5. 0.2 mL nuclease-free PCR wells/plate. 6. PCR well/plate seals. 7. Thermal cycler—program detailed in Table 2.

Table 1 Taqman miRNA assays for quality control (QC), including housekeeping and hemolysis assessment

miRNA

Role in reaction

miRBase accession number

cel-miR-393p

Nonhuman spike-in

MIMAT0000010

UCACCGGGUGUAAAUCAGC UUG

miR-30b-5p

Housekeeping miRNA

MIMAT0000420

UGUAAACAUCCUACAC UCAGCU

miR-23a-3p

Hemolysis assessment

MIMAT0000078

AUCACAUUGCCAGGGA UUUCC

miR-451a

Hemolysis assessment

MIMAT0001631

AAACCGUUACCAUUACUGAG UU

50 –30 nucleotide sequence

Table 2 Thermal cycler program for reverse transcription (RT) step Temperature

Time

16  C

30 min



42 C

30 min

85  C

5 min

4 C

1

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2.4 Custom miRNA Panel Multiplex Reverse Transcription (RT) Step

1. NFW. 2. ThermoFisher Scientific TaqMan MicroRNA Reverse Transcription Kit (Cat No. 4366597): 10
RT buffer, dNTP mix w/dTTp (100 M), RNase inhibitor (20 U/μL), MultiScribe RT enzyme (50 U/μL). 3. Custom multiplex primer mix: Multiple selected ThermoFisher Scientific TaqMan MicroRNA Assay primer 5
. The primers are part of the ThermoFisher Scientific TaqMan microRNA Assays, Cat No. 4427975. The malignant GCT miRNA biomarker panel (and relevant Taqman miRNA assay IDs) comprises miR-371a-3p (assay ID 002124), miR-372-3p (000560), miR-373-3p (000561) and miR-367-3p (000555), as originally described [13], for the malignant GCT panel per se and miR-30b-5p (000602) and cel-miR39-3p (000200) for QC/normalization. See Note 2. Nuclease-free Tris-EDTA (10 mM Tris, 1 mM EDTA) pH 8.0. Each multiplex RT reaction uses a total of 3 μL of primer made up of equal quantities of primers for each target miRNA, each diluted to 0.1
. Therefore, combine equal volumes of each Taqman miRNA-specific primer and pipette gently to mix. Using nuclease-free Tris-EDTA (10 mM Tris, 1 mM EDTA) pH 8.0, dilute the primer mix so that each individual primer in the multiplex is at a concentration of 0.1
. For the malignant GCT miRNA panel, six miRNA assays are used: the four test miRNAs (namely miR-371a-3p, miR-372-3p, miR-373-3p and miR-367-3p), as well as cel-miR-39-3p and miR-30b-5p for QC/normalization purposes (see Note 2). Thus, take 10 μL of each primer (5
) and pipette gently to mix, to give a total 60 μL primer volume. This is then diluted with 440 μL of Tris– EDTA to give a total 500 μL primer mix volume, with each primer at 0.1
. This multiplex primer mix can be aliquoted and stored at 20  C for future use, if required (see Note 3). Three microliter of this multiplex primer mix is used for each RT reaction. This is then scaled up by a factor of the number of reactions needed, that is, 12 reactions would require a total volume of 36 μL of multiplex primer mix (see Table 3). 4. 0.2 mL nuclease-free PCR wells/plate. 5. RNA sample. 6. PCR well/plate seals. 7. Thermal cycler—program detailed in Table 2.

2.5 Multiplex Preamplification Step

1. ThermoFisher Scientific 2
Taqman Pre-amp Master Mix (Cat No. 4391128). 2. Custom multiplex probe mix: Multiple selected ThermoFisher Scientific TaqMan MicroRNA Assay probes 20
. The probes are part of the ThermoFisher Scientific TaqMan microRNA

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Table 3 Reaction volumes for malignant GCT multiplex reverse transcription (RT) mastermix

Reagent

Volume for one reaction

Example volume for 12 reactions

Nuclease-free water (NFW)

4.16 μL

49.92 μL

10
RT buffer

1.50 μL

18.00 μL

dNTP mix w/dTTp (100 M)

0.15 μLa

1.80 μL

RNase inhibitor (20 U/μL)

0.19 μL

2.28 μL

Custom multiplex primer mix 5
(see Subheading 2.4, item 3)

3.00 μL

36.00 μLc

MultiScribe RT enzyme (50 U/μL)

1.00 μL

12.00 μL

Total

10.00 μL

120.00 μL

a b

a

Note very small volumes for single reaction. To minimize any potential technical variation, options include ensuring samples are processed in batches of at least 12 samples, or, if this is not possible, diluting the dNTP tenfold to 10 M and RNase inhibitor tenfold to 2 U/μL for the RT step, and reducing the volume of input NFW accordingly. Thus, for one reaction, the volumes would be as follows: l NFW ¼ 1.1 μL l diluted dNTP mix (10 M) ¼ 1.5 μL l diluted RNase inhibitor (2 U/μL) ¼ 1.9 μL b Volume of each individual RT primer for one reaction ¼ 3 μL/6 miRNAs tested ¼ 0.5 μL. c For 12 reactions the volume of each individual RT primer is 6 μL.

Assays, Cat No. 4427975. Nuclease-free Tris–EDTA (10 mM Tris, 1 mM EDTA) pH 8.0. Taqman MicroRNA Assay probes 20
should be protected from the light. Each multiplex preamplification (pre-amp) reaction uses a total of 12.5 μL of custom multiplex probe mix (see Note 3), made up of equal quantities of each probe, each diluted to 0.2
. Therefore, combine equal volumes of each Taqman miRNA-specific probe and pipette gently to mix. Using Tris-EDTA (10 mM Tris, 1 mM EDTA) pH 8.0, dilute the probe mix so that each individual probe in the multiplex is at a concentration of 0.2
. For the malignant GCT miRNA panel, six miRNA assay probes are used in the multiplex (see Note 2): thus take 10 μL of each probe (20
) and pipette gently to mix, to give 60 μL probe volume. This is then diluted with 940 μL of Tris-EDTA to give a total 1000 μL probe mix volume, with each probe at 0.2
. This multiplex probe mix can be aliquoted and stored at 20  C for future use, if required. 3. 0.2 mL nuclease-free PCR wells/plate. 4. cDNA sample. 5. PCR well/plate seals. 6. Thermal cycler—program detailed in Table 4. 7. NFW.

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Table 4 Thermal cycler program for malignant GCT multiplex preamplification step Temperature 

95 C

Time 10 min

Repeat 14 cycles of 95  C 

60 C

15 s 4 min

Hold 4 C

1

Table 5 Quantitative PCR (qPCR) program for individual microRNAs Temperature

Time

95  C

10 min

Repeat 45 cycles of

2.6 Quantitative PCR (qPCR) for Individual miRNAs

95  C

15 s

60  C

1 min

1. ThermoFisher Scientific TaqMan Universal Master Mix II, no UNG (e.g., Cat No. 4440040, but note this item can be purchased in larger packs, each with a different catalog number). 2. ThermoFisher Scientific TaqMan MicroRNA Assay probes 20
. The probes are part of the ThermoFisher Scientific TaqMan microRNA Assays, Cat No. 4427975. Note that Taqman MicroRNA Assay probes 20
should be protected from the light. 3. Half-skirted white 96-well PCR plate. 4. Optical qPCR adhesive film. 5. Real-time qPCR thermal cycler—program detailed in Table 5.

3

Methods Use the flowchart (Fig. 1) to direct the protocol workflow through the QC and multiplex steps, including verification. Nuclease-free working practices should be implemented, for example cleaning with nuclease neutralizing agents, use of filter pipette tips etc. In addition, it is recommended that the RNA extraction, and the

A Circulating MicroRNA Panel for Malignant Germ Cell Tumors

Start

End

Blood collection and sample processing [3.1]

Malignant GCT miRNA panel - Data analysis: ΔΔ Cq method and normalization [3.4.4]

RNA extraction [3.2]

Malignant GCT miRNA panel - qPCR for individual miRNAs [3.4.3/3.3.2]

Quality control – Reverse transcription step [3.3.1]

Malignant GCT miRNA panel - Multiplex preamplification step [3.4.2]

Quality control – qPCR for individual miRNAs [3.3.2]

Malignant GCT miRNA panel - Multiplex reverse transcription step [3.4.1] NO

Quality control – Data analysis [3.3.3]

YES

Quality control (QC) pass?

Fig. 1 Workflow plan. Numbers relate to chapter subheadings. GCT germ cell tumor

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preparation of the multiplex reverse transcription (RT) and preamplification steps, are performed in a different laboratory area/room from the setting up of the final singleplex individual miRNA quantification, to avoid potential issues with contamination. Ensure all solutions are prepared prior to beginning each section. Serum isolation is performed at room temperature, all other steps are to be performed on ice (4 ˚C) unless otherwise stated. 3.1 Serum Isolation from Whole Blood

1. Collect whole blood by venepuncture into an appropriate tube with clotting activator and gel for serum separation. 2. Invert the tube 8–10 times then store in a vertical position for 30 min. 3. Centrifuge the tube in a swinging bucket rotor at 2500
g for 10 min at room temperature. 4. Transfer 1 mL aliquots of the top serum layer into 2 mL nuclease-free cryovials. The isolated serum may be used immediately or stored at 80  C.

3.2

RNA Extraction

Prior to starting, ensure that all materials for the RNA extraction are prepared (see Subheading 2.2). 1. Defrost the serum samples on ice, then centrifuge at 400
g for 5 min at 4  C. 2. Transfer 200 μL of the sample supernatant to a 2 mL nucleasefree tube, ensuring any pellet is not disturbed (see Note 4). 3. Add 1000 μL of QIAzol-MS2-cel-miR-39-3p mix (prepared according to Subheading 2.2, items 2–4) to each sample and vortex for 10 s. Incubate at room temperature for 5 min. 4. Add 200 μL of chloroform to each sample and vortex for 15 s. Incubate at room temperature for 3 min. 5. Centrifuge the samples at 12,000
g for 30 min at 4  C. Steps 6–14 are performed at room temperature. 6. Transfer the top aqueous phase layer into a 2 mL nuclease-free tube, ensuring that the middle protein layer is not disturbed (see Note 5). Discard the bottom and middle layers. 7. Measure the volume of the aqueous phase, and add 1.5 times the volume of 100% ethanol. Mix well by pipetting. 8. Ensure that the RNeasy MinElute spin columns are inserted into 2 mL collection tubes. Transfer 750 μL of the sample to the column and centrifuge at >8000
g for 30 s at room temperature. Discard the flow-through. Repeat until the entire volume of sample has been passed through the column. 9. Add 700 μL of buffer RWT to the column and centrifuge at >8000
g for 30 s. Discard the flow-through.

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10. Add 500 μL of buffer RPE to the column and centrifuge at >8000
g for 30 s. Discard the flow-through. 11. Add 500 μL of 80% ethanol to the column and centrifuge at >8000
g for 2 min. Discard both the collection tube and the flow-through. 12. Transfer the column to a fresh 2 mL collection tube and centrifuge at >8000
g for 1 min. 13. Transfer the column to a 2 mL nuclease-free collection tube and add 50 μL of NFW to the membrane. Incubate for 2 min at room temperature, then centrifuge at >8000
g for 1 min. 14. Add a further 50 μL NFW to the membrane and incubate for 2 min at room temperature. Centrifuge at >8000
g for 1 min. The eluate contains the extracted total RNA (see Note 6) which may be used immediately or stored at 80  C for subsequent use. 3.3 Quality Control (QC) 3.3.1 Quality Control (QC) Reverse Transcription (RT) Step

For serum QC, the levels of cel-miR-39-3p, miR-30b-5p, miR-23a-3p and miR-451a are quantified using the method described below. Details of the assays used are found in Table 1 and Subheading 2.3. 1. Create a separate RT-mastermix for each QC miRNA. Prepare the mastermixes in 2 mL nuclease-free tubes according to the volumes stated in Table 6. The volume prepared should be sufficient to test the samples and a no-template control (NTC) sample, plus an additional 10% volume to allow for potential pipetting error. Add the reagents in the order listed in the table and mix by gently pipetting.

