Translation of DNA methylation markers for the early detection of renal cell cancer: RAISING THE ODDS Kim Lommen ISBN 978-94-6419-581-1 Printed by Ipskamp Printing | Layout and design: W. Aalberts, The research presented in this thesis was performed within GROW, School for Oncology and Reproduction, Department of Pathology, Maastricht University. This research was financially supported by Kankeronderzoekfonds Limburg as part of Health Foundation Limburg (2018-03/KOFL) and the Van Koeverden-VanWijk Foundation. © Copyright Kim Lommen, Maastricht 2022 All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without prior written permission from the author, or when applicable, from the copyright-owning journals for previously published chapters.

Translation of DNA methylation markers for the early detection of renal cell cancer: RAISING THE ODDS PROEFSCHRIFT Ter verkrijging van de graad van doctor aan de Universiteit Maastricht, op gezag van de Rector Magnificus, Prof. dr. Pamela Habibović volgens het besluit van het College van Decanen, in het openbaar te verdedigen op maandag 17 oktober 2022 om 16.00 uur door Kim Wilhelmina Jacoba Martina Petronella Lommen Geboren op 23 februari 1994 te Helden

Promotoren Prof. Dr. M. van Engeland Dr. L.J. Schouten Copromotor Dr. K.M. Smits Beoordelingscommissie Prof. Dr. J. P.F.A. Heesakkers (voorzitter) Prof. Dr. I. Boutron (INSERM– METHODS team, CRESS, Paris) Dr. L.C.H.W. Lutgens Dr. I.J.H. van Vlodrop (HagaZiekenhuis, den Haag)

TABLE OF CONTENTS Chapter 1 General introduction 7 Chapter 2 Diagnostic DNA methylation biomarkers for renal cell carcinoma: A systematic review 23 Chapter 3 Technical considerations in PCR-based assay design for diagnostic DNA methylation cancer biomarkers 63 Chapter 4 Biobanking in molecular biomarker research for the early detection of cancer 85 Chapter 5 Novel diagnostic DNA methylation biomarkers for renal cell carcinoma 101 Chapter 6 Exploring a DNA methylation field effect in renal cell carcinoma and its implications for biomarker research 121 Chapter 7 General discussion 141 Impact paragraph 157 Summary 163 Acknowledgements / Dankwoord 169 About the author 179 List of publications 183


General introduction 9 1 RENAL CELL CARCINOMA In 2020, approximately 430.000 new cases of kidney cancer were reported globally, representing 2.2% of all cancers diagnosed1. In that same year, an estimated 180.000 deaths could be attributed to kidney cancer1, 2. Renal cell carcinoma (RCC) is the most prevalent kidney cancer, responsible for 90-95% of all cases, and the incidence rates have been rising with 2% per year over the past two decades2, 3. Although a shift towards detection of smaller masses can be observed, 25-40% of RCC patients still present with locally advanced disease or distant metastases at time of diagnosis2, 3. These late stages often represent an incurable stage of the disease4, 5. The 5-year survival rate of RCC confined to the kidney is 93%, whereas the 5-year survival of distant metastasized RCC is only 13%6. Major risk factors for RCC include modifiable risk factors such as obesity, smoking and hypertension7, 8. Among the non-modifiable risk factors, age and sex are the most important factors, as RCC incidence peaks at approximately 75 years and men are twice as likely to develop RCC compared to women1, 9, 10. Pre-existing (chronic) kidney diseases including kidney stones and kidney injury, and the use of several analgesics are also associated with an increased risk of developing RCC. The vast majority of RCCs is sporadic, but a small proportion, 2-5%, are hereditary RCCs7, 8, 11, 12. DIAGNOSIS OF RENAL CELL CARCINOMA Early diagnosis has become a main focus in cancer research over the past decades, as it often allows curative treatment, corresponding to a favorable prognosis and low disease burden13. However, a primary RCC tumor does not often cause symptoms, thereby complicating the early diagnosis of this disease. Whenever symptoms occur, these mostly include a palpable mass, flank pain and hematuria; also referred to as the classic triad14. Because of the increasing use of cross-sectional imaging techniques, renal masses are also frequently detected coincidently during unrelated procedures; these masses are called incidentalomas4, 8. Computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, positron emission tomography (PET) scans and ultrasounds are used to diagnose RCC15. A drawback of these imaging techniques is that for small renal masses (SRM) (<4 centimeters in diameter), they cannot always clearly distinguish whether the SRM is benign or malignant. Only an estimated 50-70% of all SRMs can be accurately categorized as benign or malignant based on imaging3, 16-19. Recently, several radiological imaging characterizations, composite models, nomograms and deep learning strategies to distinguish benign and malignant SRMs have been published, but these have not been able to improve discrimination rates yet18-20. In some countries, needle biopsies are

