Table of Contents 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 202

Hanneke van der Wijngaart

Clinical application of genomics- and phosphoproteomics-based selection of targeted therapy in patients with advanced solid tumors Hanneke van der Wijngaart

Clinical application of genomics- and phosphoproteomics-based selection of targeted therapy in patients with advanced solid tumors Hanneke van der Wijngaart

The author gratefully acknowledges the Barcode for Life foundation (BFL), the Center for Personalized Cancer Treatment (CPCT), the Dutch Cancer Society (KWF) and the Dutch Research Council (NWO) for financially supporting the research in this thesis. Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis Oncology, Eisai, Eli Lilly, Ipsen, Janssen, Merck Sharp & Dohme, Novartis, Pfizer and Roche generously provided drugs and financial support for the DRUP trial. ISBN: 978-94-6483-538-0 doi:10.5463/thesis.350 Provided by thesis specialist Ridderprint, ridderprint.nl Printing: Ridderprint Layout and cover design: Timo Wolf Kamp, persoonlijkproefschrift.nl Copyright © 2023, Hanneke van der Wijngaart All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, photocopying, or otherwise, without the permission of the author, or, when appropriate, of the publishers of the publications.

Voor mijn moeder

TABLE OF CONTENTS Chapter 1 General introduction 9 Chapter 2 The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs 23 Chapter 3 Patients with Biallelic BRCA1/2 Inactivation Respond to Olaparib Treatment Across Histologic Tumor Types 55 Chapter 4 Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics 87 Chapter 5 Advancing wide implementation of precision oncology: A liquid nitrogen-free snap freezer preserves molecular profiles of biological samples 129 Chapter 6 Summarizing discussion and future perspectives 157 Chapter 7 Nederlandse samenvatting 177 Chapter 8 Appendices Dankwoord Curriculum vitae List of publications 187 188 195 196

CHAPTER 1 General Introduction

10 CHAPTER 1 GENERAL INTRODUCTION Accounting for almost one in six deaths, cancer is the second leading cause of death worldwide1. In 2020, an estimated 19.3 million new cancer cases occurred, and nearly 10 million people have died from the disease2. The global cancer burden continues to increase and a 47% rise in incidence is expected between 2020 and 2040 to an incidence of 28.4 million cases2. A GENETIC DISEASE Cancer is a generic term for a large group of diseases characterized by the uncontrolled growth and spread of abnormal cells that can result in death if not treated1. It is a genetic disease with nine essential characteristics (Hallmarks): self-sufficiency in growth signals, evasion of growth suppressors, resistance to cell death, replicative immortality, induction of angiogenesis, activation of invasion and metastasis, reprogramming of energy metabolism, evading immune destruction and the creation of a “tumor microenvironment”. Underlying these Hallmarks are two enabling capabilities: genome instability and mutation and tumor-promoting inflammation3,4. Using these Hallmarks to describe the pathophysiology of cancer provides a better understanding of the drivers and enablers of the disease, and, equally important, may contribute to the development of new effective systemic anti-cancer treatments. SYSTEMIC ANTI-CANCER TREATMENT: CHEMOTHERAPY The first written prescriptions of remedies for the treatment of cancer date back to 2000 BC, usually in the form of ointments, medicated herbal solutions and powders5. Luckily, we have come a long way since then, and the systemic anti-cancer therapies have become more and more effective. Between 1948 and 1956, folic acid antagonists, vinca alkaloids and methotrexate were introduced as effective chemotherapies for the treatment of different types of cancer5. These agents were among the first modern chemotherapeutic drugs and are still in use today. Since the late 1950’s, systemic anti-cancer therapies have continued to improve in terms of efficacy and survival due to the discovery of new chemotherapeutic agents, new combinations of drugs, new dosing regimens and the use of chemotherapy (neo)adjuvant to surgery and radiotherapy6. Conventional chemotherapy interferes with the DNA, hindering cell division and thereby stopping tumor growth but also damaging healthy tissues. Not all tumors respond (equally) to treatment with chemotherapy while most patients experience (serious) toxic side effects. It has proven to be difficult to upfront predict which patients will benefit from the treatment. Part of the solution to the problem of treatment selection for individual patients may lie in the fact that cancer is a genetic disease, which is characterized by dysregulation of growth signaling cascades and the escape from suppressive signaling and the immune response. NEW CLASSES OF ANTI-CANCER DRUGS In the past 30 years, global overall cancer survival and five-year relative survival has improved significantly7. Many factors have contributed to this worldwide decrease in mortality7. Development of, and access to, new types of anti-cancer drugs has played a major role in multiple tumor types. Especially drugs that interfere with aberrantly activated signaling cascades (i.e.

