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SETD2 and PBRM1 inactivation in the development of clear cell renal cell carcinomaLi, Jun

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SETD2 anD PBRM1 inacTivaTion in ThE DEvEloPMEnT of

clEaR cEll REnal cEll caRcinoMa

Jun Li

The studies described in this thesis were financially supported byGraduate School of Medical Sciences, University Medical Center Groningen

Jun Li was financially supported byChina scholarship Council (CSC)

Printing of this thesis was financially supported byGraduate School of Medical Sciences, University Medical Center Groningen

ISBN: 978-94-6182-714-2

Cover design, layout & printing: Off Page, Amsterdam

Copyright © 2016 Jun LiAll rights reserved. No parts of this book could be reproduced or transmitted in any form or by any means without prior permission of the author.

SETD2 anD PBRM1 inacTivaTion in ThE DEvEloPMEnT of clEaR cEll REnal cEll caRcinoMa

Proefschrift

ter verkrijging van de graad van doctor aan deRijksuniversiteit Groningen

op gezag van derector magnificus prof. dr. E. Sterken

en volgens besluit van het College voor Promoties.

De openbare verdediging zal plaatsvinden opwoensdag 28 september 2016 om 12.45 uur

Door

Jun Li

geboren op 10 juni 1984te Henan, China

Promotores: Prof. dr. R.H. Sijmons Prof. dr. J.H.M. van den Berg

Copromotores: Dr. K. Kok Dr. H. Westers Dr. J.L. Kluiver

Beoordelingscommissie: Prof. dr. R. Medeiros Prof. dr. M. van Engeland Prof. dr. M.G. Rots

TaBlE of conTEnTS

Chapter 1 General introduction and aims of this thesis 7

Chapter 2 SETD2: an epigenetic modifier with tumor suppressor 21functionalityOncotarget, 2016

Chapter 3 Functional studies on Primary Tubular Epithelial 49Cells indicate a tumor suppressor role of SETD2 in clear cell renal cell carcinomaNeoplasia, 2016

Chapter 4 PBRM1 loss in Primary Tubular Epithelial Cells leads to 85aberrant expression of immune response genesManuscript in preparation

Chapter 5 A long noncoding RNA signature of clear cell renal cell 111carcinoma and the impact of SETD2 and PBRM1 lossManuscript in preparation

Chapter 6 Summary, discussion and future perspectives 175

AddendumNederlandse samenvatting 191Acknowledgements/Dankwoord 193List of Abbreviations 196

GEnERal inTRoDucTion anD aiMS of ThiS ThESiSRenal cell cancer

EpidemiologyHistology

Risk factors and genetic predispositionClinical aspects

Molecular pathology of renal cell cancerLoss of 3p in renal cell cancerTumor suppressor genes at 3p

Noncoding RNAs

Aims of this thesis

c h a P T E R 1

Chapter 1

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REnal cEll cancEREpidemiologyRenal cell cancer (RCC) accounts for 2.4% of adult cancer and approximately 2% of all new cancer diagnoses worldwide (Ferlay et al., 2013). In 2013, about 340,000 RCC cases were diagnosed globally (Ferlay et al., 2013). In the Netherlands, the RCC incidence was 2,343 per 100,000 inhabitants in 2015 (The Netherlands Cancer Registry, www.cijfersoverkanker.nl, accessed 23-05-2016). RCC-related mortality is about 140,000 cases worldwide, and this accounts for 1.7% of all cancer-related deaths (Ferlay et al., 2013). The incidence of RCC increases annually and the World Health Organization (WHO) predicts a worldwide incidence of more than 465,000 RCC cases per year in 2030 (Ferlay et al., 2010). RCC occurs more frequently in males than in females, with a ratio of 1.5:1. The incidence of RCC peaks between 60 and 70 years of age. Geographically, developed regions (North America, Europe and Australia) have a higher incidence than developing regions (Africa, the Pacific and Asia) (Levi et al., 2008).

HistologyRenal cell cancer refers to a group of heterogeneous tumors that all arise from the renal parenchyma. Based on different pathological features and genetic aberrations, prognosis and therapeutic responses, RCC is further subdivided into 10 subtypes (World Health Organization (WHO) (Lopez-Beltran et al., 2006) (Table 1). Clear cell RCC (ccRCC), which is the focus of this thesis, is the most common subtype, accounting for 75%-80% of all RCC cases (Ljungberg et al., 2015). Clear cell renal cell cancer (ccRCC) originates from mature proximal tubular epithelial cells (Thoenes et al., 1986).

Renal Cell Cancer is characterized by expression of multiple cytokeratins, such as CK7, CK8, CK18 and CK19, consistent with their epithelial origin. In addition to these markers ccRCC shows a strong expression of vimentin (Vim) (Skinnider

Table 1. WHO classification of Renal Cell Cancer (Lopez-Beltran et al., 2006)

Clear cell renal cell carcinoma Multilocular clear cell renal cell carcinoma Papillary renal cell carcinoma Chromophobe renal cell carcinoma Carcinoma of the collecting ducts of Bellini Renal medullary carcinoma Xp11 translocation carcinomas Carcinoma associated with neuroblastoma Mucinous tubular and spindle cell carcinomaRenal cell carcinoma, unclassified

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et al., 2005), and an increased expression of epithelial membrane antigen (EMA) (Langner et al., 2004). ccRCC cells typically show a “clear” cytoplasm, which is caused by accumulation of glycogen and lipids. In high-grade and poorly differentiated tumors, the cytoplasm loses this characteristic and acquires a granular eosinophilic appearance (Zhou and He, 2013).

Risk factors and genetic predispositionWell-established risk factors of RCC include obesity (Bergstrom et al., 2001), hypertension (Corrao et al., 2007) and smoking (Theis et al., 2008). Consumption of red meat has also been suggested to be associated with RCC development (Rohrmann, Linseisen et al. 2015). Moderate consumption of alcohol shows a negative association with RCC development (Karami et al., 2015). Strong inherited predisposition to RCC is relatively rare and is responsible for about 2-4% of the RCC cases. Several hereditary tumor syndromes are associated with an increased RCC risk (Menko and Maher, 2016). Clear cell RCC is the only, or most frequent, subtype of RCC found in von Hippel-Lindau disease, associated with germline mutations in the VHL gene (VHL somatic mutations are discussed below), Hereditary Paraganglioma, caused by mutations in the SDHx and TMEM127 genes and Tuberous Sclerosis, caused by mutations in the TSC1 and TSC2 genes (Menko and Maher, 2016). Although the number of reported cases is still low, germline mutations in the BAP1 gene have also been suggested to cause familial ccRCC (Farley et al., 2013). This is not unexpected as somatic mutations in BAP1 play an important role in ccRCC development (further discussed below).

Clinical aspectsRCC is the deadliest urologic malignancy with an estimated 5-year survival rate of 50-60% (Scelo and Brennan, 2007). For patients with localized ccRCC, surgical removal is the standard curative treatment, which results in a 5-year survival of 69-73% (Ljungberg et al., 2015). Adjuvant therapy in patients undergoing surgery did not improve survival (Ljungberg et al., 2015). About one third of the ccRCC patients present with distant metastases at the time of diagnosis (Motzer et al., 1999). The prognosis of patients with metastatic disease is poor with a 5-year survival rate of 28% (Ljungberg et al., 2015). Systemic treatment including chemotherapy, immunotherapy and targeted therapies are generally applied to this group of patients, although tumor response is low. In a small subset of patients with metastatic RCC increased survival was achieved by immunotherapy. Immunotherapy includes (combinations of) the use of Interleukin 2, Interferon-alpha (IFN-α), lymphokine-activated killer cells, and several antibodies to block or enhance lymphocyte receptors. Currently, these therapies are only used in selected RCC cases (Motzer et al., 2015; Ljungberg et al., 2015). More recently, targeted therapies that act against the key components involved in the RCC-associated VHL-HIF pathway have shown good responses in a subgroup of metastatic ccRCC patients. (Ljungberg et al., 2015).

Chapter 1

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MolEculaR PaTholoGy of REnal cEll cancERLoss of 3p in renal cell cancerIn both The Cancer Genome Atlas (TCGA) and Sato’s study loss of heterozygosity (LOH) at 3p was the most common genetic aberration observed in more than 90% of the ccRCC cases (Cancer Genome Atlas Research, 2013; Sato et al., 2013). Early studies identified several regions of allelic loss on 3p, including 3p12-14, 3p21 and 3p25 (van den Berg et al., 1997). More recent array-based CGH (comparative genomic hybridization) studies, including our own unpublished results, suggest loss of the entire p-arm in most, if not all, cases of ccRCC. These events have always been explained as a first step in the inactivation of a tumor suppressor gene. In line with Knudson’s two hit hypothesis (Knudson, 1971) of tumorigenesis, inactivation of a tumor suppressor gene (TSG) is the result of two independent hits with functional loss of both alleles. Often one of the two hits is a deletion of a large genomic region that includes the TSG locus, and the second hit is a smaller alteration affecting the other allele of that TSG. The first TSG identified in ccRCC tumors is the VHL gene, which is located at 3p25. Loss of 3p and a concomitant VHL point mutation (Latif et al., 1993), or aberrant promoter methylation (Clifford et al., 1998), lead to its biallelic inactivation. According to the COSMIC database somatic mutations of VHL are detected in approximately 43% of non-familial ccRCC tumors (Forbes et al., 2015).

The presence of a wild type VHL gene in the majority of the ccRCC cases indicated presence of additional TSGs on 3p (Kok et al., 1997). Indeed, with the rise of next generation sequencing techniques three new candidate ccRCC TSGs were identified in the 3p21 region within a period of three years. Duns et al. (2010) and Dalgliesh et al. (2010) were the first to report somatic mutations in the histone modifier SETD2. This study was followed by two studies reporting inactivating mutations in PBRM1 (Varela et al.,2011; Duns et al 2012) and studies reporting inactivating mutations in BAP1 (Guo et al., 2012; Duns et al., 2012; Peña-Llopis et al., 2012). More recently, two independent studies (Cancer Genome Atlas Research, 2013; Sato et al., 2013) of 417 and 240 ccRCC cases respectively, showed that these four genes, all from the short arm of chromosome 3, i.e. VHL, PBRM1, SETD2 and BAP1, represent the top-4 most commonly mutated genes in ccRCC (Table 2).

Together, SETD2, BAP1 and PBRM1 are mutated in about 50% of ccRCCs, suggesting their essential contribution to the tumorigenesis (Cancer Genome Atlas Research 2013; Sato et al., 2013). Mutations of PBRM1 and BAP1 are mutually exclusive (Figure 1) (Peña-Llopis et al., 2012), whereas SETD2 mutations are observed at a higher frequency in PBRM1 mutant cases (Li et al., 2016). In two consecutive studies, Gerlinger et al. (2012 and 2014) showed that ccRCC is a very heterogeneous tumor: different mutations can be detected in different parts of the tumor. In some cases, mutations in the 3p genes were present in only part of the tumor, suggesting that the mutations were not the initial driving events in ccRCC development (Gerlinger et al., 2012).

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Figure 1. Mutation overlaps between the top 4 3p mutants in ccRCC tumors. Venn diagram shows the overlap of mutations in VHL, BAP1, SETD2 and PBMR1 in 424 cases of ccRCC from The Cancer Genome Atlas (TCGA), Nature 2013, http://www.cbioportal.org). Genetic aberrations in at least one of the four genes were observed in 72% (305 out of 424) of the samples: 51% for VHL, 36% for PBRM1, 13% for SETD2 and 10% for BAP1.

Table 2. Frequently mutated chromatin modifiers in ccRCC.

Gene nameMutation frequency

Genomic location Chromatin remodeling

PBRM1/BAF180 30% 3p21.1 SWI/SNF complexBAP1 11% 3p21.1 H2AK119ub1 deubiquitinationKMT3A/SETD2 10% 3p21.31 H3K36 trimethylationKDM5C/JARID1C 6% Xp11.22 H3K4 demethylationKMT2C/MLL3 3% 7q36.1 H3K4 methylationKMT2D/MLL2 3% 12q13.12 H3K4 methylationARID1A/BAF250A 2% 1p36.11 SWI/SNF complexSMARCA4/BRG1 2% 19p13.2 SWI/SNF complexKDM6A/UTX 1% Xp11 H3K27me2/3 demethylationARID1B/BAF250B 1% 6q25.3 SWI/SNF complexARID2/BAF200 1% 12q12 SWI/SNF complexSMARCA2/BRM 1% 9p24.3 SWI/SNF complexSMARCB1/BAF47 1% 22q11.23 SWI/SNF complexSMARCC1/BAF155 1% 3p21.31 SWI/SNF complex

Data presented in the table is retrieved from the COSMIC database (cancer.sanger.ac.uk, accessed in 24-05-2016). In the column of chromatin remodeling: K, lysine; ub, ubiquitin.

Chapter 1

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Tumor suppressor genes at 3pVHL (at 3p25) is a component of the E3 ubiquitin ligase complex, which under hypoxic conditions, induces degradation of hydroxylated hypoxia inducible factor α (HIFα). Thus, the role of the VHL protein is to maintain HIFα at low levels. Bi-allelic loss of VHL leads to loss of the E3 ubiquitin ligase complex dependent degradation of HIFα. This will result in accumulation of HIFα, which forms heterodimers with HIFβ, translocates to the nucleus and promotes transcription of a set of hypoxia responsive genes, e.g. VEGF, PDGF-β, EPO, and TGF-α. Binding of VEGF to its receptor (VEGFR) leads to phosphorylation of downstream kinases and activation of the RAS-RAF-MEK-ERK and PI3K-AKT-mTOR pathways (Figure 2). Activation of the PI3K-AKT-mTOR pathway confers resistance to VEGF and mTOR inhibitors (Pantuck et al., 2007).

PBRM1 (at 3p21) is a subunit of a subset of the SWItch/Sucrose Non-Fermenting (SWI/SNF) complexes, which play a role in remodeling of DNA around histones. The ATPase activity of the SWI/SNF complex provides energy to intrude the interactions between DNA and histones, and either remove or “slide” the histone octamers along

Figure 2. VHL-HIF axis and its downstream signaling in RCC development. (A) In hypoxic conditions, the von Hippel-Lindau (VHL) protein targets hypoxia-inducible factor alpha (HIFα) and recruits the E3 ubiquitin ligase complex for ubiquitylation-mediated degradation of hydroxylated HIF. (B) When VHL is not available, as happens in VHL-inactivated ccRCC tumors, HIFα will be stabilized by phosphorylation and forms a heterodimer with HIFβ. The heterodimer subsequently translocates to the nucleus, where it functions as a transcription factor, inducing the expression of a set of genes, including vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF)-β, tumor growth factor (TGF)-α, and erythropoietin (EPO). (C) The proteins encoded by these genes bind to their corresponding receptors, thus activating the phosphoinositide 3-kinase (PI3K)-AKT-mTOR and RAS-RAF-MEK-ERK pathways that subsequently promote angiogenesis, proliferation, and apoptosis resistance.

A

B C

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the DNA (Lorch et al., 1999; Hamiche et al., 1999). This makes specific DNA segments accessible to other proteins or complexes (Wang et al., 2004), and in this way strongly influences the expression of the targetted genes. SWI/SNF complexes are divided into two main subtypes, i.e. BAF and BPAF, based on their subunit composition. PBRM1 is a component specific for PBAF (Roberts and Orkin, 2004). The bromodomains of PBRM1 recognize acetylated lysine patterns on histone tails and this facilitates binding of the PBAF complex to the chromatin (Thompson, 2009). Presence of a defective bromodomain in PBRM1 as the result of nonsynonymous mutations may compromise the binding of the PBAF complex to its normal target regions (Liao et al., 2015; Barbieri et al., 2013) and thereby results in altered expression of its target genes. In ccRCC, genetic mutations of others SWI/SNF components have also been observed albeit at lower frequencies, i.e. ARID1A mutations in 3% of the ccRCC tumors (Cancer Genome Atlas Research, 2013) and with lower frequencies in SMARCA2/4, ARID2/1B, SMARCC2/D1, and SMARCB1 (Brugarolas 2014) (Table 2). Overall, dysfunction of a subset of the SWI/SNF complexes due to inactivating mutations in PBRM1 or one in of the other genes of the complex is a common finding in ccRCC tumors.

SETD2 (at 3p21) is a histone modifier, responsible for trimethylation of histone H3 lysine-36 (H3K36me3). Functional loss of SETD2 leads to absence of H3K36me3. Increased expression of lysine (K)-specific demethylase 4A (KDM4A) observed in a 3-4% of ccRCC cases leads to enhanced de-methylation of H3K36me3 (Klose et al., 2006). Thus, both events result in a decrease of H3K36me3 and have been proposed to contribute to ccRCC development. H3K36me3 is enriched at actively transcribed genes and functions as a beacon to recruit multiple H3K36me3 readers to carry out their specific functions, i.e. transcription elongation, RNA processing and DNA mismatch repair (Li et al., 2016 ).

BRCA1 associated protein 1 (BAP1, at 3p21) is a catalytic subunit of the Polycomb repressive deubiquitinase (PR-DUB) complex, which specifically mediates the de-ubiquitination of Lys-119 of mono-ubiquitinated H2A. Loss of BAP1 function in ccRCC results in loss of the BRCA1-mediated suppression of cell growth, which is consistent with a TSG role in ccRCC development (Peña-Llopis et al, 2012).

In addition to BAP1, SETD2 and PBRM1, two other histone modifiersi.e. KDM5C and UTX/KDM6A, both located on the X chromosome, are also mutated in ccRCC, albeit at low frequencies (Table 2). KDM5C demethylates the trimethylated and dimethylated Lys-4 of histone H3 (Christensen et al., 2007) and UTX demethylates the trimethylated and dimethylated Lys-27 of histone H3 (Hong et al., 2007). Overall, four of the top-five most commonly mutated genes in ccRCC are chromatin modifier genes. Approximately 54% of ccRCC carry a mutation in at least one of the chromatin modifier genes listed in Table 2. Thus, it appears that alterations in the chromatin structure are an important pathogenic feature of ccRCC. Understanding the impact of functional loss of these chromatin modifiers in the development of ccRCC will be a crucial next step to unravel the underlying malignant transformation process.

Chapter 1

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NoN-codiNg RNAsBesides protein-coding genes, multiple studies suggest the involvement of non-coding RNAs (ncRNAs) in initiation and progression of cancer. In the past decade, it has become clear that a large proportion of the genome is actively transcribed as noncoding RNA (ncRNA) and that these transcripts play crucial roles in regulatory networks involved in various biological processes (Esteller 2011). The ncRNAs are classified by their length as either small ncRNAs that are <200nt, e.g. microRNAs, or long ncRNAs (lncRNAs) that are >200 nt. MicroRNAs regulate gene expression at the post-transcriptional level by binding to partly complementary regions in the target gene transcripts. Deregulated expression of microRNAs has been observed in multiple cancers and they have been shown to act as oncogenes or TSGs. Aberrant microRNA expression signatures have been reported for different subtypes of RCC (Jung et al., 2009; Cheng et al., 2013; Silva-Santos et al., 2013). We identified decreased expression of miR-205 and the miR-200 seed family in ccRCC-derived cell lines (Duns et al., 2013). The miR-200 family of miRNAs suppresses the epithelial to mesenchymal transition (Korpal & Kang, 2008). The miR-17-92 cluster was overexpressed in ccRCC tumors as compared to normal kidney (Tsz-fung et al., 2010). Inhibition of the miR-17-92 cluster members, miR-17-5p and miR-20a, led to decreased proliferation (Tsz-fung et al., 2010). Expression of miR-215 was shown to be decreased in ccRCC tumors, and its overexpression decreased migration and invasive potential of ccRCC cells (White et al., 2011), suggesting a tumor suppressor function for this miRNA.

LncRNA are defined as RNA transcripts of >200nt that lack protein coding potential (Nagano and Fraser, 2011). In recent years, aberrant expression of lncRNAs is emerging as another molecular mechanism underlying RCC development (reviewed by Seles et al., 2016). Expression profiling of lncRNAs in ccRCC tumors revealed 4 different molecular ccRCC subtypes (Malouf et al., 2015), which were not distinguishable by histological phenotyping. For some of the lncRNAs a role as oncogene or TSG has been suggested in ccRCC. Increased expression of MALAT1 and H19 was identified in ccRCC tumor tissues and cell lines, as compared to normal kidney tissues (Hirata et al., 2015; Wang et al., 2015). Depletion of MALAT1 in RCC cell lines reduced proliferation, migration and invasion of the cells, and increased apoptosis (Hirata et al., 2015). Expression of GAS5 was decreased in ccRCC compared to normal renal tissue. Overexpression of GAS5 in ccRCC cells led to reduced proliferation, increased apoptosis and cell cycle arrest at G1 phase (Qiao et al., 2013).

aiMS anD ouTlinE of ThiS ThESiSIt has become evident that multiple 3p21 tumor suppressor genes contribute to ccRCC development. Although it is known that these genes are involved in chromatin structure modification, it remains unclear how they contribute to ccRCC pathogenesis. Most of the currently available functional studies on SETD2 and PBRM1 have been

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performed on ccRCC-derived cell lines (Kanu et al., 2015; Pfister et al., 2014). The consequences of loss of these TSGs in PTECs, the postulated normal counterparts of ccRCC, remains unknown.

The aim of the study reported in this thesis was to explore the functional consequences of loss of PBRM1 and SETD2 in otherwise normal tubular epithelial cells of the kidney. In chapter 2, we discuss current insights on the role of SETD2 and the relevance of SETD2 inactivation in cancer. In chapters 3 and 4, we report on the effects of stable inhibition of SETD2 and PBRM1 expression in PTECs on proliferation and defined the expression signatures upon stable knockdown of these genes in PTECs. In chapter 5, we report the lncRNA expression profile of ccRCC-derived cell lines and defined the set of lncRNA genes regulated by SETD2 and PBRM1 in PTECs. In chapter 6 we summarize our data and present future perspectives.

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SETD2: an EPiGEnETic MoDifiER wiTh TuMoR SuPPRESSoR funcTionaliTy

Jun Li1, Gerben Duns2, Helga Westers1, Rolf Sijmons1, Anke van den Berg3 and Klaas Kok1

1Department of Genetics, 3Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, The Netherlands

2Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, Canada

Oncotarget, 2016, May 14

c h a P T E R 2

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aBSTRacTIn the past decade important progress has been made in our understanding of the epigenetic regulatory machinery. It has become clear that genetic aberrations in multiple epigenetic modifier proteins are associated with various types of cancer. Moreover, targeting the epigenome has emerged as a novel tool to treat cancer patients. Recently, the first drugs have been reported that specifically target SETD2-negative tumors. In this review we discuss the studies on the associated protein, Set domain containing 2 (SETD2), a histone modifier for which mutations have only recently been associated with cancer development. Our review starts with the structural characteristics of SETD2 and extends to its corresponding function by combining studies on SETD2 function in yeast, Drosophila, Caenorhabditis elegans, mice, and humans. SETD2 is now generally known as the single human gene responsible for trimethylation of lysine 36 of Histone H3 (H3K36). H3K36me3 readers that recruit protein complexes to carry out specific processes, including transcription elongation, RNA processing, and DNA repair, determine the impact of this histone modification. Finally, we describe the prevalence of SETD2-inactivating mutations in cancer, with the highest frequency in clear cell Renal Cell Cancer, and explore how SETD2-inactivation might contribute to tumor development.

SETD2 iS a Tumor SupprESSivE EpigEnETic moDifiEr

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inTRoDucTionIn recent years, SETD2 has attracted a lot of interest as a gene whose inactivation is involved in tumor initiation and progression. However, Faber et al. (1998) had already identified a protein encoded by SETD2 in 1998 using a two-hybrid-based approach to search for proteins that interact with Huntingtin, the protein known to be associated with Huntington’s disease (HD). They identified several candidates, three of which contained a WW domain. One of these three proteins was Huntingtin Yeast Partner B (HYPB). Around the same time Mao et al. (1998) and Zhang et al. (2000) identified and analyzed a large set of transcripts from human umbilical cord CD34+ hematopoietic stem/progenitor cells. One of these transcripts, HSPC069, had a sequence identical to HYPB and represented the same gene. A few years later, HSPC069 was shown to contain an AWS-SET-PostSET domain and to possess histone methyl transferase activity specific for lysine 36 of histone 3 (H3K36) (Sun et al., 2005). In a study focusing on proteins that interact with a DNA-binding motif in the E1A promoter, a transcript identical to HYPB was identified and named HBP231 (Rega et al., 2001). The associated gene is ubiquitously expressed in all tissues and cell lines tested, including many cancer-derived cell lines. Edmunds et al. (2007) introduced the gene symbol SETD2 in 2008, and made a more detailed analysis of the global and transcription-dependent distribution of tri-methylated histone H3 lysine 36 (H3K36me3) in mammalian cells. This was in line with the role of the Saccharomyces cerevisiae homologue of SETD2, ySET2, which had been identified in 2002 (Strahl et al., 2002). An important step in understanding the biology of ySET2 was its interaction with the serine2 phosphorylated C-terminal domain (CTD) of RNA polymerase II (RNA Pol II), linking ySET2 to the transcription elongation process (Li et al., 2002). A similar interaction was later confirmed for mammalian SETD2 (Sun et al., 2005; Li et al., 2005). It was, however, not just its role in regulating transcription that attracted the interest of researchers over the years. The presence of inactivating mutations in a range of tumor types, most notably in clear cell renal cell cancer (ccRCC), sparked an additional focus of research: exploring the role of SETD2 in cancer development. In this review the domains and functions of SETD2 in normal biology will be discussed in more detail. In the final part of the review, we focus on how loss of SETD2 function can contribute to cancer development.

ThE funcTional DoMainS of SETD2The human SETD2 gene is located at the cytogenetic band p21.31 of chromosome 3, a region frequently targeted by copy number loss in various tumors (Kok et al., 1997). SETD2 encompasses a genomic region of 147Kb, and the 21 exons encode an 8,452nt transcript. The SETD2 protein consists of 2,564 amino acids and has a molecular weight of 287.5 KD. Three conserved functional domains have been identified in the SETD2 protein: the triplicate AWS-SET-PostSET domains, a WW domain and a Set2 Rpb1 interacting (SRI) domain.

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AWS-SET-PostSET domainThe human SET domain is a motif of 130 amino acids that is evolutionarily conserved from mammals to yeast and even in some bacteria and viruses (Rea et al., 2000; Tschiersch et al., 1994). The SET domain was identified by comparison of the protein sequence of the Drosophila position-effect variegation suppressor gene, Su(var)3-9, with the protein sequence of several other genes (Jenuwein et al., 1998). The acronym SET stands for Suppressor of Variegation, Enhancer of zeste and Trithorax, which are the three genes that led to the discovery of this domain.

The SET domain is usually present as part of a multi-domain, flanked by an AWS (Associated with SET) and a PostSET domain. Generally, SET-domain- containing proteins transfer one or several methyl groups from S-adenosyl-L-methionine to the amino group of a lysine or an arginine residue of histones or other proteins (Dillon et al., 2005). This transfer is dependent on the flanking AWS and PostSET regions, which contain several conserved cysteine residues. In contrast to other methyltransferases, SET-domain-containing methyltransferases have a β-sheet structure that facilitates multiple rounds of methylation without substrate disassociation (Zhang et al., 2003).

WW domainThe term WW domain was originally described in 1995 by Sudol et al. (1995) and refers to the presence of two conserved tryptophan (W) residues spaced 20-22 amino acids apart. Binding assays showed that the WW domain preferentially binds to proline-rich segments, mediating protein-protein interactions to participate in a variety of molecular processes (Ingham et al., 2005). The WW domain recognizes motifs like Proline-Proline-x-Tyrosine (PPxY) (Macias, Hyvönen et al., 1996), phospho-Serine-Proline (p-SP) or phospho-Threonine- Proline (p-ST) (Lu et al., 1999), and mediates protein binding (Sudol and Hunter, 2000). Aberrant expression of WW-domain-containing genes has been associated with different diseases such as HD (Passani et al., 2000), Alzheimer’s disease (Sze et al., 2004), and multiple cancer subtypes (Bednarek et al., 2000; Yendamuri et al., 2003). The WW domain in the C-terminal region of SETD2 interacts with the Huntingtin protein via its proline-rich segment, regardless of the length of the HD- associated polyglutamine track (Faber et al., 1998), and may also interact with TP53 (Xie et al., 2008). Gao et al. (2014) performed a detailed nuclear magnetic resonance study on the interaction of SETD2 with Huntingtin. SETD2 contains a proline-rich stretch that precedes the WW domain. This proline-rich stretch functions as an intramolecular WW-interacting domain that can block the WW domain of SETD2 from interacting with the proline-rich stretch of Huntingtin, and most likely of other proteins as well.