10 Chapter 1 used to decide upon (partial) nephrectomy in case imaging cannot confirm or rule out a malignant tumor21, 22. However, the use of needle biopsies to molecularly characterize a tumor in the kidney is debatable23, 24. Because of intratumor heterogeneity, needle biopsies are not always representative of the tumor. As a result, up to 20% of needle biopsies cannot be used to diagnose, and in another 10%, they provide a false diagnosis in terms of subtype, stage or grade18. Both intratumor heterogeneity and the risk of tumor seeding therefore drive the lack of worldwide consensus about the clinical use and value of biopsies in diagnosing SRMs20. The increasing amount of incidentalomas and the challenge to diagnose these masses accurately based on imaging and biopsies emphasize the room for improvement in diagnosing early-stage RCC. LOCALIZED RENAL CELL CARCINOMA Localized renal tumors are treated by either partial or radical nephrectomy (laparoscopic or open)25, 26. According to the European Association of Urology (EAU) Guidelines on RCC, nephron sparing partial nephrectomy is the standard procedure for tumors up to 7 centimeters14, and radical nephrectomy is performed when a RCC >7 centimeters is present. Despite the increased detection rates of small and early stage renal tumors, mortality rates have not decreased over the last decade, indicating that resecting SRMs might not be beneficial in all patients1, 27-29. SRMs have low growth rates of approximately 1-3 mm per year and rarely metastasize (1-3%)30. Therefore, to avoid overtreatment, preserve kidney function, and limit surgery risks, an active surveillance policy could be decided upon for SRMs, also considering patient characteristics like age, comorbidities and physical state30, 31. A recent prospective cohort study in which SRM patients were given the choice for either surgical resection (47%) or active surveillance (53%) indicates that active surveillance might decrease overall survival compared to surgical resection (66% vs. 85-90%)31, 32. Additionally, a statistically significant decreased quality of life in active surveillance compared to partial nephrectomy was measured, but that difference was attributed to the variation in physical state of patients in both groups32. In contrast, another prospective study concluded that there is no difference in quality of life between active surveillance and surgical intervention33. After surgical resection, RCC patients are classified by stage using the widely used tumor node metastasis (TNM) classification system, to guide clinicians in optimal treatment decisions. This system considers three components: primary tumor size and degree of invasion in neighboring tissues (T), metastases in regional lymph nodes (N), and distant metastases (M)25. This information is combined with histopathological features of the tumor, which indicate aggressiveness of the tumor34, 35. Until 2016, Fuhrman was the standard grading system for RCC, but it has been replaced by the WHO/ISUP system.

General introduction 11 1 The WHO/ISUP grading system is based on nucleolar prominence rather than nucleolar size that was used in the Fuhrman grading system36. However, individual patients can have varying outcomes despite similar TNM stage and WHO/ISUP grade. Therefore, additional classification systems like the stage, size, grade, and necrosis (SSIGN) score that includes tumor necrosis37 and the UCLA Integrated Staging System (UISS)38 that includes performance status have been introduced. Even though these prognostic models are considered to be good indicators, they cannot predict patient outcome with the level of accuracy desired39. METASTATIC RENAL CELL CARCINOMA In contrast to the often curative treatment regime of localized RCC, metastatic RCC (mRCC) has proven difficult to treat as it is highly resistant to both chemotherapy and radiation therapy14. The response rate of mRCC to chemotherapy alone is only 5-10%; this chemotherapy resistance may be related to expression of the multidrug resistance transporter in the proximal tubule cells, from which the majority of RCCs arise40, 41. RCC is considered insensitive to conventional radiation therapy, as it requires a relatively high dose to kill the RCC cells, while surrounding tissues like the jejunum, duodenum, and colon are highly susceptible to radiation toxicity42-44. Therefore, it is rarely used in a curative setting; however, it can be used as a palliative treatment to relieve symptoms at the sites of metastases45. Recent advances in radiotherapy options and refinements, such as stereotactic radiotherapy and proton therapy, has regained the interest in using radiation therapy either alone or in combination treatments44, 46-49. Until recently, first- and second-line systemic treatment for mRCC included targeted treatment with cytokines like INF-α, angiogenesis inhibitors like sorafenib and sunitinib and mTOR inhibitors temsirolimus and everolimus combined with sunitinib14, 25, 26, 50. Recent advances in the field of immunotherapy have resulted in the availability of amongst others bevacizumab and the combination of nivolumab and ipilimumab as first-line treatment for mRCC14, 51, 52. As many immune- and combination therapies are currently in a clinical trial setting, new treatment options for mRCC are expected in the near future14, 50, 52. The major expansion in targeted treatment and immunotherapy options for RCC has resulted in the necessity for tools that can select patients that will benefit from certain treatments. In order to work towards such a personalized approach, molecular features of the disease could provide information that is lacking in current models, and act as biomarkers for diagnosis, prognosis and treatment response prediction.