Table 6 Reaction volumes for microRNA-specific (initial QC) reverse transcription (RT) mastermix

a

Reagent

Volume for one reaction

Example volume for 12 reactions

Nuclease-free water (NFW)

4.16 μL

49.92 μL

10
RT buffer

1.50 μL

dNTP mix w/dTTp (100 M)

18.00 μL

0.15 μL

a

1.80 μL

RNase inhibitor (20 U/μL)

0.19 μL

a

2.28 μL

Taqman microRNA Assay primer 5


3.00 μL

36.00 μL

MultiScribe RT enzyme (50 U/μL)

1.00 μL

12.00 μL

Total

10.00 μL

120.00 μL

Note very small volumes for single reaction. To minimize any potential technical variation, options include ensuring samples are processed in batches of at least 12 samples, or, if this is not possible, diluting the dNTP tenfold to 10 M and RNase inhibitor tenfold to 2 U/μL for the RT step, and reducing the volume of input NFW accordingly. Thus, for one reaction, the volumes would be as follows: l NFW ¼ 1.1 μL l diluted dNTP mix (10 M) ¼ 1.5 μL l diluted RNase inhibitor (2 U/μL) ¼ 1.9 μL

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2. Transfer 10 μL aliquots of each miRNA specific RT-mastermix into the appropriate number of 0.2 mL PCR wells. There should be one well per sample, including the NTC, for each miRNA tested. 3. Gently mix the RNA eluate. Add 5 μL of RNA sample to its corresponding well containing RT-mastermix. For the NTC well, add 5 μL NFW. On addition, mix the well contents by gently pipetting. Seal the wells appropriately. 4. Briefly centrifuge at 1000
g before incubating on ice for 5 min. 5. Place in a thermal cycler and run the reverse transcription program detailed in Table 2. 6. Dilute the resulting cDNA product by adding 86.5 μL of NFW to the 15 μL of reaction product. Mix gently by pipetting (see Note 7). 7. The resulting solution contains diluted cDNA which may be used immediately or stored at 20  C for subsequent use (see Note 7). 8. The dilution performed here means that the diluted cDNA is “qPCR-ready” and no further NFW needs to be added at the qPCR stage (see Subheading 3.3.2). The immediate dilution step of the cDNA product aims to minimize subsequent technical variance, as larger volumes will be pipetted. 3.3.2 Quantitative PCR (qPCR) for Individual miRNAs

Taqman MicroRNA Assay probes 20
should be protected from the light. 1. Create a separate qPCR mastermix for each target miRNA. Prepare the qPCR mastermixes in 2 mL nuclease-free tubes according to the volumes stated in Table 7. The volume prepared should be sufficient to test the samples (including the NTC from the reverse transcription step) and a qPCR no-template control (qPCR-NTC) in triplicate, plus an additional 10% volume to allow for potential pipetting error. Add the reagents in the order listed in the table and mix by gently pipetting. 2. Transfer 11 μL aliquots of each miRNA specific qPCRmastermix into the appropriate number of wells of a white 96-well qPCR plate, that is, enough to test each sample, the NTC and the qPCR-NTC (using NFW) in triplicate. 3. Add 9 μL of the diluted cDNA sample [from Subheading 3.3.1 above (quality control) or Subheading 3.4.3 below (multiplex)] to each corresponding miRNA-specific qPCR-mastermix well. On addition, mix the well contents by gently pipetting.

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Table 7 Reaction volumes for microRNA-specific quantitative PCR (qPCR) mastermix Volume for one reaction

Reagent

TaqMan Universal Master-Mix II, no UNG 10.00 μL

Example volume for 36 reactions 360.00 μL

Specific TaqMan microRNA Assay probe 20


1.00 μL

36.00 μL

Total

11.00 μL

396.00 μL

4. Seal the plate with an optical qPCR seal. 5. Centrifuge briefly at 1000
g (see Note 8). 6. Place the plate in a qPCR thermal cycler. Ensure the cycler is set to FAM fluorophore detection (520 nm wavelength) and background correction for the plate has been applied, then run the qPCR program as detailed in Table 5. 3.3.3 Quality Control (QC) Data Analysis

1. For each sample, calculate the mean raw Cq value for each miRNA from the triplicate raw Cq values. 2. Calculate the hemolysis score (ΔCqHem) for each sample using the equation below: ΔCqHem ¼ mean Cq miR-23a-3p  mean Cq miR-451a. 3. Use the following criteria to evaluate sample quality: (a) If the ΔCqHem is >8, it is considered significantly hemolyzed. If possible, obtain a new serum sample for analysis. (b) If the cel-miR-39-3p mean raw Cq is >25, the RNA extraction step should be repeated, if possible. (c) If the miR-30b-5p mean raw Cq is >30, there is a potential problem with sample integrity (see Note 9). Caution should be applied when interpreting results from these samples. If possible, repeat the RNA extraction or obtain a fresh serum sample.

3.4 Custom miRNA Panel Multiplex

Prior to starting, ensure that all materials for the multiplex RT step are prepared (see Subheading 2.4).

3.4.1 Custom miRNA Panel Multiplex Reverse Transcription (RT) Step

1. Prepare one multiplex RT-mastermix in a 2 mL nuclease-free tube according to the volumes stated in Table 3. The volume prepared should be sufficient to process all samples and an NTC sample, plus an additional 10% volume to allow for potential pipetting error. Add the reagents in the order listed in the Table 3 and mix by gently pipetting. 2. Transfer 10 μL aliquots of the multiplex RT-mastermix into 0.2 mL PCR wells. There should be one well per sample, including the NTC.

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3. Gently mix the RNA eluate. Transfer 5 μL of each RNA sample to an individual multiplex RT mastermix well. For the NTC well, transfer 5 μL NFW. Seal the wells appropriately. 4. Briefly centrifuge the wells at 1000
g before incubating on ice for 5 min. 5. Place in a thermal cycler and run the program detailed in Table 2. The resulting 15 μL solution contains multiplex cDNA. 3.4.2 Multiplex Preamplification Step

Prior to starting, ensure that all materials for the multiplex pre-amp step are prepared (see Subheading 2.5). 1. Prepare the pre-amp mastermix in a nuclease-free tube according to the volumes stated in Table 8. The volumes prepared should be sufficient to process all samples (including the NTC from the multiplex RT step) and a pre-amp no-template control (pre-amp-NTC) sample, plus an additional 10% volume to allow for potential pipetting error. Mix the pre-amp mastermix by gently pipetting. 2. Transfer 37.5 μL aliquots of pre-amp mastermix into 0.2 mL PCR wells. 3. Add 12.5 μL of cDNA (from Subheading 3.4.1) from each sample to an individual pre-amp mastermix well and mix the contents by gently pipetting. Seal the wells appropriately. 4. Briefly centrifuge the wells at 1000
g before placing in a thermal cycler and running the program detailed in Table 4. The resulting 50 μL solution contains amplified cDNA derived from the copies of sequence of the miRNAs of interest. 5. In separate 2 mL nuclease-free tubes, dilute the entire 50 μL volume of each individual cDNA sample with 200 μL of NFW, then pipette gently to mix. Diluted cDNA may be used immediately or stored at 20  C for subsequent use.

Table 8 Reaction volumes for multiplex preamplification mastermix

Reagent

Volume for one reaction

Example volume for 12 reactions

Taqman Pre-amp Master Mix 2


25.0 μL

300.0 μL

Custom multiplex probe mix (Subheading 2.5, item 2)

12.5 μL

150.0 μL

Total

37.5 μL

475.0 μL

A Circulating MicroRNA Panel for Malignant Germ Cell Tumors 3.4.3 Quantitative PCR (qPCR) for Individual miRNAs

3.4.4 Data Analysis: ΔΔCt Method and Normalization

239

Using the qPCR protocol described in Subheading 3.3.2, separately quantify the levels of each single miRNA using the diluted multiplex cDNA from the preamplification step (Subheading 3.4.2). Note that despite the preceding (reverse transcription and pre-amp) steps being performed in multiplex, this step must be performed in singleplex. In addition, this step should be performed in a different laboratory area/room from the RNA extraction and multiplex RT and preamplification steps, to avoid potential issues with contamination. 1. To calculate malignant GCT biomarker miRNA (miR-371a3p, miR-372-3p, miR-373-3p and miR-367-3p) expression relative to the endogenous housekeeping miRNA miR-30b5p, firstly calculate the raw mean biomarker Cq values, then apply the following equation: c ¼ba Where: a ¼ Raw mean housekeeping miR-30b-5p Cq b ¼ Raw mean biomarker miRNA Cq c ¼ miR-30b-5p normalized biomarker miRNA ΔCq 2. To compare levels of malignant GCT biomarker miRNAs (miR-371a-3p, miR-372-3p, miR-373-3p, and miR-367-3p) in a sample with those from a control patient group, calculate the biomarker miRNA ΔΔCq. Firstly, the malignant GCT biomarker levels in each group must be adjusted for levels of cel-miR-39-3p and the housekeeping miRNA miR-30b-5p, as in steps 2 and 3. Next, calculate the mean housekeeping normalized malignant GCT biomarker miRNA ΔCq of the control group. Then, for each sample, subtract the housekeeping normalized biomarker miRNA ΔCq from the mean control group value. d ¼ c control  c sample Where: d ¼ ΔΔCq of control and sample biomarker miRNA ccontrol ¼ mean housekeeping (miR-30b-5p) normalized biomarker miRNA ΔCq of the control group csample ¼ housekeeping (miR-30b-5p) normalized biomarker miRNA ΔCq value of the sample Then, to find sample relative expression Sample relative expression ¼ log2 d

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Notes 1. Cel-miR-39-3p stock solution (2
1010 copies) may be aliquoted and stored at 80  C, but neither of the dilute cel-miR39-3p working solutions should be stored for further use due to risk of degradation at low concentrations. Instead a fresh solution should be prepared for each RNA extraction batch. To ensure that variation in the efficiency of RNA recovery is not associated with the preparation of the spike-in control, we recommend including a reference normal serum sample in all RNA extraction batches. The ratio for this sample between the spike-in singleplex Ct value and the housekeeper gene singleplex Ct value should not vary between processed batches. Any variation of this ratio would indicate variation in the amount of spike-in added, rather than changes in RNA recovery. A reference serum sample for which a large volume is available (e.g., 5 mL or more) would be most suitable for this exercise, as it would allow for multiple 200 μL aliquots to be prepared. 2. The malignant GCT panel comprises four circulating miRNAs (namely, miR-371a-3p, miR-372-3p, miR-373-3p, and miR-367-3p) which are highly sensitive and specific for malignant GCT diagnosis [8, 19, 22]. In addition, use of the exogenous nonhuman spike-in cel-miR-39-3p and endogenous miRNA housekeeping gene miR-30b-5p are included for QC/normalization purposes. Because the 14-cycle preamplification step of the protocol is essential for maximizing assay sensitivity, it is important that the primers used for the custom miRNA panel multiplex RT step are highly specific, that is, do not produce detectable nonspecific amplification. ThermoFisher Scientific states that assays run in a standard, singleplex NTC qPCR (without preamplification) will not produce detectable signal, that is, will have a raw Cq > 38. We recommend increasing this threshold to a raw Cq of >40, to avoid the potential for nonspecific amplification to limit the ability to interpret test sample diagnostic results. All new batches of primers should therefore be tested with the QC protocol (Subheading 3.3), using NFW as template (i.e., NTC), before they are used for the multiplexed diagnostic test. If the raw Cq is 30, as use of more dilute serum may increase the likelihood of false negative results when testing for malignant GCT biomarker miRNA (miR-371a-3p, miR-372-3p, miR-373-3p, and miR-367-3p) levels.