SRI domainBy analyzing a series of SET2-deletion-mutants, Kizer et al. (2005) identified a novel domain that specifically interacted with the hyperphosphorylated C-terminal domain


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(CTD) of Rpb1, the largest subunit of RNA Pol II. This Set2 Rpb1 Interacting (SRI) domain is conserved from yeast to human (Kizer et al., 2005). In human, the primary C-terminal domain-docking site of RNA Pol II is located at the first and second helices of SETD2 (Li et al., 2005). This domain directs the activity of SETD2 towards actively transcribed genes. Yeast experiments by Li et al (Li et al., 2002) revealed a high affinity of ySET2 to the Ser2-phosphorylated CTD of RNA Pol II that is present only when transcription is well under way. ySET2 binds to the Ser5-phosphorylated CTD with intermediate affinity, while it has no affinity to the unphosphorylated CTD (Li et al., 2003). This interaction is dependent on the activity of the RNA Pol II CTD kinase CTK1, the enzyme responsible for the phosphorylation of Ser2 (Krogan et al., 2003).

fRoM PRoTEin STRucTuRE To BioloGical funcTionThe above-mentioned functional domains define the biological function of SETD2. By virtue of its AWS- SET-PostSET domains, SETD2 mediates trimethylation of H3K36 (Sun et al., 2005). In vitro, human SETD2 can carry out mono-, di-, and tri-methylation of H3K36 (Wagner and Carpenter 2012), but in vivo the scenario is different. While ySET2 catalyzes all methylation levels of H3K36 (Strahl et al., 2002), SETD2 only modulates H3K36me3 in mammals. Knockdown of SETD2 induces a complete absence of H3K36me3 without disturbing the levels of H3K36me1 and H3K36me2 (Edmunds et al., 2007). In human, trimethylation of H3K36 is carried out by a complex, of which SETD2 and Heterogeneous Nuclear Ribonucleoprotein L (hnRNPL) are the major subunits (Yuan et al., 2009). Based on these studies, it has become evident that SETD2 is solely responsible for this modification. Catalyzing H3K36 trimethylation is now regarded as the main function of SETD2. H3K36me3 is recognized by so-called readers, effector proteins that are recruited by specific histone modifications and determine the functional outcome of those modifications (Yun et al., 2011)(Table 1). A schematic representation of how SETD2-mediated- trimethylation of H3K36 is involved in various biological processes is shown in Figure 1.

The most prominent function of SETD2 is thus indirectly determined by the factors that target SETD2 to specific nucleosomes to be trimethylated on the one hand, and the so-called readers of this modification on the other. Vezzoli et al. (2010) showed that BRPF1 (Bromodomain And PHD Finger Containing 1) interacts with H3K36me3 through its PWWP domain, a finding later corroborated by a study of Wu et al. (2011). Subsequently, several other readers were identified that interact with H3K36me3 by virtue of their PWWP domain (Dhayalan et al. 2010; Vermeulen et al., 2010; Qin and Min, 2014). More recently, additional proteins were identified that interact with H3K36me3 through their tudor domain (Cai et al., 2013) or chromodomain (Sun et al., 2008).

In addition to its role in histone modification, SETD2 may also interact directly with other proteins, most likely through its WW domain. The BioGRID database (http:// thebiogrid.org) lists multiple proteins that directly interact with SETD2.

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Co-immunoprecipitation assays showed that the C-terminal domain of SETD2 interacts with the N-terminal domain of TP53 (Xie et al., 2008). Binding of SETD2 to TP53 modulates the expression of a specific set of TP53 downstream target genes, including the apoptosis related genes puma, noxa, and p53AIP1. However, because no follow-up studies have corroborated these findings, the importance of the SETD2-TP53 interaction remains to be established. Given the well-known role of TP53 in cancer development, exploring its interactions with SETD2 may be relevant to elucidating the role of SETD2 mutations in cancer. To date, most studies have focused on the SETD2- dependent trimethylation of H3K36.

Table 1. Overview of currently known H3K36me3 readers and their interacting domains.

Gene symbolbinding domain Function Ref.

BRPF1/2 PWWP Histone acetylation (Vezzoli et al., 2010; Wu et al., 2011)DNMT3A/B PWWP DNA methylation (Dhayalan et al., 2010)GLYR1 PWWP Histone methylation (Vermeulen et al., 2010)HDGF PWWP DNA binding (Lukasik et al., 2006)IWS1 PWWP Transcription elongation,

splicing, and mRNA export(Maltby et al., 2012)

MORF4L1 Chromo Alternative splicing (Sun et al., 2008; Zhang et al., 2006; Xu et al., 2008)

MSH6 PWWP DNA mismatch repair (Vermeulen et al., 2010; Pfister et al., 2014)

MTF2 Tudor Histone methylation (Cai et al., 2013; Qin et al., 2013)MSL3 Chromo Histone acetylation (Larschan et al., 2007)MUM1 PWWP DNA damage repair (Wu et al., 2011; Huen et al., 2010)NSD1 PWWP Histone methylation (Vermeulen et al., 2010; Li et al., 2009)PHF1/19 Tudor Histone methylation (Cai et al., 2013; Qin et al., 2013;

Musselman et al., 2012)PSIP1 PWWP Splicing and HR repair (Eidahl et al., 2013; Pradeepa et al.,

2012)SPT16H PWWP Facilitate transcription and

repress cryptic transcription(Carvalho et al., 2013)

WHSC1/L1 PWWP Histone methylation (Vermeulen et al., 2010; Kim et al., 2011)

ZMYND11 PWWP Transcription elongation (Wang et al., 2014)

Note: BRPF1/2, Bromodomain And PHD Finger Containing 1 and 2; GLYR1, Glyoxylate Reductase 1 Homolog; HDGF, Hepatoma-Derived Growth Factor; MSH6, MutS Homolog 6; MTF2, Metal Response Element Binding Transcription Factor 2; MSL3, Male-Specific Lethal 3 Homolog; MUM1, Melanoma As-sociated Antigen 1;NSD1, nuclear receptor binding SET domain protein 1; PHD1/19, PHD Finger Protein 1/19; WHSC1, Wolf-Hirschhorn Syndrome Candidate 1; WHSC1L1, Wolf-Hirschhorn Syndrome Candidate 1-Like 1; ZMYND11, Zinc Finger MYND-Type Containing 11.


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Chapter 2

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Distribution of H3K36me3Krogan et al (2003) were the first to report a specific distribution of H3K36me3 over the yeast genome, with enrichment of H3K36me3 in actively transcribed coding regions. In C. elegans, actively transcribed genes also have much higher levels of H3K36me3 than transcriptionally repressed genes (Kolasinska-Zwierz et al., 2009). The same pattern is observed in higher eukaryotes, with high H3K36me3 levels downstream of the first exon of actively transcribed genes and across the whole gene body with a peak near the 3’ end (Bannister et al., 2005; Barski et al., 2007).

In both human and mouse, intron-containing genes showed relatively higher levels of H3K36me3 than intron- less genes, irrespective of transcriptional activity (De Almeida et al., 2011). Along the gene body, H3K36me3 enrichment also appears to be discrete, co-localizing to exons rather than introns, and with higher levels of H3K36me3 at constitutively included exons as compared to alternatively spliced exons (Kolasinska-Zwierz et al., 2009). The distribution pattern of H3K36me3 indicates a role for SETD2 in modulating splicing events by marking exonic and intronic regions.

It should be noted that H3K36me3 is not confined to actively transcribed genes. A study by Chantalat et al. (2011) showed a high level of H3K36me3 at the silenced Snurf-Snrpn region in mice, a well-known facultative heterochromatin domain. Pericentromeric regions, which consist mainly of constitutive heterochromatin, are also enriched for H3K36me3 (Chantalat et al., 2011). In these regions the H3K36me3 mark is apparently not correlated with transcriptional events. In the remainder of this review we will discuss how the loss of or decrease in H3K36me3 caused by functional loss of SETD2 could contribute to cancer development.

H3K36me3-mediAted biologicAl fuNctioNsH3K36me3 participates in transcription elongation and splicing selection Deletion of the SRI domain of SETD2 not only abolishes its interaction with RNA Pol II but also leads to a defect in trimethylation of H3K36, suggesting that H3K36 trimethylation and transcription elongation are coupled processes (Kizer et al., 2005; Li et al., 2003). Splicing and transcription are also coupled processes regulated by many factors, including chromatin remodeling complexes (Batsché et al., 2006), RNA Pol II elongation rate (Ip et al., 2011), RNA binding elements (Fu and Ares Jr, 2014) and histone modifications (Zhou et al., 2014). Direct evidence to support participation of SETD2 in splicing came from studies on alternative splicing of the human fibroblast growth factor receptor 2 (FGFR2) gene (Luco et al., 2010). FGFR2 is spliced into two mutually exclusive and tissue-specific isoforms: FGFR2-IIIb (exon IIIb is included) and FGFR2-IIIc (exon IIIc is included). Alternative splicing is modulated by polypyrimidine tract binding protein 1 (PTBP1, also known as PTB). PTBP1 is recruited by histone tail-binding protein Mortality Factor 4 like 1 (MORF4L1, also known as Eaf3 and MRG15), which recognizes H3K36me3 through its chromo domain (Sun et al., 2008;


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Zhang et al., 2006) . Overexpression of ySET2 leads to a global increase of H3K36me3 and a decreased inclusion of exon IIIb, whereas siRNA-mediated knockdown of human SETD2 resulted in inclusion of the PTB1-repressed exon IIIb (Luco et al., 2010) .

This chromatin affects splicing model does not, however, explain by what mechanism chromatin is modified to direct splicing. Subsequently a splicing affects chromatin model was proposed (De Almeida et al., 2011). Inhibiting splicing, either by knockdown of splicing factor Sin3A- associated protein (SAP130) or D-ribofuranosyl- benzimidazole (DRB) treatment, leads to a decreased recruitment of SETD2 and reduced H3K36me3 levels (De Almeida et al., 2011). Thus, the splicing machinery itself may play a role in the recruitment of SETD2 by RNA Pol II and the subsequent trimethylation of H3K36. DRB-treatment of HeLa cells reduced the H3K36me3 levels on internal exons to a level that remained higher than the level in intergenic regions, even though both regions have a comparable RNA Pol II occupancy. This indicates that, although splicing is not required for trimethylation, it does modulate H3K36me3 levels (De Almeida et al., 2011). Kim et al. (2011) showed that introducing mutations that prevent splicing, or interfere with the splicing machinery using splicing inhibitor spliceostatinA (SSA), led to a redistribution of H3K36me3 with a shift towards the 3’ region, again indicating a direct causal relationship between splicing and H3K36me3.

H3K36me3 prevents spurious transcriptionModification of nucleosomes plays an important role in protecting genomic DNA and regulating its accessibility. A compact nucleosome structure of the gene body is needed to prevent spurious transcription initiation from cryptic promoters. Removal of this barrier during transcription elongation upon passage of RNA pol II results in a more accessible chromatin. Reconstitution of completely evicted nucleosomes with acetylated nucleosomes from the soluble pool after passage of RNA pol II could result in a more accessible chromatin structure of transcribed genes. This would allow intergenic transcription initiation from cryptic promoter sequences. Trimethylation of H3K36 during transcription elongation by RNA pol II-bound SETD2 is thought to prevent spurious transcription from cryptic promoters. H3K36me3 recruits Facilitates Chromatin Transcription complex (FACT) (Carvalho et al., 2013) and Polycomb repressive complex 2 (PRC2) (Cai et al., 2013; Qin et al., 2013) to restore the repressed chromatin structure after elongation. The FACT complex disassembles the H2A- H2B dimer from the nucleosomes. After passage of RNA Pol II, the same complex promotes the replacement of the H2A-H2B dimers. This allows the transcription elongation complex to pass without the need to remove histone H4 and H3 (Belotserkovskaya et al., 2003). Thus, the H3K36 trimethylated nucleosomes are kept on their position. The IWS1:SPT6:CTD complex is needed for the recruitment of SETD2 to RNA Pol II for trimethylation of H3K36 (Yoh et al., 2007). SPT6 was already known to enhance the elongation rate by displacing the nucleosomes in front of RNA pol II (Bortvin and Winston, 1996). However, several studies have indicated that SPT6 also enhances the

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elongation rate in the absence of nucleosomes (Endoh et al., 2004; Ardehali et al., 2009; Kwak and Lis, 2013). Experiments in S. cerevisiae have shown that inactivation of SPT6 or the FACT subunit Suppressor Of Ty 16 Homolog (SPT16H, also known as SPT16) resulted in intragenic transcription initiation events from cryptic promoters (Carvalho et al., 2013; Mason and Struhl 2003; Kaplan et al., 2003). Taken together, the prevention of spurious intragenic transcription initiation is an important function of H3K36me3 and thus, indirectly, of SETD2.

H3K36me3 maintains genomic integrity and stabilityThe enriched level of H3K36me3 in transcribed regions not only serves to restore chromatin structure after transcription but also functions in maintaining genomic integrity. H3K36me3 is a crucial factor in the repair of DNA damage in transcribed regions by modulating two different pathways: (i) the DNA Mismatch Repair (MMR) pathway responsible for the repair of nucleotide mismatches and small insertion/deletion loops of simple repeat sequences and (ii) the homologous recombination (HR) repair of DNA double strand breaks (DSBs).

DNA MMR is a mechanism for correcting base-base mismatches and insertion/deletion loops produced during replication. The most abundant machinery responsible for DNA MMR is the hMutSα (MSH2-MSH6) complex. Li et al. (2013) showed that the binding of hMutSα to chromatin is H3K36me3-dependent as its subunit MSH6 reads the H3K36me3 signal by virtue of its PWWP domain. Depletion of SETD2 abolished the localization of hMutSα, which led to a DNA-MMR-deficient mutator phenotype. The DNA MMR defect in SETD2-deficient UOK143 cells could be restored by enforced expression of ySET2. This demonstrates the crucial role of H3K36me3 in recruiting the DNA MMR repairing machinery.

DNA MMR predominantly occurs during the S-phase of the cell cycle, whereas HR repair preferentially takes place in the late S/G2 phase. H3K36me3 consistently peaks in the late G1/early S phase and disappears in the late S/G2 phase (Ryba et al., 2010; reviewed by Li et al., 2015). This is additional proof that H3K36me3-modification enables a safe transition from the G1 to the S phase by recruiting repairing machineries to correct the errors produced during replication. When H3K36me3 is abolished due to SETD2 inactivation, the repair machinery cannot localize to damaged sites, resulting in an accumulation of errors and genomic instability, a hallmark of tumorigenesis.

H3K36me3 also serves as a signal to recruit proteins to DNA double strand breaks (DSBs) to initiate repair. An accurate repair of DSBs relies on HR. The PWWP domain of PC4 And SFRS1 Interacting Protein 1 (PSIP1, also known as Lens Epithelium-Derived Growth Factor, LEDGF) is the basis of this HR process, and H3K36me3 plays a key role through the recruitment of PSIP1 (Eidahl et al., 2013; Pradeepa et al., 2012; Pfister et al., 2014). This is consistent with the finding that SETD2 is required for ATM-activation upon DSBs (Carvalho et al., 2014) and the notion that SETD2-deficient cells fail to activate a proper DNA damage response, including activation of TP53 (Carvalho


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et al., 2014). SETD2 inactivation abolishes H3K36me3 and consequently the binding of PSIP1 to the damage sites. To compensate for the HR deficiency, cells have to use alternative mechanisms to repair the DSBs, such as nonhomologous end-joining and/or microhomology- mediated end-joining (Carvalho et al., 2014). These approaches are error prone and may lead to deletions (Symington and Gautier, 2011). Although the HR repair machinery in SETD2-inactivated cells is still competent (Pfister et al., 2014; Carvalho et al., 2014), these cells are not capable of recruiting the DNA repair components to the damaged sites due to loss of the H3K36me3 signal.

H3K36me3 and DNA methylationSeveral publications have indicated that actively transcribed genes are extensively methylated at the gene body (Hellman and Chess, 2007; Lister et al., 2009; Jjingo et al., 2012; Varley et al., 2013). This has raised the question of whether H3K36 trimethylation is associated with gene body DNA methylation. Hahn et al. (2011) carried out a detailed study of the association of a number of epigenetic markers in human bronchial epithelial cells and colorectal cancer cell line HCT116, focusing on chromosome 19 genes. Of the expressed genes, 74% had a high level of both gene body DNA methylation and H3K36me3. DNA methylation and H3K36me3 have been linked in both yeast and mouse (Morselli et al., 2015). In addition, a group of genes, mostly Zinc Finger genes, were identified in which H3K36me3 occurred in combination with the repressive intragenic H3K9me3 mark (Morselli et al., 2015). On average these genes were expressed at a low level and had a relatively low number of intragenic CpG dinucleotides that were largely unmethylated. By analyzing cells that were either made defective in H3K36 trimethylation or in CpG methylation, Hahn et al. (2011) further showed that the levels of these two epigenetic markers are established independently. However, Dhalayan et al. (2010) demonstrated a high affinity of DNA (cytosine-5)- methyltransferase 3A (DNMT3A) to H3K36me3 in vitro. DNMT3A is targeted to H3K36me3-containing nucleosomes, e.g. in heterochromatic regions as well as gene bodies, by virtue of its PWWP domain. DNMT3A/B interacts with PU.1 to form a complex for de novo site-specific methylation (Suzuki et al., 2006). This indicates that the H3K36me3 mark could recruit DNMT3A/B to establish DNA methylation.

SETD2 knock-out mouseIn mice, SETD2-/- knockout is embryonic lethal in E10.5 to E11.5 due to defects in angiogenesis in the yolk sac and placenta (Hu et al., 2010). Expression profiling of SETD2-/- and wild-type yolk sacs revealed significantly altered expression levels of genes involved in vascular remodeling. Both SETD2-/- embryonic bodies derived from embryonic stem cells and from cultured human endothelial cells treated with siRNAs-directed against SETD2 showed defects in cell migration and invasion (Hu et al., 2010; Zhang et al., 2014). Thus, SETD2 appears to be crucial for a proper embryonic development although many cancer cells appear to function well without SETD2. In

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the literature, no clues can be found of heterozygous SETD2 knockout mice being predisposed to any kind of disease or cancer.

SETD2 in DiSEaSELuscan et al. (2014) identified a missense and a nonsense SETD2 mutation in 2 out of 11 patients with Sotos syndrome, an overgrowth syndrome first described by Sotos et al. (1964). It is unknown if these mutations were present in the germline and there is no direct functional evidence that links these mutations to SOTOS. However, it is remarkable that the gene most frequently mutated in SOTOS is the PWWP-domain-containing Nuclear Receptor Binding SET Domain Protein 1 (NSD1, also known as KMT3B) gene responsible for mono- and di-methylation of H3K36 (Wagner and Carpenter, 2012; Li et al., 2009). We are not aware of any reports that link SETD2 germline mutations to an inherited syndrome in humans.

SETD2 in cancerThe first report on SETD2 mutations in cancer dates from 2010 when Dalgliesh et al. (2010) identified inactivation mutations in ccRCC. At the same time, using a Gene Identification by Nonsense-mediated mRNA decay Inhibition (GINI) strategy, our group identified inactivating SETD2 mutations in 5 out of 10 ccRCC- derived cell lines (Duns et al., 2010). All cell lines showed copy number loss for most of the short arm of chromosome 3, indicating complete functional loss of SETD2 in these cell lines. Subsequent targeted sequencing of the SETD2 coding regions revealed SETD2 mutations in 2 out of 10 primary ccRCC tumors (Duns et al., 2012). This bi-allelic inactivation of SETD2 was the first clue that the gene might be a tumor suppressor gene. Two large cohort studies revealed an overall frequency of SETD2 mutations of approximately 11% in ccRCC (Cancer Genome Atlas Research, 2013; Sato et al., 2013). The fraction of truncating mutations in ccRCC was more than 50% in the study of Hakimi et al (2013) and 57% in COSMIC, which is significantly higher than the fraction of truncating mutations in non-ccRCC tumors (32%, COSMIC). Still, whole-exome sequencing studies did reveal somatic SETD2 mutations in various types of cancer (Table 2), and this can be seen as an indication that SETD2 inactivation plays a role in the development of other tumors, albeit with low frequencies in most of them (COSMIC (Forbes et al., 2015), Tumorportal (Lawrence et al., 2014) and cBIOPortal (Gao et al., 2013; Cerami et al., 2012; accessed in January 2016). It should be noted that in many studies it is not clear if the mutation resulted in a bi-allelic inactivation of SETD2. Moreover, the majority of somatic SETD2 mutations were missense mutations for which the functional consequences are often unclear (Table 2). This is illustrated by the study of Zhu et al. (2014) of 241 cases of leukemia (134x acute myeloid leukemia (AML) and 107x acute lymphcytic leukemia (ALL)) in which only 8 of the 19 somatic SETD2 mutations identified in 15 patients were truncating. Bi- allelic mutations


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Table 2. Overview of SETD2 mutation frequencies in a selection of tumors based on the COSMIC database (Feb 2016)*.

Tissue/tumor subtype

Percentage of samples with mutationCases testedtruncating missense

Kidney 4.19 3.10 2197ccRCC 5.43 4.14 1473Lung 1.26 1.42 1826Adenocarcinoma 3.51 3.51 550Skin 1.08 2.65 1017Liver 0.74 1.55 1611Hepatocellular carcinoma 0.78 1.12 893Soft tissue 0.70 4.67 428Biliary tract 0.66 0.66 152Adenocarcinoma 0.67 0.67 150Endometrium 0.63 3.49 631Endometrioid carcinoma 0.74 4.08 539Large intestine 0.59 3.05 1345Adenocarcinoma 0.62 3.10 1298Breast 0.58 0.94 1378Central nervous system 0.47 0.38 2128Pancreas 0.46 0.33 1521Ductal carcinoma 0.40 0.57 1240Stomach 0.34 2.04 587Urinary tract 0.30 0.90 666Haematopoietic and lymphoid 0.24 0.87 2519Acute lymphoblastic B cell leukaemia 1.54 2.32 258Acute lymphoblastic T cell leukaemia 0.97 0.97 207Diffuse large B cell lymphoma 0.00 3.20 250Ovary 0.24 0.59 843Serous carcinoma 0.31 0.78 641Bone 0.20 0.60 496Prostate 0.10 0.88 1019Adenocarcinoma 0.12 0.48 827

* Tumor subtypes with a sample size less than 100 cases have been excluded.

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were detected in only 4 patients. It cannot be excluded that in ALL, and possibly in other tumors as well, SETD2 haploinsufficiency does lead to a disease phenotype. SETD2 mutations appeared to be most frequent in leukemias that carried a MLL gene rearrangement (Zhu et al., 2014).

In ccRCC, SETD2 is ranked in the top-5 most commonly mutated genes (COSMIC, rank 4), indicating its specific role in this tumor type. In Tumorportal, SETD2 mutations are indicated as highly significant in ccRCC and glioblastoma multiform and indicated as near significant in bladder cancer. In all cancers combined, there is a slight clustering of SETD2 missense mutations in an approximately 200 amino acid segment (p.M1468 up to p.Q1668) that overlaps with the SET domain (Figure 2). The same region is relatively devoid of missense variants in the normal population (ExAC database, http://exac. broadinstitute.org, accessed January 2016, and Figure 2), indicating that missense mutations in this domain might be more often damaging. SETD2 nonsense mutations leading to loss-of-function can be located throughout the entire gene (Figure 2). Further studies on the potential functional consequences of SETD2 missense mutations are required to establish their role in tumor development and/or progression.

Pena-Llopis et al. (2013) collected data on 924 primary ccRCC of which 300 cases had a PBRM1 mutation and 66 cases had a SETD2 mutation, while 33 cases had a mutation in both genes. This number was shown to be significantly higher than the expected number of cases with mutations in both genes (n = 21, Fisher exact test,

Figure 2. Schematic representation of SETD2 with the location of functional domains and nonsynonymous mutations and variants. The location of nonsynonymous mutations was obtained from ExAC (Germline variants in ~120000 alleles; January 2016) and COSMIC (somatic variants in 23,249 cases; January 2016). Intronic regions and 3’- and 5’-untranslated regions are not shown. Red, position of inactivating variants; Blue, position of missense variants. For the COSMIC data, the height of the bar is relative to the number of mutations. For the ExAC data, the height of the bars indicate 1, 2-5, 6-10, or >10 variants per triplet.


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p = 0.003). This suggests that mutations of PBMR1 and SETD2 may have a synergistic effect in ccRCC, possibly by disrupting different pathways. Moreover, the Cancer Genome Atlas database (TCGA) reveals co- mutation of PBRM1 and SETD2 in multiple tumors despite the low mutation frequency of both genes in these cancers. Thus, having both SETD2 and PBRM1 mutations might strengthen their oncogenic potential, and the underlying mechanism deserves exploration. Sato et al. (2013) found that SETD2 mutations predominantly occurred in tumors with pre-existing VHL mutations, again indicating a role in tumor progression. However, in other studies SETD2 mutations were also identified in ccRCC cases with wild type VHL (Hakimi et al. 2013; Varela, Tarpey et al. 2011).

The high frequency of inactivating SETD2 mutations in ccRCC points to a tumor-suppressor-like function of this gene. Additional proof for a tumor suppressor role of SETD2 came from Sleeping Beauty transposon experiments. This approach is based on the assumption that commonly observed transposon insertion sites can harbor tumor-driver genes. These studies revealed transposon integration sites in SETD2 in various tumors such as leukemia’s (Berquam-Vrieze et al., 2011) and colorectal cancer (March et al., 2011), albeit at a low frequency.

Correlation with clinical dataAl Sarakbi et al. (2009) found a negative association of SETD2 expression levels with increasing tumor stage in breast cancers. In gliomas, SETD2 mutations were predominantly seen in high-grade (16 out of 178 cases) but not in low-grade cases (0 out of 45 cases) (Fontebasso et al., 2013). ccRCC patients with somatic SETD2 mutations had a higher relapse rate compared to cases with wild-type SETD2, but no effect was observed on overall survival. In a study including 185 ccRCC patients, SETD2 mutations were significantly associated with advanced tumor stage (P = 0.02) (Hakimi et al., 2013). In the TCGA, SETD2 mutations were found to be associated with worse cancer-specific survival (P = 0.036; HR 1.68; 95% CI 1.04-2.73), and the presence of SETD2 mutations was a predictor of ccRCC recurrence in an univariant analysis (P = 0.002; HR 2.5; 95% CI 1.38-4.5) (Hakimi et al., 2013). Further evidence supporting a role of SETD2 inactivation in progression of tumors comes from a recent study performed by Ho et al. (2015). Using immuno-histochemical approaches, Ho et al. (2015) observed a decrease in H3K36me3 levels in metastatic ccRCC as compared to primary ccRCC. Either acquired SETD2 mutations or alternate mechanisms may be the cause of this, suggesting that a decreased level of H3K36me3 is correlated with progression. They also noted that loss of one allele of SETD2, a common event due to the widespread copy number loss of the short arm of chromosome 3 in ccRCC, did not result in a reduced level of H3K36me3. Thus, SETD2 haploinsufficiency does not cause a H3K36me3-related phenotype in ccRCC. In addition, intra-tumor heterogeneity studies have indicated that SETD2-inactivation may be a late event in cancer development. Gerlinger et al. (2012) carried out a genomic analysis of multiple

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regions of four primary ccRCC tumors and detected intratumor heterogeneity in every case. Using whole exome sequencing and H3K36me3-staining of tissue sections, they identified different SETD2 mutations in different regions of the same primary tumor in three cases. This suggested that loss of SETD2 can be a late event that provides a selective advantage to tumor cells (Gerlinger et al., 2012). Lentiviral-mediated knockdown of SETD2 in pre-leukemic cells carrying a MLL fusion-gene increased both the colony-forming capacity and the growth rate of these cells (Zhu et al., 2014). This indicates that loss of functional SETD2 facilitates initiation as well as progression of leukemias. Thus, it appears that SETD2-inactivation may function not only in driving the development of tumors, but also in promoting progression of the disease.