12 Chapter 1 GENETICS AND EPIGENETICS OF RENAL CELL CARCINOMA Even though most RCCs are diagnosed and treated in a similar way, RCC is a collective name for several subtypes which all originate from different parts of the nephron and therefore present with distinct (epi)genetic, molecular, histological and clinical characteristics9. Clear cell RCC (ccRCC) is the most common (75%) and aggressive type of RCC, which has a high tendency to metastasize and a poor prognosis. The 5-year and 10-year cancer-specific survival rates for ccRCC are 71% and 62% respectively, and distant metastases-free survival rates are 76% and 69% respectively53-56. Histologically, ccRCC is characterized by clear cytoplasm, and associated with loss or silencing of either one or both Von Hippel-Lindau (VHL) alleles in 60-90% of sporadic cases25. Inactivation of VHL results in upregulation of hypoxia inducible factors 1α (HIF1α) and 2α (HIF2α), which drive angiogenesis26. Due to upregulated angiogenesis promoting VEGFA, KDM5C and KDM6A, ccRCCs are highly vascularized26. As a result, ccRCC shows clusters of tumor cells, surrounded by networks of capillaries. Large genome-wide characterization studies, including the Cancer Genome Atlas (TCGA) Research Network and the TRACERx Renal study, have revealed the molecular landscape of ccRCC57-62. A critical genetic event in over 90% of ccRCCs is the loss of chromosome 3p, which holds four genes involved in chromatin remodeling, that are often inactivated in the remaining chromosomal copy. These include mutations in VHL; 60-70% of cases, PBRM1; 40% of cases, BAP1; 10% of cases, and SETD2; 10% of cases57, 58, 60. Additional chromosomal aberrations associated with ccRCCs are the gain of chromosome 5q (65-70% of cases) and the less frequent loss of chromosomes 8p, 9p and 14q9, 58-60, 63, 64. In addition to these sporadic ccRCC mechanisms, Von Hippel-Lindau disease is a hereditary disease associated with developing ccRCC through germline mutations in VHL12, 65, 66. Papillary RCC (pRCC) is less prevalent (15% of all RCCs) and aggressive compared to ccRCC. The 5-year and 10-year cancer-specific survival rates are 91% and 86% respectively, and distant metastases-free survival rates are 94% and 91% respectively53-56. Morphologically, pRCC can be subdivided into type 1 and type 2; apart from necrosis as a general histological feature, these pRCC subtypes present differently26. Type 1 pRCC is characterized by papillae lined with pale cytoplasm and low-grade nuclei tumor cells. In comparison, type 2 pRCC shows eosinophilic cytoplasm and large nuclei64. Although germline mutations in proto-oncogene MET are frequent in hereditary pRCC (75%), only approximately 6% of sporadic pRCC are associated with mutated MET63, 64. Its activated form drives cell growth, motility, migration, and differentiation9. Type 2 pRCC is associated with the Hereditary Leiomyomatosis and Renal Cell Carcinoma (HLRCC) syndrome, and is characterized by a germline mutation in FH67. Loss of this gene leads to accumula-

General introduction 13 1 tion of fumarate in the cytoplasm of renal cells, resulting in inactivation of the HIF1α pathway as described above64, 67. Even though chromophobe RCC (chRCC; 5% of RCCs) is a malignant tumor, patients generally have a favorable prognosis compared to other RCCs (5-year and 10-year cancer-specific survival rates of 88% and 86% respectively, and distant metastases-free survival rates of 92% and 88% respectively53-56). Histologically, chRCC presents with large cells, clear cell borders and atypical nuclei with perinuclear halo9. PTEN alterations have been clearly linked to sporadic chRCC25, 26. More frequent genetic events in chRCC are the loss of chromosome 1, 2, 6, 10, 13, 17, 21 and X9, 25, 26. Birt-Hogg-Dubé patients often present with chRCCs as a result of germline mutations in FLCN. Although the function of FLCN is not yet fully understood, it seems to be a modulator of mTOR activity64, 68. Recently, several novel RCC subtypes have been proposed. Amongst others, succinate dehydrogenase-deficient RCC and thyroid-like follicular carcinoma of the kidney have already been acknowledged in the 2016 WHO classification of urological tumors, whereas additional subtypes are emerging and might be acknowledged in the near future69. In contrast to the limited amount of common genetic events in RCC summarized above, epigenetic alterations like DNA methylation are more frequent and early events in renal carcinogenesis. DNA methylation is the addition of a methyl group to the 5-carbon position of a cytosine, resulting in the inaccessibility of DNA for transcription and to gene silencing63, 70. Global methylation analyses in the TCGA database showed that high methylation correlated to higher stage and grade of all RCC subtypes, and the hypermethylated phenotype of all subtypes were correlated with poorer survival compared to their unmethylated counterparts (P<0.0001)58, 71, 72. In addition, a rare subset (5.6%) of pRCC tumors featuring a genome-wide CpG island methylator phenotype (CIMP) has been identified58, 71, 72. Despite being pRCC, this CIMP phenotype has been correlated to early onset and high stage disease, and was associated with the poorest survival among all RCC subtypes58, 71, 73. The VHL gene, but also other genes involved in the VHL-HIF signaling pathway involved in angiogenesis, like PTEN, GREM1, and TIMP3, are also commonly inactivated through hypermethylation63. In literature, a wide range of 3-42% of ccRCC cases are described to be affected by hypermethylated and thereby inactivated VHL63. In a TCGA network study, 7% of ccRCC were hypermethylated for VHL72. In the same study, an additional 289 additional genes were identified to be silenced through hypermethylation in at least 5% of RCCs and therefore considered functionally involved in RCC tumorigenesis. The most prominently methylated gene correlated to gene silencing in the TCGA study was UQCRH (methylated in 36% of all RCCs), which had already been recognized as a tumor-suppressor gene, but had never been linked to RCC72. A recent study by Luo et al. found that the loss of UQCRH expression by hypermethylation promotes RCC tumorigenesis by gaining a metabolic advantage through accelerating mitochondrial function decline74. In addition, hypermethylation of WNT