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Acknowledgments We thank Dawn Ward for technical assistance. We acknowledge grant funding from The St. Baldrick’s Foundation, the National Institutes of Health (PAR UH2/UH3), the Isaac Newton Trust and Great Ormond Street Hospital Children’s Charity/Children with Cancer UK. We are also grateful for support from the Max Williamson Fund and from Christiane and Alan Hodson, in memory of their daughter Olivia. References 1. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D et al (2005) MicroRNA expression profiles classify human cancers. Nature 435(7043):834–838 2. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL et al (2008) Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A 105 (30):10513–10518 3. Murray MJ, Watson HL, Ward D, Bailey S, Ferraresso M, Nicholson JC et al (2018) “Future-proofing” blood processing for measurement of circulating miRNAs in samples from biobanks and prospective clinical trials. Cancer Epidemiol Biomark Prev 27 (2):208–218 4. Bell E, Watson HL, Bailey S, Murray MJ, Coleman N (2017) A robust protocol to quantify circulating cancer biomarker microRNAs. Methods Mol Biol 1580:265–279 5. Kirschner MB, Edelman JJ, Kao SC, Vallely MP, van Zandwijk N, Reid G (2013) The impact of hemolysis on cell-free microRNA biomarkers. Front Genet 4:94 6. Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N et al (2011) Haemolysis during sample preparation alters microRNA content of plasma. PLoS One 6(9):e24145 7. Pritchard CC, Kroh E, Wood B, Arroyo JD, Dougherty KJ, Miyaji MM et al (2012) Blood cell origin of circulating microRNAs: a cautionary note for cancer biomarker studies. Cancer Prev Res (Phila) 5(3):492–497 8. Murray MJ, Bell E, Raby KL, Rijlaarsdam MA, Gillis AJ, Looijenga LH et al (2016) A pipeline to quantify serum and cerebrospinal fluid microRNAs for diagnosis and detection of relapse in paediatric malignant germ-cell tumours. Br J Cancer 114(2):151–162 9. Palmer RD, Murray MJ, Saini HK, van Dongen S, Abreu-Goodger C, Muralidhar B

et al (2010) Malignant germ cell tumors display common microRNA profiles resulting in global changes in expression of messenger RNA targets. Cancer Res 70(7):2911–2923 10. Murray MJ, Nicholson JC, Coleman N (2015) Biology of childhood germ cell tumours, focussing on the significance of microRNAs. Andrology 3(1):129–139 11. Murray MJ, Huddart RA, Coleman N (2016) The present and future of serum diagnostic tests for testicular germ cell tumours. Nat Rev Urol 13(12):715–725 12. Murray MJ, Coleman N (2012) Testicular cancer: a new generation of biomarkers for malignant germ cell tumours. Nat Rev Urol 9 (6):298–300 13. Murray MJ, Halsall DJ, Hook CE, Williams DM, Nicholson JC, Coleman N (2011) Identification of microRNAs from the miR-371~373 and miR-302 clusters as potential serum biomarkers of malignant germ cell tumors. Am J Clin Pathol 135(1):119–125 14. Anheuser P, Radtke A, Wulfing C, Kranz J, Belge G, Dieckmann KP (2017) Serum levels of microRNA371a-3p: a highly sensitive tool for diagnosing and staging testicular germ cell tumours: a clinical case series. Urol Int 99 (1):98–103 15. Belge G, Dieckmann KP, Spiekermann M, Balks T, Bullerdiek J (2012) Serum levels of microRNAs miR-371-3: a novel class of serum biomarkers for testicular germ cell tumors? Eur Urol 61(5):1068–1069 16. Dieckmann KP, Radtke A, Geczi L, Matthies C, Anheuser P, Eckardt U et al (2019) Serum levels of microRNA-371a-3p (M371 Test) as a new biomarker of testicular germ cell tumors: results of a prospective multicentric study. J Clin Oncol 37(16):1412–1423 17. Dieckmann KP, Radtke A, Spiekermann M, Balks T, Matthies C, Becker P et al (2017) Serum levels of microRNA miR-371a-3p: a

A Circulating MicroRNA Panel for Malignant Germ Cell Tumors sensitive and specific new biomarker for germ cell tumours. Eur Urol 71(2):213–220 18. Dieckmann KP, Spiekermann M, Balks T, Flor I, Loning T, Bullerdiek J et al (2012) MicroRNAs miR-371-3 in serum as diagnostic tools in the management of testicular germ cell tumours. Br J Cancer 107(10):1754–1760 19. Gillis AJ, Rijlaarsdam MA, Eini R, Dorssers LC, Biermann K, Murray MJ et al (2013) Targeted serum miRNA (TSmiR) test for diagnosis and follow-up of (testicular) germ cell cancer patients: a proof of principle. Mol Oncol 7:1083–1092 20. Spiekermann M, Belge G, Winter N, Ikogho R, Balks T, Bullerdiek J et al (2015) MicroRNA miR-371a-3p in serum of patients with germ cell tumours: evaluations for establishing a serum biomarker. Andrology 3(1):78–84 21. Spiekermann M, Dieckmann KP, Balks T, Bullerdiek J, Belge G (2015) Is relative quantification dispensable for the measurement of microRNAs as serum biomarkers in germ cell tumors? Anticancer Res 35(1):117–121 22. Syring I, Bartels J, Holdenrieder S, Kristiansen G, Muller SC, Ellinger J (2015) Circulating serum miRNA (miR-367-3p, miR-371a-3p, miR-372-3p and miR-373-3p)

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as biomarkers in patients with testicular germ cell cancer. J Urol 193(1):331–337 23. van Agthoven T, Eijkenboom WMH, Looijenga LHJ (2017) microRNA-371a-3p as informative biomarker for the follow-up of testicular germ cell cancer patients. Cell Oncol (Dordr) 40(4):379–388 24. van Agthoven T, Looijenga LHJ (2017) Accurate primary germ cell cancer diagnosis using serum based microRNA detection (ampTSmiR test). Oncotarget 8(35):58037–58049 25. Murray MJ, Coleman N (2019) MicroRNA dysregulation in malignant germ cell tumors: more than a biomarker? J Clin Oncol 37 (16):1432–1435 26. Murray MJ, Coleman N (2019) Can circulating microRNAs solve clinical dilemmas in testicular germ cell malignancy? Nat Rev Urol 16:505 27. Murray MJ, Turnbull C (2018) Testicular cancer in 2017: sequencing advances understanding. Nat Rev Urol 15(2):79–80 28. Fendler A, Stephan C, Yousef GM, Kristiansen G, Jung K (2016) The translational potential of microRNAs as biofluid markers of urological tumours. Nat Rev Urol 13 (12):734–752

Chapter 16 Detection of Circulating Tumor Cells (CTCs) in Patients with Testicular Germ Cell Tumors Paulina Nastały, Friedemann Honecker, Klaus Pantel, and Sabine Riethdorf Abstract While the majority of patients with advanced testicular germ cell tumors (GCT) achieve complete responses after chemotherapy and if indicated after postchemotherapy resection of residual lesions, about 20% of patients have incomplete responses or show relapses. Moreover, toxicity of chemotherapy is high, and severe adverse chronic effects have been described. Therefore, there is an urgent need for biomarkers that could help to improve tumor staging, and support decision-making, ideally including monitoring of therapy response and prediction of relapse. Besides the well-established serum markers lactate dehydrogenase, α-fetoprotein, and β-subunit of human chorionic gonadotropin, during recent years new noninvasive liquid biopsy markers have been investigated in GCT, including cell-free nucleic acids like microRNAs, and circulating tumor cells (CTCs). Prognostic relevance has been demonstrated for circulating tumor cells (CTCs) in patients with different cancers. However, little is known in GCT patients. Histologically, GCT are a very heterogeneous group of tumors comprising pure seminomas (consisting of cells that remember primordial germ cells) and nonseminomas, which are either undifferentiated (embryonal carcinoma) or differentiated, exhibiting different degrees of embryonic (teratoma) or extraembryonic (yolk sac tumor and choriocarcinoma) differentiation. This heterogeneity hampers capture and detection of CTCs deriving from those tumors using a single method or a single antibody. To date, label-independent capture methods that enrich tumor cells according to the density of GCT cells, which is similar to that of mononuclear cells, have been successfully applied. Since testicular GCT might also express epithelial proteins, methods based on enrichment of CTCs using epithelial markers are promising to detect CTCs in certain subgroups of patients with GCTs as well. Here, we describe and discuss a combination of methods to capture and detect GCT cells with epithelial and germ cell characteristics in blood. Key words Germ cell tumors, Circulating tumor cells, Immunofluorescence, OCT3/4, SALL4, Keratin, EpCAM, CellSearch®, Fluorescence in situ hybridization

1

Introduction Testicular GCT account for approximately 2% of tumors in men, but they are the most commonly diagnosed malignancy in young men [1]. Evaluation of serum tumor markers α-fetoprotein,

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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β-subunit of human chorionic gonadotropin and lactate dehydrogenase is mandatory for adequate staging in GCT at initial diagnosis, and they are also used for monitoring of treatment and for detection of relapse during follow-up [2, 3]. Tumor markers have a sensitivity of 60–80%, and different histologic subgroups and clinical stages show a wide variation in marker expression levels [4, 5]. Recent studies have identified microRNAs as potential new biomarkers, for example miR-371-3 and miR-302/367 cluster [6– 8]. Although a promising sensitivity of 86% (95% confidence interval 79.7–90.4) and a specificity of 93% (95% CI: 89.0–95.9) have been reported for miR-371-3 in clinical investigations [9], there are still issues around laboratory standardization and availability of the test that must be resolved before this new biomarker can be recommended for routine clinical use. During recent years liquid biopsy-based biomarkers have come into the focus of interest in oncology. Especially blood or urine samples are readily collectable, and both cellular or cell-free components can be easily investigated [10]. Numerous clinical studies provide evidence for a prognostic relevance of CTCs in cancer patients, and first interventional studies based on CTC detection and characterization have already been initiated, for example, for breast cancer [11, 12]. Most clinical trials use the FDA (Food and Drug Administration)-cleared CellSearch® system that facilitates CTC detection in a semiautomated manner. CTCs are positively enriched by anti-EpCAM antibodies coated to ferrofluid and detected by positivity for keratin expression and negativity in immunostaining using anti-keratin and anti-CD45 antibodies, respectively [13]. If GCT cells are EpCAM and keratin-positive, they can be readily identified with the help of this system. The requirements, performance and evaluation of this approach will be described in this article. There is only a limited number of studies using blood samples derived from GCT patients so far. For example, CTCs and tumor-specific mRNAs have been identified in apheresis products of GCT patients undergoing peripheral stem cell transplantations [14–16]. Moreover, two studies detected putative CTCs in peripheral blood of testicular GCT patients by reverse transcriptase chain reaction (RT-PCR), using AFP and betaHCG-specific mRNA as markers [17, 18]. In our recently published, so far largest study on CTCs in this tumor entity, we analyzed blood samples from the peripheral vein from 143 patients, and intraoperatively taken from the testicular vein from 19 patients. CTCs were detected using Ficoll density gradient centrifugation and immunocytochemistry with a combination of germ cell tumor (anti-SALL4, anti-OCT3/4) and epithelium-specific (anti-KER, anti-EpCAM) antibodies. Parallel samples from 122 patients investigated only with the CellSearch® system revealed to be CTC-positive in 11.5% of cases, while the combination of all methods resulted in a positivity rate of 17.5%.