11 General Introduction protein kinase inhibitors (PKI’s)) or the immune system (i.e. immune checkpoint inhibitors (ICI)), or that target specific weaknesses in cancer cells caused by genetic aberrations (e.g. PARP inhibitors), have proven to be effective. For patients with metastatic melanoma or renal cell carcinoma for example, these new treatment options have dramatically improved the overall survival and quality of life. Historically, patients with advanced melanoma, an aggressive and chemotherapy-resistant form of cancer, had a median overall survival of around 8 months and a 5-year survival of 10%. With the introduction of immune checkpoint inhibitors ipilimumab (monoclonal antibody (mAb) directed against CTLA4), nivolumab and pembrolizumab (mAb directed against PD-1) and combinations of these drugs, the overall survival has improved to several years, with a 5-year survival of 52%8. Approximately 50% of patients with advanced melanoma has a pathogenic mutation in the V-Raf Murine SarcomaViral Oncogene Homolog B (BRAF) gene in their tumor DNA. Treatment of these patients with an inhibitor of BRAF combined with an inhibitor of mitogen-activated protein kinase 1 (MEK1 or MAP2K1) resulted in a median progression free survival of 9.9 months, with an objective response rate of 68%9. Treatment strategies combining these BRAF/MEK inhibitors with ICI are currently under investigation10. For patients with metastatic clear cell renal cell carcinoma, the introduction of anti-angiogenic tyrosine kinase inhibitors (TKI’s), such as sunitinib, sorafenib, axitinib, pazopanib and cabozantinib, has also dramatically improved survival. Since their introduction, the median overall survival (OS) has improved from 15-17 months before 200411-14 to 23-29 months with TKI monotherapy15-17. Combining TKI’s with ICI has further improved the 12-month overall survival rate from 72%18 to 90%19,20. MOLECULAR PROFILING TO ASSESS TUMOR BIOLOGY A corner stone for successful targeted treatment of patients with cancer is the presence of a biomarker that is associated with sensitivity for a certain targeted agent. Targets for treatment can be identified in multiple layers of cancer cell biology, but the challenge remains where to look for the most reliable biomarkers that best predict the treatment outcome to a targeted therapy. DNA holds a permanent copy of the genetic information. The genes in DNA encode proteins, the driving force of cellular function, including intracellular signaling and immune response. All genetic information together is called the genome. The conversion of the genetic information stored in DNA to a functional product, such as a protein, is a complicated process that has two major steps. First, during transcription, the information in the double-stranded DNA is transferred to a messenger RNA (mRNA) molecule, which is a single-stranded temporary copy of the gene26. The sum of all the mRNA molecules expressed from the genes is called the transcriptome. During the process of translation, the second major step, the transcribed code on the mRNA molecules is used to assemble a chain of specifically sequenced amino acids 1

12 CHAPTER 1 that form a protein26. All proteins in an organism together are called the proteome. Through regulation of gene expression, cellular functions can be controlled. Another way that the function and activity of proteins are regulated is through reversible chemical changes to the protein after translation, known as posttranslational modifications (PTM). Phosphorylation is one of the most common PTM. During phosphorylation, a phosphate group is added to one of the amino acids tyrosine, serine or threonine by a kinase, thereby regulating the protein function. Especially tyrosine phosphorylation (pTyr) plays an important role in the regulation of signaling cascades in cancer. All phosphorylated proteins together are called the phosphoproteome. GENOMICS-BASED PRECISION ONCOLOGY The development of a large number of targeted- and immunotherapies, targeting specific molecular alterations and aberrant pathways in tumor cells, has dramatically changed the treatment paradigm in oncology. Coming from a histology-centered one-size-fits-all approach, the major focus has now shifted to precision oncology, a patient-centered biomarker-driven personalized approach to systemic treatment of patients with cancer21. Precision oncology is also known in literature as “personalized oncology”, “personalized cancer medicine” or “precision cancer medicine”. Many targeted- and immunotherapies have already received FDA/EMA approval and are available for patients with certain tumor types, harboring a specific molecular feature that predicts sensitivity for these drugs9,22-24. Though this is an important step towards precision oncology, the maximum potential of this approach is currently not used. A pan-cancer whole-genome analysis of metastatic solid tumors showed that in 31% of patients, across tumor types, an “actionable” genomic event was identified that predicted sensitivity to a drug. In 18% this was a biomarker for which on-label medication was available, and 13% of patients had a genomic target for which drugs were available, but not for the tumor type25. Due to the histology-specific registrations of these drugs, a significant number of patients with other tumor types harboring the qualifying genomic aberration does not have access to these potentially active treatment options. Clinical evidence for efficacy of these drugs in other tumor types is often not available, and large trials with conventional design are usually not feasible due to the small and diverse subgroups of patients. PROTEOME- AND MULTI-OMICS-BASED PRECISION ONCOLOGY For the identification of tissue-based biomarkers, research often focused on abnormal protein expression, as found by immunohistochemistry, or genomic aberrations, such as activating mutations or amplifications of oncogenes or deletions of tumor suppressor genes, as found by targeted or broad panel sequencing. With recent advancements in sequencing- and bioinformatics techniques, also more complex genomic features such as gene fusions, microsatellite instability (MSI) and homologous repair deficiency (HRD) signatures can be computed and may serve as genomic biomarkers for treatment response to targeted agents.