14 Chapter 1 pathway genes, including WIF1, the SFRPs and DKKs (methylated in 8-73%, 9-80% and 7-58% respectively) dysregulates cell proliferation and differentiation, and can thereby induce tumorigenesis58, 63, 73, 75-77. DNA methylation of additional key genes involved in cell proliferation, differentiation and adhesion, amongst others PBRM1, CDH1, FBN2 and APC (methylated in 41%, 6-83%, 21-52% and 5-54% respectively), are known to promote epithelial-to-mesenchymal transition and subsequent invasion and metastasis in RCC63. DNA METHYLATION BIOMARKERS FOR RENAL CELL CARCINOMA AND ITS CHALLENGES As the molecular landscape of RCC has become more clear over the years, molecular markers involved in RCC such as (epi)genetic alterations have been investigated for the diagnosis, prognosis and disease prediction of localized RCC14, 78, 79. In addition, (epi) genetic biomarkers might contribute to better discrimination of benign and malignant SRMs prior to nephrectomy, thereby preventing surgical resection of benign SRMs. Current diagnostic procedures such as CT and MRI scans are costly and may be perceived as unpleasant because of scan duration, noise and space- and motion restriction80, 81. Next to that, they are considered time-consuming, not only because of the duration of the scan, but also because patients have to travel to and from the hospital80. To limit and potentially substitute part of such imaging procedures, researchers have been aiming to improve cancer diagnostics by focusing on molecular markers in liquid biopsies. These minimally-invasively collected bodily fluids like blood, stool or urine are assumed to represent the molecular composition of a malignant tumor, including its (epi)genetic make-up, and are therefore considered valuable sample types82, 83. Cell-free DNA (cfDNA), including circulating tumor DNA (ctDNA), is released into the blood stream as a result of apoptosis and necrosis of a solid tumor. Only small cfDNA and ctDNA fragments (~100 bp) can pass glomerular filtration and also end up in urine84. Next to ctDNA, other cancer-derived components such as proteins, circulating tumor cells (CTCs), RNA, and extracellular vesicles can be detected in liquid biopsies, providing information about the transcriptomic, proteomic, genetic and epigenetic features of a tumor85. As DNA methylation is a frequent and early event in carcinogenesis, it remains stable over time and can be analyzed by relatively simple, accurate and low-cost techniques, it is very suitable to act as a biomarker63. Even though for many types of cancer, several (epi)genetic biomarkers measured in liquid biopsies like blood and urine have been described, the translation rate of these biomarkers to a clinical setting is very low86-88. As advocated by several researchers, the field of (cancer) biomarkers produces a substantial amount of research waste, mainly caused by inappropriate research methodology, including a lack of validation, lack of standardization and lack of reproducibility of biomarkers87-90.

General introduction 15 1 Ioannidis et al. described the current biomarker development process as ‘a tortuous series of linearly connected pipes’ with several phases; biomarker discovery, validation, translation, evaluation and implementation66. All of these phases harbor their own issues, which hamper biomarker development and clinical translation89. For instance, in the research-oriented biomarker discovery and validation phase, non-empirically identifying candidate biomarkers, and how and where to measure these biomarkers, can hamper identification of suitable candidate biomarkers. A major technical consideration is designing an optimal assay regarding the technique, the primers and the genomic location of the assay. In addition, inappropriate study design such as low sample sizes and sampling bias may limit applicability of the biomarker in the general population. The fact that very few published biomarkers eventually reach clinical care emphasizes the importance of appropriate and standardized biomarker research methodology.

16 Chapter 1 AIM AND OUTLINE OF THIS THESIS The aim of this thesis is to identify and evaluate the utility of DNA methylation biomarkers for the non-invasive diagnosis of RCC. In addition, we aimed to evaluate reasons for the lack of clinical translation of diagnostic DNA methylation biomarkers and discuss how to overcome these. In Chapter 2, we performed a systematic literature review in which we provided an overview of all published diagnostic DNA methylation biomarkers for RCC and summarized their current Level of Evidence (LoE). In addition, we identified issues that may hamper clinical translation of these biomarkers. In Chapter 3, we evaluated technical considerations in PCR-based assay design for diagnostic DNA methylation biomarkers. We specifically looked into the genomic location of the assay and assessed the primer and probe quality of included assays. The availability of large study cohorts of appropriate samples, complemented by extensive and well-annotated clinical and pathological patient data is crucial for fast and adequate validation of biomarkers. Therefore, in Chapter 4 we elaborated on considerations for establishing new biobanks, as well as for using existing biobanks, both in general and specific for certain specimen types, in order to develop optimal conditions for validation of biomarkers for early detection of cancer. We concluded that the lack of clinically useful diagnostic DNA methylation biomarkers for RCC might be attributed to, amongst others, the lack of empirical biomarker identification. Therefore, in Chapter 5, we used a novel in silico approach to identify RCC specific DNA methylation biomarkers for the early detection of RCC. The lack of reproducibility of biomarkers could be caused by amongst others the choice of inappropriate control samples. The fact that normal appearing tissues adjacent to the tumor might be molecularly predisposed to become malignant, emphasizes the importance of carefully selecting appropriate control tissues in biomarker studies. In Chapter 6, we therefore aimed to evaluate the existence of a DNA methylation field effect in RCC, and to illustrate the impact of this field effect and choice of control tissues in biomarker identification and development. In the general discussion of Chapter 7, the findings of this thesis are discussed and reflected upon. In addition, we provide future perspectives and recommendations relevant to the development of clinically useful diagnostic DNA methylation biomarkers for cancer management.