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A significant association of CTCs with advanced tumor stages was found. In detail, 41% of patients with metastatic tumors, and 100% of patients with recurrent and chemotherapy-refractory disease were CTC-positive. Patients with nonseminomatous tumors were more frequently CTC-positive than those suffering from pure seminomas, and presence of CTCs correlated with elevated AFP, beta-HCG, and LDH levels. Finally, both the incidence as well as absolute CTC counts were higher in blood samples collected from the testicular vein compared to those from peripheral blood samples [19]. Ficoll density gradient centrifugation is performed to separate peripheral blood mononuclear cells (PBMCs) from whole blood samples, based on cellular density. Tumor cells can be coenriched within this fraction by their density similar to that of PBMCs. Subsequently, immunocytochemistry is conducted in order to identify CTCs that are present with a frequency of 10
7 to 10
6. In the proposed methodology, we used germ cell–specific (SALL4 and OCT3/4) and epithelial cell–specific (keratins and EpCAM) markers. In order to avoid loss of CTCs during density gradient centrifugation and following steps, several methodological aspects have to be taken into account as for example blood layering and aspiration of PBMC fraction, optimization of centrifugation parameters (temperature, turning the brake off) and washing steps as well as cytospin preparation. Moreover, specificity can be improved by combining immunocytochemical markers with fluorescence in situ hybridization (FISH) using germ cell-specific genomic aberrations. Another approach to capture and detect CTCs with epithelial cell features is the CellSearch® system which has been cleared by the FDA for the detection of CTCs in blood samples from patients with metastatic breast, prostate, and colon cancer; moreover, results from numerous studies on patients with other carcinomas have demonstrated clinical relevance of CTCs found by CellSearch [13, 20]. These different methods for detection of CTCs in patients with GCT will be described and discussed in detail in the following paragraphs.

2

Materials

2.1 Cell Lines and Media

TCam-2 (origin: patient with a seminoma) [21]

2.1.1 Cell Lines

NCCIT (origin: patient with an embryonal carcinoma) [23]

2102Ep (origin: patient with an embryonal carcinoma) [22] NTERA-2 (origin: patient with an embryonal carcinoma/teratoma) [24, 25]

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2.1.2 Media

RPMI medium: 90% RPMI 1640, 10% fetal calf serum, 0.5% penicillin–streptomycin mix (50 U/mL), and 2 mM L-glutamine. DMEM medium: 90% DMEM medium, 10% fetal calf serum, 0.5% penicillin–streptomycin mix (50 U/mL), and 2 mM Lglutamine.

2.2 CTC Detection Using Ficoll-Hypaque Density Gradient Centrifugation

1. Peripheral and testicular vein blood samples in EDTA tubes for Ficoll density gradient centrifugation. 2. Adhesive microscope slides (Super frost Plus). 3. Ficoll-Hypaque. 4. Neubauer cell-counting chamber. 5. Centrifuge: suitable for 50 mL reaction tubes and with acceleration and brake on/off function. 6. Centrifuge: suitable for cytospin preparation.

2.3 Double Immunofluorescence Staining of CTCs

1. Fat-containing pen. 2. Mouse monoclonal anti-human SALL4 antibody, clone 6E3 (Abnova, Taipei, Taiwan). 3. Goat polyclonal anti-human OCT3/4 antibody, sc-8629 (Santa Cruz Biotechnology, Dallas, TX, USA). 4. Mouse monoclonal antibody against human pan-keratin A45-B/B3 directly labeled with Cy3 (Micromet, Munich, Germany). 5. Mouse monoclonal antibody against human pan-keratin AE1/AE3 eFluor 570, eBioscience™ (Thermo Fisher Scientific, Schwerte, Germany). 6. Mouse monoclonal antibody against human pan-Keratin C11 (Alexa Fluor® 555 conjugate, Cell Signaling Technology, Frankfurt, Germany). 7. Mouse monoclonal Alexa Fluor® 647 anti-human CD45 antibody, clone HI-30 (BioLegend, San Diego, CA, USA). 8. Mouse monoclonal anti-human EpCAM antibody, clone NCL-ESA (Novocastra, Brøndbyvester, Denmark). 9. Secondary anti-mouse Alexa (Thermo Fisher Scientific).

488-conjugated

antibody

10. Secondary anti-goat Alexa 488-conjugated antibody (Thermo Fisher Scientific). 11. Secondary anti-mouse Alexa (Thermo Fisher Scientific).

546-conjugated

antibody

12. 1 PBS. 13. Fixation Solution B Epithelial Cell Detection Kit (Formaldehyde, 37%, stabilized with 10% methanol and calcium carbonate, working solution: 135 μL in 10 mL 1 PBS, Micromet).

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14. Blocking Solution I: 10% AB blocking serum (Bio-Rad Medical Diagnostics GmbH, Dreieich, Germany). 15. Blocking Solution II: DakoCytomation Protein Block SerumFree Ready-To-Use: 0.25% casein in PBS, containing stabilizing protein and 0.015 M sodium azide (Dako, Glostrup, Denmark). 16. Permeabilization Solution: 0.1% Triton X in 1 PBS. 17. Antibody Diluent I: Tris–HCl buffer containing stabilizing protein and 0.015 M sodium azide (Dako). 18. Antibody Diluent II: 0.05 M Tris–HCl buffer containing 0.1% Tween, stabilizing proteins to reduce background, and 0.015 M sodium azide (Dako). 19. Mounting Medium Containing DAPI: VECTASHIELD® with DAPI (Vector Laboratories, Burlingame, CA, USA). 20. 20 SSC stock solution: 3.0 M NaCl, 0.3 M Na-citrate, dissolve 175.3 g NaCl and 88.2 g Na-citrate in 800 mL distilled water, add distilled water to 1 L, adjust pH to 7.0, autoclave NOTE: store up to 1 year at RT. 21. Fluorescence microscope equipped with fluorescence filters for visualizing the appropriate immunofluorescence stainings. Store all reagents at 4  C. For antibodies, follow the manufacturer’s instructions for storage. 2.4 Fluorescence In Situ Hybridization in CTCs

1. Nuclease-free water. Store at
20  C. 2. DNA Labeling System allowing fluorescence detection of nucleic acids (e.g., BioPrime™, Thermo Fisher Scientific). Follow the manufacturer’s instructions for storage. 3. Bio-Spin 30 Tris Columns (Bio-Rad, Hercules, CA, USA). Store at 4  C. 4. Denaturation Solution [pH ¼ 7.4]: 70% formamide in 2 SSC. Store at
20  C. 5. Proteinase-K Solution [pH ¼ 7.5]: 20 mM Tris–HCl [pH ¼ 7.4], 2 mM CaCl2, 0.1 μg/mL Proteinase-K. Prepare fresh before using. 6. Hybridization Buffer [pH ¼ 7.0]: 50% Formamide, 20% Dextran in 2 SSC buffer. Aliquot and store at
20  C. 7. Centromere 12 detection probe (SpectrumGreen™, Abbott Molecular, Field Drive, IL, USA). Store at
20  C. 8. PAC clone 876C13 from region 12p11.2-p12.1 [26]. 9. Cot 1 DNA (Roche, Mannheim, Germany). Store at
20  C. 10. Rubber cement.

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11. Wash Buffer: 2 SSC Buffer containing 0.1% NP-40. Prepare fresh before using. 12. Mounting Medium Containing DAPI: VECTASHIELD® with DAPI (Vector Laboratories). Store at 4  C. 13. Centrifuge: suitable for 1.5 mL reaction tubes with temperature regulation. 14. FISH-dedicated thermoblock suitable for microscopic slides. 15. Humidified chamber suitable for microscopic slides. 16. Fluorescence microscope equipped with fluorescence filters for visualizing the appropriate immunofluorescence stainings. 2.5 CellSearch® Analysis of CTCs

1. CellSave preservative tubes for blood collection (Menarini Silicon Biosystems, Castel Maggiore, Italy, former Janssen Diagnostics, Raritan, NJ, USA). 2. Centrifuge: suitable for 15 mL reaction tubes and with acceleration and brake on/off function. 3. CellSearch® System: CellTracks® Autoprep System and CellTracks® Analyzer II, MagNest® (Menarini). 4. CellSearch® Circulating Tumor Cell Kit: includes dilution buffer, enrichment and staining reagents, sample tubes and caps, cartridges (Menarini). 5. Centrifuge: suitable for 15 mL reaction tubes and with acceleration and brake on/off function. Store all enrichment and staining reagents at 4  C, but the dilution buffer at room temperature.

3

Methods

3.1 Cultivation of GCT Cell Line Cells

The cell line TCam-2 is grown in RPMI medium at 37  C and 5% CO2. The cell lines 2102Ep, NCCIT, and NTERA-2 are grown in DMEM medium at 37  C and 10% CO2.

3.2 CTC Detection Using Ficoll-Hypaque Gradient Centrifugation

1. Mix the collected peripheral blood (9–17 mL) or testicular vein blood (0.5–3.5 mL) (see Notes 1 and 2) 1:1 with 1 PBS, carefully layer on 20 mL Ficoll-Hypaque in a 50 mL tube (see Note 3), and centrifuge for 30 min at 450  g with low acceleration and brake turned off (see Note 4). 2. Aspirate the upper layer leaving the mononuclear cell layer (lymphocytes, monocytes, and thrombocytes) undisturbed at the interphase (see Note 5). 3. Carefully transfer the mononuclear cell fraction, potentially containing CTCs, to a new 50 mL tube, fill with 1 PBS and spin for 30 min at 450  g.

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4. Carefully aspirate supernatant completely. Resuspend the cell pellet in 1 PBS and count the cells using the Neubauer cellcounting chamber. 5. Proceed with microscopic slide preparation (see Note 6). Place the microscopic slide in the cytospin pocket, secure the funnel, and add cell suspension. Spin down for 3 min at 300  g. 6. Leave the slides to air-dry at room temperature overnight. For long-term storage, wrap them in aluminum foil back to back and store them at
80  C. 3.3 Double Immunofluorescence Staining of CTCs

3.3.1 SALL4/Keratin

Allow the microscopic slides to thaw for at least 30 min (see Note 7) in the foil before proceeding with the immunoreaction. Draw a circle around the cell area using a fat-containing pen to create a hydrophobic barrier. 1. Carefully drop Fixation Solution B for Epithelial Cell Detection Kit onto the cells. Incubate for 10 min. Wash the cells three times for 3 min in 1 PBS. 2. Permeabilize cells for 5 min using the Permeabilization Solution (see Note 8). Wash the cells three times for 3 min in 1 PBS. 3. Incubate cells for at least 20 min with Blocking Solution I. 4. Prepare a dilution of primary mouse antibody against SALL4 in Antibody Diluent I (final concentration 1.3 μg/mL) (see Note 9). Before using the antibody, prepare the titration experiment to identify the optimal dilution. Incubate cells with antibody for 45 min at room temperature. Wash cells three times for 3 min in 1 PBS. 5. Prepare a 1:200 dilution of the secondary anti-mouse antibody labeled with Alexa 488 in Antibody diluent II. Incubate cells with antibody for 45 min at room temperature in a dark environment. Wash the cells three times for 3 min in 1 PBS. 6. Incubate the cells at room temperature for 20 min with Blocking Solution II. 7. Prepare a 1:300 dilution of A45 antibody directly labeled with Cy3 in Antibody Diluent I (see Note 10). Incubate the cells with antibody for 45 min at room temperature in the dark. Wash the slides three times for 3 min in 1 PBS (see Note 11). 8. Counterstain nuclei with Mounting Medium Containing DAPI and cover slides with coverslips. Seal the coverslips with transparent nail polish. 9. Evaluate the results by fluorescence microscopy (see Note 12) (Fig. 1a). For the detection of CTCs, screen the whole cytospin area first with a 100 or 200 magnification in a meandering fashion for either keratin or SALL4 expressing cells. Than