SETD2 functional studies in cancerAlternative splicing is considered as a major impetus driving proteome diversity and promoting progression of cancer (Oltean and Bates, 2013). SETD2-mutated ccRCC tumors showed an altered chromatin accessibility in the H3K36me3 marked regions, which led to widespread defects in transcript processing, including intron retention, exon utilization and different transcriptional start and stop site usage, especially in highly expressed genes (Simon et al., 2014). A specific set of transcripts showed an increased retention of introns in H3K36me3-deficient tumors, and several of the affected genes, including PTEN, TP53, ATR, RAD50, POLN, XRCC1, CCNB1, and CCND3, are important in tumor development. Since intron retention could lead to loss of function of the protein product, SETD2-inactivation will probably also have an impact on the functionality of these genes. Additionally, in the study of Ho et al (Ho et al., 2015), decreased levels of H3K36me3 in ccRCC, most likely due to SETD2-inactivating mutations, resulted in alternative exon usage for a selection of genes (Ho et al. 2015). Li et al. (2015) carried out a detailed study on the splicing of CDH1 in gastric cancer cell lines in comparison to the human gastric mucosal epithelial cell line GES-1. In all samples, the wild type product and a transcript that lacks part of exon 8 were identified. A higher level of H3K36me3 appeared to favor the use of the splice donor site within exon 8. Attempts to influence the ratio between the two transcript variants were most successful using siRNA directed against SETD2 and, to a lesser extent, using an HDAC inhibitor.

HR repair and DNA MMR defects have been observed in SETD2-inactivated tumor cell lines, although the repair machineries themselves are not abolished in these cells (Li et al., 2013) . The SETD2-deficient ccRCC-derived cell line UOK143 showed insufficient MutSα-mediated DNA MMR in S phase. In contrast, in the SETD2-proficient ccRCC cell line UOK12, abundant MSH6 foci were formed during S phase and most of those loci co- localized with the H3K36me3 signal. SETD2-inactivated ccRCC cell lines RCC-MF and RCC-FG2 showed defects in DSB repair (Carvalho et al., 2014) . These studies indicated that SETD2 is important to maintain the genomic integrity in ccRCC.


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Additional factors modulating H3K36me3 levelsWhen examining several databases, it becomes clear that SETD2 is ubiquitously expressed in most if not all tissues. This is not surprising given its function as the sole gene responsible for the trimethylation of H3K36. However, two factors have been identified in cancer- related studies that can modulate the level of SETD2 in cancer cells, and may also do so in non-cancerous cells (Figure 3). A recent study on liver cancer demonstrated a negative correlation between expression of SETD2 and the HOX transcript antisense RNA (HOTAIR) (Li et al., 2015). HOTAIR expression has been associated with several cancers and is shown to be an oncogenic long noncoding RNA (Tang et al., 2013). HOTAIR suppressed the transcription of SETD2, and reduced the level of H3K36me3. Thus, HOTAIR overexpression is linked to various cellular processes mediated by H3K36me3 readers.

Xiang et al. (2015) showed that miR-106b-5p could bind to, and inhibit translation of, the SETD2 mRNA transcript in ccRCC. SETD2 levels increased by inhibiting miR-106b-5p and this resulted in suppression of cell proliferation and a G0/G1 cell cycle arrest.

A number of genes other than SETD2 can influence H3K36me3 levels. KDM4A, -B and -C are known to demethylate H3k36me3 (Labbe et al., 2013). Overexpression of these genes, which is a relative common event in various types of cancer (Berry and Janknecht, 2013), may thus interfere with all processes that involve H3K36me3 readers. As an example, it was recently shown that an enhanced expression of KDM4A-C promotes genomic instability (Awwad and Ayoub, 2015). By demethylating H3K36me3 the recruitment of MSH6 is prevented.

Figure 3. Regulation of SETD2 expression. The long non-coding RNA HOTAIR regulates SETD2 expression at the transcriptional level by competitively blocking loading of CREB-P300-RNA Pol II complex to the SETD2 promoter. MicroRNA-106-5p (miR-106-5p) regulates SETD2 expression at the translational level by binding to the 3’-UTR of the SETD2 mRNA transcript.

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EMERGinG ThERaPEuTic oPPoRTuniTiESNow that it is evident that SETD2-inactivation can be an important factor in tumor development and progression, especially in ccRCC, understanding the SETD2-inactivation-related pathways may offer new targets for therapy. The Genomics of Drug Sensitivity in Cancer database (Yang et al., 2013) lists four chemical compounds with a selective inhibitory capacity for SETD2-/- cell lines. Two of these components target P13Kβ. Feng et al. (2015) further analyzed the effects of AZD6482 on SETD2-/- ccRCC cell lines and showed that tumor cells were selectively inhibited. This represents the first indication that novel compounds targeting SETD2-/- tumors might become feasible treatment for ccRCC patients. In recent years many studies have focused on the ability of small molecules to target specific histone modifications, which could eventually be used in targeted therapies. A recent study shows that the combination of WEE1-inactivation by the AZD1775 inhibitor and H3K36me3-deficiency is lethal for cultured human cells (Pfister et al., 2015). These results were then validated in xenograft models of two tumor- derived SETD2-/- cell lines. The underlying mechanism appears to be inhibition of the replication process. These recent developments may open the doors that allow for the development of targeted therapies for H3K36me3- deficient tumors in combination with WEE1 inhibitors. The WEE1 inhibitor is currently being tested in several phase II clinical trials (http://www.clinicaltrials.gov).

concluDinG REMaRkSSETD2 is responsible for the trimethylation of H3K36 in the gene body of actively transcribed genes and its inactivation interferes with the function of readers of this specific histone modification. The role of H3K36me3 on specific cellular functions is becoming more and more clear. Loss of one allele of SETD2, most likely a common event in many tumors due to widespread and frequent 3p copy number loss, may not be enough to cause a significant change in H3K36me3. On the other hand, biallelic inactivation of SETD2 is not the only mechanism that may cause loss of H3K36me3. Loss of SETD2 may also cause regional genomic instability, RNA processing defects and intragenic transcription initiations. Both genomic instability and alternative splicing are known as hallmarks of cancer. The former is a key force in carcinogenesis. The latter is an important mechanism for driving proteome diversity, which contributes to cancer development. In combination with the presence of SETD2-inactivating mutations in a substantial proportion of ccRCC, this clearly demonstrates SETD2’s role as a suppressor of both tumor initiation and progression.

Our knowledge on SETD2-regulated signaling pathways is quite limited, especially in the context of SETD2 binding proteins. Recent studies have indicated that SETD2 may interact with multiple proteins (Huttlin et al., 2015; Hein et al., 2015; KirlI et al., 2016). The challenge will be to unravel novel SETD2 functionalities that are independent of its function as trimethylator of H3K36. Conditional, and/or tissue- specific, SETD2


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knockout mice may be of help to identify the crucial pathways that are affected upon inactivation of SETD2. Loss of SETD2 appears to play an essential role in a substantial subset of ccRCC. However, the specific effect of SETD2 inactivation on ccRCC precursor cells, kidney primary tubular epithelial cells, is still unknown. As SETD2 mutations are also seen in other cancer types, understanding the role of SETD2 in ccRCC will contribute to our understanding of these tumors.

acknowlEDGEMEnTSWe are grateful to Kate McIntyre for critically editing the manuscript.

funDinGJL was supported by a China Scholarship Council of Research fellowship.

conflicTS of inTERESTThere is no conflict of interest for any of the authors.

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funcTional STuDiES on PRiMaRy TuBulaR EPiThElial cEllS inDicaTE a TuMoR SuPPRESSoR RolE of SETD2 in clEaR cEll

REnal cEll caRcinoMa

Jun Li 1, Joost Kluiver 2,3, Jan Osinga 1, Helga Westers 1, Maaike B van Werkhoven 4, Marc A. Seelen 4, Rolf H. Sijmons 1, Anke van den Berg 2,3 and Klaas Kok 1

1 Department of Genetics, 2 Department of Medical Biology, 3 Department of Pathology and 4 Department of Nephrology, University of Groningen, University Medical Center Groningen,

PO Box 30.001, 9700 RB Groningen, the Netherlands

Neoplasia, 2016; 18(6), 339-346

c h a P T E R 3

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aBSTRacTSET domain-containing 2 (SETD2) is responsible for the trimethylation of histone H3 lysine36 (H3K36me3) and is one of the genes most frequently mutated in clear cell renal cell carcinoma (ccRCC). It is located at 3p21, one copy of which is lost in the majority of ccRCC tumors, suggesting that SETD2 might function as a tumor suppressor gene. However, the manner in which loss of SETD2 contributes to ccRCC development has not been studied in renal primary tubular epithelial cells (PTECs). Therefore, we studied the consequences of SETD2 knockdown through lentiviral shRNA in human PTECs. Consistent with its known function, SETD2 knockdown (SETD-KD) led to loss of H3K36me3 in PTECs. In contrast to SETD2 wild-type PTECs, which have a limited proliferation capacity; the SETD2-KD PTECs continued to proliferate. The expression profiles of SETD2-KD PTECs showed a large overlap with the expression profile of early- passage, proliferating PTECs, whereas nonproliferating PTECs showed a significantly different expression profile. Gene set enrichment analysis revealed a significant enrichment of E2F targets in SETD2-KD and proliferating PTECs as compared with nonproliferating PTECs and in proliferating PTEC compared with SETD2-KD. The SETD2-KD PTECs maintained low expression of CDKN2A and high expression of E2F1, whereas their levels changed with continuing passages in untreated PTECs. In contrast to the nonproliferating PTECs, SETD2-KD PTECs showed no β-galactosidase staining, confirming the protection against senescence. Our results indicate that SETD2 inactivation enables PTECs to bypass the senescence barrier, facilitating a malignant transformation toward ccRCC.

SETD2-loSS prEvEnTS pTECs from SEnESCEnCE

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inTRoDucTionClear cell renal cell carcinoma (ccRCC) represents the most common and lethal subtype of kidney cancer, accounting for 80% to 90% of renal cell carcinomas and 3% of all cancers (Ferlay et al., 2014). A better understanding of the processes that underlie ccRCC development might help in designing more successful ways to treat these tumors (Haddad et al., 2015). ccRCC arises from the primary tubular epithelial cells (PTECs) of the kidney (Thoenes et al., 1986), but the malignant transformation process is poorly understood. The most common genomic aberration in ccRCC is 3p loss (Kok et al., 1997), indicating the presence of ccRCC-associated tumor suppressor genes (TSGs). The first TSG identified in ccRCC was Von Hippel–Lindau (VHL) (Seizinger et al., 1988), which maps to 3p25 and is mutated in approximately 55% of tumors (Dalgliesh et al., 2010). In recent years, three additional 3p genes (PBRM1, BAP1, and SETD2) have been identified as being frequently mutated in ccRCC. Mutations in SETD2 were first reported in two independent studies. Dalgliesh et al. identified SETD2-inactivating mutations in 15/342 ccRCC cases (Dalgliesh et al., 2010), and we identified SETD2-inactivating mutations in 5/10 ccRCC-derived cell lines (Duns et al., 2010). SETD2-inactivating mutations occur at a frequency of 11% in ccRCC (Sato et al., 2013; Cancer Genome Atlas Research Network, 2013). According to the Catalogue of Somatic Mutations in Cancer database (http://cancer.sanger.ac.uk/cosmic, accessed in October 2015), ccRCC is the only tumor type that SETD2 ranks into the top five mutated genes. Together, these studies support the relevance of SETD2 inactivation in the development of ccRCC. Loss of one allele of SETD2 and functional inactivation of the second allele by a point mutation are consistent with Knudson’s classic two-hit model to inactivate TSGs.

SETD2 is a histone methyltransferase responsible for the histone H3 lysine 36 trimethylation (H3K36me3), a histone mark enriched at the gene body of actively transcribed genes (Edmunds et al., 2007). The SRI domain of SETD2 interacts with RNA-polymerase II, causing SETD2 to be present during transcription. Many of the biological processes in which SETD2 has been suggested to participate revert to its presence during the transcriptional process. In ccRCC-derived cell lines, loss of 3p and mutation of the remaining SETD2 allele result in a complete loss of H3K36me3, whereas cell lines with one functional SETD2 allele show at most slightly reduced or even normal H3K36me3 levels (Duns et al., 2010). It is still unclear how SETD2 inactivation might contribute to the pathogenesis of ccRCC. We aimed to determine if SETD2 acts as a TSG in ccRCC and how SETD2 inactivation contributes to the malignant transformation.

MaTERial anD METhoDSA schematic representation of the workflow and detailed experimental procedures are presented in the Supplementary material and methods.

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Isolation of PTECs and Cell CulturesPTECs were isolated from the healthy human kidney cortex segment. The isolation procedures and phenotype identification were performed as previously described (van Ark J et al., 2013). Both PTECs and HKC8 were maintained in Dulbecco’s modified Eagle’s medium/F-12 GLUTMAX-1 containing10% fetal bovine serum (FBS), 100 U/ml of penicillin, 100 μg/ml of streptomycin, 1% Insulin-Transferrin-Selenium, and 5 ng/ml of epidermal growth factor (EGF). Human embryonic kidney 293T (HEK293T) cells were cultured in Dulbecco’s modified Eagle’s medium supplemented with 10% fetal bovine serum (FBS), 100 U/ml of penicillin, and 100 μg/ml of streptomycin (all used for cell culturing are from Sigma-Aldrich, St. Louis, MO). All the cells were maintained at 37°C under humidified air containing 5% CO2. Mycoplasma, bacteria, and fungi were tested as negative in these cultures.

ShRNA Constructs, Lentiviral Transductions, and Growth Competition AssayOligos (Eurogentec, Seraing, Belgium) to generate shRNA constructs were cloned into the pGreenpuro lentivector (Systems Biosciences, Mountain View, CA) using standard procedures (see shRNA construct sequences in Supplementary Table 1). Lentiviral particles were produced by calcium phosphate–mediated transfection of HEK293T cells. Transduction of target cells was performed with multiple dilutions of concentrated virus stock in the presence of 4 μg/ml of polybrene (Sigma-Aldrich). Green fluorescent protein (GFP) was measured on the FACS Calibur flow cytometer (BD Biosciences, San Jose, CA), and data were analyzed with Kaluza Flow Analysis Software v 1.3 (Beckman Coulter, Brea, CA). Cultures with a high percentage of transduced cells were used to confirm knockdown of SETD2. Cell cultures with a mix of GFP+ and GFP− cells were used in the GFP-competition assay. Percentages of GFP+ cells were normalized to the percentage of GFP+ cells at the first measurement. GFP was measured at indicated time points.

RNA Isolation and Reverse-Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)Total RNA was isolated by Gene JET RNA purification kit (Fermentas, St. Leon-Rot, Germany). RNA quality was evaluated on an HT RNA LabChip GX/GXII kit (Caliper GX; Life Sciences, Hopkinton, MA). To quantify the expression levels of target genes, equal amount of RNA was synthesized to first-strand cDNA using the RevertAid H Minus First Strand cDNA synthesis kit (Thermo Fisher Scientific, Rockford, IL). Quantitative PCR was performed on the ABI 7900HT Fast Real-Time PCR system (Applied Biosystems, Foster City, CA) with iTaq Universal SYBR Green Supermix (Bio-Rad, Hercules, CA), and the results were analyzed by SDS 1.3.0 software (Life Technologies, Foster City, CA). Unpaired one-tailed t tests were used to determine whether significant changes in SETD2 levels were obtained upon shRNA-mediated knockdown (see RT-qPCR primers in Supplementary Table 1).


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Histone Isolation and Western Blot AnalysisCells were lysed in TEB buffer (PBS containing 0.5% Triton X 100 [v/v], 2 mM phenylmethylsulfonyl fluoride, and 0.02% [w/v] NaN3), and histones were isolated by acid extraction. Histones extracts were separated with 15% sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a PVDF membrane (Roche, Mannhein, Germany) for blotting. The proteins of interest were probed with antibodies against tri-methyl-histone H3 (Lys36) (1:1000; Cell Signaling, Danvers, MA) or histone H3 (1:1000; Cell Signaling). Target proteins were detected with HRP-conjugated Alexa Fluor 488 Donkey Anti-Rabbit IgG antibody (H + L) (1:10,000; Life Technologies, NY). Positive staining was visualized by incubation with Lumi-light Western Blotting substrate (Roche). Images were captured by the ChemiDOC MP imaging system with Image lab v4.1 software (Bio-Rad).

Microarray and Expression AnalysisA custom-designed microarray was used for expression profiling (Agilent ID 050524), and the procedure was performed according to the manufacturer’s instructions (Agilent Technologies, Santa Clara, CA). Total RNA was labeled using the Low Input Quick Amp Labeling Kit and the Cyanine5 CTP Dye Pack (Agilent Technologies). cRNA was purified using the RNeasy Mini Kit (Qiagen, Valencia, CA), quantified on a NanoDrop 1000 spectrophotometer (Thermo Fisher Scientific), and hybridized on the custom array using the Gene Expression Hybridization Kit (Agilent Technologies). Arrays were scanned with the Agilent DNA Microarray Scanner and analyzed with Agilent Feature Extraction software v 10.7.3.1. The resulting raw data were analyzed with GeneSpring GX 13.1.1 software (Agilent Technologies). To exclude a possible bias caused by the multiplicity of infection (MOI), we performed principle component analysis and compared wild-type (WT) to nontargeting (NT) PTECs at both day 6 and day 16. In addition, we performed a moderated t test with Bonferroni family-wise error rate (FWER) multiple testing correction. One-way analysis of variance (ANOVA) using Tukey’s honestly significant difference post hoc test was used to identify differentially expressed genes between the three experimental groups, and Bonferroni FWER adjusted P values < 0.05 were considered statistically significant. The experimental groups were 1) proliferating PTECs at day 6 including both WT and NT PTECs, 2) nonproliferating WT and NT PTECs at day 16, and 3) SETD2-KD PTECs at day 25. Microarray data are available through the GEO database (GSE72792).

Senescence β-Galactosidase (β-gal) and Immunohistochemistry (IHC) StainingThe senescence β-gal Staining Kit (Cell Signaling) was used according to the manufacturer’s instructions. Images were captured by TissueFax (TissueGnostics, Vienna, Austria) equipped with Zeiss objective LD “Plan-Neofluar” 20 ×/0.4 Corr Dry, Ph2 objectives. Formaldehyde- or acetone-fixed cells were processed for IHC staining by standard procedures. Representative images were captured by an Olympus BX41

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microscope (Olympus, Hamburg, Germany). Antibodies used in the staining are listed in the Supplementary material and methods.

RESulTS anD DiScuSSionSETD2 Depletion in Immortalized Kidney Epithelial Cell Lines To study the role of SETD2 in epithelial cells, we transduced HEK293T and HKC8 cells with lentiviruses containing SETD2-targeting or NT shRNAs coexpressed with GFP. Both SETD2 shRNA constructs induced a 60% to 70% decrease in SETD2 mRNA levels. A virtual absence of H3K36me3, commonly used as a measure of SETD2 loss (Edmunds et al., 2007), in SETD2-shRNA treated HEK293 cells confirmed efficient downregulation of SETD2 at the protein level (Figure 1A). To study the effect of SETD2 knockdown on cell growth, we performed a GFP competition assay in both cell lines. In HEK293T cells, a significant reduction of GFP+ SETD2-KD cells (60%-80%) was observed at day 20 for both shRNA constructs relative to the GFP+ percentage at day 3. In HKC8 cells, the reduction was also significant, although less pronounced, with a drop of 40% to 60%. No significant differences were observed in the growth competition assays for the NT-shRNA construct transduced cell lines (Figure 1B). Thus, SETD2 depletion caused a marked decrease in cell growth in immortalized human embryonic kidney (HEK293T) cells and kidney epithelial (HKC8) cells. The decrease in GFP+

Figure 1. SETD2 knockdown in immortalized kidney epithelial cell lines. (A) Transduction at high MOI of HEK293T cells with sh1 and sh2 directed against SETD2 results in a decreased level of SETD2 mRNA determined by RT-qPCR. Results are presented as 2-∆Ct; HPRT was used for normalization. Ctrl, wild-type HEK293T cells; NT, nontargeting shRNA transduced HEK293T cells. Western blot shows a strong decrease of the global level of H3K36me3 in SETD2 knockdown cells as compared with control HEK293T cells and the NT-treated HEK293T cells. The level of histone H3 was used as a loading control. (B) Growth competition assay in HEK293T and HKC8 cells. HEK293T and HKC8 cells were transduced with a nontargeting sequence (NT) or with constructs targeting SETD2 (sh1 and sh2) at low MOI. The percentage of GFP+ cells was measured at the indicated time points (X-axis). The relative changes in GFP-positive cells were normalized to the percentage of GFP-positive cells on day 3 (Y-axis). The data are presented as mean ± SD from triplicate experiments. One-way ANOVA with Dunnett multiple testing correction showed a significant difference of SETD2-sh1 and -sh2 compared with NT, **P < 0.01, ***P < 0.001.

A B


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cells might be related to a reduced transcription elongation rate of multiexon protein coding genes as a consequence of loss of H3K36me3 (Li et al., 2002). These findings are not consistent with a tumor suppressor function of SETD2 in ccRCC. However, the impact of SETD2 knockdown in these immortalized and highly proliferating kidney cell lines might not represent an optimal model to study the tumor-suppressing function of SETD2 in ccRCC.

SETD2-KD in PTECsTo further study the possible tumor suppressor function of SETD2 in ccRCC oncogenesis, we switched to renal primary tubular epithelial cells (PTECs), which are generally regarded as the normal counterparts of ccRCC (Thoenes et al., 1986). These PTECs can be isolated from the kidney cortex segment and cultured in vitro for a limited number of population doublings (Qi et al., 2007). We isolated PTECs and authenticated their phenotype as described previously (van Ark J et al., 2013).

Early-passage PTECs derived from three different individuals were transduced with viral particles containing SETD2-shRNA constructs. Again, both constructs induced a significant decrease in SETD2 mRNA levels (40%-60%) and an almost complete loss of H3K36me3 (Figure 2A). This loss of H3K36me3 is consistent with a complete functional loss of SETD2 as observed in ccRCC cell lines caused by loss of one allele and an inactivating mutation in the remaining SETD2 allele (Duns et al., 2010). We next assessed the effect of SETD2 knockdown in a GFP-competition assay. At day 22 of the growth competition assay, the proportion of GFP+ cells showed a significant increase of 140% and 70% in SETD2-sh1 and SETD2-sh2 transduced PTECs, respectively, over the GFP− cells compared with day 2. The percentage of GFP+ cells in the NT-shRNA transduced PTECs (NT-PTECs) did not show a significant change over time (Figure 2B). These experiments revealed an apparent growth advantage of SETD2-KD PTECs relative to SETD2-WT PTECs consistent with a possible tumor suppressor function of SETD2. The proliferative capacity of untreated and NT-shRNA treated PTECs gradually decreased, and cells stopped proliferating around day 15 (passage 5), consistent with the known limited proliferative capacity of PTECs (Qi et al., 2007). We therefore stopped the GFP-competition assay at day 22.

SETD2-KD PTECs continued to proliferate until we stopped these cultures at day 40. Staining of the SETD2-KD PTECs at day 40 revealed an immunophenotype consistent with the wild-type PTECs at passage 3 (Supplementary Figure 1), i.e., positive for epithelial markers cytokeratin 8/18 (CK 8/18), epithelial membrane antigen (EMA), cytokeratin clone AE1/3 (CK AE1/3), and C5α receptor (c5α R) and negative for fibroblast marker α smooth muscle actin (α-SMA). SETD2-KD PTECs were also positive for liver-type fatty acid–binding protein 1 (L-FABP) (Figure 2C), a marker of human kidney proximal tubular cells (Maatman et al., 1992). Thus, we showed that SETD2 knockdown in PTECs abolished H3K36me3 and rendered a relative proliferative advantage while preserving the expected immune phenotype of PTECs.

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A B

C

Figure 2. Knockdown of SETD2 in kidney PTECs. (A) SETD2 knockdown in PTECs. PTECs were transduced with shRNA constructs as described in Figure 1A. The relative abundance of SETD2 mRNA was normalized to RNA polymerase II (RP II). Y-axis shows the 2-∆Ct from three independent experiments (mean ± SD, one-way ANOVA with Dunnett multiple testing correction, *P < 0.05, **P < 0.01). The level of H3K36me3 in SETD2 wild-type PTECs and shRNA transduced PTECs was shown by Western blotting; Histone H3 was used as a loading control. (B) Growth competition assay in PTECs. PTECs were transduced with shRNA virus particles as described in Figure 1B; GFP-positive cells were measured at the indicated time points (X-axis). The fold change relative to the percentage at day 2 (Y-axis) is shown. The data are presented as mean ± SD of three independent experiments. One-way ANOVA with Dunnett multiple testing correction showed a significant difference of SETD2-sh1 and -sh2 compared with NT, *P < .05, **P < .01, ***P < .001. (C) Immunohistochemical staining of SETD2-KD PTECs at day 40 with four epithelial markers (CK8/18, EMA, CA AE1/3, and C5α receptor), one fibroblast marker (α-SMA), and one proximal tubular marker (L-FABP). The staining was done in three independent PTEC cultures, and the images shown represent one of these cultures (400 ×).


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Expression Signature of SETD2-KD PTECsTo elucidate the mechanism underlying the enhanced proliferative capacity of SETD2-KD PTECs, we generated gene expression signatures of proliferating SETD2-WT PTECs at day 6 (WT-day 6), nonproliferating SETD2-WT PTECs at day 16 (WT-day 16), and SETD2-KD PTECs at day 25 (KD-day 25) that had overcome the restricted proliferating capacity. PTECs transduced with NT shRNA constructs (days 6 and 16) were used as controls. To obtain sufficient cells for the analysis, we infected the NT cells at a high MOI. To exclude a potential bias caused by comparing untreated PTECs to PTECS infected with a high MOI (NT) or a low MOI (SETD2-KD shRNAs), we carried out a principle component analysis (Supplementary Figure 2). Component 1 discriminated between nonproliferating WT- and NT-day 16 cells and the proliferating WT/NT-day 6 and KD-day 25 cells. Component 2 discriminated between WT/NT-day 6 and the KD-day 25 samples. NT cells clustered together with the WT cells at both day 6 and day 16, indicating that MOI did not affect the expression profile. Moreover, no significant differences in the expression profiles between the WT and NT cells were detected. These analyses clearly indicate that the high MOI used for the NT short hairpin transduction did not affect the expression signature of PTECs.

One-way ANOVA with Bonferroni FWER multiple testing correction revealed 227 differentially expressed genes between the three experimental groups, i.e., proliferating untreated/NT PTECs at day 6 (WT/NT-day 6), nonproliferating WT/NT-day 16 PTECS, and proliferating SETD2-KD day 25 PTECs. Two hundred seven genes were differentially expressed between WT/NT-day 6 and WT/NT-day16 PTECs, 207 genes between SETD2 KD-day 25 and WT/NT-day 16 PTECs, and 148 genes between WT/NT-day 6 and SETD2 KD-day 25 PTECs (Supplementary Table 2). Unsupervised hierarchical clustering revealed one cluster with all proliferating WT/NT-day 6 and SETD2 KD-day 25 PTECs and a second cluster with the nonproliferating WT/NT-day 16 PTECs (Figure 3A). The samples in the first cluster showed a further grouping, with one tree containing the WT/NT-day 6 PTECs and one tree containing the SETD2 KD-day 25 PTECS. To characterize the expression differences between these three experimental groups, a gene set enrichment analysis (GSEA) for biological function was performed (Table 1). In comparison to WT/NT-day 6 PTECs, SETD2 KD-day 25 PTECs showed a significant enrichment of nine gene sets (false discovery rate (FDR) < 0.01). Activation of the TNFα via–NF-κB signaling cascade promotes cell proliferation in ccRCC cell lines (Ikemoto et al., 2003). Epithelial-Mesenchymal-Transition (EMT) has been shown as an important expression signature of ccRCC (Tun et al., 2010). We previously identified differential expression of a set of EMT-related microRNAs between PTEC cells and ccRCC-derived cell lines (Duns et al., 2013). Moreover, activation of a membrane-bound interleukin-15 isoform was also shown to stimulate EMT (Yuan et al., 2015). These studies indicate an oncogenic potential of the SETD2-KD PTECs. Seven gene sets, including E2F_TARGETS and G2M_CHECKPOINT, were enriched in WT/NT-day 6 PTECs in comparison to KD-day 25 cells. Compared

Chapter 3

58

Figu

re 3

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3

59

Tabl

e 1.

Enr

iche

d G

ene

Sets

in W

T-D

ay 6

and

KD

-Day

25

PTEC

s Com

pare

d w

ith W

T-D

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6 PT

ECs.