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General introduction 19 1 multicenter study. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2004;​22(16):​3316-22. 39. Joosten SC, Odeh SNO, Koch A, Buekers N, Aarts MJB, Baldewijns MMLL, et al. Development of a prognostic risk model for clear cell renal cell carcinoma by systematic evaluation of DNA methylation markers. Clinical Epigenetics. 2021;​13(1):​103. 40. Makhov P, Joshi S, Ghatalia P, Kutikov A, Uzzo RG, Kolenko VM. Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies. Mol Cancer Ther. 2018;​ 17(7):​1355-64. 41. Vogelzang NJ. Another step toward the cure of metastatic renal cell carcinoma? J Clin Oncol. 2010;​28(34):​5017-9. 42. Deschavanne PJ, Fertil B. A review of human cell radiosensitivity in vitro. International Journal of Radiation Oncology*Biology*Physics. 1996;​34(1):​251-66. 43. Rühle A, Andratschke N, Siva S, Guckenberger M. Is there a role for stereotactic radiotherapy in the treatment of renal cell carcinoma? Clin Transl Radiat Oncol. 2019;​18:​104-12. 44. Berghen C, Albersen M, De Roover R, Rans K, Beuselinck B, Decaestecker K, et al. The role of radiation therapy and particle therapy in renal cell carcinoma: current evidence and future perspectives. Journal of Cancer Metastasis and Treatment. 2021;​7:​58. 45. Society AC. Radiation Therapy for Kidney Cancer 2017 [Available from: https:/​ / cancer/kidney-cancer/treating/radiation.html#references. 46. Funayama S, Onishi H, Kuriyama K, Komiyama T, Marino K, Araya M, et al. Renal Cancer is Not Radioresistant: Slowly but Continuing Shrinkage of the Tumor After Stereotactic Body Radiation Therapy. Technol Cancer Res Treat. 2019;1​ 8:​1533033818822329-. 47. De Felice F, Tombolini V. Radiation therapy in renal cell carcinoma. Crit Rev Oncol Hematol. 2018;​ 128:​82-7. 48. Miccio JA, Oladeru OT, Jun Ma S, Johung KL. Radiation Therapy for Patients with Advanced Renal Cell Carcinoma. Urol Clin North Am. 2020;​47(3):​399-411. 49. Parashar B, Patro KC, Smith M, Arora S, Nori D, Wernicke AG. Role of radiation therapy for renal tumors. Semin Intervent Radiol. 2014;​31(1):​86-90. 50. Bedke J, Albiges L, Capitanio U, Giles RH, Hora M, Lam TB, et al. The 2021 Updated European Association of Urology Guidelines on Renal Cell Carcinoma: Immune Checkpoint Inhibitor-based Combination Therapies for Treatment-naive Metastatic Clear-cell Renal Cell Carcinoma Are Standard of Care. Eur Urol. 2021;​80(4):​393-7. 51. Albiges L, Tannir NM, Burotto M, McDermott D, Plimack ER, Barthélémy P, et al. Nivolumab plus ipilimumab versus sunitinib for first-line treatment of advanced renal cell carcinoma: extended 4-year follow-up of the phase III CheckMate 214 trial. ESMO Open. 2020;5​ (6):​e001079. 52. Atkins MB, Tannir NM. Current and emerging therapies for first-line treatment of metastatic clear cell renal cell carcinoma. Cancer Treat Rev. 2018;​70:​127-37. 53. Leibovich BC, Lohse CM, Crispen PL, Boorjian SA, Thompson RH, Blute ML, et al. Histological subtype is an independent predictor of outcome for patients with renal cell carcinoma. The Journal of urology. 2010;​183(4):​1309-15. 54. Morris MR, Latif F. The epigenetic landscape of renal cancer. Nature reviews Nephrology. 2017;​ 13(1):​47-60. 55. Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM. The 2016WHO Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. Eur Urol. 2016;​70(1):​93-105.