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Fig. 1 Circulating tumor cells (CTCs) enriched by Ficoll density gradient centrifugation of blood samples drawn from GCT (germ cell tumor) patients. Representative images of CTCs detected by immunofluorescence among the majority of leukocytes (a, b). Nuclei are stained by DAPI (40 ,6-diamidino-2-phenylindole, dihydrochloride, blue). (a) Three CTCs enriched from peripheral blood displaying SALL4 (green) and keratin (antibody, pan keratin A45)-specific immunofluorescence (red). (b) CTC cluster identified in a blood sample taken from the testicular vein of a patient with GCT. Five CTCs demonstrate nuclear Oct3/4-specific immunofluorescence (green) and seven CTCs are OCT3/4 (green) and EpCAM-positive (red). Fluorescence in situ hybridization on CTCs for the detection of chromosome 12p11.23 aberrations (c, d). Nuclei are stained by DAPI. (c, d) CTCs bottom left with 2 or 3 centromere 12 signals (green), and at least four and five 12p11.23-specific signals (red), respectively. Leukocytes present with each two centromere 12 (green) and two 12p11.23 signals (red)

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increase the magnification to 400 and evaluate each positive event carefully by switching between the fluorescence channels thereby ensuring that the DAPI signal is localized within the cell identified by nuclear SALL4 or cytoplasmic keratin staining. Exclude EpCAM- or OCT3/4-positive leukocytes by CD45 negativity. 3.3.2 OCT3/4/EpCAM

1. Carefully drop Fixation Solution B for Epithelial Cell Detection Kit onto the cells. Incubate for 10 min. Wash the cells three times for 3 min in 1 PBS. 2. Permeabilize cells for 5 min using the Permeabilization Solution (see Note 8). Wash the cells three times for 3 min in 1 PBS. 3. Incubate cells for at least 20 min with Blocking Solution I. 4. Prepare the 0.3 μg/mL dilution of primary goat antibody against OCT3/4 in Antibody Diluent I (see Note 9). Before using the antibody prepare the titration experiment to choose the optimal dilution. Incubate cells with antibody for 45 min at room temperature. Wash cells three times for 3 min in 1 PBS. 5. Prepare the 1:200 dilution of the secondary anti-goat antibody labeled with Alexa 488 in Antibody diluent II. Incubate cells with antibody for 45 min at room temperature in the dark. Wash the cells three times for 3 min in 1 PBS. 6. Incubate cells for 20 min with Blocking Solution II. 7. Prepare the 0.4 μg/mL dilution of primary mouse antibody against EpCAM in Antibody Diluent I (see Note 9). Before using the antibody prepare the titration experiment to choose the optimal dilution. Incubate cells with antibody for 45 min at room temperature. Wash cells three times for 3 min in 1 PBS. 8. Prepare the 1:200 dilution of secondary anti-mouse antibody labeled with Alexa 546 in Antibody diluent II. Incubate cells with antibody for 45 min at room temperature in the dark. Wash the cells three times for 3 min in 1 PBS (see Note 11). 9. Counterstain nuclei with Mounting Medium Containing DAPI and cover slides with coverslips. Secure the coverslips with transparent nail polish. 10. Evaluate the results by fluorescence microscopy (see Note 12) (Fig. 1b). For the detection of CTCs, screen the whole cytospin area first with a 100 or 200 magnification in a meandering fashion for either EpCAM or OCT3/4 expressing cells. Then increase the magnification to 400 and evaluate each positive event carefully by switching between the fluorescence channels thereby ensuring that the DAPI signal is localized within the cell identified by nuclear OCT3/4 or membranous EpCAM staining. Exclude cells coexpressing EpCAM- and/or OCT3/4- and CD45.

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3.4 Fluorescence In Situ Hybridization in CTCs

In order to confirm the germ-cell origin of CTCs, fluorescence in situ hybridization (FISH) can be conducted using a probe derived from the Homo sapiens PAC (bacteriophage P1 artificial chromosomes) or BAC (bacterial artificial chromosome) clone (see Note 13) from short arm of chromosome 12 (region 12p11.2– p12.1). Gain of the short arm of chromosome 12, mostly due to isochromosome (i12p), is the most frequently found (structural) chromosomal abnormality in invasive testicular GCT [26].

3.4.1 Probe Preparation by Random Priming

1. Dissolve 1 μg of isolated BAC or PAC DNA in nuclease-free water to obtain a total volume of 18 μL. Denature DNA by immersing the tube in boiling water for 5 min. 2. Immediately cool down the denatured DNA on ice and prepare the labeling reaction using for example the DNA Labeling System as presented in Table 1. Gently mix the sample, spin down briefly and incubate for at least 3 h at 37  C in a thermoblock. Proceed with purification of the probe. 3. Prepare the Bio-Spin 30 Tris Column by inverting it a few times, cut off the tip and open the cap. Centrifuge the empty column for 2 min at 1000  g. Load the labeled probe onto the Bio-Spin 30 Tris Column and spin it down for 4 min at 1000  g (see Note 14). 4. Precipitate the product using 5 μL of 3 M sodium acetate and 150 μL of 100% ethanol for a minimum 1 h at 4  C. 5. Spin down the probe for 30 min at 20,000  g at 4  C. Remove the supernatant and air-dry the pellet. Dissolve it in 25 μL of Hybridization Buffer (overnight at 37  C in thermoblock) and store it at
20  C.

3.4.2 Fluorescence In Situ Hybridization Procedure

1. Incubate the cells with Denaturation Solution for 5 min at 75  C. Subsequently, dehydrate the slides by immersing them in a series of ascending ethanol concentrations (70% < 80% < 90% < 100%).

Table 1 Preparation of the labeling reaction Reagent

Volume [μL]

1 μg BAC DNA in water

18

10 dNTPs

5

0.5 mM fluorescently labeled dUTPs

5

2.5 random hexamers

20

Klenow fragment

1

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Table 2 Preparation of the hybridization mixture Reagent

Volume [μL]

Labeled probe

3

Centromere probe

1

Cot 1 DNA

1

Hybridization Buffer

6

2. Perform enzyme pretreatment of cells in Proteinase-K Solution for 7 min at 37  C in a humidified chamber to prevent them from drying. 3. Dehydrate the slides in a series of ascending ethanol concentrations (70% < 80% < 90% < 100%) and air-dry them. 4. Prepare the hybridization mixture by mixing the ingredients as shown in Table 2. Gently transfer the mixture onto the microscopic slide. Cover the slide with a coverslip and secure it with rubber cement. 5. Using the FISH-dedicated thermoblock, denature the slides at 75  C for 7 min, and allow the probe to hybridize for at last 16 h at 37  C. 6. Perform post-hybridization washes at 72  C and at room temperature in Wash Buffer for 3 min each. Wash the slides for 3 min in 1 PBS. 7. After dehydration in ascending concentrations of ethanol (70% < 80% < 90% < 100%) and air-drying, mount the microscopic slides with Mounting Medium Containing DAPI and secure them with nail polish. 8. Evaluation of FISH results (see Note 15) (Fig. 1c, d). For the detection of CTCs, screen the whole cytospin area first with an at least 400 magnification (better: 630 or 1000 using a drop of oil and an oil-immersion objective) for 12p11.23specific signals (orange) in a meandering fashion. Test any suspicious cell with more than two orange fluorescence signals for the number of green centromere seven signals. A higher number of orange than green signals implies amplification at least of part of this chromosomal region. Formation of frequently appearing isochromosomes in GCTs is to be seen by 12p11.23 signals in close proximity to each other. 3.5 CellSearch® Detection of Circulating Tumor Cells

The CellSearch® System is an automated system for the enrichment (CellTracks® Autoprep) and detection of CTCs in peripheral blood samples (see Note 16). The CellTracks® AutoPrep system allows automation of the sample processing, including all reagent addition, mixing, incubation, and aspiration steps. In the first step

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plasma is aspirated. In order to capture epithelial cells, anti-EpCAM antibody–coated ferrofluids and capture enhancement reagent are added. After incubation and magnetic separation, unbound cells and remaining plasma are removed, and ferrofluid-labeled cells are resuspended in buffer, permeabilized, and fluorescently labeled using the phycoerythrin (PE)-conjugated anti-keratin antibodies recognizing keratins (predominantly keratins 8, 18, and 19). Additionally, an antibody against CD45 conjugated with allophycocyanin (APC) to identify white blood cells is added. The cell nuclei are fluorescently labeled with a nuclear dye [40 ,6-diamidino-2-phenylindole (DAPI)]. After incubation and repeated magnetic separation, unbound staining reagents are aspirated, and a cell fixative is added. Then, labeled cells are kept in a strong magnetic field for at least 20 min and scanned using the CellTracks® Analyzer. Detailed protocol for sample preparation: 1. Prior to use, let enrichment and staining reagents adjust to room temperature. 2. Gently mix 7.5 mL of blood with 6.5 mL of dilution buffer in a sample tube (see Notes 1 and 15). 3. Centrifuge sample at 800  g for 10 min with a low acceleration and brake turned off. 4. Transfer the sample into the CellTracks® AutoPrep system. After enrichment and immunocytochemical staining, keep immunomagnetically labeled cells for 20 min in a strong magnetic field. 5. Proceed with the scanning using the CellTracks® Analyzer II according to instructions of the manufacturer. 6. Analyze and interpret the images generated and presented by the CellTracks® Analyzer II in an image gallery. Using this system, also apoptotic cells can be assessed by characteristic morphology—presence of small pycnotic nuclei or apoptotic bodies or speckled cytoplasmic staining. 7. Interpret images according to the following criteria [27] (see Notes 17–19) (Fig. 2): A nucleated cell (as to be seen by DAPI staining) is designated as CTC if it is positive for keratin (PE channel) and negative for CD45 (APC channel). Measured with the CellSearch® software, the diameter of CTCs should be at least 4 μm and CTCs should be of oval or round shape. Exclude pixelated images that contain less than three distinct grey levels. If there is no additional antibody in the fourth fluorescence channel (FITC, Fluorescein isothiocyanate) supplied, no signal should be seen there. According to the 50% rule, the nucleus has to be for at least 50% within the cytoplasm. Speckled keratin and DAPI signals have to be evaluated appropriately and apoptotic CTCs have to be excluded or separately counted according to the appropriate protocols.

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Fig. 2 Galleries of representative CellSearch® images from GCT cell line cells (left) and circulating tumor cells from four GCT patients (a–d, right). Cells from all used GCT cell lines spiked into blood from healthy donors were detected with the CellSearch® system. GCT cell line cells and CTCs were identified by keratin (KER) positivity and negativity for CD45. No additional antibody was supplied in the fourth fluorescence channel (FITC, Fluorescein isothiocyanate). Cells derived from the different GCT cell lines displayed different intensity of keratin immunofluorescence, with TCam-2, a pure seminoma cell line, demonstrating the strongest keratin expression. NCCIT (cell line from an embryonal carcinoma) and 2102Ep cells (also originating from an embryonal carcinoma) showed weaker keratin expression. The smallest cells were derived from the NT2 cell line (embryonal carcinoma/teratoma), and these cells exhibited a dot-like paranuclear keratin expression pattern. CTCs (right image galleries) differed in sizes, shapes and keratin expression. Thus, CTCs detected in blood from patient A (histology of the primary tumor: 50% embryonal carcinoma, 25% yolk sac tumor and 25% choriocarcinoma) displayed different sizes and intensity of keratin immunofluorescence as well as various keratin expression patterns including dot-like keratin accumulation (cells 2 and 3)

4

Notes 1. Dispose first 2–3 mL of blood after injecting the needle into the vein (venipuncture) before collecting 7.5 mL blood into the EDTA or the CellSave® tube to avoid contamination of the sample with epithelial cells derived from the skin. Mix the blood sample with the preservative in the tube by turning it upside down immediately after blood draw. 2. Process the blood samples collected into EDTA-tubes within a maximum of 24 h after blood drawing.