1.93

/< 0

.001

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ene

Set

NES

/FD

R q

-Val

ueN

ES/F

DR

q-V

alue

NES

/FD

R q

-Val

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KD

-Day

25

vs W

T-D

ay 1

6W

T-D

ay 6

vs W

T-D

ay 1

6K

D-D

ay 2

5 vs

WT-

Day

6

E2F_

TARG

ETS

2.81

< 0

.001

–2.

95 <

0.0

01–

–2.

43 <

0.0

01G

2M_C

HEC

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2.66

< 0

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19 <

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1

Chapter 3

60

with WT/NT-day 16 PTECs, two gene sets were specifically enriched in SETD2 KD-day 25 PTECs and two in WT/NT-day 6 PTECs. Four gene sets were significantly enriched in both WT/NT-day 6 and SETD2 KD-day 25 PTECs (Table 1). In accordance with their proliferation status, both WT/NT-day 6 and SETD2 KD-day 25 PTECs showed significant enrichment of E2F_TARGETS (Figure 3B), G2M_CHECKPOINT, MITOTIC_ SPINDLE, and MYC_TARGETS_V1 gene sets in comparison to the nonproliferating WT/NT-day 16 PTECs. G2/M checkpoint genes regulate the transition of G2 to M phase in cells and prevent division of cells with DNA damage (Zhou et al., 2000). The E2F family of transcription factors orchestrates the expression of hundreds of genes in multiple biological processes, including senescence (Narita et al., 2003). Collectively, these results demonstrate that SETD2-KD PTECs remain in an active proliferation status well beyond a passage that would have caused senescence in the WT-PTECs. Given the known association between E2F targets and senescence, as well as the results of the growth competition assay, we next studied the expression of known senescence markers.

Inhibition of CDKN2A-E2F signaling in SETD2-KD PTECsSenescent cells are characterized by growth arrest, enlarged and flat cellular morphology, and an expression profile characterized by senescence-associated genes. The most commonly used marker to identify senescent cells is β-gal activity (Campisi et al., 2013). As shown in Figure 4, almost all NT-day 20 PTECs (both GFP+ and GFP−) stained positive for β-gal, indicative of a senescent status. In the mixed SETD2-KD cultures at day 20, containing both transduced GFP+/SETD2-KD PTECs and nontransduced GFP-/SETD2-WT PTECs, only a subpopulation of the cells stained positive for β-gal. After 40 days, almost all SETD2 KD cells were negative for β-gal while being positive for GFP. The decrease of β-gal–positive cells, in combination with the increasing number of GFP+ cells in the SETD2-KD PTECs culture, is consistent with a rescue of senescence of the SETD2-KD cells. These results indicate that knockdown of SETD2 prevents the transition of proliferating PTECs to nonproliferating, senescent PTECs. The two main pathways associated with regulation of senescence are the tumor protein (TP)53-cyclin-dependent kinase inhibitor 1A (CDKN1A) and the CDKN2A-E2F pathway (Campisi et al., 2007). Activation of TP53 results in induction of CDKN1A and senescence. The expression levels of TP53 did not change, whereas its downstream target CDKN1A was increased in both nonproliferating WT/NT-day 16 PTECs and proliferating SETD2 KD-day 25 PTECs (Supplementary Figure 3). Activation of CDKN2A induces senescence by inhibiting E2F family members through binding to the retinoblastoma protein. As GSEA showed enrichment of E2F targets in WT/NT-day 6 and SETD2 KD-day 25 PTECs compared with WT/NT-day 16 PTECs, we studied the expression of CDKN2A and E2F1 in these three cohorts. Compared with WT/NT-day 6 PTECs, we observed a significant increase of CDKN2A and a significant decrease of E2F1 in WT-day 16 PTECs. In SETD2 KD-day 25 PTECs, the expression of CDKN2A


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Figure 4. SETD2 inactivation prevents PTECs from senescence by active E2F signaling. (A) GFP and β-gal staining results of NT-day 20, SETD2-KD PTECs at day 20 and 40 PTEC cultures. Representative microscopic views are shown. Quantification of GFP- and β-gal–positive cells was performed by using ImageJ software (National Institutes of Health, Bethesda, MD). The results are present as mean ± SD value of three independent experiments (right panels). (B) The mRNA expression of CDKN2A and E2F1 in SETD2-WT PTECs at day 6 (WT-day 6), SETD2-WT PTECs at day 16 (WT-day 16), and SETD2-KD PTECs at day 25 (KD-day 25) was determined by RT-qPCR. The expression level of target genes was normalized to RP II. The results are presented as 2-∆Ct values of three independent experiments with mean ± SD. One-way ANOVA with Dunnett multiple testing corrections showed significant differences between WT-day 6 and KD-day 25 PTECs compared with nonproliferating WT-day 16 PTECs. *P < 0.05, **P < 0.01.

A

B

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62

and E2F1 was maintained at levels comparable to WT/NT-day 6 PTECs (Figure 4B). These findings are consistent with a previous report showing that the expression of the E2F1 was decreased in senescent cells, whereas E2F1 overexpression enabled resistance to senescence in primary fibroblast cells (Dimri et al., 2000). Thus, it appears that SETD2 knockdown prevents senescence in PTECs by maintaining the CDKN2A-E2F pathway. In the immortalized HEK293T cells, we observed decreased expression of both CDKN2A and E2F1 as a result of SETD2-KD (Supplementary Figure). Since the phosphorylation of RB is abolished as a result of the immortalization, the decreased expression of CDKN2A cannot activate E2Fs. The decreased expression level of E2F1 might be caused by the genome-wide absence of H3K36me3 in gene bodies.

To examine if SETD2 inactivation could reverse the senescent nature of PTECs at high passage number, β-gal staining was performed on PTECs 6 days after transduction with lentiviral SETD2-shRNA at passage 6 (day 20). Although the majority of the cells were GFP+, they also stained positive for β-gal (Supplementary Figure 5), indicating that the senescent state could not be reverted upon SETD2 knockdown. Our data show that SETD2 inactivation represents an escape of senescence mechanism of PTECs, in line with its tumor suppressor function in ccRCC. This is consistent with the proposed role of SETD2 inactivation in acute leukemia (Zhu et al., 2014). Senescence is a response that prevents proliferation of cells with DNA damage, and it serves as a barrier for malignant transformation (Zhou et al., 2000). We now show that SETD2 inactivation in PTECs bypasses the senescence barrier by maintaining CDKN2A-E2F signaling. Collectively, these studies emphasize the importance of the senescence-associated pathway in the development of ccRCC.

The major known consequence of SETD2 inactivation is loss of H3K36me3 on actively transcribed multiexon genes. This histone mark is recognized by so-called readers, most often by virtue of their PWWP domain (Qin et al., 2014). These readers are important components of several cellular pathways that are linked to cancer. SETD2 mutated ccRCC tumors and/or cell lines showed altered chromatin accessibility, resulting in widespread transcript processing defects (Simon et al., 2014). This is consistent with the known regulatory role of H3K36me3 methylation on transcription regulation (Li et al., 2002). Loss of H3K36me3 in ccRCC prevented recruitment of the mutS homolog 6, which is essential for DNA mismatch repair (Li et al., 2013), and recruitment of Lens epithelium- derived growth factor, which is required for homologous recombination of DNA double-strand breaks (Pfister et al., 2014). Loss of H3K36me3 also hinders the recruitment of RAD51 to DNA damage sites, resulting in failure of the TP53-mediated DNA damage response (Carvalho et al., 2014). A disrupted interaction of BRCA1 with RAD51 was shown to lead to microtubule organizing center amplification, causing chromosomal instability (Jung et al., 2014). Thus, it might be speculated that loss of SETD2 leads to accumulation of DNA damage. However, it remains unknown how SETD2 loss exactly prevents senescence in PTECS; most likely, the effect is modulated by CDKN2A, which is strongly induced upon


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senescence and prevents induction of E2Fs and their targets. On the other hand, it cannot be excluded that another, undiscovered mode of action of SETD2 is responsible for the phenotype as observed upon SETD2-KD. Loss of a direct interaction of SETD2 with TP53 could play a role in this process (Xie et al., 2008). The regulation of several TP53 downstream targets appeared to be dependent on its interaction with SETD2. Loss of puma, one of these targets, is suggested to prevent DNA-damage–induced apoptosis (Zhou et al., 2014).

concluSionSIn summary, we demonstrate that functional loss of SETD2 enables PTECs to bypass the senescence barrier by maintaining CDKN2A-E2F signaling. The prolonged proliferating potential might result in accumulation of DNA damage and thereby result in the development of ccRCC. Our results thus support a tumor suppressor role for SETD2 in ccRCC, consistent with Knudson’s two-hit model.

acknowlEDGEMEnTSThe research was supported by the Graduate School of Medical Sciences, University Medical Center Groningen, University of Groningen. J. L. was supported by a China Scholarship Council of research fellowship. We are grateful to Prof. Dr. Ir. Jo Vandesompele, Pieter-Jan Volders, and Dr. Pieter Mestdagh (Center for Medical Genetics Ghent) for sharing the design of the microarray used in this study. We thank Jackie Senior for critically editing the manuscript.

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SuPPlEMEnTaRy DaTaExperiment workflow and detailed procedures

PTECs isolation, culturing and treatmentThe proximal cortex of a normal kidney was cut into 12-15 kidney cubes of less than 2 by 2 mm. Twelve to fifteen kidney cubes were put into each FCS-precoated and collagen-1 coated T25 flasks (BD Biosciences, San Jose, CA). Next the fluid around the kidney cubes was removed intermittently, and the flasks were dried upright for 1.5-2 hours to make sure the cubes adhere to the surface. Then 5 ml DMEM/F-12 GLUTMAX-1 medium containing 5ng/ml EGF, 5μg/ml Insulin-Transferrin-Selenium (ITS), 100 U/ml penicillin and 100μg/ml streptomycin (all from Sigma-Aldrich, St. Louis, MO) was added to each flask (No fetal bovine serum, FBS). The primary cells were kept at 37°C in 5% CO2, and the medium was changed for the first time between day 5-7. When the PTECs reached a confluence of 80%-90%, they were divided over new flasks and maintained as passage 1 (P1) culture. When the confluence was again 80%-90%, the PTECs were harvested and stored at -80 °C. At passage 3 the PTECs were routinely characterized with the following markers: Cytokeratin 8/18 (CK 8/18), epithelial membrane antigen (EMA), pan cytokeratin clone AE1/3 (CK AE1/3), C5α receptor (c5αR), and liver-type fatty acid-binding protein 1 (L-FABP). The α-Smooth Muscle Actin (αSMA) fibroblast marker was included as a negative control (Supplementary Figure S1). The PTECs were cultured in DMEM/F-12 GLUTMAX-1 containing 10% FBS, 5 μg/ml Insulin-Transferrin-Selenium (ITS), 100 U/ml penicillin and 100 μg/ml streptomycin, and 5ng/ml EGF (all from all from Sigma-Aldrich, St. Louis, MO) at 37°C and 5% CO2. Lentiviral transduction was performed on PTECs at P2. A schematic presentation of the workflow is shown at the top.

ShRNA constructs, lentiviral transductions and growth competition assayShort hairpin oligo’s obtained from Eurogentec (Eurogentec, Seraing, Belgium) (Supplementary table S1) were annealed and subcloned using BamH1 and EcoR1


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restriction sites of the pGreenpuro vector (Systems Biosciences, Mountain View, CA). The sequence of the inserts was verified by Sanger sequencing. Lentiviral particles were produced by calcium phosphate (CaPO4)-mediated co-transfection of HEK293T cells of these constructs with three packaging plasmids (pCMV-VSV-G, pRSV-REV and pMDL-gPRRE) using standard protocols. Lentiviral particles were collected 48 hours after transfection and passed through a 0.45µm Millex-HV filter (Millipore, Watertown, US). Transduction was performed with serial dilutions of the concentrated viral stocks in the presence of polybrene (final concentration 4 μg/ml; Sigma-Aldrich, St. Louis, MO) in 6-well plate with a cell confluence of about 50%. For the GFP growth competition assay the transduction was performed with a lower volume of virus particles to obtain a mixed culture that contained both GFP+ and GFP- PTECs. The percentage of GFP+ cells in the mixed culture was monitored by FACS Calibur flow cytometer (BD Biosciences, San Jose, CA) at each passage until Day 22. The data were normalized to the first measurement.

Βeta-galactosidase (β -gal) staining and ImmunohistochemistryThe PTECs were seeded into 6-well plate and stained using the Senescence β-galactosidase Staining Kit (Cell Signaling, Danvers, USA) following the instructions provided by the manufacturer at indicated time points. Images were captured by TissueFax (TissueGnostics, Vienna, Austria) equipped with Zeiss objective LD “Plan-Neofluar” 20x/0.4 Corr Dry, Ph2 objectives. For IHC, PTECs were seeded into 12-well plates covered with coverslips, followed by fixation with 4% formaldehyde histology fixative or 90% acetone in demi-water. After fixation the cells were washed with PBS, and endogenous peroxidases were blocked by treatment with a 0.09% H2O2. The primary antibodies used are αCKs 8/18 (BD biosciences 345779, clone CAM5.2, San Jose, CA, 1:100), αEMA (Dako M0613, clone E29, Cytomation, Denmark, 1:20), αCKs AE1.3 (Dako M3513, clone AE1/AE3, 1:100), αC5aR (Hycult, Uden, The Netherlands ,1:1000), αSMA (Dako M0851, clone 1A4, Cytomation, Denmark, 1:100), and αL-FABP (HyCult HK404, Uden, The Netherlands,1:100). After incubation with the primary antibody, secondary and tertiary antibody incubation steps were performed and binding visualization was done with AEC (3-amino-9-ethylcarbazole) or DAB (3,3’-Diaminobenzidine) using standard procedures. Images were captured by DM2000 LED microscope system (Leica, Wetzlar, Germany).

Histone isolation and Western blottingCells were lysed in Triton Extraction Buffer [TEB: PBS containing 0.5% Triton X 100 (v/v), 2mM phenylmethylsulfonyl fluoride (PMSF), and 0.02% (w/v) NaN3] at a volume of 1ml per 107 cells. Histones were isolated by acid extraction overnight, size-separated in 0.2N HCl using 15% SDS-PAGE and transferred to a PVDF membrane (Roche, Mannheim, Germany). The primary antibodies used for Western Blotting were as follows: rabbit anti-histone 3 (#9715, 1:1000; Cell Signaling), rabbit

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anti-tri-methyl-histone H3 (Lys36)(#4909, 1:1000; Cell Signaling, Danvers, USA). The secondary antibody used is Alexa Fluor® 488 Donkey Anti-Rabbit IgG (H+L) (A-21206, 1:10000; Life Technologies, NY, USA). Positive staining was visualized by incubation with Lumi-light Western Blotting substrate (Roche, Mannhein, Germany) and images were produced with the ChemiDOCTM MP imaging system using Image lab v4.1 software (BIO-RAD, Hercules, CA).

Gene expression microarraysExpression profiles were generated by hybridization to a custom-designed microarray (Agilent Technologies, Santa Clara, USA, Agilent ID 050524). Resulting raw data were analyzed with GeneSpring GX 13.1 software (Agilent Technologies, Santa Clara, USA) using quantile normalization without baseline transformation. All probes detecting protein-coding genes that are flagged as present by the feature extraction software in at least 12 of the 18 samples were selected (N=18513). Next, we filtered by expression, continuing with the probes with signals intensity in the 30th to 100th percentile in at least 12 out of 18 cases. This resulted in a list of 12414 probes. At this point a principle component analysis was carried out to validate that the the expression profile of the NT-PTECs did not differ significantly from the expression profile of the WT-PTECS. Next consistent probes were identified based on a <2 fold difference in a paired comparison between WT-PTECs and non-targeting (NT)-PTECs both harvested at day 6, or between WT-PTECs and NT-PTECs harvested at day 16, or between SETD2 knock down (KD) short hairpin sh1 and sh2 treated PTECs harvested at day 25. All probes retained in at least one of the three comparisons were included in the final analysis (N=12197 probes). Statistically significant changes in expression among WT/NT-PTECs-day 6, WT/NT-PTECSs-day 16, and SETD2 KD-PTECs-day 25 were determined by one-way ANOVA using Tukey’s honestly significant difference post hoc test. P value was adjusted by Bonferroni Family-wise error rate (FWER) multiple testing correction. Heatmap was generated with Genesis software v1.7.6 using Euclidean distance as the distance metric. GSEA using the hallmark gene sets (MSigDB, Collection H, n=50) was applied to identify the biological processes enriched in the experimental PTEC groups. The analysis was performed using gene-set permutations with an FDR of 1% and a P-value < 0.05.


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69

SuPPlEMEnTaRy fiGuRES anD TaBlES

Supplementary Figure 1. Immunostaining of primary PTECs at passage 3. PTECs stain positive for epithelial cell markers CK8/18, EMA, CK AE1/3, and C5α receptor, as well as for the proximal tubular marker L-FABP. Meanwhile, PTECs stain negative for fibroblast marker α-SMA (DAB staining, magnification 400 ×).

Supplementary Figure 2. Principal component analysis of the microarray data. To investigate the similarities and differences of the global expression features, we performed PCA by using Genespring software. The PCA plot indicates the first and second principal components of all 18 samples, including WT/NT PTECs at day 6 (blue) and day 16 (brown) and SETD2-KD PTECs at day 25 (red).

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Supplementary Figure3. Relative expression levels of TP53 and CDKN1A were determined by RT-qPCR. Levels of three different PTECs are grouped as untreated and NT-PTECs on day 6 (WT-day 6), untreated and NT-PTECs on day 16 (WT-day 16), and SETD2 KD-PTECs on day 25 (KD-day 25). Results are presented as 2−∆Ct of three independent experiment using RP II as housekeeping gene. Values shown are mean ± SD. Significant differences between WT-day 6 and KD-day 25 as compared with WT-day 16 are determined by one-way ANOVA with Dunnett multiple testing correction that both WT-Day6 and KD-day25 are compared with WT-day 16. *P< .05.

Supplementary Figure 4. Expression levels of CDKN2A and E2F1 in HEK293T cells after SETD2 depletion. HEK293T cells were transduced with NT shRNA or with SETD2 targeting shRNAs (sh1 and sh2). Total RNA was isolated from sorted cells, and the mRNA level of CDKN2A and E2F1 was determined by RT-qPCR. Results are presented as 2−∆Ct values of two independent experiments using HPRT as endogenous control (mean ± SD).

Supplementary Figure 5. PTECs transduced at day 20 with SETD2-sh1 virus were stained with SA-β gal at day 26. Images captured in bright field and GFP field are from the same region. The experiments were performed with PTECs of all three donors, and one representative example is shown. Scale bar indicates 50 μm.


3

71

Supplementary Table S1. Overview of all oligo- and primer-sequences.

Name Sequence (5´→ 3´)

shR

NA

s

sh1 Forward GATCCCAGGGAGAACAGGCGTAATAATTCAA GAGATTATTACGCCTGTTCTCCCTGTTTTTG

Reverse AATTCAAAAACAGGGAGAACAGGCGTAATAA TCTCTTGAATTATTACGCCTGTTCTCCCTGG

sh2 Forward GATCCAGTAGTGCTTCCCGTTATAAATTCAAG AGATTTATAACGGGAAGCACTACTTTTTTG

Reverse AATTCAAAAAAGTAGTGCTTCCCGTTATAAAT CTCTTGAATTTATAACGGGAAGCACTACTG

q RT

PC

R p

rim

ers

HPRT Forward GGCAGTATAATCCAAAGATGGTCAAReverse GTCTGGCTTATATCCAACACTTCG

RP II Forward CGTACGCACCACGTCCAATReverse CAAGAGAGCCAAGTGTCGGTAA

SETD2 Forward TGCCAAAGACCTTCCTTCGReverse CGTGCATACTCCTTCACTC

CDKN2A Forward CCCAACGCACCGAATAGTTAReverse ACCAGCGTGTCCAGGAAG

E2F1 Forward AAGTCCAAGAACCACATCCAGReverse TGCGTAGTACAGATATTCATCAGG

CDKN1A Forward TGTCACTGTCTTGTACCCTTGReverse GGCGTTTGGAGTGGTAGAA

TP53 Forward CCTCAGCATCTTATCCGAGTGReverse ACATGTAGTTGTAGTGGATGGTG

NT-shRNA construct is included in the pGreenPuro™ shRNA Expression Lentivector kit.sh, short hairpin; SETD2, SET-domain containing 2; RP II, RNA polymerase II; E2F1, E2F Transcription Factor 1; CDKN2A, cyclin-dependent kinase inhibitor 2A; CDKN1A, cyclin-dependent kinase inhibitor 1A.

Chapter 3

72

Supp

lem

enta

ry T

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Gen

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gnifi

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ly ch

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t day

6, S

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A_2

3_P4

1380

ABC

E10.

0124

1.10

up1.

61up

-1.4

7do

wn

chr4

:146

0496

42-1

4604

9701

A_2

3_P5

9528

ACN

90.

0309

-1.0

6do

wn

1.63

up-1

.73

dow

nch

r7:9

6810

787-

9681

0846

A_2

3_P2

8279

ACTR

1B0.

0078

1.78

up1.

44up

1.24

upch

r2:9

8273

139-

9827

3080

A_2

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60up

1.21

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r9:1

8910

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r10:

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7-11

1894

886

A_2

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9810

AGPA

T90.

0027

1.26

up3.

30up

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2do

wn

chr4

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2658

4-84

5266

43A

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P158

76A

LPK

20.

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-1.8

7do

wn

-5.7

0do

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3.06

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r18:

5614

8827

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4876

8A

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0.03

861.

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1do

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1017

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2.41

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3do

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1.19

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r1:2

0210

2732

-202

1026

73A

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P391

607

ARR

DC1

0.03

71-1

.87

dow

n-1

.46

dow

n-1

.28

dow

nch

r9:1

4050

9391

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5094

50A

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P278

299

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130.

0462

1.94

up1.

46up

1.33

upch

r10:

5681

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dow

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r11:

6215

9917

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5997

6A

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P321

4105

ATF3

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5-1

.02

dow

n-6

.39

dow

n6.

26up

chr1

:212

7885

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1278

8561

A_2

3_P3

4915

ATF3

0.02

331.

61up

-5.2

3do

wn

8.41

upch

r1:2

1279

3856

-212

7939

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P215

111

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9do

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1.75

up-5

.59

dow

nch

r7:1

3839

1206

-138

3911

47A

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P129

417

BMP1

0.00

172.

22up

1.07

up2.

07up

chr8

:220

5431

4-22

0547

72A

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14BR

AP

0.04

85-1

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.37

dow

n-1

.14

dow

nch

r12:

1120

9655

1-11

2093

412

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3_P1

1492

9BR

P44

0.03

97-1

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dow

n1.

74up

-2.0

5do

wn

chr1

:167

8892

88-1

6788

9229


3

73

Supp

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Gen

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6, S

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A_3

3_P3

2380

52C1

2orf

240.

0161

1.79

up2.

31up

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9do

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chr1

2:11

0927

982-

1109

2804

1A

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P106

544

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01.

02up

1.32

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dow

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r16:

8100

9944

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0988

5A

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P747

78C1

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40.

0209

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8do

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9do

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chr1

:150

2532

51-1

5025

3310

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7043

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6505

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9894

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661.

92up

1.29

up1.

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chr1

5:44

7076

27-4

4707

686

A_3

3_P3

3137

96C

CDC3

40.

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1.94

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49up

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8do

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chr1

1:27

3601

76-2

7360

117

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CCD

C99

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2.41

up-1

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r5:1

6903

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6903

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5do

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-1.3

6do

wn

-1.2

9do

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chr6

:999

9141

1-99

9913

52A

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P668

91CD

C42E

P40.

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1.61

up1.

80up

-1.1

2do

wn

chr1

7:71

2802

69-7

1280

210

A_2

4_P8

1841

CDK

N1B

0.01

92.

18up

2.00

up1.

09up

chr1

2:12

8749

40-1

2874

999

A_3

3_P3

4105

07CE

P170

0.03

371.

46up

1.05

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39up

chr1

:243

2881

71-2

4328

8112

A_2

3_P3

4403

7CH

FR0.

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-1.6

2do

wn

-1.3

6do

wn

-1.1

9do

wn

chr1

2:13

3417

771-

1334

1771

2A

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P112

279

CHTF

80.

0099

1.72

up2.

22up

-1.3

0do

wn

chr1

6:69

1526

18-6

9152

559

A_2

3_P2

5674

CKB

0.03

64-2

.96

dow

n-2

.43

dow

n-1

.22

dow

nch

r14:

1039

8652

5-10

3986

466

A_2

3_P1

3549

9CL

IC4

0.00

292.

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1.51

up1.

32up

chr1

:251

7005

3-25

1701

12A

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P212

608

CLST

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0.00

21-2

.02

dow

n-3

.78

dow

n1.

87up

chr3

:140

2863

79-1

4028

6438

A_3

2_P9

2399

CO

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0.00

171.

14up

1.56

up-1

.36

dow

nch

r16:

6936

2884

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6282

5A

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P328

5077

CO

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29-1

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dow

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22up

-1.5

4do

wn

chr4

:474

5513

6-47

4550

77

Supp

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enta

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(con

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Chapter 3

74

Supp

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8do

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-1.4

7do

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-1.0

1do

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chr3

:128

9940

46-1

2899

4105

A_2

3_P2

5944

2CP

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0391

2.98

up-1

.15

dow

n3.

44up

chr4

:166

4192

32-1

6641

9291

A_2

3_P8

1880

CTD

SP2

0.04

81.

96up

1.47

up1.

33up

chr1

2:58

2138

70-5

8213

811

A_2

4_P3

7655

6CY

CS0.

0369

1.27

up1.

84up

-1.4

5do

wn

chr7

:251

5829

6-25

1582

64A

_23_

P329

38D

DX

100.

0025

1.50

up1.

56up

-1.0

4do

wn

chr1

1:10

8811

424-

1088

1148

3A

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P162

982

DH

RS4

0.03

651.

43up

1.76

up-1

.23

dow

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r14:

2442

9166

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3498

3A

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P698

26D

HX

150.

0482

1.22

up1.

76up

-1.4

5do

wn

chr4

:245

3134

4-24

5312

85A

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9090

DIX

DC1

0.00

012.

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1.03

up2.

04up

chr1

1:11

1893

216-

1118

9327

5A

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P151

337

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U1

0.00

751.

82up

2.84

up-1

.56

dow

nch

r13:

5067

9272

-506

7933

1A

_23_

P192

26D

SE0.

0349

1.22

up-1

.15

dow

n1.

40up

chr6

:116

7584

40-1

1675

8499

A_2

4_P1

8249

4D

USP

100.

0459

-1.9

0do

wn

-2.5

8do

wn

1.36

upch

r1:2

2187

5832

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8757

73A

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P170

518

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731.

32up

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0do

wn

1.45

upch

r18:

4657

0276

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7021

7A

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P124

672

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45-1

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dow

n1.

33up

-1.4

3do

wn

chr1

2:12

0935

925-

1209

3598

4A

_23_

P150

45E4

F10.

0362

-1.4

0do

wn

-1.3

7do

wn

-1.0

2do

wn

chr1

6:22

8544

0-22

8549

9A

_23_

P156

842

EEF1

E10.

0002

1.16

up1.

73up

-1.5

0do

wn

chr6

:809

7591

-809

7532

A_2

4_P2

1631

3ER

GIC

30.

0028

2.12

up1.

89up

1.12

upch

r20:

3413

6265

-341

3632

4A

_23_

P123

905

EXO

SC3

0.04

311.

28up

1.78

up-1

.39

dow

nch

r9:3

7780

699-

3778

0640

A_2

3_P4

9448

FA2H

0.00

11-7

.34

dow

n1.

05up

-7.6

8do

wn

chr1

6:74

7470

23-7

4746

964

A_2

4_P7

9712

FAM

36A

0.00

04-1

.54

dow

n1.

33up

-2.0

5do

wn

chr1

:245

0076

01-2

4500

7660

A_2

3_P1

3663

FAM

60A

0.03

-1.5

7do

wn

-1.7

6do

wn

1.12

upch

r12:

3143

5673

-314

3561

4A

_23_

P337

20FA

RS2

0.00

21.

74up

1.98

up-1

.13

dow

nch

r6:5

4312

90-5

4313

49

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)


3

75

Supp

lem

enta

ry T

able

S2.

Gen

es th

at si

gnifi

cant

ly ch

ange

d in

the o

ne-w

ay A

NO

VA te

st o

f 3 g

roup

s of s

ampl

es: S

ETD

2-W

T PT

ECs a

t day

6, S

ETD

2-W

T PT

ECs a

t day

16,

and

SET

D2-

KD

PTE

Cs a

t day

25.