20 Chapter 1 56. Baldewijns MM, van Vlodrop IJ, Schouten LJ, Soetekouw PM, de Bruine AP, van Engeland M. Genetics and epigenetics of renal cell cancer. Biochimica et biophysica acta. 2008;1​ 785(2):​133-55. 57. Creighton CJ, Morgan M, Gunaratne PH, Wheeler DA, Gibbs RA, Gordon Robertson A, et al. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;4​ 99(7456):​ 43-9. 58. Ricketts CJ, De Cubas AA, Fan H, Smith CC, Lang M, Reznik E, et al. The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep. 2018;​23(1):​313-26. e5. 59. Turajlic S, Xu H, Litchfield K, Rowan A, Horswell S, Chambers T, et al. Deterministic Evolutionary Trajectories Influence Primary Tumor Growth: TRACERx Renal. Cell. 2018;​173(3):​595-610.e11. 60. Mitchell TJ, Turajlic S, Rowan A, Nicol D, Farmery JHR, O’Brien T, et al. Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal. Cell. 2018;1​ 73(3):​611-23.e17. 61. Scelo G, Riazalhosseini Y, Greger L, Letourneau L, Gonzàlez-Porta M, Wozniak MB, et al. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nature Communications. 2014;​5(1):​5135. 62. Sato Y, Yoshizato T, Shiraishi Y, Maekawa S, Okuno Y, Kamura T, et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat Genet. 2013;​45(8):​860-7. 63. Joosten SC, Smits KM, Aarts MJ, Melotte V, Koch A, Tjan-Heijnen VC, et al. Epigenetics in renal cell cancer: mechanisms and clinical applications. Nat Rev Urol. 2018;​15(7):​430-51. 64. Schmidt LS, Linehan WM. Genetic predisposition to kidney cancer. Semin Oncol. 2016;4​ 3(5):​56674. 65. Furuya M, Hasumi H, Yao M, Nagashima Y. Birt-Hogg-Dubé syndrome-associated renal cell carcinoma: Histopathological features and diagnostic conundrum. Cancer Sci. 2020;​111(1):​15-22. 66. Nielsen SM, Rhodes L, Blanco I, Chung WK, Eng C, Maher ER, et al. Von Hippel-Lindau Disease: Genetics and Role of Genetic Counseling in a Multiple Neoplasia Syndrome. J Clin Oncol. 2016;​ 34(18):​2172-81. 67. Schmidt LS, Linehan WM. Hereditary leiomyomatosis and renal cell carcinoma. Int J Nephrol Renovasc Dis. 2014;​7:​253-60. 68. Schmidt LS, Linehan WM. FLCN: The causative gene for Birt-Hogg-Dubé syndrome. Gene. 2018;​ 640:​28-42. 69. Trpkov K, Hes O. New and emerging renal entities: a perspective post-WHO 2016 classification. Histopathology. 2019;​74(1):​31-59. 70. Herman JG, Baylin SB. Gene silencing in cancer in association with promoter hypermethylation. N Engl J Med. 2003;​349(21):​2042-54. 71. LinehanWM, Spellman PT, Ricketts CJ, Creighton CJ, Fei SS, Davis C, et al. Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma. N Engl J Med. 2016;3​ 74(2):​135-45. 72. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;​ 499(7456):​43-9. 73. Linehan WM, Ricketts CJ. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nat Rev Urol. 2019;​16(9):​539-52. 74. Luo Y, Medina Bengtsson L, Wang X, Huang T, Liu G, Murphy S, et al. UQCRH downregulation promotes Warburg effect in renal cell carcinoma cells. Scientific Reports. 2020;1​ 0(1):​15021. 75. Ricketts CJ, Hill VK, Linehan WM. Tumor-specific hypermethylation of epigenetic biomarkers, including SFRP1, predicts for poorer survival in patients from the TCGA Kidney Renal Clear Cell Carcinoma (KIRC) project. PLoS One. 2014;​9(1):​e85621.

General introduction 21 1 76. Morris MR, Ricketts C, Gentle D, Abdulrahman M, Clarke N, Brown M, et al. Identification of candidate tumour suppressor genes frequently methylated in renal cell carcinoma. Oncogene. 2010;​ 29(14):​2104-17. 77. Urakami S, Shiina H, Enokida H, Hirata H, Kawamoto K, Kawakami T, et al. Wnt antagonist family genes as biomarkers for diagnosis, staging, and prognosis of renal cell carcinoma using tumor and serum DNA. Clin Cancer Res. 2006;1​ 2(23):​6989-97. 78. LuY, SongY, XuY, Ou N, Liang Z, Hu R, et al. The prevalence and prognostic and clinicopathological value of PD-L1 and PD-L2 in renal cell carcinoma patients: a systematic review and meta-analysis involving 3,389 patients. Transl Androl Urol. 2020;9​ (2):​367-81. 79. Klatte T, Rossi SH, Stewart GD. Prognostic factors and prognostic models for renal cell carcinoma: a literature review. World J Urol. 2018;​36(12):​1943-52. 80. Oztek MA, Brunnquell CL, Hoff MN, Boulter DJ, Mossa-Basha M, Beauchamp LH, et al. Practical Considerations for Radiologists in Implementing a Patient-friendly MRI Experience. Topics in Magnetic Resonance Imaging. 2020;​29(4). 81. Rosenkrantz AB, Pysarenko K. The Patient Experience in Radiology: Observations From Over 3,500 Patient Feedback Reports in a Single Institution. J Am Coll Radiol. 2016;1​ 3(11):​1371-7. 82. Wang J, Chang S, Li G, Sun Y. Application of liquid biopsy in precision medicine: opportunities and challenges. Front Med. 2017;​11(4):​522-7. 83. Di Meo A, Bartlett J, Cheng Y, Pasic MD, Yousef GM. Liquid biopsy: a step forward towards precision medicine in urologic malignancies. Mol Cancer. 2017;​16(1):​80. 84. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol. 2017;​14(9):​531-48. 85. Martins I, Ribeiro IP, Jorge J, Gonçalves AC, Sarmento-Ribeiro AB, Melo JB, et al. Liquid Biopsies: Applications for Cancer Diagnosis and Monitoring. Genes (Basel). 2021;1​ 2(3). 86. Koch A, Joosten SC, Feng Z, de Ruijter TC, Draht MX, Melotte V, et al. Analysis of DNA methylation in cancer: location revisited. Nat Rev Clin Oncol. 2018;​15(7):​459-66. 87. Poste G. Bring on the biomarkers. Nature. 2011;​469(7329):​156-7. 88. Kern SE. Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer research. 2012;​72(23):​6097-101. 89. Ioannidis JPA, Bossuyt PMM. Waste, Leaks, and Failures in the Biomarker Pipeline. Clin Chem. 2017;​63(5):​963-72. 90. Ioannidis JP, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, et al. Increasing value and reducing waste in research design, conduct, and analysis. Lancet (London, England). 2014;​ 383(9912):​166-75.