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3. Adapt the Ficoll volume to the amount of blood that has to be analyzed. If only 3 mL or a lower volume of blood is available, use 5 mL Ficoll and prepare the gradient in a 15 mL Falcon tube. 4. In order to assess the tumor cell recovery rate, prepare the controls by spiking blood samples from healthy donors with different numbers of tumor cells (e.g., 10–500) from germ cell tumor cell lines using Ficoll-Hypaque gradient centrifugation. It will allow assessment of detection thresholds of CTC enrichment. 5. When too many erythrocytes are visible, apply a method to remove erythrocytes. Give 1 mL of H-lyse buffer (R&D Systems, Human Erythrocyte Lysing Kit, Cat. No. WL 1000, Minneapolis, MN, USA) to the sediment and resuspend for 3 min on a mixer. Fill it up with 30 mL PBS, pellet the leukocytes by centrifugation for 10 min at 450  g with low acceleration and brake turned off, decant or aspirate the supernatant and resuspend the cells by adding 10 mL 1 PBS buffer. 6. Cell number depends on the size of the funnels. Follow the manufacturer’s instructions. Funnel size small: at maximum 30,000 cells, intermediate: 200,000, large: 500,000. 7. To avoid detachment of cells during immunostaining procedure, follow the thawing time exactly. 8. Carefully drop the Permeabilization Solution onto the cells in order to avoid its detachment. 9. Before using a new batch of antibody always perform a titration assay in order to establish the best antibody concentration for your experiments. 10. Other anti-pan-keratin antibodies also can be applied, such as a cocktail of the pan-keratin monoclonal antibodies AE1/AE3 ( eFluor 570, eBioscience™, San Diego, CA, USA), dilution 1:100 and the pan-keratin mouse monoclonal antibody C11 (Alexa 555, Cell Signaling, Frankfurt, Germany), dilution 1:300. 11. To exclude unwanted keratin immunofluorescence of leukocytes, an anti-CD45 antibody should be added. Here the clone HI30 labeled with Alexa Fluor® 647 (BioLegend, San Diego, CA, USA) is recommended. 12. If available, use semiautomated image analyses for rare cell detection (Ariol—Clinical IHC and FISH Scanner, Leica, Wetzlar, Germany). 13. BAC or PAC clones of interest can be purchased via several BAC/PAC resources centers.

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14. Load the sample to the center of the column without touching the silica membrane on the bottom. 15. Combining FISH with immunocytochemistry using antikeratin or anti-EpCAM antibodies facilitates detection of CTCs, but might impair the quality of FISH signals. 16. Blood samples drawn into CellSave® tubes can be processed within 96 h after collection when stored at room temperature. Storage and transport of blood samples at 4  C have to be avoided since this low temperature strongly increases the number of images that have to be evaluated. 17. Evaluation of images presented in the gallery requires experience [27]. Occasionally seen contaminating superficial squamous cells are easily to exclude because of their typical morphology (e.g., small nuclei and large cytoplasm). Other normal epithelial cells are more difficult to differentiate from tumor cells. Characteristic features are for example clustering in “acinus-like” cell associations or regular small nuclei, however classification might be subjective. Here, characterization of genomic aberrations in suspicious cells by single cell methods [28, 20] is recommended to discriminate between normal and tumor cells. Some white blood cells demonstrate autofluorescence and present with similar images in all or at least two fluorescence channels, sometimes even without CD45 immunostaining. In these cases comparison of fluorescence intensity, nuclear morphology, and cell shape has to be considered carefully among a larger number of images. 18. Also the 50% rule cannot be applied to all tumor cells. If the keratin staining pattern is heterogeneous, cell margins are not always clearly to define. Therefore, using the software, evaluating the cell of interest in the “Cell select” mode and reboxing is helpful to evaluate the whole cytoplasm. Moreover, there are also CTCs with a dot-like keratin expression pattern that do not always fulfil the 50% rule. 19. Since there is only one study using the CellSearch® system to detect CTCs in blood samples from patients with testicular germ cell tumors, evaluation still is subjective, but can be improved by larger clinical studies including this approach for CTC detection. Furthermore, molecular characterization of CTCs can provide insights in the biology of this tumor entity.

Acknowledgments This work was supported by the ERC-2010-AdG_20100317 grant DISSECT to KP.

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FA, Wallwiener D, Janni W, Fehm T (2016) Therapeutic intervention based on circulating tumor cell phenotype in metastatic breast cancer: concept of the DETECT study program. Arch Gynecol Obstet 293:271–281 12. Ignatiadis M, Rack B, Rothe F, Riethdorf S, Decraene C, Bonnefoi H, Dittrich C, Messina C, Beauvois M, Trapp E, Goulioti T, Tryfonidis K, Pantel K, Repollet M, Janni W, Piccart M, Sotiriou C, Litiere S, Pierga JY (2016) Liquid biopsy-based clinical research in early breast cancer: the EORTC 9009110093 Treat CTC trial. Eur J Cancer 63:97–104 13. Cristofanilli M, Budd GT, Ellis MJ, Stopeck A, Matera J, Miller MC, Reuben JM, Doyle GV, Allard WJ, Terstappen LW, Hayes DF (2004) Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N Engl J Med 351:781–791 14. Fan Y, Einhorn L, Saxman S, Katz B, Abonour R, Cornetta K (1998) Detection of germ cell tumor cells in apheresis products using polymerase chain reaction. Clin Cancer Res 4:93–98 15. Hildebrandt MO, Blaser F, Beyer J, Siegert W, Mapara MY, Huhn D, Salama A (1998) Detection of tumor cells in peripheral blood samples from patients with germ cell tumors using immunocytochemical and reverse transcriptase-polymerase chain reaction techniques. Bone Marrow Transplant 22:771–775 16. Bokemeyer C, Gillis AJ, Pompe K, Mayer F, Metzner B, Schleucher N, Schleicher J, Pflugrad-Jauch G, Oosterhuis JW, Kanz L, Looijenga LH (2001) Clinical impact of germ cell tumor cells in apheresis products of patients receiving high-dose chemotherapy. J Clin Oncol 19:3029–3036 17. Yuasa T, Yoshiki T, Isono T, Tanaka T, Hayashida H, Okada Y (1999) Expression of transitional cell-specific genes, uroplakin Ia and II, in bladder cancer: detection of circulating cancer cells in the peripheral blood of metastatic patients. Int J Urol 6:286–292 18. Hautkappe AL, Lu M, Mueller H, Bex A, Harstrick A, Roggendorf M, Ruebben H (2000) Detection of germ-cell tumor cells in the peripheral blood by nested reverse transcription-polymerase chain reaction for alpha-fetoprotein-messenger RNA and beta human chorionic gonadotropin-messenger RNA. Cancer Res 60:3170–3174 19. Nastaly P, Ruf C, Becker P, Bednarz-Knoll N, Stoupiec M, Kavsur R, Isbarn H, Matthies C, Wagner W, Hoppner D, Fisch M,

Circulating Tumor Cells in Patients with Testicular Germ Cell Tumors Bokemeyer C, Ahyai S, Honecker F, Riethdorf S, Pantel K (2014) Circulating tumor cells in patients with testicular germ cell tumors. Clin Cancer Res 20:3830–3841 20. Riethdorf S, O’Flaherty L, Hille C, Pantel K (2018) Clinical applications of the CellSearch platform in cancer patients. Adv Drug Deliv Rev 125:102–121 21. Mizuno Y, Gotoh A, Kamidono S, Kitazawa S (1993) Establishment and characterization of a new human testicular germ cell tumor cell line (TCam-2). Nihon Hinyokika Gakkai Zasshi 84:1211–1218 22. Andrews PW (1982) Human embryonal carcinoma cells in culture do not synthesize fibronectin until they differentiate. Int J Cancer 30:567–571 23. Damjanov I, Horvat B, Gibas Z (1993) Retinoic acid-induced differentiation of the developmentally pluripotent human germ cell tumor-derived cell line, NCCIT. Lab Investig 68:220–232 24. Andrews PW (1984) Retinoic acid induces neuronal differentiation of a cloned human

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Chapter 17 Developing and Using a Data Commons for Understanding the Molecular Characteristics of Germ Cell Tumors Bo Ci, Shin-Yi Lin, Bo Yao, Danni Luo, Lin Xu, Mark Krailo, Matthew J. Murray, James F. Amatruda, A. Lindsay Frazier, and Yang Xie Abstract Germ cell tumors (GCTs) are a rare disease, but they account for 15% of all malignancies diagnosed during adolescence. The biological mechanisms underpinning their development are only starting to be explored. Current GCT treatment may be associated with significant toxicity. Therefore, there is an urgent need to understand the molecular basis of GCT and identify biomarkers to tailor the therapy for individual patients. However, this research is severely hamstrung by the rarity of GCTs in individual hospitals/institutes. A publicly available genomic data commons with GCT datasets compiled from different institutes/studies would be a valuable resource to facilitate such research. In this study, we first reviewed publicly available web portals containing GCT genomics data, focusing on comparing data availability, data access, and analysis tools, and the limitations of using these resources for GCT molecular studies. Next, we specifically designed a GCT data commons with a web portal, GCT Explorer, to assist the research community to store, manage, search, share, and analyze data. The goal of this work is to facilitate GCT molecular basis exploration and translational research. Key words Germ cell tumor, Data commons, Data standards

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Introduction Germ cell tumors (GCTs) are a rare disease, but they account for 15% of all malignancies diagnosed during adolescence [1]. The biological mechanisms underpinning their development are only starting to be unraveled [2]. Standard platinum-based chemotherapy for GCTs is associated with toxicity [2], and children are especially vulnerable to therapy-related late effects [3]. Therefore, there is an urgent need to further understand GCT pathogenesis— to facilitate both the discovery of new therapeutic avenues and the identification of biomarkers, in order to tailor therapies for individual patients. Genomic profiling techniques such as DNA and RNA sequencing have been developing over the last few years, rapidly advancing

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_17, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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the understanding of cancer’s molecular basis and clinical behavior, which promotes translational practice in oncology [4, 5]. These techniques also benefit GCT research [2, 6–9]. However, the ability to undertake this research is hamstrung by the rarity of GCTs [2, 10, 11]. The availability of GCT samples within each institute is limited, making it a challenge to derive appropriate wide-ranging insights from such restricted resources. Thus, a publicly available GCT data commons comprising datasets compiled from different institutes/studies will be an invaluable resource to enhance local studies and support broader collaborations. Besides the data availability issue, transmission and analysis of genomic data are also challenging. The costs of data transmission in time and digital space can be prohibitively high. In addition, data accuracy and quality usually decrease as the number of transmission steps increases. Moreover, performing bioinformatics and statistical analysis on genomic data are still challenging for many research groups. Thus, there is a need for a single platform entity where researchers and clinicians could find their data of interest and perform exploratory analysis. In this study, we will briefly review several existing cancer genomic data commons and their data availability, analysis tools and limitations. Moreover, we will introduce the GCT Explorer, a data commons specifically designed to assist the community to store, manage, search, share, and analyze GCT data. The goal was to build a data sharing platform that would make the data findable, accessible, interoperable, and reusable (FAIR) [12]. We hope that this platform will promote research to deepen our understanding of the molecular basis of GCTs and facilitate translation of such findings into clinical practice.