Prob

e N

ame

Gen

e Sy

mbo

lp

(Cor

r)FC

K

D v

s SR

egul

atio

n

KD

vs S

FC

NS

vs S

Reg

ulat

ion

NS

vs S

FC KD

vs N

SR

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n K

D v

s NS

Gen

omic

Coo

rdin

ates

A_2

3_P1

4006

9FB

XL3

0.03

591.

75up

1.71

up1.

02up

chr1

3:77

5799

39-7

7579

880

A_3

3_P3

2555

09FC

HO

10.

0277

-2.7

7do

wn

-1.3

1do

wn

-2.1

2do

wn

chr1

9:17

8992

95-1

7899

354

A_2

3_P3

9718

FEZ2

0.00

421.

63up

1.38

up1.

18up

chr2

:367

7976

3-36

7797

04A

_23_

P215

341

FKBP

140.

011

-1.0

3do

wn

-1.5

0do

wn

1.46

upch

r7:3

0053

880-

3005

3821

A_2

4_P3

3413

0FN

10.

0003

1.32

up-3

.72

dow

n4.

92up

chr2

:216

2888

95-2

1628

8217

A_3

3_P3

3946

15FU

ND

C20.

0002

1.41

up1.

91up

-1.3

6do

wn

chrX

:154

2617

68-1

5426

1827

A_3

3_P3

3312

42G

3BP1

0.04

71.

80up

1.57

up1.

15up

chr5

:151

1848

22-1

5118

4881

A_3

3_P3

2277

16G

ATSL

30.

0084

-1.1

9do

wn

-1.6

7do

wn

1.40

upch

r22:

3068

1194

-306

8113

5A

_23_

P117

933

GCS

H0.

0124

1.08

up1.

47up

-1.3

6do

wn

chr1

6:81

1160

42-8

1115

983

A_2

3_P2

0918

3G

LT25

D1

0.00

942.

25up

1.61

up1.

40up

chr1

9:17

6932

37-1

7693

296

A_3

2_P9

7169

GPC

60.

0092

3.29

up1.

43up

2.30

upch

r13:

9505

9334

-950

5939

3A

_33_

P341

4422

GPH

N0.

0213

1.65

up1.

74up

-1.0

5do

wn

chr1

4:67

6483

73-6

7648

432

A_2

3_P2

5525

GTF

3A0.

0064

1.37

up1.

76up

-1.2

8do

wn

chr1

3:28

0096

46-2

8009

705

A_3

2_P1

5338

8G

ULP

10.

0002

-3.0

5do

wn

-2.5

1do

wn

-1.2

1do

wn

chr2

:189

2485

25-1

8924

8584

A_2

3_P3

3948

0H

AT1

0.02

841.

68up

1.97

up-1

.17

dow

nch

r2:1

7284

8187

-172

8482

46A

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P155

765

HM

GB2

0.01

43.

82up

5.72

up-1

.50

dow

nch

r4:1

7425

3072

-174

2530

13A

_32_

P222

383

HM

GN

20.

0099

2.30

up4.

03up

-1.7

5do

wn

chr1

:268

0219

2-26

8022

51A

_32_

P414

87H

MG

N2

0.00

572.

47up

4.39

up-1

.78

dow

nch

r1:2

6802

354-

2680

2413

A_3

3_P3

2490

72H

OG

A1

0.04

94-2

.08

dow

n-1

.22

dow

n-1

.72

dow

nch

r10:

9936

1936

-993

6199

5A

_24_

P105

191

HS6

ST2

02.

79up

2.92

up-1

.05

dow

nch

rX:1

3176

0147

-131

7600

88A

_32_

P469

81H

SBP1

L10.

0159

-1.7

1do

wn

-1.1

4do

wn

-1.5

0do

wn

chr1

8:77

7266

42-7

7728

111

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 3

76

Supp

lem

enta

ry T

able

S2.

Gen

es th

at si

gnifi

cant

ly ch

ange

d in

the o

ne-w

ay A

NO

VA te

st o

f 3 g

roup

s of s

ampl

es: S

ETD

2-W

T PT

ECs a

t day

6, S

ETD

2-W

T PT

ECs a

t day

16,

and

SET

D2-

KD

PTE

Cs a

t day

25.

Prob

e N

ame

Gen

e Sy

mbo

lp

(Cor

r)FC

K

D v

s SR

egul

atio

n

KD

vs S

FC

NS

vs S

Reg

ulat

ion

NS

vs S

FC KD

vs N

SR

egul

atio

n K

D v

s NS

Gen

omic

Coo

rdin

ates

A_3

3_P3

3350

42H

SD17

B12

0.01

69-2

.17

dow

n-1

.98

dow

n-1

.10

dow

nch

r11:

4377

5611

-437

7567

0A

_23_

P162

579

HSP

B80.

0391

-1.3

6do

wn

-2.0

2do

wn

1.49

upch

r12:

1196

1746

4-11

9624

868

A_2

3_P4

1803

1IF

FO2

0.02

173.

96up

-1.0

2do

wn

4.05

upch

r1:1

9230

868-

1923

0809

A_2

3_P7

6078

IL23

A0.

0054

-6.1

1do

wn

-2.0

4do

wn

-3.0

0do

wn

chr1

2:56

7340

83-5

6734

142

A_2

3_P1

5146

IL32

0.03

03-2

.20

dow

n-2

.27

dow

n1.

03up

chr1

6:31

1930

8-31

1936

7A

_33_

P340

5424

IL4I

10.

0087

-3.5

4do

wn

-1.7

3do

wn

-2.0

5do

wn

chr1

9:50

3929

76-5

0392

917

A_3

3_P3

2904

03IM

PA2

0.00

412.

57up

4.30

up-1

.67

dow

nch

r18:

1199

9126

-119

9918

5A

_33_

P323

7096

INPP

5F0.

0338

1.41

up1.

63up

-1.1

5do

wn

chr1

0:12

1551

626-

1215

5168

5A

_23_

P205

007

IPO

50.

0092

1.67

up1.

69up

-1.0

1do

wn

chr1

3:98

6762

14-9

8676

273

A_2

3_P2

1837

5IT

GA

E0.

0113

1.62

up2.

08up

-1.2

8do

wn

chr1

7:36

2366

8-36

2360

9A

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P659

18IT

PKA

0.01

793.

23up

1.72

up1.

88up

chr1

5:41

7956

40-4

1795

699

A_2

4_P1

5649

0KC

NM

A1

0.01

492.

55up

2.17

up1.

18up

chr1

0:78

6448

26-7

8644

767

A_3

2_P7

0724

KD

M5B

0.02

81.

35up

-1.3

6do

wn

1.84

upch

r1:2

0269

6804

-202

6967

45A

_23_

P117

852

KIA

A01

010.

0231

8.27

up22

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up-2

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dow

nch

r15:

6465

7906

-646

5784

7A

_23_

P106

505

LCM

T20.

0116

1.26

up1.

64up

-1.3

0do

wn

chr1

5:43

6203

14-4

3620

255

A_2

3_P5

3476

LDH

B0.

0261

1.49

up1.

82up

-1.2

2do

wn

chr1

2:21

7885

08-2

1788

449

A_2

4_P9

6474

LDO

C1L

0.01

511.

55up

1.36

up1.

14up

chr2

2:44

8887

91-4

4888

732

A_3

3_P3

4087

62LM

NA

0.04

55-1

.08

dow

n1.

34up

-1.4

5do

wn

chr1

:156

1060

99-1

5610

6158

A_2

3_P3

0278

7LO

C375

295

0.00

051.

53up

2.61

up-1

.71

dow

nch

r2:1

7749

4727

-177

4946

68A

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P180

971

LOC7

2832

30.

0173

-2.1

1do

wn

-2.2

8do

wn

1.08

upch

r2:2

4303

7063

-243

0371

22A

_23_

P317

184

LRRF

IP2

0.02

8-1

.87

dow

n-1

.37

dow

n-1

.36

dow

nch

r3:3

7100

322-

3709

6639

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)


3

77

Supp

lem

enta

ry T

able

S2.

Gen

es th

at si

gnifi

cant

ly ch

ange

d in

the o

ne-w

ay A

NO

VA te

st o

f 3 g

roup

s of s

ampl

es: S

ETD

2-W

T PT

ECs a

t day

6, S

ETD

2-W

T PT

ECs a

t day

16,

and

SET

D2-

KD

PTE

Cs a

t day

25.

Prob

e N

ame

Gen

e Sy

mbo

lp

(Cor

r)FC

K

D v

s SR

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n

KD

vs S

FC

NS

vs S

Reg

ulat

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NS

vs S

FC KD

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n K

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Gen

omic

Coo

rdin

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A_2

3_P2

1180

6LR

RFIP

20.

0034

-1.7

3do

wn

-1.5

3do

wn

-1.1

3do

wn

chr3

:370

9485

5-37

0947

96A

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P111

961

MA

K16

0.01

461.

77up

2.05

up-1

.16

dow

nch

r8:3

3358

375-

3335

8434

A_2

4_P5

6317

MBN

L20.

0007

2.10

up1.

38up

1.52

upch

r13:

9804

6226

-980

4628

5A

_23_

P202

594

MCM

BP0.

0458

1.47

up2.

27up

-1.5

4do

wn

chr1

0:12

1589

539-

1215

8948

0A

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P596

02M

IOS

0.01

681.

21up

1.62

up-1

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dow

nch

r7:7

6359

96-7

6360

55A

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P690

58M

LH1

0.01

481.

64up

1.68

up-1

.02

dow

nch

r3:3

7092

163-

3709

2222

A_3

3_P3

4197

20M

LH1

0.03

741.

66up

1.71

up-1

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dow

nch

r3:3

7092

085-

3709

2144

A_2

3_P6

1050

MLK

L0.

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1.37

up2.

24up

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3do

wn

chr1

6:74

7060

38-7

4705

979

A_2

3_P1

1224

MM

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0.02

971.

43up

1.69

up-1

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dow

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rX:1

3504

4415

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0443

56A

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938

MO

RN4

0.04

27-2

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dow

n-2

.21

dow

n-1

.14

dow

nch

r10:

9937

9274

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7921

5A

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P474

97M

RPL1

60.

0155

1.36

up1.

81up

-1.3

3do

wn

chr1

1:59

5737

99-5

9573

740

A_2

3_P1

3784

8M

RPL2

40.

004

1.25

up1.

46up

-1.1

7do

wn

chr1

:156

7075

13-1

5670

7454

A_2

3_P4

9768

MRP

L27

0.01

651.

20up

1.44

up-1

.20

dow

nch

r17:

4844

5492

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4543

3A

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P209

989

MRP

L46

0.00

691.

41up

1.74

up-1

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dow

nch

r15:

8901

0441

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1038

2A

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P991

38M

RPL5

10.

0013

1.18

up1.

54up

-1.3

0do

wn

chr1

2:66

0159

5-66

0153

6A

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P169

050

MRP

S28

0.00

01-1

.04

dow

n1.

41up

-1.4

6do

wn

chr8

:808

3131

5-80

8312

56A

_23_

P157

352

MRP

S33

0.01

7-1

.12

dow

n1.

18up

-1.3

3do

wn

chr7

:140

7102

58-1

4070

6316

A_3

3_P3

2341

68M

TDH

0.04

841.

80up

1.84

up-1

.02

dow

nch

r8:9

8738

442-

9873

8501

A_2

4_P5

5465

MTP

N0.

0079

1.74

up1.

62up

1.07

upch

r7:1

3561

2318

-135

6122

59A

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P335

3692

MYH

90.

0072

-1.5

5do

wn

-2.6

8do

wn

1.73

upch

r22:

3672

2673

-367

2261

4A

_33_

P324

8794

NA

B20.

009

1.82

up2.

09up

-1.1

5do

wn

chr1

2:57

4891

62-5

7489

221

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 3

78

Supp

lem

enta

ry T

able

S2.

Gen

es th

at si

gnifi

cant

ly ch

ange

d in

the o

ne-w

ay A

NO

VA te

st o

f 3 g

roup

s of s

ampl

es: S

ETD

2-W

T PT

ECs a

t day

6, S

ETD

2-W

T PT

ECs a

t day

16,

and

SET

D2-

KD

PTE

Cs a

t day

25.

Prob

e N

ame

Gen

e Sy

mbo

lp

(Cor

r)FC

K

D v

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n

KD

vs S

FC

NS

vs S

Reg

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NS

vs S

FC KD

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n K

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Gen

omic

Coo

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A_2

4_P2

7812

6N

BN0.

0143

1.96

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31up

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8do

wn

chr8

:909

4644

3-90

9463

84A

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P330

3385

NCA

PD2

0.04

361.

91up

2.27

up-1

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dow

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r12:

6641

042-

6641

101

A_2

3_P5

551

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0.00

261.

19up

1.67

up-1

.40

dow

nch

r2:2

3231

9780

-232

3197

21A

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P149

470

ND

UFS

20.

0203

1.35

up1.

89up

-1.4

0do

wn

chr1

:161

1801

37-1

6118

0397

A_3

3_P3

3910

05N

EDD

4L0

-2.4

7do

wn

-2.3

3do

wn

-1.0

6do

wn

chr1

8:56

0167

84-5

6016

843

A_2

3_P1

4729

6N

FU1

0.01

92-1

.21

dow

n1.

21up

-1.4

6do

wn

chr2

:696

2751

4-69

6233

82A

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P550

73N

OL1

10.

002

1.69

up2.

22up

-1.3

1do

wn

chr1

7:65

7356

57-6

5735

716

A_2

4_P1

0140

2N

OP5

60.

0002

1.26

up1.

64up

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1do

wn

chr2

0:26

3888

0-26

3893

9A

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P885

89N

R2F2

0.00

022.

22up

2.22

up1.

00up

chr1

5:96

8819

57-9

6882

016

A_2

3_P6

3190

NRA

S0.

0158

1.56

up2.

09up

-1.3

4do

wn

chr1

:115

2499

92-1

1524

9933

A_3

3_P3

3015

14N

RCA

M0

-3.9

8do

wn

-2.1

9do

wn

-1.8

1do

wn

chr7

:107

7999

85-1

0779

9926

A_2

4_P9

2805

2N

RP1

0.00

013.

07up

2.03

up1.

51up

chr1

0:33

4669

95-3

3466

936

A_2

4_P3

5471

5N

T5E

0.01

641.

80up

2.80

up-1

.56

dow

nch

r6:8

6204

891-

8620

4950

A_2

4_P2

2953

1O

BFC2

A0.

0001

-1.8

5do

wn

-2.3

8do

wn

1.29

upch

r2:1

9254

3838

-192

5467

14A

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P129

829

ORM

DL3

0.01

24-1

.21

dow

n-1

.67

dow

n1.

38up

chr1

7:38

0775

58-3

8077

499

A_2

4_P2

0042

7PA

ICS

0.00

061.

63up

2.11

up-1

.29

dow

nch

r4:5

7327

235-

5732

7294

A_3

3_P3

3687

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0.00

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66up

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0do

wn

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5:69

6998

95-6

9699

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34-1

.64

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9736

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3575

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2.16

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wn

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0:50

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0099

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2.15

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1.79

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41

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tinue

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and

SET

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KD

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25.

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4681

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849

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4077

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3636

0-44

4364

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dow

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1:73

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58-7

3942

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0172

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0.00

321.

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1.62

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dow

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0.03

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28up

1.79

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dow

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6866

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1.72

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29up

1.33

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7478

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A_2

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6291

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0.04

82-1

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dow

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37up

chr1

2:10

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721

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.31

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9:43

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3258

490

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6380

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1.48

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chr9

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1.81

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2848

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636

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2.26

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6do

wn

chr5

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8556

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5985

5703

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8579

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62up

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chr4

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2-37

9624

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88up

1.15

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0.04

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9:42

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2460

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8632

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2799

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ly ch

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and

SET

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KD

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4813

2927

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3248

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9178

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0-2

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dow

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r12:

4813

8315

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3825

6A

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316

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r12:

4812

8514

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2845

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2.49

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4548

9768

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8982

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248

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dow

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1:35

8890

68-3

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0.03

611.

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6311

5775

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6:11

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1.45

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6869

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1.65

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7918

8826

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8876

7A

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.79

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8:13

7426

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3746

230

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8841

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8890

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1.45

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chr7

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6682

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6623

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1.43

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7420

4009

A_3

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9350

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8525

6-37

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5:90

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32-9

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591

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KD

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3249

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2485

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0:48

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7:18

9653

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441

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2849

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Supp

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7:49

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438

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PBRM1 loSS in PRiMaRy TuBulaR EPiThElial cEllS lEaDS To aBERRanT ExPRESSion of iMMunE RESPonSE GEnES

Jun Li1, Joost Kluiver2, Jan Osinga1, Helga Westers1, Anke van den Berg2, Rolf H. Sijmons1 and Klaas Kok1

1Department of Genetics, and 2Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, PO box 30.001, 9700 RB Groningen,

the Netherlands

Manuscript in preparation

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aBSTRacTClear cell Renal Cell Carcinoma (ccRCC) is characterized by loss of the short arm of chromosome 3 in more than 90% of the cases. The Polybromo-1 (PBRM1) gene maps to this region and is the second most frequently mutated gene in ccRCC. PBRM1 is a subunit of the PBAF complex, a subgroup of the SWI/SNF complexes that modify local accessibility of chromatin, and in that way contributes to the regulation of gene expression. With a mutation frequency of 30%, of which about 80% are presumably inactivating, it is clear that inactivation of PBRM1 is a major contributor to the development of ccRCC. However it is unclear how this event contributes to the early steps of ccRCC development. To study the role of PBRM1 in ccRCC initiation, we performed lentiviral-based shRNA knockdown of PBRM1 in kidney primary tubular epithelial cells (PTECs), the presumed normal counterparts of ccRCC. Interestingly, knockdown of PBRM1 did not give the PTECS an clear growth advantage, nor did it extend the proliferative capacity as compared to control PTECs. At the gene expression level, both the gene set enrichment analyses and the Gene Ontology analysis pointed towards a significant effect of PBRM1-KD on the expression on immune responsive genes. Previous studies have already shown aberrant expression of IFN responsive genes in malignant cells with defective SWI/SNF complexes, but mostly without specifying the specific subgroup of these complexes. Based on our data we suggest that functional loss of the wild type PBAF complex could be one of the events triggering the development of ccRCC.

PBRM1-loss in PTECs lEads To ExPREssion ChangEs in iFns REsPonsE gEnEs

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inTRoDucTionClear cell Renal Cell Carcinoma (ccRCC) is characterized by copy number loss of a large part of the short arm of chromosome 3 (Kok et al., 1997; van den Berg et al., 1997), which occurs in more than 90% of the cases (Hakimi et al., 2013). This frequent allelic loss indicates the location of one or more tumor suppressor genes (TSGs) at this chromosome arm. Any of these genes might be bi-allelicly inactivated due to a mutation in the remaining allele following the model proposed by Knudson (Knudson, 1971).

Linkage studies of von Hippel-Lindau cancer syndrome families paved the way for the identification of the Von Hippel–Lindau (VHL) gene, the first identified TSG located at 3p (Latif et al., 1993). In recent years, a series of next generation sequencing studies revealed three additional candidate tumor suppressor genes on 3p, i.e. PBRM1, SETD2 and BAP1 (Duns et al., 2010; Duns et al., 2012; Cancer Genome Atlas Research 2013; Sato et al., 2013). In ccRCC, PBRM1 is the second most frequently mutated gene after VHL. Importantly, more than 80% of the nonsynonymous mutations in PBRM1 are inactivating mutations (COSMIC database). This high mutation frequency indicates that PBRM1 inactivation is a crucial event in the development of ccRCC tumors.

PBRM1 encodes the BAF180 protein, a subunit of a specific group of SWI/SNF complexes (Xue et al., 2000; Roberts and Orkin, 2004). In general, SWI/SNF complexes are recruited to chromatin and function to mediate ATP-dependent chromatin remodeling processes. The human SWI/SNF complex consists of multiple subunits including one of two known ATPases (Roberts and Orkin, 2004; Kadoch and Crabtree, 2015). SWI/SNF complexes are divided into two different subtypes known as BAF (BRM-associated factors) and PBAF (polybromo-associated BAF) (Nie et al., 2003). The BCL11, BCL7, CRD9 and ARID1 subunits are specific for BAF complexes, whereas PBRM1 (also known as BAF180), BRD7, and ARID2 (also known as BAF200) are specific for PBAF complexes (Xue et al., 2000; Hohmann and Vakoc, 2014). BAF and PBAF target different genomic segments (Angus-Hill et al., 2001; Lemon et al., 2001). By virtue of its bromodomains, PBRM1 functions as a reader of acetylated Lysines at H3K4 and H3K9 and enables targeting of PBAF to these regions (Kupitz et al., 2008; Thompson, 2009). The specific epigenetic locus recognition mechanism of the BAF complex is still not clear (Kadoch and Crabtree, 2015).The inactivation of one or more subsets of the SWI/SNF complexes can promote the development of cancer (reviewed by Reisman et al., 2009). PBRM1 inactivation will result in loss of the PBAF complex, and this will lead to loss of its tumor suppressive function. Missense mutations in PBRM1 seem to occur more frequently in the 4th bromodomain than in the other functional domains. Since the bromodomains are crucial for the interaction of the PBAF complex with the chromatin, these missense mutations are potentially pathogenic.

The consequence of PBRM1 loss in the ccRCC precursor cells, i.e. primary tubular epithelial cells of the kidney (PTECs) (Thoenes et al., 1986), is still unknown. To evaluate this, we generated PTECs stably transduced with viral short hairpin RNA overexpressing

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constructs. We monitored changes in their phenotype over a period of three to four weeks and determined changes in gene expression profiles at day 6 after transduction.

MaTERial anD METhoDSPTECs isolation and cell cultureKidney primary tubular epithelial cells (PTECs) were isolated from healthy renal cortex segments as previously described (Li et al., 2016). Briefly, the tissue block was cut into small cubes and seed into T25 FCS-pre-coated and Collagen-1-coated T25 flasks (BD Biosciences, San Jose, CA, USA, BD BioCoat 25cm2, Cat#356484). The isolated cells were cultured in DMEM/F-12 GLUTMAX-1 supplemented with 1% ITS (5μg/ml insulin, 5μg/ml transferrin, 5ng/ml selenium ITS), 0.1% EGF (5ng/ml) and 1% P/S (100U/ml penicillin and 100μg/ml streptomycin), at 37°C, 5% CO2. When the cells reached 80%-90% confluence (day 5 to 7), they were split and frozen for use in the experiment as passage 1. At passage 3 the primary PTECs were characterized with the following markers: Cytokeratin 8 (CK8.18), epithelial membrane antigen (EMA), pan cytokeratin (CK AE1.3), C5α receptor (c5αR), and liver-type fatty acid-binding protein 1 (L-FABP). During the experiment the PTECs were maintained in DMEM/F-12 GLUTMAX-1 containing 10% FBS, 1% ITS, 0.1% EGF and 1% P/S. All the reagents used for cell culturing are from Sigma-Aldrich (St. Louis, MO, USA). CcRCC cell lines RCC1, RCC4, RCC5, and RCC6 are a gift from Dr. C.D. Gerharz (Institute of Pathology, University Hospital, Düsseldorf, Germany), who established these cell lines. CcRCC cell lines RCC-ER, RCC-MF, RCC-JF, RCC-HS, RCC-GW, and RCC-FW were purchased from Cell Line Services, Eppenheim, Germany. The ccRCC cell lines were maintained in RPMI 1640 supplemented with 10% FBS, 1% ITS, and 1% P/S. All the cells were maintained at 37°C in humidified air containing 5% CO2.

Construction of shRNA vectors and generation of lentiviral particles Oligonucleotides (Eurogentec, Liège, Belgium) were annealed and subcloned into the pGreenpuro shRNA cloning and expression lentivector (Systems Biosciences, Mountain View, CA, USA). The non-targeting shRNA lentiviral vector was obtained from Systems Biosciences (Mountain View, CA). The insert sequences were confirmed by Sanger sequencing (sh-PB1: 5’-GATCCAGCTAAATTTGCCGAGTTATTCAAGAGATAAC TCGGCAAATTTAGCTTTTTTG-3’; sh-PB2: 5’-GATCCGTTAGGAGTTGTCGGAA TATTCAAGAGATATTCCGACAACTCCTAACTTTTTG-3’). Lentiviral particles were produced by co-transfection of 7x105 HEK293T cells in a 6-well plate by the calcium phosphate (CaPO4)-mediated method, with 2µg pGreenPuro shRNA expression lentivector (sh-PB1, sh-PB2 or non-targeting (NT)) in combination with a plasmid mix containing 1µg pCMV-VSV-G, 1µg pRSV.REV, and 1µg pMDL-gPRRE. Lentiviral particles were harvested 48 hours after transfection and passed through a 0.45µm pore PVDF Millex-HV filter (Millipore, Billerica, MA, USA).


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Cell transduction for expression studies and growth competition assayPTECs were transduced with a serial dilution of viral stocks in the presence of 4μg/ml polybrene (Sigma-Aldrich, St. Louis, MO, USA). For expression studies, PTECs were transduced at high multiplicities of infection (MOI) resulting in more than 85% GFP positive (GFP+) cells. For the GFP growth competition assay, PTECs were transduced at low MOI aiming at approximately 20% GFP+ cells. The percentage of GFP+ cells in the mixed cultures was determined for 3 weeks by a FACS Calibur flow cytometer (BD Biosciences). FACS results were analyzed using Kaluza software (v1.3, Beckman Coulter, Brea, CA, USA). The relative change in the fraction of GFP+ cells in the cultures was calibrated to the percentage of GFP+ cells at the first measurement, carried out on day 2.

Senescence-associated beta-galactosidase (β-gal) stainingTransduced and untransduced PTECs were cultured for 20 days (passage 5) and subjected to β-gal staining by using the senescence β-galactosidase (β-gal) Staining Kit (Cell Signaling, Danvers, USA) according to the manufacturer’s instructions. Images were captured by a TissueFax (TissueGnostics, Vienna, Austria) equipped with a Zeiss objective LD Plan-Neofluar 20x/0.4 Corr Dry, Ph2 objectives.

RNA extraction and RT-qPCRTotal RNA was extracted using the GeneJET RNA purification kit (Fermentas, St. Leon-Rot, Germany) according to the manufacturer’s instructions. RNA integrity and quantity were measured by using the HT RNA LabChip GX/GXII kit (Caliper GX, Life Sciences, Hopkinton, MA). Total RNA (1µg) was used for reverse transcription using the RevertAidTM H Minus First Strand cDNA Synthesis Kit with random primers (Fermentas, St. Leon-Roth, Germany). Quantitative PCR was performed in triplicate with equal amounts of cDNA mixed with the iTaqTM Universal SYBR® Green Supermix (BIO-RAD, Hercules, CA) and 5pmol of both forward and reverse primers. The sequences of primers (5’→3’) used in this study: RP II (F’: GGTTCAGGCAGAAGACTTTG; R’: TTGGGAGAAGCCATGTCATC), PBRM1 (F’: GGTTCAGGCAGAAGACTTTG; R’: TTGGGAGAAGCCATGTCATC). The quantification of transcript abundance was determined on the ABI 7900T Fast Real-Time PCR system (Applied Biosystems, Foster City, CA, USA). Results were analyzed by SDS software (V1.3.0, Life Technologies, Foster City, CA, USA). The relative quantification of target genes was analyzed by using the 2–ΔCT method and presented as mean ± SD of triplicate experiments. RPII was used as the endogenous control.

Gene expression microarraysThe microarray-based gene expression procedure was performed as described previously (Winkle et al., 2015). First 50-100ng total RNA was used for cDNA synthesis, amplification, and labeling with Cy5 dyes (Agilent Technologies, Santa Clara, CA, USA).

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Labelled RNA was purified using the RNeasy Mini Kit (Qiagen, Valencia, USA). The resulting cRNA concentration and dye incorporation was quantified by a NanoDrop 1000 UV-VIS spectrophotometer (Thermo Fisher Scientific, Rockford, IL, USA). All reagents and equipment used for the subsequent hybridization were purchased from Agilent (Agilent Technologies). Each Cy-5 sample was mixed with the same amount of a Cy3-labeled sample, which was non-relevant for this study. The samples were hybridized at 65°C overnight on Agilent SurePrint G3 Custom Human 8x60K Microarrays (ID-050524). Next, the microarray array slides were washed and scanned on the Agilent DNA Microarray Scanner with Agilent Feature Extraction software v10.7.3 (Agilent Technologies). Data preprocessing and normalization was performed using GeneSpring GX 12.6 software (Agilent Technologies). The resulting data were subject to quantile normalization without baseline transformation. The 34,134 Agilent probes, specific for protein coding genes, were selected for further analyses. In the comparison of both PBRM1-KD PTECs vs PBRM1-WT PTECs the probes that are flagged present in all samples of one out of two conditions, and whose expression intensity falls within the 30-100th percentile were selected for statistical analysis. This filtering resulted in 11,579 (PBRM1-KD vs PBRM1-WT) probes, which were used for further analysis. Heatmaps of differentially expressed genes were generated by Genesis software (v 1.7.6). Unsupervised clustering of the samples and genes was calculated using Euclidian distance metric.

Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) analysisGene Set Enrichment Analysis (Broad Institute) was performed for the 50 hallmark gene sets from the MSigDB collection using the Java GSEA implementation (V2.2.0). An enrichment score (ES) is assessed by walking down the ranked list of genes, and normalized by gene set size and correlations between gene sets and the expression profile. Functional annotation of genes by Gene Ontology (GO) was done using the Database for Annotation, Visualization and Integrated Discovery (DAVID, v6.7). The GO analysis is performed by using official gene symbols on the tool available at the DAVID website (http://david.abcc.ncifcrf.gov/). Gene ontology option GOTERM_BP (biology process)_ALL was used for generating an enrichment chart for up-and down-regulated genes separately.

Statistical analysisFor three-group comparisons in the RT-qPCR experiments to determine the PBRM1 expression and in the growth competition assay to evaluate the GFP changes, the significance of the PBRM1-KD and the change in GFP percentages was determined by one-way ANOVA comparing PBRM1-shPB1 and -shPB2 treated PTECs to NT-shRNA and untreated PTECs. The resulting P value was adjusted by Dunnett’s multiple testing correction. Significantly differentially expressed genes in the microarray data were determined by using moderated t-test with Benjamini-Hochberg correction. P-values <0.05 were considered to be significant.


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RESulTSPBRM1 loss neither conveys PTECs growth advantage, nor extends their proliferation capacityWe tested the knockdown efficiency of the shRNA constructs in HEK293T cells and HKC8 cells by RT-qPCR. This revealed a more than 80% reduction of PBRM1 mRNA levels in both cell lines (Supplementary Figure S1). In three independent PTEC cultures the knockdown efficiency ranged from 60%-70% (Figure 1A). We were unable to measure the actual decrease in the amount of PBRM1 protein due to lack of reliable antibodies.

In the growth competition assay, sh-PB1 transduced PTECs appear to proliferate slightly faster as compared to the NT-shRNA transduced PTECs; but the increase is significant only at day 10 (p = 0.019) and day 22 (p = 0.022). No significant changes were observed for sh-PB2 transduced PTECs in comparison to NT-shRNA transduced PTECs (Figure 1B). Thus loss of PBRM1 did not appear to promote the proliferation of PTECs. Analysis of the morphology of the cells during the growth competition assay also revealed no changes upon PBRM1 knockdown. After 20 days of culturing, both PBRM1-WT and PBRM1-KD PTECs showed a flattened appearance and enlarged nuclei, which are the characteristics of senescent cells. β-galactosidase (β-gal) staining of the treated and untreated PTECs at day 22 indeed revealed a positive staining for the fast majority of the cells consistent with a senescence state (Figure 1C). In summary, PBRM1-KD PTECs neither showed proliferative advantage, nor prolonged proliferation capacity as compared to PBRM1-WT PTECs.

PTECs show changes in their expression profile after PBRM1-KDTo further investigate the effect of PBRM1 loss on PTECs, we determined the gene expression changes upon PBRM1-KD in PTECs at day 6 after transduction. Principal component analysis (PCA) showed a good separation of PBRM1-KD and PBRM1-WT PTECs in the first component explaining 30.4% of all variation (Figure 2A). A moderated t-test with Benjamini-Hochberg multiple testing correction revealed significant changes for2,747 probes (1,475 up and 1,272 down in PBRM1-KD). A fold change in the signal intensity of more than 2 was observed for 301 of the significant probes corresponding to 285 genes. Of these, 136 probes corresponding to 130 genes were upregulated and 165 probes corresponding to 155 genes were downregulated in PBRM1-KD cells compared to PBRM1-WT PTECs (Supplementary Table S1). Unsupervised hierarchical clustering of the 301 probes revealed two distinct clusters with PBRM1-WT PTECs samples in the first and PBRM1-KD in the second cluster (Figure 2B).

Using expression data of 10 different ccRCC-derived cell lines (see chapter 5) we analyzed the expression pattern of these 301 probes by unsupervised hierarchical clustering. This resulted in one cluster including PBRM1-WT PTECs and PBRM1-KD PTECs, and a second cluster including all ccRCC cell lines (Figure 3). Visual inspection of the heatmap indicated that the expression level of the downregulated genes in PBRM1-KD PTECs was even lower in the ccRCC cell lines. In contrast, the expression

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Figure 1. Consequences of PBRM1 knockdown in PTECs. (A) Quantification of PBRM1 mRNA abundance by RT-qPCR 6 days after transduction at high MOI of 3 independent cultures of PTECs with PBRM1-targeting (sh-PB1 and sh-PB2) or non-targeting (NT) shRNAs. The results are presented as 2-∆Ct values with mean ± SD from 3 independent experiments, RPII serves as a reference gene. (B) Three independent cultures of PTECs were transduced at low MOI with the shRNA constructs sh-PB1, sh-PB2 and NT at passage 2 (day 0). The fraction of GFP+ cells was determined by FACS at each passage until day 22. The relative change of the fraction of GFP+ cells in the mixed cultures was compared to the first measurement (day 2), and is presented as fold changes (mean ± SD from 3 independent experiments). The significance of observed differences between PBRM1-sh-PB1 and sh-PB2 compared with NT (both in RT-qPCR and GFP competition assay), are calculated by one-way ANOVA with Dunnett’s multiple testing correction. *P < 0.05, ***P < 0.001. (C) PTECs were transduced with sh-constructs as described in panel (A), and processed for β-gal staining at day 20 (passage 5) to determine the senescence status. Images are representative for one of 3 independent experiments. The scale bar indicates 100µm. NT: non-targeting shRNA transduced PTECs, KD: sh-PB1 transduced PTECs.

levels of the genes upregulated upon PBRM1-KD in PTECS showed a mixed expression pattern in ccRCC cell lines (Figure 3).

PBRM1-KD induces changes in the basal expression of immune responsive genesTo characterize the nature of the genes with altered expression levels upon PBRM1-KD in PTECs, we performed a gene set enrichment analysis (GSEA). Compared with PBRM1-WT PTECs, PBRM1-KD PTECs were significantly enriched in gene sets related to Immune Response, E2F-TARGETS, and MYC-TARGETS-V2 (FDR<0.005, Table 1). Interferon-α (IFN-α) and interferon-γ (IFN-γ) response gene sets were the top enriched gene sets in PBRM1-KD PTECs (Figure 4A).

A B

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Figure 2. Expression features of PBRM1-KD PTECs. (A) PCA plot shows the distribution of PBRM1-WT (black, including both control and NT-shRNA treated) PTECs and PBRM1-KD (gray, including both sh-PB1 and sh-PB2 treated) PTECs. The plot was generated using the 11,579 probes that were present in at least 1 out of 2 conditions with a signal intensity between the 30-100 percentile in at least 1 of the conditions. (B) Heatmap including the 301 probes that are differentially expressed between PBRM1-KD and PBRM1-WT PTECs (moderated t-test with Benjamini-Hochberg multiple testing correction, P<0.05 and fold change>2). Unsupervised clustering of the samples and genes was calculated using Euclidian distance metric.

A B

Gene ontology (GO) analysis of the significantly differentially expressed genes upon PBRM1-KD revealed enrichment of genes involved in the immune system process and the nucleoside metabolism in the upregulated genes and enrichment of genes implicated in cell response to stimulus and cell differentiation in the downregulated gene set (Figure 4B).

DiScuSSionPBRM1 mutations are detected in more than 20 different tumor types, with by far the highest frequency in clear cell Renal Cell carcinoma (ccRCC) (COSMIC database). In ccRCC, PBRM1 is the second most frequently inactivated gene next to VHL. Based on the presence of PBRM1 inactivating mutations in the “ trunk”, Gerlinger et al (2014) concluded that PBRM1 inactivation is a driver event in ccRCC development. In the cancer genome atlas (TCGA), 44 out of 157 ccRCC patients with a PBRM1 mutation have wild-type VHL, SETD2 and BAP1 genes (Cancer Genome Atlas Research, 2013). These observations indicate that PBRM1 inactivation can initiate ccRCC development.

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Figure 3. Genes downregulated upon PBRM1-KD in PTECs are expressed at low levels in ccRCC cell lines. A heatmap was generated using the 301 probes described in Figure 2B keeping the same ordering of genes. Unsupervised clustering was performed on the samples using Euclidian distance metric.

Inactivation of PBRM1 in primary tubular epithelial cells of the kidney (PTECs) did not induce significant differences in the growth characteristics nor in the morphology of these cells. PBRM1-KD PTECs became senescent at approximately the same passage as wild type PTECs. Thus, PBRM1 loss does not interfere with the process of senescence in these cells. This is in contrast to the results of an shRNA screen in primary fibroblasts set up to identify genes that regulate replicative senescence (Burrows et al., 2010). In this particular screen, PBRM1 was identified as a protein whose inactivation delayed the process of senescence. We showed that inactivation of SETD2 resulted in an escape from senescence in PTEC cells (Li et al., 2016, chapter 3), while knockdown of SETD2 in bronchial epithelial cells did not (our own preliminary and unpublished results). Likewise, also the effect of PBRM1 inactivation may be cell type and/or tissue specific.

The Gene Set Enrichment Analysis (GSEA) of our expression data showed a significant enrichment for the E2F-TARGETS gene set in PBRM1-KD cells as compared to PBRM1-WT PTECS (Table 1). In chapter 3 of this thesis, this gene set was enriched in the WT-PTECs (day 6) as compared to the SETD2-KD PTECs (day 25) (Supplementary Figure S2). Thus, with respect to the E2F target gene set there appears to be a reciprocal effect of the two knock-down experiments. To what extend these


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Table 1. Enriched gene sets in PBRM1-KD PTECs.

NAME SIZE NES FDR q-val

HALLMARK_INTERFERON_ALPHA_RESPONSE 65 -3.08 <.001HALLMARK_INTERFERON_GAMMA_RESPONSE 119 -2.80 <.001HALLMARK_E2F_TARGETS 131 -1.90 <.005HALLMARK_MYC_TARGETS_V2 51 -1.81 <.005

PBRM1-WT PTECs were compared to PBRM1-KD PTECs using the hallmark gene sets retrieved from Broad institute (https://www.broadinstitute.org). No gene set was significantly enriched in PBRM1-WT PTECs. NES, normalized enrichment score, FDR, false discovery rate.

Figure 4. Functional interpretations of the expression features of PBRM1-KD PTECs. (A) Enrichment plots showing the hallmark gene sets of INTERFERON_ALPHA _RESPONSES (left) and INTERFERON_GAMMA _RESPONSES (right) in the comparison between PBRM1-WT PTECs and PBRM1-KD PTECs. The hallmark gene sets were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www.broadinstitute.org/gse). The false discovery rate (FDR) and normalized enrichment score (NES) for each gene set are indicated. (B) Gene ontology (GO) analysis using the 285 differentially expressed genes from Supplementary Table 1. A minimum enrichment score of 1.5 is presented. The left graph shows GO analysis using the 150 genes upregulated upon PBRM1-KD in PTECS and the right graph shows the GO analysis for the 135 genes downregulated upon PBRM1-KD.

A

B

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differences explain the phenotypical differences of PBRM1-KD vs SETD2-KD PTECs, including those in the process of senescence, is unclear.

PBRM1 (BAF180) is a subunit specific for the PBAF type SWI/SNF complex (Xue et al., 2000). However, it has been suggested that BAF180-deficient PBAF complexes retain part of their functionality (Yan et al., 2005). PBRM1 targets the PBAF complex to specific genomic loci and in that way makes the promoter region available for transcription factors. Using an in vitro chromatin transcription assay Lemon et al. (2001) showed that PBAF was indispensable for an effective activation of transcription by nuclear hormone receptors. Wang et al. (2004) identified a set of genes whose expression level was significantly changed upon PBRM1 knock-out in a mouse model. For a subset of genes their expressions were upregulated, suggesting that a functional PBAF complex can induce a suppressive chromatin state. Vice versa, presence of a subset of downregulated genes indicates that PBAF also can induce an activated chromatin state. Consistent with these findings we indeed found significantly up- and downregulated genes. The different approaches and cell types used within studies precludes a meaningful comparison of the genes altered upon PBRM1-KD.

Several studies have shown that a functional SWI/SNF complex is a prerequisite for an efficient and fast response to IFN-α stimulation, i.e. change of expression of IFN-α target genes. These effects have been shown to be dependent on the presence of the BRG1 and BAF47 components of the SWI/SNF complex. Expression of BRG1 in BRG1-deficient SW13 cells caused upregulation of a number of genes (Liu et al., 2001), and restored the quick response of IFN-α target genes to IFN-α (Liu et al., 2002). Knockdown of BAF47, another subunit of the SWI/SNF complex, in HeLa cells prevented the activation of a set of IFN-α responsive genes (Cui et al., 2004). This indicated that the SWI/SNF complex is responsible for the maintenance of an open chromatin configuration of the IFN-α responsive genes facilitating a quick response to IFN-α exposure. Huang et al. (2002) showed that the interaction of BRGI with STAT2 is responsible for at least some of the effects of the SWI/SNF complex. At the same time this study indicated that not all IFN-α responsive genes depend on presence of a functional SWI/SNF complex. As BAF47 and BRG1 are present in all human SWI/SNF complexes (Kadoch and Crabtree, 2015), these studies still did not identify the specific components that are responsible for regulating the expression of IFN-α responsive genes. Indeed, Yan et al. (2005) showed that the BAF and BPAF complexes regulate different IFN-α responsive genes. In our GSEA, INTERFERON_ALPHA_ RESPONSE was the most significant enriched gene set, indicating that the PBRM1-containing PBAF complex indeed is involved in regulating the expression of these genes. As we did not treat the cells with IFN-α, our observations at this moment mainly reflect the basal expression levels of these genes. This is consistent with the mode of action proposed by Kadoch and Crabtree (2015), i.e. that in general SWI/SNF complexes induce a local open or closed chromatin structure, and in this way regulate the basal expression level and the response time of this gene set upon IFN-α exposure.


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Another gene set that was significantly enriched in our study was the IFN-γ response gene set. This finding is not surprising, as 75% of the IFN-α gene set is also part of the IFN-γ gene set (Supplementary Figure S3). Of the 50 IFN-α leading edge genes 39 were also present in the IFN-γ leading edge gene set. Zhang et al. (2010) reported a BRG1-mediated interaction of the SWI/SNF complex with STAT1 in vitro. This resulted in the recruitment of SWI/SNF complex to the IFN-γ activated sequences and induction of IFN-γ responsive genes (Zhang et al., 2010). The effect of this interaction on gene expression may well depend on the presence of PBRM1.

The ccRCC cell lines showed a more pronounced downregulation of the genes that were also downregulated upon PBRM1-KD. This suggests that loss of PBRM1 indeed pushes the cells towards malignant transformation of PTECs. GO analysis of the genes significantly downregulated upon PBRM1-KD revealed enrichment of genes involved in cell differentiation, synapse organization and cytoskeleton organization, processes known to be essential for tumor progression (Quail and Joyce, 2013; Fife et al., 2014) This pinpoints potential changes in cell-cell or cell-matrix contacts as possible changes involved in transformation of PTECs.

Our findings may add to our understanding of immunotherapy induced treatment resistance in ccRCC tumors. Wolf et al. (2012) showed IFN-α treatment resistance is neither caused by the defective IFN receptors, nor by suppression of cytokine signaling. Our data indicates that PBRM1 depletion disturbs the expression signature of IFN-α and IFN-γ responsive genes, maybe by disturbing the balance between different subtypes of SWI/SNF complex in PTECs. Thus it will be interesting to determine whether immunotherapy-induced treatment resistant tumors have changes in PBRM1 expression levels or mutation status.

Our preliminary analysis did not give a clear-cut answer as to how loss of PBRM1 could contribute to, or even initiate the development of ccRCC. However, our data are a good basis to design further studies aiming at elucidating the role of PBRM1 loss in the pathogenesis of ccRCC.

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Supplementary Figure S1. PBRM1-knockdown (KD) in cell lines HEK293T and HKC8. HEK293T and HKC8 cells were transduced with PBRM1 targeting shRNAs sh-PB1 and sh-PB2. A non-targeting (NT) shRNA was included as a control. Total RNA was isolated from sorted GFP positive cells for cDNA synthesis. The mRNA abundance of PBRM1 was determined by RT-qPCR. The results are presented as 2-∆Ct values (mean ± SD) from 3 independent experiments using HPRT as a reference gene. Statistical significance is determined by one-way ANOVA with Dunnett’s multiple testing correction. ***P < 0.001.

Supplementary Figure S2. Enrichment plots for the E2F_TARGETS gene set. Enrichment plots of the E2F_TARGETS gene set in the comparison between WT (day 6) vs PBRM1-KD (day 6) PTECs, and WT (day 6) vs SETD2-KD (day 25) PTECs (Chapter 3, this thesis) are shown. The hallmark gene sets were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www.broadinstitute.org/gse). The false discovery rate (FDR) and normalized enrichment score (NES) in each comparison are shown.


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Supplementary table 1. Significantly and differentially expressed genes between PBRM1-WT PTECs and PBRM1-KD PTECs.

Probe Name p (Corr) Regulation FC Gene Symbol

A_33_P3393821 0.0138 up 8.58 C1RA_23_P167983 0.0012 up 6.58 HIST1H2ACA_32_P101031 0.0019 up 5.91 LYPD1A_23_P152782 0.0364 up 5.51 IFI35A_33_P3400578 0.0066 up 4.99 HLFA_23_P100711 0.0026 up 4.80 PMP22A_33_P3284129 0.0032 up 4.67 LYPD1A_24_P317762 0.0400 up 4.65 LY6EA_24_P119685 0.0001 up 3.85 OBSCNA_23_P82503 0.0049 up 3.71 PEG10A_33_P3399208 0.0489 up 3.69 HLA-BA_23_P75741 0.0325 up 3.66 UBE2L6A_23_P216655 0.0333 up 3.48 TRIM14A_23_P145238 0.0005 up 3.33 HIST1H2BKA_23_P88626 0.0008 up 3.28 ANPEPA_33_P3397865 0.0021 up 3.28 TNNT1A_23_P8240 0.0005 up 3.25 FAM50BA_24_P678104 0.0132 up 3.23 STMN3A_23_P120002 0.0226 up 3.22 SP110A_32_P69368 0.0044 up 3.19 ID2A_23_P50096 0.0035 up 3.17 TYMS

Supplementary Figure S3. Overlap of the genes in the gene sets of IFN-α and IFN-γ responsive genes. The total gene lists of INTERFERON_ALPHA _RESPONSES and INTERFERON_GAMMA _RESPONSES were retrieved from the Molecular Signatures Database (MSigDB v5.1) (www.broadinstitute.org/gse). The leading edge gene lists were retrieved from the GSEA.

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A_33_P3311493 0.0003 up 3.15 LOC283392A_23_P19673 0.0002 up 3.08 SGK1A_23_P218646 0.0131 up 3.07 TNFRSF6BA_24_P252078 0.0048 up 3.05 BTN3A2A_33_P3290403 0.0022 up 3.03 IMPA2A_24_P408047 0.0483 up 2.99 PLEKHA4A_23_P50146 0.0374 up 2.95 SIGLEC15A_23_P209625 0.0015 up 2.91 CYP1B1A_24_P99216 0.0002 up 2.81 LRP10A_24_P416177 0.0015 up 2.81 ADCY7A_23_P393620 0.0335 up 2.81 TFPI2A_23_P139912 0.0479 up 2.80 IGFBP6A_23_P37441 0.0449 up 2.73 B2MA_33_P3393836 0.0458 up 2.72 NT5C3A_33_P3412016 0.0021 up 2.71 SEMA4BA_23_P214208 0.0007 up 2.70 CNR1A_23_P114740 0.0091 up 2.69 CFHA_33_P3632937 0.0027 up 2.67 LOC100131262A_23_P143143 0.0263 up 2.61 ID2A_24_P346431 0.0001 up 2.58 TNS3A_32_P120895 0.0044 up 2.58 LYSMD2A_23_P95930 0.0004 up 2.57 HMGA2A_33_P3249046 0.0314 up 2.57 CLDN2A_23_P384044 0.0089 up 2.56 CNIH3A_23_P43726 0.0013 up 2.56 NUP160A_23_P119562 0.0021 up 2.55 CFDA_33_P3211520 0.0002 up 2.54 SNAP47A_24_P354715 0.0008 up 2.54 NT5EA_23_P102364 0.0489 up 2.53 NGEFA_23_P151710 0.0000 up 2.51 PTGER2A_24_P48057 0.0032 up 2.51 IRX5A_23_P216630 0.0004 up 2.51 SLC44A1A_33_P3290343 0.0005 up 2.48 CYP1B1A_33_P3344204 0.0025 up 2.48 ZDHHC11A_23_P212617 0.0019 up 2.47 TFRC

Supplementary table 1. (continued)


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A_23_P64617 0.0489 up 2.46 FZD4A_23_P251421 0.0052 up 2.44 CDCA7A_23_P140256 0.0366 up 2.43 PNPA_33_P3228325 0.0378 up 2.43 SP100A_23_P66715 0.0049 up 2.43 PIGSA_23_P62115 0.0022 up 2.42 TIMP1A_23_P137035 0.0255 up 2.40 PIRA_23_P15357 0.0366 up 2.38 LGALS3BPA_33_P3331366 0.0317 up 2.37 TRIM25A_33_P3345643 0.0131 up 2.36 ZDHHC11BA_33_P3318288 0.0068 up 2.36 CFHA_24_P278126 0.0019 up 2.35 NBNA_23_P61050 0.0061 up 2.35 MLKLA_23_P211957 0.0143 up 2.35 TGFBR2A_32_P171313 0.0017 up 2.35 GNB4A_23_P86900 0.0001 up 2.33 B3GNT1A_33_P3403117 0.0022 up 2.33 NR2F1A_33_P3229083 0.0003 up 2.32 HIST1H2BKA_23_P136978 0.0066 up 2.31 SRPX2A_23_P119478 0.0012 up 2.30 EBI3A_23_P50426 0.0040 up 2.30 KANK2A_23_P353717 0.0021 up 2.30 RMI2A_33_P3280213 0.0003 up 2.29 CTSAA_23_P302787 0.0002 up 2.29 LOC375295A_33_P3800734 0.0273 up 2.27 RYR3A_33_P3336257 0.0019 up 2.26 IRX1A_33_P3277110 0.0022 up 2.26 SLC5A3A_24_P14260 0.0002 up 2.26 CARD8A_23_P414273 0.0009 up 2.25 C5orf62A_33_P3228305 0.0035 up 2.25 ARHGAP26A_23_P165608 0.0018 up 2.24 SEMA4FA_32_P117170 0.0042 up 2.24 NAPEPLDA_33_P3397418 0.0117 up 2.24 ZC3HAV1A_32_P41487 0.0026 up 2.24 HMGN2A_24_P216313 0.0013 up 2.24 ERGIC3


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A_23_P93180 0.0002 up 2.23 HIST1H2BCA_23_P250629 0.0147 up 2.22 PSMB8A_33_P3347869 0.0397 up 2.21 C3A_23_P80040 0.0173 up 2.20 PROCRA_23_P139704 0.0189 up 2.19 DUSP6A_23_P203488 0.0037 up 2.17 SMPD1A_23_P128613 0.0037 up 2.17 KDELC1A_24_P379820 0.0148 up 2.16 ITM2CA_23_P38154 0.0012 up 2.16 FDXRA_24_P810290 0.0151 up 2.15 PPAPDC1AA_33_P3278941 0.0055 up 2.15 REC8A_33_P3398448 0.0459 up 2.14 PARP10A_23_P76914 0.0019 up 2.14 SIX1A_23_P200030 0.0015 up 2.14 FPGTA_23_P95165 0.0015 up 2.13 SEMA4BA_24_P309317 0.0077 up 2.13 PSAPA_23_P208880 0.0046 up 2.12 UHRF1A_24_P394246 0.0016 up 2.10 SHISA5A_23_P111041 0.0010 up 2.10 HIST1H2BIA_23_P88589 0.0001 up 2.10 NR2F2A_23_P391506 0.0031 up 2.10 IVNS1ABPA_23_P388433 0.0059 up 2.09 C4orf3A_23_P138680 0.0215 up 2.08 IL15RAA_24_P90097 0.0005 up 2.08 ADD3A_23_P58588 0.0323 up 2.08 SLIT3A_33_P3227788 0.0076 up 2.08 PANK1A_23_P153745 0.0358 up 2.07 IFI30A_23_P416468 0.0400 up 2.07 PIF1A_23_P13740 0.0099 up 2.06 NAV3A_23_P152235 0.0479 up 2.06 IRX3A_23_P390172 0.0020 up 2.05 RNASELA_23_P66608 0.0021 up 2.05 KAT2AA_23_P210210 0.0017 up 2.04 EPAS1A_24_P322474 0.0367 up 2.04 PDE4AA_23_P71513 0.0020 up 2.03 EFR3A



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A_32_P112279 0.0005 up 2.02 CHTF8A_32_P196142 0.0065 up 2.02 LOC100130938A_32_P32413 0.0017 up 2.02 SETBP1A_33_P3298062 0.0079 up 2.02 ABCC5A_23_P43157 0.0164 up 2.02 MYBL1A_24_P154037 0.0048 up 2.02 IRS2A_24_P146211 0.0018 up 2.02 HIST1H2BDA_33_P3348239 0.0027 up 2.01 FBN1A_24_P354689 0.0045 up 2.01 SPOCK1A_23_P106562 0.0008 up 2.00 GALNSA_23_P137016 0.0037 down -2.00 SAT1A_23_P334870 0.0014 down -2.01 TMEM217A_33_P3403867 0.0030 down -2.01 PMEPA1A_23_P154037 0.0153 down -2.01 AOX1A_33_P3371727 0.0030 down -2.02 SAT1A_33_P3229032 0.0164 down -2.02 CLEC11AA_33_P3288942 0.0157 down -2.02 FAM107BA_23_P68851 0.0014 down -2.02 KREMEN1A_23_P162766 0.0024 down -2.02 DOCK9A_33_P3294031 0.0204 down -2.02 KCNQ1OT1A_33_P3289705 0.0010 down -2.02 GOLGB1A_33_P3230658 0.0001 down -2.03 TSNAXA_23_P418199 0.0006 down -2.03 RP11-195F19.30A_23_P203445 0.0000 down -2.04 UEVLDA_23_P113005 0.0105 down -2.05 EFNA1A_33_P3383029 0.0003 down -2.05 MXI1A_32_P113436 0.0035 down -2.05 HNRNPA1L2A_33_P3397150 0.0050 down -2.05 FLJ22184A_23_P36888 0.0024 down -2.06 FAM113BA_32_P104432 0.0056 down -2.06 NCRNA00087A_33_P3353502 0.0002 down -2.06 PLCB4A_33_P3381827 0.0018 down -2.07 OSBPL2A_24_P358305 0.0129 down -2.07 GS1-44D20.1A_23_P258002 0.0195 down -2.07 CDKN2AIPA_24_P82880 0.0077 down -2.08 TPM4