1 2 3 4 5 6 7 I S A A P CHAPTER 2 DIAGNOSTIC DNA METHYLATION BIOMARKERS FOR RENAL CELL CARCINOMA: A SYSTEMATIC REVIEW Kim Lommen, Nathalie Vaes, Maureen J. Aarts, Joep G. van Roermund, Leo J. Schouten, Egbert Oosterwijk, Veerle Melotte, Vivianne C. Tjan-Heijnen, Manon van Engeland, and Kim M. Smits European Urology Oncology. 2021;4(2):215-26

24 Chapter 2 ABSTRACT Context: The 5-year survival of early stage renal cell carcinoma (RCC) is approximately 93%, but once metastasized, the 5-year survival plummets to 12%, indicating that early RCC detection is crucial to improve survival. DNA methylation biomarkers have been suggested to be of potential diagnostic value; however, their current state of clinical translation is unclear and a comprehensive overview is lacking. Objective: To systematically review and summarize all literature regarding diagnostic DNA methylation biomarkers for RCC. Evidence acquisition: We performed a systematic literature review of PubMed, EMBASE, Medline and Google Scholar up to January 2019, according to the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines. Included studies were scored according to the Standards for Reporting of Diagnostic Accuracy Studies (STARD) criteria. Forest plots were generated to summarize diagnostic performance of all biomarkers. Level of Evidence (LoE) and potential risk of bias were determined for all included studies. Evidence synthesis: After selection, 19 articles reporting on 44 diagnostic DNA methylation biomarkers and 11 multi-marker panels were included; however, only 15 biomarkers were independently validated. STARD scores varied from 4-13 out of 23 points, with a median of 10 points. Large variation in subgroups, methods and primer locations was observed. None of the reported biomarkers exceeded LoE III, and the majority of studies reported inadequately. Conclusions: None of the reported biomarkers exceeded LoE III, indicating limited clinical utility. Moreover, study reproducibility and further development of these RCC biomarkers is greatly hampered by inadequate reporting.

Diagnostic DNA methylation biomarkers for renal cell carcinoma 25 2 INTRODUCTION Worldwide, 400 000 people were diagnosed with renal cell carcinoma (RCC) and 175 000 people died of this disease in 20181. The significant health burden of RCC is mainly caused by the high number of patients (up to 17%) that present advanced disease at time of diagnosis2,3. This is attributed to the typical lack of symptoms of the primary RCC, leading to a substantial number of metastasized RCC cases that could have been prevented if diagnosed earlier. Currently, the majority of patients are diagnosed after a coincidental finding (incidentaloma) during unrelated imaging procedures4,5. While 5-year survival rates of early stage RCC are around 93%, patients presenting with metastasized RCC have poor 5-year survival rates, around 12%6. These numbers indicate the great importance to accurately diagnose RCC at an early stage. Because the current diagnostic RCC imaging techniques leave room for improvement, several studies have focused on molecular techniques instead7,8. The possibility to diagnose RCC using a non-invasive liquid-biopsy based molecular test, in addition to imaging, could not only enhance early diagnosis, but also facilitate differentiation of benign and malignant masses, proven to be challenging in case a small renal mass (≤4 cm) is discovered9,10,11. Recently, within the TRACERx Renal study, seven evolutionary subtypes were identified for the most common RCC subtype: clear cell RCC (ccRCC), for which the most prevalent abnormality was found to be the simultaneous loss of 3p and 5q gain (36% of ccRCC patients) 12,13. The well-known VHL, PBRM1, BAP1, and SETD2 genes are the most frequently mutated (60-70%, 40%, 10% and 10% respectively) and subsequently inactivated genes in ccRCC as a result of these chromosomal abberations13. For the other RCC subtypes however, genetic mutations such as mutations in MET or FH in papillary RCC (pRCC), and mutations of PTEN or FLCN in chromophobe RCC (chRCC) are less frequent14-16. Compared to genetic alterations, DNA hypermethylation is more pronounced and frequently found in all RCC subtypes, and involved in several RCC related pathways such as angiogenesis14,15. As DNA methylation is considered a common, early and stable event in tumorigenesis that is easily detectable in small amounts of DNA, these alterations could be interesting cancer biomarkers17. This is illustrated by the successful implementation of seven DNA methylation biomarkers in 4 clinical diagnostic tests for prostate, colorectal and lung cancer18. However, despite their potential, no diagnostic RCC DNA methylation marker has reached the clinic yet. In addition, there is currently no overview showing which markers can be considered as potential diagnostic RCC biomarkers and for which further validation or development is desirable. We have systematically reviewed the literature on diagnostic DNA methylation biomarkers in RCC to provide this overview and summarize current evidence for these biomarkers.