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Current Cancer Genomic Data Commons Review Although there is no standard definition, a data commons should include datasets, computing environments (e.g., cloud or high performance computing), software service/tools, and FAIR digital object compliance as suggested in the NIH Commons Overview, Framework & Pilots - Version 1 [13]. It should be noted that some databases or data portals introduced in the following sections may not meet the strict criteria above, but for ease of reference we will still apply the term “commons” to describe them in this chapter.

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Overview

As various large public cancer genomic projects (e.g., The Cancer Genome Atlas (TCGA, https://cancergenome.nih.gov/), Therapeutically Applicable Research to Generate Effective Treatments (TARGET, https://ocg.cancer.gov/programs/target) and International Cancer Genome Consortium (ICGC, https://dcc.icgc. org/)) advance, terabytes of cancer genomic data have been

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collected. To facilitate the sharing of data from these cancer studies, several cancer genomic data commons have been established, most of which provide access to data hubs, visualization tools, and application programming interface (API). This section offers a brief overview of some emerging data commons [14, 15]. We have listed eight data commons in Table 1 and introduce three of them in the next section. From the table, we can see that six data commons focus on cancer: the National Cancer Institute’s Genomic Data Commons (the NCI’s GDC), University of California Santa Cruz (UCSC) Xena, cBioPortal, International Cancer Genome Consortium (ICGC), Catalogue Of Somatic Mutations In Cancer (COSMIC), and FireBrowse. The other two data commons don’t focus on specific disease areas: European Genomephenome Archive (EGA) and Gene Expression Omnibus (GEO). With the exception of COSMIC, which mainly focuses on harmonizing mutation-related data, and GEO harmonizing array—and sequence-based data, all six other data commons cover different types of molecular profiling data, from genome to transcriptome and proteome as well as epigenetic changes. Some of these data commons contain human GCT-related datasets and visualization tools as well as API, which would be useful resources for GCT research. 2.2 The National Cancer Institute’s Genomic Data Commons

The NCI’s GDC (https://portal.gdc.cancer.gov/) is a research program funded by the National Cancer Institute. As of March 2018, it has harmonized 32,555 cases (310,859 files) from 40 projects of three programs, TCGA, TARGET, and Foundation Medicine (FM). It covers 61 cancer types and various data types (openaccess and controlled-access): clinical, biospecimen, raw sequencing, simple nucleotide variation (SNV), copy number variation (CNV), DNA methylation, and transcriptome profiling. The TCGA-Testicular Germ Cell Tumor (TGCT) and FM-Adult Cancer are the two projects containing GCT-related genomic data. The NCI’s GDC provides online cohort comparison and data visualization as well as API access to that data. The NCI’s GDC also provides well-written documentations that explain the concepts and procedures behind the data preparation.

2.3 European Genome-phenome Archive

European Genome-phenome Archive (EGA; https://www.ebi.ac. uk/ega/) is a database of genotype information hosted at the European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI). EGA is designed to be a hub for all types of genotype and sequence experiments. Thus, it is not a cancer-focused data hub and includes both cancer-related and noncancer disease data. As of March 2018, EGA has 1815 studies and 3838 datasets, access to which requires the authorization of data assessment committees. The EMBL-EBI portal (https://www.ebi.ac.uk/), which covers EGA, harmonizes large sources of biology analysis tools and

http://www. cbioportal.org

https://dcc.icgc.org

https://cancer.sanger. Cancerac.uk/cosmic related

https://www.ncbi. nlm.nih.gov/geo/

http://firebrowse. org/

https://qbrc.swmed. edu/projects/gct/

cBioPortal

International Cancer Genome Consortium

Catalogue Of Somatic Mutations In Cancer

Gene Expression Omnibus

FireBrowse

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Cancerrelated

Cancerrelated

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Non–disease Array- and specific sequencebased

Cancerrelated

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https://xena.ucsc. edu/

University of California Santa Cruz Xena

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European Genome-phenome Archive

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The National Cancer Institute’s https://portal.gdc. Genomic Data Commons cancer.gov/

Data commons

Table 1 Genomic data commons

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valuable databases, for example, ArrayExpress (https://www.ebi.ac. uk/arrayexpress/) and Expression Atlas (https://www.ebi.ac.uk/ gxa/home). 2.4 University of California Santa Cruz Xena

UCSC Xena (https://xena.ucsc.edu/) is a system consisting of public and private data hubs as well as data visualization tools. The public hubs host 132 cohorts and 1494 datasets from the TCGA, ICGC, Global Alliance for Genomics and Health (GA4GH, https://www.ga4gh.org/), Treehouse Childhood Cancer Project (https://treehousegenomics.soe.ucsc.edu/) and the NCI’s GDC. Data types include somatic mutation genes, copy number, DNA methylation, exon, microRNA, and protein expression. The private hub enables users to upload their data securely and analyze the public and private data together. The visualization tools from the UCSC Xena portal were well-designed and implemented to facilitate users in selecting the variables of interest, whether genomic or phenotypic, to perform association analysis, and to generate intuitive plots to understand the results of the data.

2.5

The data commons described above are invaluable resources for searching cancer genomic data and exploring molecular mechanisms. However, there are several limitations for researchers in using such resources for GCT research: (1) No data commons is comprehensive for GCT genomic data. Due to the tumor’s rarity, the size of these online GCT cohorts is usually less than 200 patients. Most current data commons only include adult testicular cancer data, and other types of GCTs are rarely collected. It would be helpful to have one data commons to collect diverse types of GCT data from different sources. (2) No unified data standards have been applied among datasets. Currently, the datasets from different hospitals/ institutes in the data commons usually retain their own naming conventions, clinical annotation standards and controlled terminology. When combining these datasets, the variable mapping procedure will cost a great deal of time and effort. Additionally, this procedure is prone to error, resulting in subsequent information loss. Standardization of data reformatting would be beneficial before delivery to users. To do this, clinical expertise in GCT is needed to make the terminology used in the data commons consistent with GCT clinical care and research. (3) A need for more diverse and powerful analysis tools. Most of the data commons provide data analysis tools, which are helpful for users to quickly review the available data. However, the need for analysis functions, such as differential expression and other advanced analyses, is largely unmet. It would be more advantageous if users could find the data and complete their desired analysis seamlessly in one stop. Thus, we devoted efforts to develop a GCT-specific data commons, GCT Explorer, for which we not only harmonized the data but also strove to address the above limitations, in order to facilitate GCT research to understand the molecular basis.

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Germ Cell Tumor Explorer Data Resources

As proof of concept to develop a GCT-specific data commons (https://qbrc.swmed.edu/projects/gct/), including both public and private genomic data, we have collected, processed and included data from three different resources, including the TCGA-TGCT (mainly from the NCI’s GDC), the work of Dr. Bagrodia A et al. [16] and the work of Dr. Palmer RD et al. [7]. Meanwhile, we are collaborating with the Malignant Germ Cell International Consortium (MaGIC) to integrate the clinical trial data as well as genomic data collected by MaGIC members over the past several decades. MaGIC is a collaborative group of international oncologists, surgeons, pathologists, epidemiologists, bioinformaticians, statisticians, and basic scientists, with the same motivation to improve outcomes of patients with GCTs through the development of more effective management and treatment. In total, the data of 1798 patients, 370 samples, 835 genomics from four programs, and 14 clinical trials are hosted in our data commons. The data types cover clinical annotations, SNVs, CNVs, and mRNA and microRNA expression (Fig. 1a).

Fig. 1 The dashboard and cohort discovery web page of the GCT Explorer. (a) Dashboard. Shows the total number of available data in the data commons; (b) Cohort discovery. Blue frame, filter choices; red frame, current selected filters; green frame, the number of patients and samples satisfying the current selected filters; orange frame, the pie charts showing summary of the patients satisfying the current selected filters

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Data Standards

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Uniform data standards are essential for dismantling the barriers to analyzing data from different sources. If data was curated according to the same standards, it would make the data more reusable, interoperable, and comparable for other users. Thus, in the past 2 years, a group of statisticians, informaticians, and oncologist/ surgeons/pathologists in MaGIC have been working together to develop GCT clinical data standards to facilitate data sharing across hospitals/institutes worldwide. Data standards define the variables and controlled terminology. Clinical data standards should be comprehensive enough to cover all possible events in the patient journey: diagnosis, treatment, follow-up, relapse, and so on. In this manuscript, we will focus on the sample data standards rather than the clinical data standards. From patient samples to genomic datasets, it goes through multiple steps: specimen preparation and storage, molecule extraction, array-based/sequencing-based experiment, and raw data analysis. All these steps affect the quantity and quality of the final genomic data. It would be helpful to incorporate detailed information from each step into the data commons for users to better understand the data and how it was generated. We developed the GCT genomic data commons sample data standards based on the protocols from OpenSpecimen, TCGA and Minimum Information About a Microarray Experiment (MIAME) [17]. These cover the workflow from tissue to end data in three sections: specimen, experiment and analysis. The specimen section describes how to handle all types of samples, for example, tissue, liquid, and cells. It includes information about how the specimen was obtained (Fig. 2, Procedure type, e.g., biopsy and surgery), the Pathological status (e.g., normal, primary, and metastasis tumor), how the specimen was processed (Specimen type, e.g., fresh, frozen, and formalin-fixed, paraffinembedded (FFPE)), and the anatomical site and laterality of the specimen origin. The experimental section addresses how the specimen was processed into raw microarray/sequencing data. It includes detailed experimental protocols about how the specimen was

1-n Specimen

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Fig. 2 Schema of the prototype germ cell tumor sample data standards

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transformed into experiment-ready molecules (e.g., the kit and how the library was built) and platform information (e.g., sequencer and microarray). The analysis section focuses on the computational pipeline from the raw data to the end-user genomic data, for example, the genome reference and packages used in the workflow. 3.3

Cohort Discovery

To facilitate users in finding data/studies of their interest, a Cohort Discovery module was developed and embedded in the GCT Explorer (Fig. 1b). Users could set up the criteria via the filters on the left panel (blue frame in Fig. 1b). The currently selected filters will be presented automatically in the red frame in Fig. 1b. The number of available patients and samples that satisfy the criteria will update in real time (green frame in Fig. 1b). Meanwhile, the pie charts showing the summary statistics of patients will be updated as well based on the criteria (orange frame in Fig. 1b). With this module, users could narrow down the population to a specific subgroup of interest. Currently, two types of filters are available: (1) clinical variables, sex, race, age at diagnosis, histology, primary site, relapse and vital status; and (2) genomic data availability: whether the SNV, CNV, mRNA, and microRNA data are available in the data commons.