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A_33_P3408054 0.0008 down -2.08 HSP90AB2PA_24_P102053 0.0104 down -2.08 OCLNA_33_P3398862 0.0015 down -2.08 RHOBA_23_P161727 0.0360 down -2.09 HSPB2A_23_P213102 0.0243 down -2.09 PALLDA_33_P3306964 0.0003 down -2.10 PPP1R2A_33_P3222380 0.0001 down -2.11 AHNAK2A_33_P3347928 0.0068 down -2.11 CCNL1A_23_P388168 0.0027 down -2.12 RAB3BA_33_P3371718 0.0080 down -2.13 SAT1A_24_P703830 0.0014 down -2.13 NANOS3A_24_P334130 0.0166 down -2.13 FN1A_32_P49844 0.0009 down -2.13 RHOQA_32_P116556 0.0114 down -2.13 ZNF469A_23_P383422 0.0076 down -2.14 NFKBIDA_33_P3343145 0.0008 down -2.14 MAP1BA_23_P52761 0.0091 down -2.14 MMP7A_23_P157865 0.0042 down -2.14 TNCA_23_P316850 0.0358 down -2.15 ODF3L2A_33_P3326312 0.0045 down -2.16 naA_24_P348925 0.0002 down -2.16 CCNKA_23_P132718 0.0297 down -2.17 SEMA3BA_23_P122216 0.0147 down -2.17 LOXA_24_P203502 0.0003 down -2.17 RSL24D1P11A_33_P3337277 0.0037 down -2.17 LOC100129846A_33_P3286621 0.0031 down -2.18 SCARNA16A_24_P282309 0.0003 down -2.18 MYOFA_23_P39766 0.0046 down -2.18 GLSA_23_P151307 0.0039 down -2.19 RAPGEF3A_24_P67681 0.0038 down -2.19 LOC100508670A_33_P3429242 0.0346 down -2.19 LOC339988A_33_P3332885 0.0006 down -2.20 BTN2A1A_33_P3539345 0.0015 down -2.21 MYO6A_33_P3375314 0.0022 down -2.21 ATP9AA_23_P162719 0.0049 down -2.22 DIAPH3



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A_33_P3270863 0.0119 down -2.23 XDHA_33_P3285545 0.0094 down -2.23 CLDN4A_33_P3399064 0.0063 down -2.24 RN5-8S1A_33_P3232011 0.0178 down -2.24 RAB17A_23_P421423 0.0213 down -2.24 TNFAIP2A_23_P144465 0.0001 down -2.24 PAPSS1A_33_P3332487 0.0022 down -2.24 FANK1A_33_P3299754 0.0002 down -2.25 RAB18A_33_P3224380 0.0001 down -2.26 DLG1A_23_P39237 0.0070 down -2.26 ZFP36A_33_P3212575 0.0008 down -2.26 NNATA_33_P3317815 0.0015 down -2.27 KRASA_23_P373119 0.0019 down -2.27 HMGB3P1A_32_P191895 0.0002 down -2.28 SDCBPP2 A_33_P3368188 0.0057 down -2.29 SEP9A_23_P403335 0.0047 down -2.29 EXPH5A_33_P3685216 0.0179 down -2.30 A1BGA_33_P3268304 0.0146 down -2.30 LIMS2A_23_P155900 0.0005 down -2.31 NPFFR2A_33_P3353692 0.0022 down -2.31 MYH9A_23_P214080 0.0378 down -2.33 EGR1A_23_P25674 0.0020 down -2.33 CKBA_33_P3260066 0.0011 down -2.35 BEAN1A_33_P3315719 0.0039 down -2.36 PLEKHH2A_32_P153388 0.0015 down -2.37 GULP1A_23_P93269 0.0219 down -2.37 ZNF165A_33_P3210099 0.0159 down -2.37 ALPK3A_23_P34597 0.0464 down -2.37 CDAA_23_P748 0.0440 down -2.38 IRF6A_33_P3320197 0.0084 down -2.41 FAM150BA_23_P88303 0.0121 down -2.41 HSPA2A_23_P212608 0.0226 down -2.42 CLSTN2A_23_P111395 0.0157 down -2.43 SLC22A2A_33_P3221303 0.0014 down -2.44 CCR10A_23_P376488 0.0019 down -2.45 TNF


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A_33_P3388391 0.0157 down -2.46 GJB4A_24_P295590 0.0084 down -2.47 RASSF4A_23_P319583 0.0021 down -2.47 RIMS3A_23_P81898 0.0099 down -2.47 UBDA_23_P16834 0.0037 down -2.48 FNDC4A_33_P3389842 0.0043 down -2.48 PROM1A_33_P3232798 0.0152 down -2.49 RAB11FIP1A_24_P339944 0.0091 down -2.50 PDGFBA_23_P145397 0.0002 down -2.50 CCNCA_23_P53663 0.0004 down -2.51 PAWRA_33_P3245178 0.0045 down -2.51 BEX2A_32_P150891 0.0024 down -2.52 DIAPH3A_33_P3335042 0.0002 down -2.53 HSD17B12A_23_P2181 0.0129 down -2.53 CYB5R2A_23_P339119 0.0003 down -2.56 ACSS3A_23_P8801 0.0120 down -2.60 CYP3A5A_24_P4705 0.0096 down -2.61 PPME1A_23_P19182 0.0466 down -2.62 REEP2A_23_P12343 0.0181 down -2.63 GSTM3A_23_P71328 0.0094 down -2.63 MATN2A_33_P3252359 0.0029 down -2.68 BDH1A_23_P115785 0.0003 down -2.69 FANK1A_33_P3332492 0.0002 down -2.71 FANK1A_32_P393316 0.0016 down -2.71 RAPGEF3A_23_P133408 0.0123 down -2.72 CSF2A_33_P3410279 0.0094 down -2.73 DOCK9A_23_P8571 0.0219 down -2.73 SRCRB4DA_33_P3391005 0.0001 down -2.73 NEDD4LA_32_P180971 0.0000 down -2.73 LOC728323A_23_P358917 0.0133 down -2.76 CYP3A7A_24_P392110 0.0420 down -2.77 PSG8A_24_P390060 0.0290 down -2.82 IQCDA_23_P429998 0.0198 down -2.82 FOSBA_32_P131031 0.0325 down -2.86 MACC1A_23_P71946 0.0210 down -2.87 BSPRY



4

109



A_23_P4494 0.0010 down -2.89 DSC2A_33_P3382271 0.0001 down -3.03 ATXN3A_23_P119943 0.0015 down -3.13 IGFBP2A_33_P3408913 0.0187 down -3.15 SAA2A_33_P3293381 0.0043 down -3.26 RASSF4A_33_P3274935 0.0136 down -3.32 C17orf28A_32_P135336 0.0001 down -3.36 LOC388242A_33_P3307013 0.0059 down -3.36 C17orf57A_23_P154217 0.0002 down -3.41 ITGB6A_23_P127565 0.0308 down -3.44 LAYNA_23_P331049 0.0012 down -3.52 DPYSL4A_32_P703 0.0010 down -3.54 LOC646626A_23_P76078 0.0039 down -3.60 IL23AA_32_P24376 0.0082 down -3.70 LOC730755A_23_P15174 0.0374 down -3.80 MT1FA_23_P203540 0.0084 down -3.95 EHFA_33_P3214948 0.0059 down -3.99 SPOCK2A_33_P3329088 0.0244 down -4.09 PRSS8A_33_P3313055 0.0234 down -4.19 NOTCH3A_23_P215720 0.0102 down -4.21 CFTRA_33_P3671291 0.0009 down -4.41 SNORA12A_33_P3229107 0.0352 down -4.50 LOC642587A_23_P94800 0.0005 down -4.53 S100A4A_24_P33895 0.0388 down -4.59 ATF3A_33_P3214105 0.0122 down -4.68 ATF3A_23_P21363 0.0009 down -4.73 AHNAKA_23_P312150 0.0172 down -5.06 EDN2A_23_P372834 0.0136 down -5.28 AQP1A_23_P161218 0.0126 down -5.46 ANKRD1A_23_P74778 0.0045 down -7.28 C1orf54A_23_P15876 0.0004 down -7.43 ALPK2A_33_P3243093 0.0013 down -7.82 RGS5A_23_P125233 0.0043 down -9.06 CNN1A_23_P17065 0.0035 down -9.10 CCL20A_23_P46045 0.0023 down -9.58 RGS5


a lonG noncoDinG Rna SiGnaTuRE of clEaR cEll REnal cEll caRcinoMa anD ThE iMPacT of SETD2 anD PBRM1 loSS

Jun Li1, Joost Kluiver2, Jan Osinga1, Debora de Jong2, Helga Westers1, Rolf H. Sijmons1, Anke van den Berg2 and Klaas Kok1

1Department of Genetics, and 2Department of Pathology and Medical Biology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

Manuscript in preparation

c h a P T E R 5

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aBSTRacTIn recent years, altered expression of long non-coding (lnc) RNAs has been shown to be functionally involved in the development of cancer. In this study, we compared lncRNA expression profiles of 10 ccRCC cell lines to those of their presumed normal counterpart, primary tubular epithelial cells (PTECs). In addition, we identified lncRNAs associated with shRNA knockdown of the ccRCC tumor suppressor genes PBRM1 and SETD2 in PTECs. Finally, we identified potential cis-acting lncRNAs based on a close proximity.

Compared with PTECs, 89 lncRNAs were significantly and >2 fold differentially expressed in ccRCC cell lines. Expression levels of 48 and 34 lncRNAs were significantly altered upon knockdown of SETD2 and PBRM1 in PTECs, respectively. The ccRCC cell lines showed an even further downregulation of the lncRNAs with a significantly reduced expression level upon SETD2 or PBRM1 knock down. A total of 39 putative cis-regulating lncRNA / protein-coding gene pairs were identified in the ccRCC cell lines, 7 in SETD2-KD PTECs and 3 in PBRM1-KD PTECs.

In conclusion, ccRCC cell lines show clear lncRNA expression changes compared to normal PTECs. Loss of SETD2 and PBRM1 induced marked changes in the lncRNA expression profile of PTECs that were even more pronounced in the ccRCC cell lines, suggesting that these two ccRCC tumor suppressor genes might contribute to ccRCC development through regulation of multiple lncRNAs.

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inTRoDucTionKidney cancer is amongst the top-10 most common cancers in men and amongst the top-15 in women worldwide (Znaor et al., 2015). Approximately 84,400 new cases and 34,700 kidney cancer-related deaths were registered in the European Union in 2012 (Ferlay et al., 2013). Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC accounting for more than 80% of all kidney tumors (Ljungberg et al., 2015). Treatment of ccRCC patients includes surgical resection and systemic therapy, which results in a 5-year cancer-specific survival rate of 91%, 74%, 67%, and 32% for TNM stage I, II, III and IV respectively.

Loss of the p-arm of chromosome 3 occurs in approximately 90% of the patients and is the most common genomic aberration observed in ccRCC (Hakimi et al., 2013). Mutations in the remaining allele of any of the four tumor suppressor genes mapping to 3p, e.g. VHL, SETD2, BAP1 and PBRM1, have been linked to the pathogenesis of ccRCC (Brugarolas, 2014). VHL is responsible for cellular oxygen sensing by targeting the E3 ubiquitin ligase complex to hypoxia-inducible factors for ubiquitynation and subsequent proteosomal degradation (Gossage et al., 2015). SETD2 is a histone methyltransferase responsible for trimethylation of histone H3 Lys-36 (Edmunds et al., 2007). BAP1 is a de-ubiquitinating enzyme that regulates several key pathways, including cell cycle, differentiation, transcription and DNA damage response (Jensen et al., 1998). PBRM1 is a subunit of the ATP-dependent SWI/SNF chromatin-remodeling complex that regulates the position of nucleosomes along the DNA strands (Hargreaves & Crabtree, 2011).

The role of non-protein coding genes in RCC is less well known. Long non-coding (lnc) RNAs coding genes are a subset of these genes, defined by RNA transcripts of more than 200 nucleotides in length that lack protein-coding potential (Derrien et al., 2012). A large proportion of the lncRNAs map to promoter or intragenic regions of protein-coding genes, either in the sense or the antisense direction (Cabili et al., 2011; Derrien et al., 2012; Rinn & Chang, 2012; Necsulea et al., 2014). In addition, they can be located in intergenic regions. Currently, more than 111,000 lncRNA transcripts have been identified across different human tissues and cell types (Volders et al., 2015). In the last decade, it has become clear that lncRNAs play main regulatory roles in almost all cellular processes (Rinn & Chang, 2012). The modes of action of lncRNAs include regulation of chromatin marks, transcription, splicing, translation and protein localization (Rinn & Chang, 2012; Quinn & Chang, 2015). LncRNAs can regulate transcription of nearby genes in cis or of distant genes in trans.

Deregulation of lncRNAs has been linked to various diseases, including the development of cancer (Huarte, 2015). For a limited number of lncRNAs a direct link with the development of cancer has been demonstrated using mouse models. For example female mice with a deleted Xist allele in the blood compartment develop highly aggressive myeloproliferative disease (Yildirim et al., 2013).

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Several lncRNAs have been suggested to act as tumor suppressor or oncogenes based on deregulated expression in kidney tumors (Martens-Uzunova et al., 2014; Seles et al., 2016). For some of them functional studies further supported a role in ccRCC pathogenesis. Overexpression of the lncRNA GAS5 inhibited growth, induced apoptosis and arrested cell cycle progression in an ccRCC cell line (Qiao et al., 2013). The lncRNA MALAT1 was shown to interact with the EZH2 subunit of PRC2 (Hirata et al., 2015; Zhang et al., 2015) and to regulate ZEB2 by acting as a competing endogenous RNA for the miR-200 family (Xiao, et al., 2015). Overexpression of the lncRNA MEG3 decreased viability ccRCC cells by reducing Bcl-2 and procaspase-9 protein levels (Wang et al., 2015a). Knockdown of the lncRNA H19 in ccRCC reduced cell proliferation, invasion, and migration (Wang et al., 2015b). Re-analysis of RNA-seq data of 475 ccRCC samples revealed four subclasses of ccRCC based on their lncRNA expression patterns. These subgroups were associated with specific clinical characteristics and with specific genomic aberrations, such as mutational status of PBRM1 (Malouf et al., 2015). Altogether, these studies show the relevance of lncRNAs in ccRCC pathogenesis.

To further explore the potential role of LncRNAs in ccRCC we studied differentially expressed lncRNAs in ccRCC cell lines as compared to renal proximal tubular epithelial cells (PTECs), the presumed normal ccRCC counterpart, using a custom design lncRNA array. In addition, we determined if knockdown of SETD2 and PBRM1 in PTECs affected the lncRNA expression signature. The custom array also included probes for all protein-coding genes allowing simultaneous identification of potential cis-acting lncRNAs.

MaTERial anD METhoDSCell culturePTECs were obtained from Dr. van Werkhoven (Rode Kruis Ziekenhuis, Netherlands) and isolated as described previously (Li et al., 2016). PTECs were maintained in DMEM/F-12 GLUTMAX-1 containing 10% fetal bovine serum (FBS), 100 units/ml penicillin and 100 µg/ml streptomycin, 1% Insulin-Transferrin-Selenium (ITS), and 5 ng/ml Epidermal growth factor (EGF) (Sigma-Aldrich, St. Louis, MO). CcRCC cell lines RCC-1, RCC-4, RCC-5 and RCC-6 were a kind gift of Dr. C.D. Gerharz (Institute of Pathology, University Hospital, Düsseldorf, Germany). CcRCC cell lines RCC-ER, RCC-MF, RCC-JF, RCC-GW, RCC-FW and RCC-HS were purchased from Cell Line Services (Eppenheim, Germany). The cell lines were cultured in RPMI 1640 supplemented with 10% FBS, 100 units/ml penicillin and 100 µg/ml streptomycin (Sigma-Aldrich, St. Louis, MO) at 37°C in humidified air containing 5% CO2. Cells were harvested at a confluency of about 80%.

Lentiviral shRNA-mediated PBRM1 and SETD2 knockdownNon-targeting shRNA (NT), SETD2 targeting shRNA (sh1 and sh2), and PBRM1 targeting shRNA (sh-PB1 and sh-PB2) were cloned into the pGreenpuro lentiviral

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vector (Systems Biosciences, Mountain View, CA)(described in Chapters 3 and 4). Lentiviral particles were generated by co-transfection of these constructs together with third generation packaging plasmids pCMV-VSV-G, pRSV.REV, and pMDL-gPRRE into HEK293T cells. The virus containing supernatant was collected, filtered through a 0.45µm filter, and store at -80°C for use. PTECs were transduced with a serial dilution of viral stocks in the presence of 4 μg/ml polybrene (Sigma-Aldrich, St. Louis, MO) in 6-well plates. Details on the generation of the constructs as well as on their knock down efficiencies have been described in Chapters 3 and 4.

RNA extraction and quality control assayTotal RNA was extracted using the Gene JET RNA purification kit (Fermentas, St. Leon-Rot, Germany) according to the manufacturer’s instructions. RNA integrity and quantity were determined on the HT RNA LabChip GX/GXII kit (Caliper GX, Life Sciences, Hopkinton, MA).

Gene expression profilingA total amount of 50-100ng total RNA was used for labeling using the Low Input QuickAmp Labeling kit and the Cyanine5 CTP Dye Pack following the protocol provided by the manufacturer (Agilent Technologies, Santa Clara, USA). The resulting Cy5-labeled cRNA was quantified by NanoDrop 1000 UV-VIS spectrophotometer (Thermo Fisher Scientific, Rockford, USA). Each Cy5-labeled sample was mixed with the same amount of a Cy3-labeled sample, the last being non-relevant for this study. The samples were hybridized at 65°C overnight on AgilentSurePrint G3 Human 8x60K Custom Microarrays (Agilent ID 050524) using the Gene Expression Hybridization Kit (Agilent). The custom gene expression microarray contains 34,131 protein-coding probes and 25,962 lncRNA probes, covering 26,088 protein-coding genes and 15,913 lncRNA genes, respectively. Microarrays were washed and scanned by the Agilent DNA Microarray Scanner with Agilent Feature Extraction software v10.7.3.1. Data were quantile normalized without baseline transformation using GeneSpring GX 12.6 software (Agilent Technologies). Probes flagged present in all samples of at least one of the two experimental conditions with an expression intensity within the 30-100th percentile were selected for statistical analysis. Consistent with the grouping used in Chapters 3 and 4, wild type (WT) and non-targeting (NT)-shRNA transduced PTECs were grouped together as WT/NT-PTECs; SETD2 targeting sh1 and sh2 transduced PTECs were grouped as SETD2-KD PTECs; PBRM1 targeting sh-PB1 and sh-PB2 transduced PTECs were grouped as PBRM1-KD PTECs. Differentially expressed probes were identified by a moderated t-test, and probes with an adjusted p<0.05, based on Benjamin-Hochberg multiple testing correction, were considered as significant. In addition, we applied a further selection of probes that showed an at least 2 fold difference in expression level. Unsupervised hierarchical clustering based on the Euclidean matrix distance was performed to generate heatmaps of the differentially expressed genes (Genesis software, v 1.7.6).

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Identification of cis-acting lncRNAs Putative cis-acting lncRNAs were identified by defining the distance between the transcriptional start sites (TSS) of each differentially expressed lncRNA to the nearest TSS of a differentially expressed protein-coding gene. We determined putative cis-regulated genes in all three data sets, i.e. differentially expressed lncRNAs and protein-coding genes in (1) ccRCC cell lines vs PTECS, (2) SETD2-KD PTECs vs WT/NT-PTECs and in (3) PBRM1-KD PTECs vs WT/NT-PTECs, separately. Differentially expressed protein-coding gene lists for SETD2-KD and PBRM1-KD were retrieved from Chapters 3 and 4, respectively. Gene-ID and mapping data of the full-length transcripts were retrieved from the UCSC genome Browser (https://genome.ucsc.edu/). Next, we identified all gene pairs in which the TSS of a lncRNA gene mapped within 300kb of the TSS of a protein-coding gene. The putative cis-acting lncRNA regulated protein-coding genes in SETD2-KD and PBRM1-KD PTECs were overlapped with the hallmark gene sets (Molecular Signatures Database) enriched upon knockdown of SETD2 and PBRM1 (Chapters 3 and 4).

RESulTSLncRNA expression signatures of ccRCC cell linesAfter normalization and filtering, 2,217 lncRNA and 12,576 protein-coding gene probes were retained for further analyses (Table 1). Principle component analysis (PCA) using the 2,217 lncRNA probes did not show a good separation between the 3 PTEC samples and the 10 ccRCC cell lines in any of the first four components (Figure 1A). PCA of the 12,576 protein-coding probes showed a nearly complete separation between PTECs and ccRCC cell lines in the first component explaining 19.4% of all variance (Figure 1B).

Statistical analysis revealed a significantly different expression level for 111 lncRNA probes, with a fold change of more than 2 for 101 of the probes (Supplementary Table S1). These 101 probes corresponded to 89 unique lncRNA genes. Thirty-four (34%) lncRNA probes showed increased signals and 67 (66%) showed decreased signals in the ccRCC cell lines compared to PTECs. Unsupervised hierarchical clustering using these 101 lncRNA probes revealed a complete separation between PTEC and ccRCC samples (Figure 1C). A similar analysis for the protein-coding gene probes revealed 916 probes with a significantly different expression level, with a fold change of more than 2 for 745 probes. These 745 probes corresponded to 683 unique protein-coding genes (Supplementary Table S2). Unsupervised hierarchical clustering using the 745 probes showed a complete separation of the PTEC and ccRCC samples with 249 (33%) up- and 496 (67%) downregulated probes (Figure 1D).

SETD2-regulated lncRNAsNext, we investigated whether knockdown of SETD2 led to an altered lncRNA expression signature in PTECs using the shRNA-transduced PTECs at day 25 compared

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Tabl

e 1.

Ove

rvie

w o

f the

arr

ay re

sults

and

ana

lysi

s of c

cRC

C v

s PTE

Cs,

SETD

2-K

D v

s WT/

NT-

PTEC

s and

PBR

M1-

KD

vs W

T/N

T-PT

ECs.

ccRC

C v

s PTE

Cs

SETD

2-K

D v

s WT

PBRM

1-K

D v

s WT

lncR

NA

pc-R

NA

lncR

NA

pc-R

NA

aln

cRN

Apc

-RN

Ab

# pr

obes

bef

ore

filte

ring

25,9

6234

,134

25,9

6234

,134

25,9

6234

,134

# pr

obes

afte

r filte

ring

2,21

712

,567

1,68

211

,659

1,68

311

,579

Sign

. and

2 fo

ld d

iff. e

xpr.

prob

es

(% u

p/%

dow

n)10

1

(34%

/66%

)74

5

(33%

/67%

)54

(1

7%/8

3%)

416

(4

7%/5

3%)

38

(26%

/74%

)30

1

(45%

/55%

)Si

gn. a

nd 2

fold

diff

. exp

r. ge

nes

(up/

dow

n)89

(3

1/58

)68

3

(235

/448

)48

(7

/41)

401

(1

92/2

09)

34

(9/2

5)28

5

(130

/155

)a D

ata

from

cha

pter

3; b

Dat

a fr

om c

hapt

er 4

; lnc

RNA

, lon

g no

n-co

ding

RN

As;

pc-R

NA

, pro

tein

-cod

ing

RNA

; ac,

alm

ost c

ompl

ete

sepa

ratio

n.

to the WT/NT shRNA-transduced PTECs at day 6 as described in Chapter 3. After normalization and filtering, 1,682 lncRNA probes were retrieved (Table 1). PCA of these probes showed an almost complete separation between SETD2-KD and WT/NT-PTECs in the first component, which explained 38.2% of the variance (Figure 2A). Statistical analysis revealed 198 lncRNA probes with a significantly different expression level, of which 54 showed a fold change of more than 2. These 54 probes corresponded to 48 lncRNA genes (Supplementary Table S3). Unsupervised hierarchical clustering using the 54 lncRNA probes with differentially expression levels showed a complete separation of the SETD2-KD PTECs and WT/NT-PTECs (Figure 2B). Nine of the lncRNA probes (17%) were up- and 45 (83%) downregulated in SETD2-KD PTECs. One of the probes with significantly reduced expression levels upon SETD2-KD corresponded to HIF1A-AS2, a lncRNA known to be upregulated in ccRCC.

Next, we carried out a cluster analysis for the 54 SETD2-altered lncRNA probes, with inclusion of the expression data from the ccRCC cell lines. In this analysis, the cell lines ended up in a completely separate cluster (Figure 3A) with a further decrease in expression of the group of lncRNAs downregulated upon SETD2-KD in PTECs. For the lncRNAs upregulated upon SETD2-KD no clear pattern could be observed in the ccRCC cell lines. Despite the overall consistent expression changes especially for the downregulated lncRNAs, the overlap between lncRNAs regulated by SETD2 and lncRNAs differentially expressed between PTECs and ccRCC cell lines with a fold change of at least 2, was limited to 10 (highlighted in Supplementary Table S3).

Chapter 5

118

Figu

re 1

. Pri

ncip

al c

ompo

nent

ana

lysi

s (P

CA

) and

sig

nific

antly

diff

eren

tial

ly e

xpre

ssed

gen

es in

ccR

CC

cel

l lin

es a

nd P

TEC

s. (

A)

PCA

plo

t of

the

1st a

nd 2

nd c

ompo

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ng th

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217

lncR

NA

pro

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hat w

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. Non

e of

the

com

pone

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ould

mak

e a

dist

inct

ion

betw

een

the

2 gr

oups

. (B)

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A p

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f the

1st a

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nd c

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nent

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ng t

he 1

2,57

6 pr

otei

n-co

ding

gen

e pr

obes

ret

aine

d aft

er fi

lteri

ng. (

C)

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tmap

of t

he

unsu

perv

ised

hie

rarc

hica

l clu

ster

ing

of th

e 10

1 di

ffere

ntia

lly e

xpre

ssed

lncR

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bes.

(D)

Hea

tmap

of t

he u

nsup

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sed

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arch

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clu

ster

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of

the

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diffe

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exp

ress

ed p

rote

in-c

odin

g ge

ne p

robe

s. Si

gnifi

cant

ly d

iffer

entia

lly e

xpre

ssed

pro

bes w

ere

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tified

by

the

Mod

erat

ed t-

test

with

Be

njam

in-H

ochb

erg

MTC

(p<0

.05)

with

a fo

ld c

hang

e >2

. Bla

ck sq

uare

s ind

icat

e W

T/N

T-PT

ECs a

nd g

ray

squa

res i

ndic

ate

ccRC

C c

ell l

ines

.

A B C

D

lncRnAs in ccRcc

5

119

Figu

re 2

. Pri

ncip

al c

ompo

nent

ana

lysi

s (P

CA

) and

sig

nific

antly

diff

eren

tial

ly e

xpre

ssed

gen

es in

SET

D2-

KD

, PBR

M1-

KD

PT

ECs,

as

com

pare

d to

WT

/NT-

PTEC

s. (A

) PC

A p

lot o

f the

1st a

nd 2

nd c

ompo

nent

bas

ed o

n th

e 1,

682

lncR

NA

pro

bes

reta

ined

afte

r fil

teri

ng in

SET

D2-

KD

and

WT/

NT-

PTEC

s. (B

) Hea

tmap

of t

he u

nsup

ervi

sed

hier

arch

ical

clu

ster

ing

of th

e 54

lncR

NA

pro

bes d

iffer

entia

lly e

xpre

ssed

bet

wee

n SE

TD2-

KD

and

WT/

NT-

PTEC

s. (C

) PC

A p

lot o

f the

1st a

nd 2

nd c

ompo

nent

usi

ng th

e 1,

683

lncR

NA

pro

bes

reta

ined

afte

r fil

teri

ng in

PBR

M1-

KD

and

WT/

NT-

PTEC

s. (D

) Hea

tmap

of t

he u

nsup

ervi

sed

hier

arch

ical

clu

ster

ing

of th

e 38

lncR

NA

pro

bes d

iffer

entia

lly e

xpre

ssed

bet

wee

n PB

RM1-

KD

and

WT/

NT-

PTEC

s. Si

gnifi

cant

ly d

iffer

entia

lly ex

pres

sed

prob

es w

ere i

dent

ified

by

a m

oder

ated

t-te

st w

ith B

enja

min

-Hoc

hber

g M

TC (p

<0.0

5) w

ith a

fold

chan

ge >

2. B

lack

sq

uare

s ind

icat

e W

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nd P

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(C).

BAC D

Chapter 5

120

Figure 3. The expression of SETD2- and PBRM1-regulated lncRNA probes in ccRCC cell lines. (A). Heatmap of the expression levels of the 54 lncRNA probes differentially expressed between SETD2-KD and WT/NT-PTECs shown in Figure 2B now with the ccRCC cell lines included. The probes are ordered in the same way as in Figure 2B, and samples are subject to unsupervised hierarchical clustering. (B) Heatmap of the expression levels of the 38 lncRNA probes significantly differentially expressed in PBRM1-KD PTECS as compared to WT/NT-PTECS shown in Figure 2D now with the ccRCC cell lines included. Probes are ordered as in Figure 2D, and samples were subject to unsupervised clustering. Black squares indicate WT/NT-PTECs, gray squares indicate PBRM1-KD PTECs, and light gray squares indicate ccRCC cell lines.