26 Chapter 2 EVIDENCE ACQUISITION Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines were applied in the process of writing this systematic review19. Search strategy, eligibility criteria & study selection Electronic literature searches (up to January 2019) of PubMed, EMBASE, Medline and Google Scholar were conducted (supplementary table 3). Articles eligible for this systematic review were all original articles on diagnostic DNA methylation biomarkers in RCC. Other inclusion criteria were: English language; specific genes being evaluated; biomarker potential was expressed in at least one measure of diagnostic value. Studies were excluded when reporting on global methylation analysis, hereditary RCC, transitional cell carcinoma, Wilms’ tumours and renal sarcomas. Because this review focuses specifically on DNA methylation, studies reporting on micro-RNA methylation were excluded. After initial screening, six additional articles were included through scanning reference lists of the full-text assessed articles. Ultimately, 19 articles were included in this systematic review (figure 1). Figure 1. PRISMA flow diagram visualizing the study selection process PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Diagnostic DNA methylation biomarkers for renal cell carcinoma 27 2 Data extraction All data were extracted by two independent authors (KL and KS) using a standardised data extraction sheet. In addition, articles were assessed for reporting quality using STARD 201520, which considers 34 items for good reporting of diagnostic accuracy studies. Each of these items were awarded 1 point if the item was fully reported, 0.5 point if part of the item was reported and 0 points if an item was not reported. Each item of the STARD criteria not applicable to biomarker research was excluded. Based on the adapted STARD criteria (supplementary table 4), the maximum reporting score was 23 points. Mutual consensus was reached whenever inter-observer variation occurred. The risk of potential bias across or within studies was analysed per study using the STARD scores (supplementary table 2). In case a study scored ≥0.5 points per item for STARD items 5-9, the potential risk of selection bias was low. Whenever this criterion was not met, potential risk of selection bias was increased. Measurement bias regarding the assay method and outcome assessment was measured similarly, using STARD items 10a, 12a and 13a for the assay method and STARD items 14, 21a and 24 for outcome assessment. Other variable assessment measurement bias was based on STARD item 20. In case of a full score (score=1), measurement bias risk was low. Whenever this item was partially or not reported, potential measurement bias risk was increased (supplementary table 2). To obtain a summary of current evidence on diagnostic DNA methylation biomarkers in RCC, the LoE for each biomarker was determined according to two established reference schemes21,22. Five LoE categories represent the current evidence for clinical utility of a diagnostic biomarker, with LoE I representing the highest evidence and LoE V representing the poorest evidence for clinical utility. Forest plots Forest plots were created to summarize diagnostic performance of all studied biomarkers. Sensitivity, specificity and 95% confidence intervals were reported where available. If sensitivity and specificity were not reported, these measures were calculated from the percentage of DNA methylation. In addition, forest plots depict the DNA methylation detection method, specimen type, LoE, genomic location of primers, TNM stage and Fuhrman grade. EVIDENCE SYNTHESIS Study characteristics Nineteen articles (published between 2003 and 2017) were included in this systematic review using a standardised selection procedure (figure 1). Four (21%) studies described one single biomarker, whereas 15 (79%) reported on multiple markers. A total of 44

28 Chapter 2 Table 1. Characteristics of the 19 studies included in this systematic review. First author, yearref Study characteristics Evaluation of DNA methylation STARD score Sample size Specimen Preservation method Tumour type Method Biomarker studied Sensitivity %a Specificity % Ahmad, 201228 196 PTT Fresh frozen ccRCC, pRCC, chRCC, TCRCC MSP APAF1 63.8 87.8 13 DAPK1 41.3 85.2 SPARC 12.2 91.8 Battagli, 20035 50 PTT NR ccRCC, pRCC, chRCC, RCC unclassified, oncocytomas, collecting duct, TCC renal pelvis MSP VHL PTT 12, urine 12 PTT NR, urine 100 12.5 Urine CDKN2A (p16) PTT 10, urine 8 PTT NR, urine 100 CDKN2A (p14) PTT 18, urine 18 PTT NR, urine 100 APC PTT 18, urine 16 PTT NR, urine 100 RASSF1A PTT 52, urine 50 PTT NR, urine 100 TIMP3 PTT 60, urine 52 PTT NR, urine 100 Panel of VHL, CDKN2A (p16), CDKN2A (p14), APC, RASSF1A, TIMP3 PTT 100, urine 88 PTT NR, urine 100 Christoph, 200829 85 PTT Fresh frozen ccRCC qMSP APAF1 89 85 10.5 CASP8 0 100 DAPK1 66 95 IGFBP3 4 100