3.4

Online Analysis

Advanced online data analysis modules will promote GCT biomarker discovery, the need for which remains largely unmet. Here we give several examples that the GCT research community will benefit from but that have not been developed in any existing data commons. The first example is to identify robust differentially expressed genes between different phenotypes using several algorithms. Identifying differentially expressed (DE) genes is usually the first step to uncovering new molecular mechanisms using genomic data. For example, DE analysis between tumor and normal samples has been used to find cancer driver genes by mRNA expression data [18, 19]. Although many computational algorithms have been developed to identify DE genes, the genes identified by different algorithms are often not fully overlapping. Therefore, it will be important to provide a comparative analysis of using two well-accepted DE analysis algorithms, and find which genes are robust to different analytics approaches. In our module, users can choose their preferred algorithm to analyze the data. Users can also select two or more algorithms to run on the same dataset. Then, the module will return the results from individual algorithms and the common hits. Figure 3 is an illustrative example with edgeR [18] and Limma [19] packages. We applied this strategy in our neuroblastoma web portal (unpublished) to compare the subgroups with and without MYCN amplification. EdgeR returned 3127 genes with significant differential

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Fig. 3 An illustrative sample of online analysis using edgeR and Limma packages. (a, b) Differential gene expression analysis in the neuroblastoma web portal (unpublished) to compare the subgroups of patients with and without MYCN amplification. (a) EdgeR returned 3127 genes with significant differential expression, and Limma returned 1735 genes, while 1404 genes occurred in both lists with the same regulation status. (b) Several examples of the 1404 genes occurring in the results of both methods. (c) The prototype results of comparing the subgroup of seminoma and nonseminoma using the NCI’s GDC TCGA-TGCT mRNA expression data. Limma returned 16,905 genes with significant differential expression, and edgeR returned 5360 genes. Five-thousand three-hundred and thirty-six genes occurred in both lists with the same regulation status

expression, and Limma returned 1735 genes, while only 1404 genes occurred in both lists and with the same regulation status (e.g., up- or downregulation) (Fig. 3a). The names and regulation statuses of these 1404 genes will be also be returned (Fig. 3b shows several examples). Similarly, we will apply this module in the GCT Explorer. Figure 3c shows the prototype results, which compared the subgroups of seminoma and nonseminoma using the NCI’s GDC TCGA-TGCT mRNA expression data. Limma returned 16,905 genes with significant differential expression, and edgeR returned 5360. These two had a large overlap of 5336 genes that occurred in both lists and with the same regulation status. 3.5

Data Security

Since most current and future data will be patient-derived, it is essential to keep patient information confidential. First, only deidentified patient data/samples will be stored in the data commons. Second, even with the deidentified data, it is critical to have

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stringent data storage, server specification, data access, and management protocols in place to protect data. In the GCT Explorer, only summary statistics like the patient number will be released (Fig. 1b) as well as high level genomic summary information (Fig. 4, an illustrative sample using TCGA-TGCT data) are accessible to all users. No individual patient data are accessible, especially those from private sources. Full access to the data requires registration (Fig. 5), and access to private datasets requires approval from the study committees.

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Future Work and Discussion Genomic data from different analysis centers could be generated using different versions of reference genomes and bioinformatic packages, which could lead to mismatched data and confusing

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Fig. 5 The login page of the GCT explorer

results. Therefore, it will be useful to access the raw genomic data, and reanalyze them in a uniform workflow, using the same or similar reference genome files and packages, which would make the data more comparable across different sources and even across disease types. In addition, it would also be useful to have a platform where users could query data and define the analysis themselves as well as to provide a convenient computational environment with high performance. Data quality and accuracy is the most important issue to a data commons. Stringent quality assessment and control procedures, version controls and documentation of each data preprocessing and analysis procedure are essential for the development and maintenance of a data commons. In addition, the procedure of transmitting data from one commons to another is usually prone to error and leads to information loss due to inconsistent formats and other reasons outside of control. Thus, if possible, the data transmission steps should be minimized to as few as possible. This requires a data commons to have access to the raw genomic data from the original source and to have the capability to perform high-quality analyses to accurately transform the raw data into analysis-ready datasets.

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Summary Here we have reviewed and compared the existing genomic data commons. We have also described a GCT-specific data commons, GCT Explorer, designed to assist molecular exploration and biomarker discovery for this under-investigated disease group. The data commons currently hosts several public genomic datasets and will be extended to include more datasets in the future. The data commons also provides other functions, for example, cohort discovery, online analysis, and user registration. Moreover, we are collaborating with MaGIC to develop the GCT Clinical and Specimen Data Standards to provide a census for seamless data sharing and transfer, making the data findable, accessible, interoperable, and reusable for the GCT research community.

Acknowledgments We acknowledge grant funding from the St. Baldrick’s Foundation, the National Institute of Health (5P30CA142543 and R01GM115473), and the Cancer Prevention Research Institute of Texas [RP180805, RP110394, and RP170152]. References 1. Oosterhuis JW, Looijenga LH (2005) Testicular germ-cell tumours in a broader perspective. Nat Rev Cancer 5:210–222. https://doi.org/ 10.1038/nrc1568 2. Frazier AL, Amatruda JF (eds) (2013) Pediatric germ cell tumors: biology treatment survivorship, vol 1. Springer Science & Business Media, New York, NY 3. Hale GA, Marina NM, Jones-Wallace D, Greenwald CA, Jenkins JJ, Rao BN et al (1999) Late effects of treatment for germ cell tumors during childhood and adolescence. J Pediatr Hematol Oncol 21:115–122 4. Mardis ER (2018) Insights from large-scale cancer genome sequencing. Annu Rev Cancer Biol 2:429–444. https://doi.org/10.1146/ annurev-cancerbio-050216-122035 5. Cieslik M, Chinnaiyan AM (2018) Cancer transcriptome profiling at the juncture of clinical translation. Nat Rev Genet 19:93–109. https://doi.org/10.1038/nrg.2017.96 6. Korkola JE, Houldsworth J, Chadalavada RS, Olshen AB, Dobrzynski D, Reuter VE et al (2006) Down-regulation of stem cell genes, including those in a 200-kb gene cluster at 12p13.31, is associated with in vivo differentiation of human male germ cell tumors. Cancer

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Correction to: Germ Cell Tumor Cell Culture Techniques John T. Lafin, James F. Amatruda, and Aditya Bagrodia

Correction to: Chapter 5 in Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9 The chapter was inadvertently published with Acknowledgement section excluded in the article. This error has now been corrected by including the Acknowledgement section in the chapter.

The updated online version of this chapter can be found at: https://doi.org/10.1007/978-1-0716-0860-9_5 Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

C1

INDEX A

F

AggreWell ............................................128, 133–135, 143 Alpha-fetoprotein (AFP).......................... 5, 6, 14, 15, 20, 21, 24–26, 226 Apoptosis ....................................... 32, 81, 100, 101, 103, 104, 106–109, 183

Fluorescence in situ hybridization (FISH) ................... 39, 49–62, 247, 249, 252, 254–255, 258, 259

B β-HCG.......................................................................14, 24 Bisulfite treatment................................................ 167–180

C Cancer stem cells (CSCs)..................................... 147–149 Cell culture .....................................65–77, 79–81, 86–88, 93, 94, 104, 106–108, 118, 121, 126–128, 130–133, 141, 142, 149, 152, 156, 158, 159, 168 Cell cycle................................33, 81, 100–102, 105–107, 109, 114, 121, 122, 167 CellSearch® .......................................................... 246, 247, 250, 255–257, 259 Cell viability ................................................ 81, 88, 93, 96, 100, 101, 105–108 Choriocarcinoma (CC) .....................................6, 7, 9, 14, 19, 21, 24, 65, 79, 82, 86, 104, 257 Circulating ....................................................174, 225–241 Circulating tumor cells (CTCs) .......................... 246–259 CRISPR/Cas9...........................................................85–96

G Gene-editing..............................................................94, 95 Genetic susceptibility ........................................... 189, 190 Genome-wide association study .......................... 189, 190 Germ cell cancers ..........................77–82, 85–96, 99–109 Germ cell tumors (GCTs) ............................ 1–11, 13–28, 31–45, 49–63, 85, 86, 88, 89, 125, 147–164, 181–186, 189–221, 225–242, 245–259, 263–274 GFP reporters.................................................................. 93

H Hanging drops ..........................................................77–82 High resolution melting ............................................... 169 Homologous recombination ............................... 113–122

I Immunofluorescence ..........................248–254, 257, 258 Immunohistochemistry (IHC)........................... 6, 13–28, 135–139, 160, 161 Induced pluripotent stem cells (iPSCs) ....................... 126 Interstrand crosslink repair ..........................100, 113–122 Isochromosome 12p/chromosomal 12p anomalies .............................................. 49–61, 148

D

K

Data commons ..................................................... 263–274 Data standards ............................................. 267, 269, 274 Differential diagnosis ...................................................... 27

Keratins ........................ 28, 246–248, 251–253, 256–259 Kras....................................... 33, 34, 148, 149, 151, 153, 161, 162, 182, 183

E

L

Embryoid bodies (EBs) ............................... 80, 127, 130, 134–136, 138, 140, 141 Embryonal carcinoma (EC) ............................. 3–5, 9, 14, 19–22, 24–27, 51, 65, 66, 78, 79, 81, 85, 86, 104, 113–122, 148, 149, 159, 247, 257 EpCAM.........................................................246–248, 253 Epidemiology ....................................................... 190, 268

Laser capture microdissection .......................... 32, 36–38, 44, 45, 138

M Methylation ............................34, 86, 167–179, 265, 267 microRNAs (miRNAs)....................................34, 40, 168, 225–241, 246, 267, 268, 270

Aditya Bagrodia and James F. Amatruda (eds.), Testicular Germ Cell Tumors: Methods and Protocols, Methods in Molecular Biology, vol. 2195, https://doi.org/10.1007/978-1-0716-0860-9, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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TESTICULAR GERM CELL TUMORS: METHODS

278 Index

AND

PROTOCOLS

Molecular genetics ........................... 32, 45, 50, 125, 265 Mouse model..................................................65, 147–164

N Naı¨ve pluripotency ...................................... 126, 127, 132 Non-seminomatous tumor ............................... v, 4, 9, 14, 20, 21, 181, 247

O OCT3/4...................................... 3, 6, 14, 18–20, 22–27, 33, 86, 130, 247, 248, 252, 253 OCT4 ......................................... 130, 135–138, 149, 161

P Polymerase chain reaction (PCR) ....................38, 40, 41, 44, 89, 90, 94–96, 150, 151, 154, 155, 160–162, 168–176, 178, 179, 185, 186, 194, 209, 210, 225, 226, 229–232, 236–239 Pre-amplification ........................................ 227, 230–232, 234, 238–240 Primed pluripotency ........................................... 126, 127, 130–132, 141, 143 Primordial germ cell-like cells ............................. 125–144 Primordial germ cells (PGCs) ..........................18, 19, 21, 85, 125–127, 159 Psoralen ................................................................ 114–118 Pten ........................... 148, 149, 151, 153, 154, 160–162

R RT-qPCR....................................................................... 227

S SALL4.............................................3, 5–8, 14, 15, 19–21, 24, 26, 27, 247, 248, 251–253 Seminomas..............................................3, 4, 7, 9, 10, 14, 19–24, 26, 27, 32, 34, 35, 49, 65, 66, 78, 79, 81, 85, 86, 104, 147, 181, 182, 226, 247, 257, 271 Serum....................................................3, 5, 6, 26, 66, 73, 79, 86, 87, 104, 118, 119, 128, 136, 138, 152, 226–229, 234, 235, 237, 240, 241, 245, 248, 249 Spin-EB................................................127, 133, 134, 143 Standard operating procedures .................................... 167

T Testicular ................................................1, 6, 7, 9, 10, 14, 18, 19, 24, 31–45, 49–62, 65, 85–96, 99, 147–164, 181–183, 189–220, 226, 227, 245–259 Testicular cancers ...................................34, 45, 125, 147, 181, 182, 227, 267 Testicular germ cell tumor (TGCT) ..............1, 113–122, 148, 155, 156, 159, 189, 265 Testis ..............................1, 2, 9, 10, 13, 32, 35, 156, 163 3d cell culture.................................................................. 77 Tissue microdissection ..............................................31–45 Transfection................................ 87, 89–93, 96, 117–122 Troubleshooting ............................................................. 55

Y Yolk sac tumors (YST) ......................................5, 6, 8, 10, 14, 24, 25, 35, 65, 66, 86, 226, 257