13 General Introduction For single oncogene-driven tumors, such as malignant melanoma with a BRAF V600E mutation, genomics-based treatment is valuable approach9. Unfortunately, not all tumors harbor a clear genomic diver mutation. Some may be driven by a multitude of aberrantly activated kinase signaling pathways, such as renal cell carcinoma27. In these tumor types, a functional pathway analysis may be a more promising approach28,29. (Phospho)proteomics based on liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) may offer insight in aberrantly activated kinase signaling pathways and potential drug targets through the global analysis of phosphorylated proteins. In particular, phosphotyrosine-(pTyr)-phosphoproteomics provides an opportunity for the identification of patient subgroups likely to benefit from tyrosine kinase inhibitors (TKI’s)30,31. In the past decade, advances in technology have enabled us to generate large-scale molecular data, allowing characterization of complex biological systems in great detail. For quite some time, research efforts have focused on unidimensional approaches to discovery of clinically useful biomarkers, i.e. genomics, transcriptomics or proteomics analysis32. The new fields of research created by these efforts are often referred to as “omics”, a field of study that focusses on large-scale data/information to understand biology33. The application of these omics techniques have enabled major improvements in the understanding of cancer biology, the identification of biomarkers and the personalized treatment of patients with cancer. The integrated use of multiple omics may hold an opportunity for further improvement of our knowledge of biological processes. This multi-omics approach is suggested by numerous recent reviews to greatly benefit the field of precision oncology34-36. To date, only limited examples of truly multi-omics studies are available32. Most so-called multi-omics analyses only describe one omics approach, complemented with a limited amount of data from additional techniques, often obtained through targeted analyses32. Given the fact that different omics datasets do not overlap extensively and the correlation between data sets is extremely limited, it is likely that different omics approaches assess disparate pieces of the puzzle of the complex pathophysiology of cancer development and progression. True multi-omics analysis of tissues obtained from patients with cancer is still in its infancy. Nevertheless, recent advances in each of the omics techniques bring the clinical application of multi-omics in the standard care for patients with cancer closer by the day. PRE-ANALYTICAL REQUIREMENTS TO ENABLE MULTI-OMICS ANALYSIS Development and wider implementation of multi-omics in clinical studies faces many challenges32. One of the most critical hurdles is tissue availability. A true multi-omics analysis requires multiple techniques to be performed on a tissue of interest. To allow for optimal correlation between these types of omics, they are ideally performed on the same piece of tissue to minimize the effect of intra-and inter-patient heterogeneity. Each of the omics techniques has its own minimally required quantity, often expressed as, for example, minimal tumor cell percentage, nanograms of DNA or RNA, or milligrams of protein. Clinical tissue samples, however, 1

14 CHAPTER 1 are often core needle biopsies, with a maximum tissue yield of only 3.5 – 7 mg when using a 16-gauge core needle37. In recent years, the omics techniques have improved tremendously, resulting in a general lowering of minimally required quantity of tissue. Whole genome- and whole transcriptome sequencing can already be performed on a single cell38-40. In the field of phosphoproteomics, important steps have been made to optimize the techniques, to facilitate analysis of small clinical samples41. Single-cell mass spectrometry-based phosphoproteomics is considered a promising opportunity for improving our understanding of individual tumor biology and facilitating phosphoproteomics-based therapy selection for individual patients in the future42,43. Furthermore, a standardized suitable method of processing and handling the acquired tissue specimen is fundamentally important to allow for a comprehensive multi-layer analysis of cancer tissue. In the past, biopsy samples were often collected in buffers that stabilized DNA and RNA, but essentially rendering the tissue useless for proteomics analysis32. Instead, high-quality fresh frozen tumor samples are required44. Standardized operating procedures for handling and preservation of the tissue are indispensable, since differences in pre-analytical handling can generate conflicting research results due to heterogeneity in the quality of samples and associated data45,46. Moreover, posttranslational modifications may be affected by certain handling and storage conditions, such as cold ischemia time47-50 and possibly freezing rate51-53. Standardized high-quality preservation of biospecimens, in order to harness the most accurate genomic, transcriptomic and protein expression properties of the tissue, is a basic requirement for the generation of these complex multi-omics data46. An even bigger challenge may be the urgent need for the development of an integrated bioinformatics pipeline for a comprehensive analysis of these high-throughput molecular assays32,54. Such an integrated approach may further increase our understanding of cancer biology and support biomarker discovery and drug repurposing55,56, both essential for the practice and advancement of precision oncology. TREATMENT SELECTION TRIALS Working towards a histology-agnostic biomarker-centric approach, many precision oncology clinical trials now focus on the use of registered or experimental (combinations of) targeted agents solely based on the presence of a validated biomarker, while evaluating the effect in the context of histology. New trial designs have been developed to investigate even modest signs of clinical activity of these targeted agents in small subgroups of patients with cancer. Many of these basket-, umbrella and N-of-1-trials have been conducted in the past ten years57, some living up to the promise of precision medicine, and others reporting disappointing results58-70. Tsimberidou et al have reviewed and summarized all these completed and ongoing trials and their distinctive features and outcomes21. A fundamental question in precision oncology remains how to select the right treatment for the right patient at the right time. An important factor contributing to the success of a precision oncology approach may be the actual process of treatment selection and the arguments for prioritizing one treatment over another.

15 General Introduction THESIS OUTLINE AND SCOPE Clinical implementation of precision oncology for patients with advanced solid tumors continues to be challenging. This thesis focused on optimizing the approach to targeted treatment selection (patient-based approach) and on identification of predictive tissue-based biomarkers for treatment benefit (drug-based approach), while contributing to an optimized infrastructure as a basic requirement for multi-omics analysis. Chapters 2 and 3 focus on the Drug Rediscovery Protocol (DRUP), an ongoing prospective, multicenter, non-randomized basket trial, in which patients with advanced solid tumors are being treated based on their tumor genomic profile, with targeted- or immunotherapy outside their registered indications. Chapter 2 describes the design and feasibility of the DRUP trial, including treatment outcomes of the first 215 patients treated in the trial. The clinical benefit rate in the first completed cohort “Nivolumab for MSI tumors” is highlighted, as well as the value of WGS in identifying targeted treatment options for patients with advanced cancer. In chapter 3 we present the results of the DRUP cohort “Olaparib for tumors with a BRCA1/2 mutation”, in which 24 patients with treatment refractory cancer with BRCA1/2 loss of function mutations were treated with the PARP inhibitor olaparib. Clinical outcome of these patients is interpreted in the context of their tumor genomic characteristics, attempting to identify potential indicators of (lack of) treatment benefit to olaparib, with special emphasis on patients with non-BRCA-associated tumor types. Chapter 4 focuses on the use of mass spectrometry-based phosphoproteomics for the identification of predictive biomarkers for response and resistance to the tyrosine kinase inhibitor sunitinib in patients with renal cell carcinoma. Using this functional read-out, we aimed to describe differences in biology between sensitive and primary resistant patients and to define a phosphosite signature for prediction of treatment outcome. In chapter 5 we describe a new liquid nitrogen-free snap freezer for snap freezing biospecimens, which was developed to conserve molecular profiles under standardized and optimized pre-analytical conditions. We compare the performance of the new snap freezer to the current golden standard for snap freezing (quenching in liquid nitrogen) in terms of conservation of phosphoproteomics- and transcriptomics profiles of samples, hypothesizing that a liquid nitrogen-free snap freezing method may advance implementation of precision oncology. The main findings of this thesis are summarized and discussed in chapter 6. With special emphasis on the approaches we used for improving patient selection and prediction of treatment outcome, we place our findings in the broader context of multi-omics for improving effective and personalized care for patients with cancer, and give recommendations for future research. 1

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18 CHAPTER 1 30. Jimenez CR, Verheul HM. Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications. Am Soc Clin Oncol Educ Book 2014:e504-10. DOI: 10.14694/EdBook_AM.2014.34.e504. 31. Klaeger S, Heinzlmeir S, Wilhelm M, et al. The target landscape of clinical kinase drugs. Science 2017;358(6367) (In English). DOI: ARTN eaan4368 10.1126/science.aan4368. 32. Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019;20(19) (In English). DOI: ARTN 4781 10.3390/ijms20194781. 33. Yadav SP. The wholeness in suffix -omics, -omes, and the word om. J Biomol Tech 2007;18(5):277. (https://www.ncbi.nlm.nih.gov/pubmed/18166670). 34. Gallo Cantafio ME, Grillone K, Caracciolo D, et al. From Single Level Analysis to Multi-Omics Integrative Approaches: A Powerful Strategy towards the Precision Oncology. High Throughput 2018;7(4). DOI: 10.3390/ht7040033. 35. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017;18(1):83. DOI: 10.1186/s13059-017-1215-1. 36. Turanli B, Karagoz K, Gulfidan G, Sinha R, Mardinoglu A, Arga KY. A Network-Based Cancer Drug Discovery: From Integrated Multi-Omics Approaches to Precision Medicine. Curr Pharm Des 2018;24(32):3778-3790. DOI: 10.2174/1381612824666181106095959. 37. Lai HW, Wu HK, Kuo SJ, et al. Differences in accuracy and underestimation rates for 14- versus 16gauge core needle biopsies in ultrasound-detectable breast lesions. Asian J Surg 2013;36(2):83-88. (In English). DOI: 10.1016/j.asjsur.2012.09.003. 38. Han YY, Wang D, Peng LS, et al. Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis. J Hematol Oncol 2022;15(1) (In English). DOI: ARTN 59 10.1186/ s13045-022-01280-w. 39. Tang XM, Huang YM, Lei JL, Luo H, Zhu X. The single-cell sequencing: new developments and medical applications. Cell Biosci 2019;9 (In English). DOI: ARTN 53 10.1186/s13578-019-0314-y. 40. Zhang YJ, Wang D, Peng M, et al. Single-cell RNA sequencing in cancer research. J Exp Clin Canc Res 2021;40(1) (In English). DOI: ARTN 81 10.1186/s13046-021-01874-1. 41. Labots M, van der Mijn JC, Beekhof R, et al. Phosphotyrosine-based-phosphoproteomics scaleddown to biopsy level for analysis of individual tumor biology and treatment selection. J Proteomics 2017;162:99-107. DOI: 10.1016/j.jprot.2017.04.014. 42. Polat AN, Ozlu N. Towards single-cell LC-MS phosphoproteomics. Analyst 2014;139(19):4733-4749. (In English). DOI: 10.1039/c4an00463a. 43. Lun XK, Bodenmiller B. Profiling Cell Signaling Networks at Single-cell Resolution. Mol Cell Proteomics 2020;19(5):744-756. (In English). DOI: 10.1074/mcp.R119.001790. 44. Mazur P. Stopping biological time. The freezing of living cells. Ann N Y Acad Sci 1988;541:514-31. DOI: 10.1111/j.1749-6632.1988.tb22288.x.

19 General Introduction 45. Mager SR, Oomen MH, Morente MM, et al. Standard operating procedure for the collection of fresh frozen tissue samples. Eur J Cancer 2007;43(5):828-34. DOI: 10.1016/j.ejca.2007.01.002. 46. Barnes RO, Parisien M, Murphy LC, Watson PH. Influence of evolution in tumor biobanking on the interpretation of translational research. Cancer Epidemiol Biomarkers Prev 2008;17(12):3344-50. DOI: 10.1158/1055-9965.EPI-08-0622. 47. Bray SE, Paulin FE, Fong SC, et al. Gene expression in colorectal neoplasia: modifications induced by tissue ischaemic time and tissue handling protocol. Histopathology 2010;56(2):240-50. DOI: 10.1111/j.1365-2559.2009.03470.x. 48. Buffart TE, van den Oord RAHM, van den Berg A, et al. Time dependent effect of cold ischemia on the phosphoproteome and protein kinase activity in fresh-frozen colorectal cancer tissue obtained from patients. Clin Proteom 2021;18(1) (In English). DOI: ARTN 8 10.1186/s12014-020-09306-6. 49. Freidin MB, Bhudia N, Lim E, Nicholson AG, Cookson WO, Moffatt MF. Impact of collection and storage of lung tumor tissue on whole genome expression profiling. J Mol Diagn 2012;14(2):140-8. DOI: 10.1016/j.jmoldx.2011.11.002. 50. Mertins P, Yang F, Liu T, et al. Ischemia in Tumors Induces Early and Sustained Phosphorylation Changes in Stress Kinase Pathways but Does Not Affect Global Protein Levels. Mol Cell Proteomics 2014;13(7):1690-1704. (In English). DOI: 10.1074/mcp.M113.036392. 51. Desrosiers P, Legare C, Leclerc P, Sullivan R. Membranous and structural damage that occur during cryopreservation of human sperm may be time-related events. Fertil Steril 2006;85(6):1744-52. DOI: 10.1016/j.fertnstert.2005.11.046. 52. Fabbri R, Porcu E, Marsella T, Rocchetta G, Venturoli S, Flamigni C. Human oocyte cryopreservation: new perspectives regarding oocyte survival. Hum Reprod 2001;16(3):411-6. DOI: 10.1093/ humrep/16.3.411. 53. Hunt CJ. Cryopreservation: Vitrification and Controlled Rate Cooling. Methods Mol Biol 2017;1590:4177. DOI: 10.1007/978-1-4939-6921-0_5. 54. Nicora G, Vitali F, Dagliati A, Geifman N, Bellazzi R. Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools. Front Oncol 2020;10:1030. DOI: 10.3389/ fonc.2020.01030. 55. Gottlieb A, Stein GY, Ruppin E, Sharan R. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol 2011;7:496. DOI: 10.1038/msb.2011.26. 56. Napolitano F, Zhao Y, Moreira VM, et al. Drug repositioning: a machine-learning approach through data integration. J Cheminformatics 2013;5 (In English). DOI: Artn 30 10.1186/1758-2946-5-30. 57. Park JJH, Hsu G, Siden EG, Thorlund K, Mills EJ. An overview of precision oncology basket and umbrella trials for clinicians. Ca-Cancer J Clin 2020;70(2):125-137. (In English). DOI: 10.3322/caac.21600. 58. Hainsworth JD, Meric-Bernstam F, Swanton C, et al. Targeted Therapy for Advanced Solid Tumors on the Basis of Molecular Profiles: Results From MyPathway, an Open-Label, Phase IIa Multiple Basket Study. Journal of Clinical Oncology 2018;36(6):536-+. (In English). DOI: 10.1200/Jco.2017.75.3780. 1

20 CHAPTER 1 59. Le Tourneau C, Delord JP, Goncalves A, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 2015;16(13):1324-1334. (In English). DOI: 10.1016/S1470-2045(15)00188-6. 60. Massard C, Michiels S, Ferte C, et al. High-Throughput Genomics and Clinical Outcome in Hardto-Treat Advanced Cancers: Results of the MOSCATO 01 Trial. Cancer Discov 2017;7(6):586-595. (In English). DOI: 10.1158/2159-8290.Cd-16-1396. 61. Rodon J, Soria JC, Berger R, et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nature Medicine 2019;25(5):751-+. (In English). DOI: 10.1038/s41591019-0424-4. 62. Rothwell DG, Ayub M, Cook N, et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TARGET study. Nature Medicine 2019;25(5):738-+. (In English). DOI: 10.1038/s41591019-0380-z. 63. Schwaederle M, Parker BA, Schwab RB, et al. Precision Oncology: The UC San Diego Moores Cancer Center PREDICT Experience. Mol Cancer Ther 2016;15(4):743-752. (In English). DOI: 10.1158/15357163.Mct-15-0795. 64. Sicklick JK, Kato S, Okamura R, et al. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nature Medicine 2019;25(5):744-+. (In English). DOI: 10.1038/s41591-019-0407-5. 65. Stockley TL, Oza AM, Berman HK, et al. Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial. Genome Med 2016;8 (In English). DOI: ARTN 109 10.1186/s13073-016-0364-2. 66. Tredan O, Wang Q, Pissaloux D, et al. Molecular screening program to select molecular-based recommended therapies for metastatic cancer patients: analysis from the ProfiLER trial. Ann Oncol 2019;30(5):757-765. (In English). DOI: 10.1093/annonc/mdz080. 67. Tsimberidou AM, Hong DS, Ye Y, et al. Initiative for Molecular Profiling and Advanced Cancer Therapy (IMPACT): An MD Anderson Precision Medicine Study. Jco Precis Oncol 2017;1 (In English). DOI: Doi 10.1200/Po.17.00002. 68. Tsimberidou AM, Iskander NG, Hong DS, et al. Personalized Medicine in a Phase I Clinical Trials Program: The MD Anderson Cancer Center Initiative. Clinical Cancer Research 2012;18(22):63736383. (In English). DOI: 10.1158/1078-0432.Ccr-12-1627. 69. Von Hoff DD, Stephenson JJ, Rosen P, et al. Pilot Study Using Molecular Profiling of Patients’ Tumors to Find Potential Targets and Select Treatments for Their Refractory Cancers. Journal of Clinical Oncology 2010;28(33):4877-4882. (In English). DOI: 10.1200/Jco.2009.26.5983. 70. Wheler JJ, Janku F, Naing A, et al. Cancer Therapy Directed by Comprehensive Genomic Profiling: A Single Center Study. Cancer Research 2016;76(13):3690-3701. (In English). DOI: 10.1158/0008-5472. Can-15-3043.

21 1

CHAPTER 2 The Drug Rediscovery protocol facilitates the expanded use of existing anticancer drugs H. van der Wijngaart*, D. L. van der Velden*, L. R. Hoes*, J. M. van Berge Henegouwen*, E. van Werkhoven, P. Roepman, R. L. Schilsky, W. W. J. de Leng, A. D. R. Huitema, B. Nuijen, P. M. Nederlof, C. M. L. van Herpen, D. J. A. de Groot, L. A. Devriese, A. Hoeben, M. J. A. de Jonge, M. Chalabi, E. F. Smit, A. J. de Langen, N. Mehra, M. Labots, E. Kapiteijn, S. Sleijfer, E. Cuppen, H. M. W. Verheul, H. Gelderblom, E. E. Voest * These authors contributed equally to this work Nature 2019 Oct;574(7776):127-131.

24 CHAPTER 2 ABSTRACT The large-scale genetic profiling of tumours can identify potentially actionable molecular variants for which approved anticancer drugs are available1-3. However, when patients with such variants are treated with drugs outside of their approved label, successes and failures of targeted therapy are not systematically collected or shared. We therefore initiated the Drug Rediscovery protocol, an adaptive, precision-oncology trial that aims to identify signals of activity in cohorts of patients, with defined tumour types and molecular variants, who are being treated with anticancer drugs outside of their approved label. To be eligible for the trial, patients have to have exhausted or declined standard therapies, and have malignancies with potentially actionable variants for which no approved anticancer drugs are available. Here we show an overall rate of clinical benefit—defined as complete or partial response, or as stable disease beyond 16 weeks—of 34% in 215 treated patients, comprising 136 patients who received targeted therapies and 79 patients who received immunotherapy. The overall median duration of clinical benefit was 9 months (95% confidence interval of 8–11 months), including 26 patients who were experiencing ongoing clinical benefit at data cut-off. The potential of the Drug Rediscovery protocol is illustrated by the identification of a successful cohort of patients with microsatellite instable tumours who received nivolumab (clinical benefit rate of 63%), and a cohort of patients with colorectal cancer with relatively low mutational load who experienced only limited clinical benefit from immunotherapy. The Drug Rediscovery protocol facilitates the defined use of approved drugs beyond their labels in rare subgroups of cancer, identifies early signals of activity in these subgroups, accelerates the clinical translation of new insights into the use of anticancer drugs outside of their approved label, and creates a publicly available repository of knowledge for future decision-making.

25 The Drug Rediscovery Protocol MAIN The precision treatment of cancer holds great promise for patients in terms of life extension and quality of life1,2,4-7. However, early studies and experiences with genetically and molecularly informed decisions regarding treatment have also identified considerable hurdles, which may jeopardize the way in which we capitalize on precision medicine8-11. First, populations of patients who are eligible for specific treatments or trials become smaller and trials accrue slower, owing to pre-selection by targeted sequencing of candidate variants and to slow implementation of pre-selection tests. Second, these candidate variants can, in general, be appreciated only when their tissue context is taken into consideration. However, with regards to drug sensitivity, the importance of a given genetic or molecular variant is usually tested in the subtype of cancer that most frequently contains this variant. The importance of the same variant in other cancers often remains unknown. Third, as drug development is challenging for rare subtypes of cancer, this can create inequality in care12. Finally, with growing pressure from society to increase the success rate of drug-development trials13, there is hesitation amongst payers to reimburse large-scale sequencing efforts before they have proof that these efforts will make healthcare more sustainable. As a result, we are not using the full potential of rapidly expanding technological advances, knowledge of biomarkers and the spectrum of approved anticancer drugs for our patients. The Center for Personalized Cancer Treatment was founded in 201014 to address these issues. In this network (which now connects 45 hospitals in the Netherlands), patients with all types of metastatic cancer are offered the opportunity to undergo a fresh tumour biopsy for whole-genome sequencing (WGS) before starting systemic anticancer treatment. The WGS results are combined with treatment outcomes in a national, centralized database for research purposes, and returned to the physician who is treating the patient for future planning of treatment. This initiative has contributed to the identification of potentially actionable variants in cancers that are not routinely tested for these variants. To provide treatment opportunities for patients in whom such variants were identified (while simultaneously collecting clinical outcomes), we began the Drug Rediscovery protocol (DRUP), in which we seek to expand the use of targeted therapies that have been approved by the European Medicines Agency (EMA) and/or US Food and Drug Administration (FDA) beyond the approved indications of these therapies. The DRUP is an ongoing, prospective multi-drug and pan-cancer trial. Patients who are eligible are those who have progression of an advanced or metastatic solid tumour, multiple myeloma or B-cell non-Hodgkin lymphoma, and no suitable standard-treatment options. A potentially actionable genetic or molecular variant, which can be matched to one of the drugs available in the study (Extended Data Table 1), must have been identified via regular diagnostics or by the Center for Personalized Cancer Treatment. In recognition of the importance of tissue context, the trial design allows for an unlimited number of parallel cohorts (each defined by tumour type, molecular variant and study treatment) (Fig. 1). For selected variant categories (such as mutational load, microsatellite instabil2

26 CHAPTER 2 ity and DNA-repair deficiency), the protocol allows for cohorts in which tumour types are combined. A Simon-like two-stage design is used per cohort15,16, in which 8 patients are enrolled in stage I and up to 24 patients are enrolled in stage II—provided that clinical benefit (which we define as complete or partial response, or stable disease beyond 16 weeks, measured 2-or-more times, ≥28 days apart) is observed at least once in stage I. A drug warrants further investigation in a particular cohort if ≥5 out of 24 patients experience a clinical benefit. If fewer responses are observed, the cohort is closed; results will be made public whether or not the cohort is successful. This design has 85% power to reject a rate of clinical benefit of 10%, if the true percentage is 30% (α error rate of 7.8%). The analysis of closed cohorts with some activity allows for the opening of new cohorts with refined criteria for inclusion. Figure 1. Study design. Schematic overview of the study and cohort design. For each study drug, a theoretically unlimited number of cohorts can be opened in parallel, depending on the tumour types and tumour profiles of submitted patients and the amount of the drug being studied that is available. A new cohort is opened for each combination of tumour type, tumour profile and study treatment. In each cohort, patients are enrolled in a two-stage design. Clinical benefit is defined as either complete or partial response, or absence of disease progression for ≥16 weeks, and must be measured 2 or more times and ≥28 days apart. Between September 2016 and September 2018, over 600 cases were submitted for central review and 294 patients started study treatment. Extended Data Figure 1 provides details of the review process, and Extended Data Figure 2 provides an overview of case submissions. To allow for sufficient follow-up (≥5 months for patients on study treatment), here we pres-

27 The Drug Rediscovery Protocol ent the results of the first 215 patients who started study treatment. The enrolment of these 215 patients resulted in the initiation of 76 cohorts (Extended Data Table 2); the baseline characteristics of these patients are provided in Table 1. Table 1. Baseline characteristics of the first 215 patients who started study treatment WHO, World Health Organization. *All patients were required to have exhausted standard therapies, but some patients refused standard chemotherapy owing to fear of toxicity. In addition, on occasion the treating physician had well-argued reasons to refrain from a given standard therapy (such as the low response rate to standard therapies in specific subgroups of patients). n = 215 Age (approximately at consent) Median (range) 62 (23 – 87) Gender Male 114 53% Female 101 47% WHO Performance Status WHO 0 60 28% WHO 1 116 54% WHO 2 14 7% Not available 25 12% Primary tumor types Colorectal cancer 49 23% Non-small cell lung cancer 37 17% Prostate cancer 19 9% Breast cancer 16 7% Gastro-intestinal stroma cell tumor 9 4% Cervical cancer 8 4% Salivary gland carcinoma 8 4% Urothelial cell carcinoma 8 4% Sarcoma 7 3% Ovarian cancer 7 3% Other 47 22% Number of prior systemic therapies Median (range) 3 (0 – 12)* Overall, clinical benefit was observed in 74 patients (34%) (Extended Data Table 3) with a median duration of 9 months (95% confidence interval, 8–11 months). Clinical benefit was observed across all types of treatment, comprising immunotherapy (n = 79 patients, clinical benefit rate of 38%), treatment with small-molecule inhibitors (including PARP inhibitors) (n = 81 patients, clinical benefit rate of 36%) and with monoclonal antibodies (n = 55 patients, 2

28 CHAPTER 2 clinical benefit rate of 27%). The median progression-free survival and overall survival were 3 months (95% confidence interval 2–4 months) and 10 months (95% confidence interval 7–13 months), respectively (Figure. 2). To put this in perspective, a large database of 854 patients who were participating in phase I studies and were treated with molecularly targeted agents indicated a median progression-free survival and overall survival of 2 and 8 months, respectively17. Figure 2. Response and survival plots. a. Waterfall plot of the best percentage change in the sum of target lesions compared to baseline tumour measurements according to ‘Response Evaluation Criteria in Solid Tumours’ (RECIST) 1.1, for all patients with ≥1 response evaluation and with a known change in the sum of target lesions (n = 166 patients). Patients with unequivocal disease progression at the first evaluation (on the basis of non-target lesions or non-RECIST measurements only) and patients who went off-study before their response could be evaluated are not included in this graph (n = 49 patients). b. Kaplan–Meier curve for estimated progression-free survival. c. Kaplan–Meier curve for estimated overall survival, with 95% confidence interval (dashed lines). One hundred and forty-one patients (66%) did not experience a clinical benefit, either because of progressive disease (n = 117 patients) or because they went off-study before they could be classified as having experienced a clinical benefit or not (n = 24 patients). Reasons for early withdrawal from the study without obtaining radiologic or clinical diagnosis of progressive disease included death (n = 9 patients), adverse events (n = 5 patients), patient preference (n = 3 patients) or were unknown (n = 7 patients). Adverse events were consistent with those observed in standard of care (Extended Data Table 4). Overall, ten patients discontinued treatment owing to toxicity. Two suspected unexpected severe adverse reactions were reported: bacterial peritonitis in a patient with ovarian carcinoma and sinus thrombosis in a patient with breast cancer. To date, two cohorts have completed accrual: the first is a tumour-type-agnostic cohort of patients with microsatellite-instable (MSI) tumours treated with nivolumab. In total, 30 patients with 8 types of tumour were enrolled in this cohort. As of 3 May 2019, one patient (3%) had a complete response. Eleven patients (37%) had a partial response, and seven patients (23%) had stable disease at ≥16 weeks. Four patients (13%) had progressive disease as a best overall response, and seven patients (23%) went off study before evaluability was reached (that is, after fewer than two cycles of nivolumab treatment and/or with insufficient response evalu-

29 The Drug Rediscovery Protocol ations to determine clinical benefit). In this cohort, the rate of clinical benefit was 63%. The median progression-free survival was not reached after a median follow-up of 16.5 months. A summary of the clinical benefits to individual patients is presented in Figure 3. The results are consistent with previous reports for immunotherapy in MSI tumours18,19. Overall, nivolumab was tolerated well, and adverse events were largely consistent with those that have previously been reported18,19 (Extended Data Table 5). One patient developed a grade-5 abdominal infection upon intestinal perforation, owing to shrinkage of a peritoneal tumour deposit. One patient experienced grade-5 dyspnoea, possibly attributable to disease progression. Baseline WGS for this cohort was successfully performed in 20 patients (67%) (Table 2). Assessment of MSI on the basis of WGS was highly representative for MSI identification on the basis of immunohistochemistry and PCR. On average, MSI tumours had 866 mutations (range of 614–1,111 mutations) in the genome. Figure 3. Treatment efficacy of nivolumab in completed MSI cohort. Swimmer plot of the time on treatment (in weeks) for each patient (n = 30 patients). Patients marked with an arrow were still on treatment at the point of data cut-off (3 May 2019). The white bars represent the time period for which nivolumab treatment was interrupted (which was optional per protocol after 12 months of treatment) for patients, who still experienced clinical benefit. 2

30 CHAPTER 2 Table 2. Tumour and biomarker details per patient of the MSI cohort Histologic tumour type, biomarker characteristics and classification of clinical benefit per patient in the MSI cohort. CB, clinical benefit; PFS, progression-free survival (given in weeks, at data cut-off; > denotes benefit ongoing at cut-off); CRC, colorectal cancer; EC, endometrial cancer; GBM, glioblastoma multiforme; Lynch, Lynch syndrome; ML, mutational load; MSIseq, microsatellite instability score; MSS, microsatellite stable; mut, mutation; NA, not applicable; NE, not evaluable; no., case number; PD, progressive disease; PR, partial response; SD, stable disease; somatic, different from germline DNA; UCC, urothelial cell carcinoma. aThe use of − denotes that no baseline WGS data are available. bCopy number gain of CD274. cUse of + denotes >20% prevalence of ‘Catalogue of Somatic Mutations in Cancer’ (COSMIC) mutational signature. Signatures are only mentioned in case of >10% prevalence (https://cancer.sanger.ac.uk/cosmic/signatures). No. Tumour type Pre-enrolment Baseline WGS CB PFS Somatic or Lynch MSI profile MSI seqa ML JAK1 and/ or JAK2 mutations CD274b COSMIC signaturec 1 CRC Somatic MLH1/PMS2 loss − NE NA 2 CRC Somatic MLH1/PMS2 loss, MLH1 methylation 76.9 1,589 3× 6+, 9, 15, 12, 17 PR >105 3 UCC Lynch MSH2 mut, MSH2 loss, MSI 35.2 973 3× 1+, 6+ PR >92 4 CRC Somatic MLH1/PMS2 loss − PD 8 5 Cervix Lynch MSH2 mut, MSH2/MSH6 loss 23.1 776 2× 6+, 1+, 14, 12 PR >88 6 CRC Somatic MLH1/PMS2 loss, MLH1 methylation 66.7 1,301 2× 12+, 6+, 9, 20 PR >83 7 CRC Somatic MLH1/PMS2 loss, MLH1 methylation 17.1 638 2× PR >90 8 CRC Somatic MLH1/PMS2 loss − NE NA 9 CRC Somatic MLH1/PMS2 loss, MSI, MLH1 methylation − SD 23 10 Breast Somatic MLH1/PMS2 loss, MLH1 not methylated 20.9 287 JAK2 p.Tyr20Asn 8× 6+, 12, 9, 1 PR 23 11 CRC Somatic MLH1/PMS2 loss,MLH1 methylation 65.9 1,036 JAK1 p.Ala639Val 2× 12+, 6+, 9 SD 24 12 CRC Somatic MLH1/PMS2 loss 10.6 346 2× 6+, 12 SD >68 13 CRC Somatic MLH1/PMS2 loss, MLH1 methylation 58.6 912 JAK1 p.Lys860fs 2× 6+, 12, 21, 9 PR >69 14 Breast Somatic MLH1/PMS2 loss, MLH1 not methylated − PR >72 15 CRC Somatic MLH1/PMS2 loss, MSI, BRAFV600E, MLH1 methylation 0.5 73 2× 1+, 8, 17, 18, 11 NE NA

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