A

B

lncRnAs in ccRcc

5

121

PBRM1-regulated lncRNAsTo identify PBRM1-regulated lncRNAs we compared the lncRNA profiles of WT/NT and PBRM1 shRNA transduced PTECs at day 6 as described in Chapter 4. A total of 1,683 lncRNA probes were retrieved after normalization and filtering (Table 1). PCA of these probes showed an almost complete separation in the second component that explained 16.6% of all variance (Figure 2C). 180 lncRNA probes showed a significant difference in their expression levels upon PBRM1 knockdown with for 38 probes a fold change of more than 2. These 38 probes corresponded to 34 lncRNAs (Supplementary Table S4). Unsupervised hierarchical clustering of the 38 lncRNA probes revealed a clear distinction between the PBRM1-KD and the WT/NT-PTECs (Figure 2D). Ten lncRNA probes (26%) were up- and 28 (74%) were downregulated. A probe detecting MALAT1 was among the lncRNA genes significantly downregulated upon PBRM1-KD in PTECs.

Analysis of the expression pattern of the PBRM1-regulated lncRNA probes in the ccRCC cell lines revealed a separate cluster with the ccRCC cell lines next to the cluster with the WT/NT-PTECs and PBRM1-KD PTECs (Figure 3B). The probes with decreased levels in PBRM1-KD PTECs showed an overall lower signal in the ccRCC cell lines. The pattern of the PBRM1-KD induced probes was, similarly to the pattern observed for SETD2-KD induced probes, less clear in the ccRCC cell lines. Of the 38 lncRNA probes with a significantly different expression level upon PBRM1 knockdown, 2 were also differentially expressed between PTECs and ccRCC cell lines (highlighted in Supplementary Table S4). The abundance of these 2 transcripts was consistently lower in PBRM1-KD cells and in ccRCC cell lines as compared to WT/NT-PTECS.

Identification of putative cis regulating lncRNAsIn the ccRCC cell lines, we identified 39 differentially expressed lncRNA / protein-coding gene pairs with a distance between the transcriptional start sites of <300kb, indicative of a putative cis-regulation (Table 2). The distance varied between 0 to 281kb, with an average distance of 112kb. Twenty-one of the lncRNA / protein-coding gene pairs were transcribed from the same strand, while 18 pairs were transcribed from the two opposite strands. Of the 18 pairs transcribed from opposite strands, 9 were in a tail-to-tail orientation (T-T) and 9 in a head-to-head orientation (H-H). A concordant expression change was observed for 30 pairs, whereas an inversed regulation was seen for nine of the pairs.

To identify putative cis-acting lncRNAs in the SETD2-KD PTECs, we retrieved the differentially expressed protein-coding gene list from Chapter 3 with in total 416 probes with a differentially expression level (196 up- and 220 downregulated). Seven lncRNA / protein-coding gene pairs were identified with a distance of <300kb, indicative of a putative cis-acting regulatory function of the lncRNA (Table 2). The distance varied between 1 to 212kb with an average of 101kb. Four pairs were expressed from the same strand and three pairs from opposite strands (2x T-T and 1x H-H). Of the seven pairs, six showed concordant expression changes and one pair showed discordant

Chapter 5

122

Tabl

e 2. O

verv

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of p

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lncR

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lncRnAs in ccRcc

5

123

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Tabl

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Chapter 5

124

Tabl

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e 2. (

cont

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d)

lncRnAs in ccRcc

5

125

changes. Next, we determined whether any of the protein-coding genes putatively regulated by these 7 lncRNAs belong to the gene sets significantly affected by SETD2-KD in PTECs (as identified in Chapter 3). This revealed one gene, i.e. Nucleolar And Spindle Associated Protein 1 (NUSAP1), which belongs to the G2M checkpoint gene set. One cis-acting lncRNA – protein-coding gene pair, i.e. the RP11-768F21.1 lncRNA / TMEM233 pair, was found consistently in ccRCC versus PTECs and in SETD2-KD versus WT/NT-PTECs, which supports a potential cis-regulating mode of action.

For PBRM1-KD we retrieved the differentially expressed protein-coding genes from Chapter 4, which included 301 protein-coding gene probes (136 up- and 165 downregulated). Three putative cis-acting lncRNAs regulated by PBRM1 were identified (Table 2) with a distance to the transcription start site of the protein-coding gene of 82, 126 and 137kb, respectively. All three pairs were expressed from opposite strands and showed concordant expression changes. One of the three protein-coding genes, i.e. purine nucleoside phosphorylase (PNP), is part of the IFN-α responsive gene set, which was shown to be significantly enriched in PBRM1-KD PTECs (Chapter 4).

DiScuSSionWe identified 89 lncRNA genes differentially expressed in ccRCC cell lines as compared to PTECs, and 48 SETD2- and 34 PBRM1-regulated lncRNA genes in PTECs. The overlap between the SETD2- and PBRM1-regulated lncRNAs and the differentially expressed lncRNAs in ccRCC cell lines is limited to 10 and 2, respectively (highlighted in Supplementary Tables S3 and S4). Interestingly, a relatively large proportion of the differentially expressed lncRNAs, i.e. 2 up- and 9 downregulated lncRNA probes, were shared between the SETD2- and PBRM1-regulated lncRNA gene sets. In the ccRCC cell lines inactivating mutations in SETD2 and PBRM1 have been identified in 5 and 3 of them. Due to the relatively low number of ccRCC cell lines and the marked overlap between cell lines with functional SETD2 and PBRM1 loss we could not separately analyze differential lncRNA expression patterns in them.

Although there are some studies that have characterized the lncRNA expression pattern of ccRCC (Yu et al., 2012; Fachel et al., 2013; Qin et al., 2014; Blondeau et al., 2015), it is hard to make a direct comparison with our data. This is caused by differences in nomenclature and annotation of lncRNA transcripts and in part by the use of different array platforms. To enable a partial comparison we determined whether the fold change based top-20 up- and downregulated lncRNAs listed in two of the studies were also differentially expressed in our study (Qin et al., 2014; Blondeau et al., 2015). This analysis revealed an overlap of 4 out of 31 (for 9 of the lncRNAs we did not have probes on our custom designed array) for the study of Qin et al. (2014), i.e. TCONS_l2_00028987, TMEM179, lincRNA-TSPAN8 and PDE1A; and an overlap of 3 out of 40 lncRNAs for the study of Blondeau et al. (2015), i.e. lnc-SCN2A-2, lnc-MED10-7 and lnc-TTC34-3. The overlap of the top-20 up- and downregulated lncRNAs listed

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126

in the study of Blondeau et al. (2015) with the complete list of differentially expressed probes of the study of Qin et al. (2014) was limited to three consistently downregulated lncRNAs. None of the lncRNAs was differentially expressed in all three studies. Several targeted approach studies showed altered expression for specific lncRNAs in ccRCC, i.e. decreased expression levels of GAS5 (Qiao et al., 2013) and MEG3 (Wang et al., 2015a) and enhanced expression levels of HIF1A-AS1 and -AS2 (Trash-Bingham and Tartof 1999; Bertozzi et al., 2016), H19 (Wang et al., 2015b), HOTAIR (Pei et al., 2014; Wu et al., 2014) and MALAT1 (Hirata et al., 2015; Xiao et al., 2015, Zhang et al., 2015). In our study, significantly reduced expression levels were observed only for MEG3. GAS5 levels were variable but overall increased in ccRCC, albeit not significant. Levels of HIF1A-AS2 were upregulated in 8 out of 10 ccRCC cell lines and HOTAIR levels were increased in 4 out of 10 cell lines. HIF1A-AS1 and H19 levels were below the detection limit in all our hybridizations, possibly due to a suboptimal probe design. In contrast to the literature, MALAT1 levels were decreased in most of the ccRCC cell lines compared to PTECs. Thus, for most of the lncRNAs previously shown to be involved in ccRCC we now show similar expression changes in comparison to PTECs. A potential explanation for the differences observed between the different studies might be related to the use of short term cultured PTECs versus ccRCC cell lines in this study as compared to total renal tissue or epithelial cell lines as normal counterparts compared to tissue of ccRCC cases in most of the other studies.

Both SETD2 and PBRM1 loss revealed a clear change in the lncRNA expression pattern of PTECs, with substantially more genes being down- than upregulated. This might be consistent with the known functions of these two proteins. SETD2 mediates H3K36me3, which is positively correlated with enhanced expression by facilitating transcription elongation (Edmunds et al., 2007). The PBRM1-containing SWI/SNF complex modifies the chromatin structure and recruits the transcriptional apparatus to nucleosomal DNA to initiate transcription (Hargreaves & Crabtree, 2011). Therefore, loss of either SETD2 or PBRM1 might be consistent with our observation that the proportion of lncRNAs downregulated was much higher than the proportion that was upregulated (Table 1).

A visual inspection of the lncRNA heatmaps that included the ccRCC data (Figure 3) led to the observation that lncRNAs downregulated in the SETD2 and PBRM1 KD-experiments were even less abundant in the ccRCC cell lines. The group of lncRNAs upregulated after SETD2-KD or PBRM1-KD did in general not show a further increase in their expression levels in the ccRCC cell lines. This suggests that the downregulated lncRNAs might be direct targets of SETD2 and PBRM1, and relevant for ccRCC pathogenesis, whereas the upregulated lncRNAs might possibly represent indirect targets. A similar pattern was seen for the SETD2- and PBRM1-regulated protein-coding genes, with an obvious further decrease in the levels of the downregulated probes (Supplementary Figure S2). All together, our data support a role especially for the downregulated lncRNAs in the pathogenesis of ccRCC. Malouf et

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127

al. (2015) reported a significant enrichment of PBRM1-mutated tumors in two out of four lncRNA-based molecular ccRCC subtypes. This supports a role for PBRM1 loss in defining the lncRNA expression pattern of ccRCC.

SETD2-KD in PTECS resulted in a significantly reduced expression level of HIF1-AS2, a lncRNA shown to be upregulated in ccRCC. This could indicate that ccRCC cases with functional SETD2 have higher HIF1-AS2 levels as compared to cases with functional loss of SETD2. We identified significantly reduced levels of MALAT1 in PBRM1-KD PTECs. Significantly enhanced expression levels of MALAT1 have been observed previously in ccRCC cases (Hirata et al., 2015; Xiao et al., 2015, Zhang et al., 2015). Together, these data suggest that high levels of MALAT1 might be more common in ccRCC cases with functional PBRM1. A further study of the potential link between SETD2, PBRM1 and these lncRNAs is warranted to unravel their functional relevance in ccRCC pathogenesis.

LncRNAs can regulate the expression of nearby protein-coding genes in cis. We identified 34 putative cis-regulating lncRNA / protein-coding gene pairs in ccRCC versus PTECs. Two of these putative cis-acting lncRNA / protein-coding gene pairs have been identified previously as possible cis-acting lncRNAs in ccRCC based on a strong positive correlation between their expression levels in ccRCC tumors (Malouf et al., 2015), i.e. RP11-768F21.1 / TMEM233 and RP11-999E24.3 / C14orf37. The protein-coding gene partners of two of the putative cis-acting lncRNAs might be relevant for the phenotype observed in PTECs upon knockdown of SETD2 and PBRM1. The SETD2-regulated lncRNA, TCONS_00023186, might regulate expression of the nearby protein-coding gene Nucleolar and spindle-associated protein 1 (NUSAP1). Both genes were upregulated upon SETD2 knockdown in PTECs. NUSAP1 expression is regulated by E2F1 (Gulzar et al., 2013) and belongs to the G2M checkpoint gene set, which is significantly enriched in SETD2-KD PTECs (Li et al., 2016). Thus, the high levels of TCONS_00023186 upon SETD2-KD might contribute to the enhanced expression level of NUSAP1. For PBRM1 we identified the lncRNA RPPH1 as a putative cis-regulating lncRNA for Purine Nucleoside Phosphorylase (PNP). Both genes were upregulated upon PBRM1 knockdown in PTECs. PNP belongs to the IFN-γ responsive gene set, which was significantly enriched in PBRM1-KD PTECs (Chapter 4, this thesis). PNP depletion in prostate cancer led to decreased proliferation, migration and invasion (Kojima et al., 2012) supporting a possible role in cancer.

In conclusion, our study revealed a distinct lncRNA expression pattern in ccRCC that might at least partly be associated with functional loss of SETD2 and PBRM1. LncRNAs with a reduced expression upon knockdown of SETD2 or PBMR1 showed a further decrease in ccRCC cell lines, suggestive of a possible role in the pathogenesis of ccRCC. Several cis-regulating lncRNA / protein-coding gene pairs were identified, but their role in ccRCC remains unknown.

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Supplementary Figure 1. Principal component analysis (PCA) plots of the protein-coding gene probes for shRNA treated PTECs. (A) PCA plot of the 1st and 2nd component using the 11,659 probes for protein-coding genes retained after filtering in SETD2-KD (gray) and WT/NT-PTECs (black). (B) PCA plot of the 1st and 2nd component using the 11,579 probes for protein-coding genes retained after filtering in PBRM1-KD (gray) and WT/NT-PTECs (black).

A

B

lncRnAs in ccRcc

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Chapter 5

132

Supplementary Table S1. List of lncRNA probes that are significantly and at least 2 fold differentially expressed between ccRCC cell lines and PTECs.

Probe name p (corr) ccRCC vs PTECs

Fold change Lincipedia name

PVD_LNCIPEDIA_2013_18085 0.0002 up 40.2 lnc-ZNF644-1PVD_LNCIPEDIA_2013_9207 0.0034 up 27.8 lnc-LRRC61-2PVD_LNCIPEDIA_2013_9206 0.0011 up 24.4 lnc-LRRC61-1PVD_LNCIPEDIA_2013_18086 0.0019 up 16.1 lnc-ZNF644-1PVD_LNCIPEDIA_2013_9622 0.0018 up 9.4 lnc-MDM1-1PVD_LNCIPEDIA_2013_22893 0.0340 up 6.0 lnc-MFSD9-4PVD_LNCIPEDIA_2013_15089 0.0330 up 5.5 lnc-SOX6-1PVD_LNCIPEDIA_2013_15893 0.0046 up 5.1 lnc-TFEB-1PVD_LNCIPEDIA_2013_7719 0.0378 up 5.0 lnc-HPS6-1PVD_LNCIPEDIA_2013_7670 0.0423 up 4.9 lnc-HNRNPU-2PVD_LNCIPEDIA_2013_1145 0.0463 up 4.6 lnc-ANKRD50-1PVD_LNCIPEDIA_2013_7596 0.0013 up 4.3 lnc-HLA-DQA2-10PVD_LNCIPEDIA_2013_1877 0.0031 up 4.2 lnc-BCL7A-1PVD_LNCIPEDIA_2013_9118 0.0375 up 4.2 lnc-LRIG2-4PVD_LNCIPEDIA_2013_6899 0.0019 up 4.0 lnc-GINS1-1PVD_LNCIPEDIA_2013_546 0.0018 up 4.0 lnc-ACTR3B-1PVD_LNCIPEDIA_2013_20558 0.0207 up 3.8 lnc-MFSD6-1PVD_LNCIPEDIA_2013_1580 0.0131 up 3.6 lnc-ATF7IP-2PVD_LNCIPEDIA_2013_1876 0.0031 up 3.6 lnc-BCL7A-1PVD_LNCIPEDIA_2013_21420 0.0077 up 3.5 lnc-MLXIP-1PVD_LNCIPEDIA_2013_16570 0.0233 up 3.4 lnc-TPST1-1PVD_LNCIPEDIA_2013_6255 0.0033 up 3.4 lnc-FBN1-2PVD_LNCIPEDIA_2013_4839 0.0018 up 3.2 lnc-DCLK3-1PVD_LNCIPEDIA_2013_16667 0.0002 up 3.1 lnc-TRIM56-1PVD_LNCIPEDIA_2013_16790 0.0017 up 3.0 lnc-TSN-3PVD_LNCIPEDIA_2013_8611 0.0022 up 3.0 lnc-KIAA1755-3PVD_LNCIPEDIA_2013_16114 0.0271 up 2.9 lnc-TMC2-2PVD_LNCIPEDIA_2013_4574 0.0463 up 2.9 lnc-CTD-

2517M22.14.1-2PVD_LNCIPEDIA_2013_10563 0.0347 up 2.8 lnc-NFAM1-2PVD_2013_lncrnadb_103 0.0048 up 2.7 lnc-TCF24-3PVD_LNCIPEDIA_2013_22192 0.0019 up 2.7 lnc-CTC1-1PVD_2013_lncrnadb_102 0.0215 up 2.4 lnc-TCF24-3PVD_LNCIPEDIA_2013_17605 0.0284 up 2.3 lnc-XRN2-2

lncRnAs in ccRcc

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PVD_LNCIPEDIA_2013_22316 0.0031 up 2.1 lnc-SEBOX-2PVD_LNCIPEDIA_2013_746 0.0034 down 2.0 lnc-AGRN-1PVD_LNCIPEDIA_2013_8803 0.0480 down 2.0 lnc-KPNA6-1PVD_LNCIPEDIA_2013_18369 0.0137 down 2.3 lnc-AF131215.6.1-1PVD_LNCIPEDIA_2013_5735 0.0375 down 2.3 lnc-ERICH1-5PVD_LNCIPEDIA_2013_20952 0.0020 down 2.5 lnc-AGRN-1PVD_LNCIPEDIA_2013_25516 0.0207 down 2.5 lnc-NRBP2-1PVD_LNCIPEDIA_2013_5101 0.0046 down 2.6 lnc-DLK1-4 / MEG3PVD_LNCIPEDIA_2013_24316 0.0075 down 2.8 lnc-CXorf69-1PVD_LNCIPEDIA_2013_3675* 0.0207 down 2.9 lnc-CDK9-1PVD_LNCIPEDIA_2013_855 0.0375 down 3.1 lnc-AL117340.1-2PVD_LNCIPEDIA_2013_18443 0.0375 down 3.6 lnc-ADAM10-1PVD_LNCIPEDIA_2013_15256 0.0129 down 3.6 lnc-SRCAP-1PVD_LNCIPEDIA_2013_23220 0.0389 down 4.0 lnc-CD58-1PVD_LNCIPEDIA_2013_24601 0.0107 down 4.1 lnc-GRHL2-1PVD_LNCIPEDIA_2013_18189 0.0376 down 4.2 lnc-ZNF843-2PVD_LNCIPEDIA_2013_14573 0.0310 down 4.2 lnc-SLC25A29-1PVD_LNCIPEDIA_2013_12881 0.0375 down 4.4 lnc-RFWD2-1PVD_LNCIPEDIA_2013_2260 0.0461 down 4.7 lnc-C12orf75-1PVD_LNCIPEDIA_2013_8068 0.0136 down 5.9 lnc-INSIG1-2PVD_LNCIPEDIA_2013_5516 0.0034 down 6.2 lnc-EIF2AK3-3PVD_LNCIPEDIA_2013_2309 0.0222 down 6.3 lnc-C14orf37-1PVD_LNCIPEDIA_2013_2741 0.0480 down 6.4 lnc-C3orf52-1PVD_LNCIPEDIA_2013_25444 0.0390 down 6.8 lnc-C1orf195-1PVD_LNCIPEDIA_2013_2379 0.0250 down 7.4 lnc-C16orf61-2PVD_LNCIPEDIA_2013_9703 0.0014 down 7.7 lnc-METAP1-3PVD_LNCIPEDIA_2013_25694 0.0207 down 8.7 lnc-RP1-1PVD_LNCIPEDIA_2013_15282 0.0001 down 9.1 lnc-SRPK2-3PVD_LNCIPEDIA_2013_169 0.0304 down 9.1 lnc-AC009336.1-2PVD_LNCIPEDIA_2013_10482 0.0468 down 9.4 lnc-NDST3-5PVD_LNCIPEDIA_2013_12139 0.0286 down 9.6 lnc-PPP1R32-1PVD_LNCIPEDIA_2013_4003 0.0000 down 9.9 lnc-CLDN14-1PVD_LNCIPEDIA_2013_13223 0.0034 down 9.9 lnc-RP11-150O12.6.1-1PVD_LNCIPEDIA_2013_6189 0.0492 down 10.0 lnc-FAM84B-8

Supplementary Table S1. (continued)

Chapter 5

134




PVD_2013_lncrnadb_24 0.0014 down 10.3 lnc-AC018816.3.1-1PVD_LNCIPEDIA_2013_4237 0.0204 down 11.4 lnc-COL28A1-1PVD_LNCIPEDIA_2013_17575 0.0004 down 11.9 lnc-XKR4-1PVD_LNCIPEDIA_2013_13228 0.0001 down 12.3 lnc-RP11-150O12.6.1-1PVD_LNCIPEDIA_2013_20288 0.0000 down 12.4 lnc-C9orf156-3PVD_LNCIPEDIA_2013_14027 0.0002 down 12.8 lnc-SCN2A-2PVD_LNCIPEDIA_2013_7691 0.0185 down 13.0 lnc-HOXC4-3PVD_LNCIPEDIA_2013_14776 0.0207 down 13.8 lnc-SLC7A6OS-2PVD_LNCIPEDIA_2013_5095 0.0000 down 14.5 lnc-DLK1-4 / MEG3PVD_2013_lncrnadb_89 0.0004 down 14.8 lnc-THNSL1-2PVD_LNCIPEDIA_2013_22354 0.0204 down 15.0 lnc-TMEM88B-1PVD_LNCIPEDIA_2013_3967 0.0002 down 15.8 lnc-CIT-1PVD_LNCIPEDIA_2013_3516 0.0000 down 16.1 lnc-CD59-1PVD_LNCIPEDIA_2013_25450 0.0000 down 16.3 lnc-TBCCD1-1PVD_LNCIPEDIA_2013_6411 0.0018 down 16.3 lnc-FGGY-6PVD_LNCIPEDIA_2013_246 0.0010 down 16.7 lnc-AC018816.3.1-2PVD_2013_lncrnadb_23 0.0014 down 17.2 lnc-AC018816.3.1-1PVD_LNCIPEDIA_2013_21004 0.0075 down 17.6 lnc-ZCRB1-1PVD_LNCIPEDIA_2013_20170 0.0000 down 17.7 lnc-KIN-2PVD_LNCIPEDIA_2013_24943 0.0330 down 19.6 lnc-ARMCX5-1PVD_LNCIPEDIA_2013_20637 0.0001 down 19.8 lnc-CD59-1PVD_LNCIPEDIA_2013_3164 0.0004 down 21.2 lnc-CAMK1G-1PVD_LNCIPEDIA_2013_13360 0.0000 down 21.4 lnc-RP11-327F22.5.1-7PVD_LNCIPEDIA_2013_7682 0.0000 down 22.8 lnc-HOXA13-1PVD_LNCIPEDIA_2013_21084 0.0203 down 27.6 lnc-HOXD3-1PVD_LNCIPEDIA_2013_6723 0.0304 down 30.1 lnc-GADD45G-4PVD_LNCIPEDIA_2013_6984 0.0297 down 33.1 lnc-GLT1D1-1PVD_LNCIPEDIA_2013_17591 0.0080 down 33.6 lnc-XRCC2-1PVD_LNCIPEDIA_2013_22110 0.0089 down 34.6 lnc-XRCC2-1PVD_LNCIPEDIA_2013_20185 0.0001 down 41.0 lnc-THNSL1-3PVD_LNCIPEDIA_2013_21390 0.0014 down 47.4 lnc-XKR4-1PVD_2013_lncrnadb_88 0.0001 down 49.8 lnc-THNSL1-2PVD_LNCIPEDIA_2013_6171 0.0000 down 56.1 lnc-FAM83B-1PVD_LNCIPEDIA_2013_6985 0.0210 down 90.4 lnc-GLT1D1-1

Supplementary Table S1. (continued)

lncRnAs in ccRcc

5

135

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chr9

:922

2140

0-92

2214

59G

AD

D45

GA

_23_

P137

035

0.03

72up

5.2

chrX

:154

0311

7-15

4030

58PI

RA

_32_

P163

858

0.03

45up

5.2

chr1

0:10

2123

917-

1021

2397

6SC

D

Chapter 5

136

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P2

0317

30.

0074

up5.

2ch

r11:

1178

7207

0-11

7872

129

IL10

RAA

_24_

P131

589

0.04

28up

5.1

chr3

:121

8392

46-1

2183

9305

CD86

A_2

3_P1

6002

50.

0235

up5.

0ch

r1:1

5902

4619

-159

0246

78IF

I16

A_2

3_P3

1480

50.

0078

up4.

9ch

r1:9

5658

415-

9565

8474

TMEM

56A

_23_

P161

297

0.04

41up

4.9

chr1

0:50

9428

57-5

0942

798

OG

DH

LA

_23_

P343

398

0.02

36up

4.6

chr1

7:38

7101

19-3

8710

060

CCR

7A

_23_

P160

720

0.00

01up

4.5

chr1

:212

8600

87-2

1286

0028

BATF

3A

_24_

P289

178

0.02

14up

4.5

chr1

6:85

7412

85-8

5741

226

C16o

rf74

A_2

3_P3

0468

20.

0081

up4.

5ch

r16:

1062

2683

-106

2262

4EM

P2A

_24_

P175

176

0.00

20up

4.5

chr7

:775

8617

9-77

5862

38PH

TF2

A_2

3_P1

5688

00.

0131

up4.

5ch

r6:1

3221

1871

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2119

30EN

PP1

A_3

2_P1

7896

60.

0012

up4.

4ch

r6:1

1583

573-

1158

3632

TMEM

170B

A_2

3_P1

0385

0.00

94up

4.4

chr1

:212

2778

52-2

1227

7911

DTL

A_2

3_P1

0668

20.

0072

up4.

3ch

r16:

1062

6759

-106

2670

0EM

P2A

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P326

3157

0.04

36up

4.2

chr7

:042

1531

48-0

4215

3207

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3A

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P132

175

0.00

67up

4.2

chr2

2:20

2290

29-2

0228

970

RTN

4RA

_24_

P935

103

0.02

23up

4.1

chr1

6:40

1282

6-40

1276

7A

DCY

9A

_24_

P139

943

0.00

31up

4.0

chr2

:208

1793

5-20

8178

76H

S1BP

3A

_23_

P388

812

0.02

81up

4.0

chr2

:113

4958

17-1

1349

5758

CKA

P2L

A_3

3_P3

3607

280.

0302

up4.

0ch

r19:

4095

3760

-409

5370

1BL

VRB

A_3

3_P3

2105

610.

0117

up3.

9ch

r2:0

2636

3183

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3631

24EN

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0004

4381

8A

_23_

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253

0.01

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chr2

:234

3806

36-2

3438

0695

DG

KD

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

137

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

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oord

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esG

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sym

bol

A_2

4_P9

1649

60.

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6480

6770

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0682

9PR

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A_2

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8002

20.

0393

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8ch

r3:1

7060

6975

-170

6069

16EI

F5A

2A

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P369

2979

0.00

81up

3.8

chr1

3:11

0777

522-

1107

7758

1LO

C283

485

A_2

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5849

30.

0126

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8ch

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2617

2483

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1725

42LM

NB1

A_2

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6822

50.

0126

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7ch

r17:

4845

8627

-484

5868

6EM

E1A

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P335

9753

0.01

57up

3.7

chr1

:229

4568

73-2

2945

6814

C1or

f96

A_2

3_P1

4576

10.

0115

up3.

6ch

r7:1

2728

281-

1272

8340

ARL

4AA

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118

0.00

36up

3.6

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:371

4310

2-37

1431

61PI

M1

A_3

3_P3

2650

300.

0234

up3.

6ch

r22:

1971

2238

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1229

7G

P1BB

A_2

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4788

0.03

61up

3.6

chr1

:452

3306

6-45

2331

25K

IF2C

A_3

3_P3

2162

970.

0379

up3.

5ch

r5:1

4265

7577

-142

6575

18N

R3C1

A_2

3_P7

4349

0.02

59up

3.5

chr1

:163

3251

44-1

6332

5203

NU

F2A

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P214

603

0.00

06up

3.5

chr6

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9789

1-30

6978

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OT1

A_2

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3651

30.

0059

up3.

4ch

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5427

0927

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2708

68G

EMIN

5A

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252

0.02

78up

3.4

chr7

:229

7009

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6609

SNX

8A

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800

0.03

29up

3.4

chr1

:143

8972

00-1

4389

7141

FAM

72D

A_3

2_P3

2254

0.04

63up

3.4

chr2

1:47

4248

36-4

7424

895

CO

L6A

1A

_23_

P169

003

0.03

64up

3.3

chr8

:192

5224

2-19

2523

01SH

2D4A

A_2

4_P2

7229

00.

0105

up3.

3ch

r6:3

7234

44-3

7233

85C6

orf1

45A

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800.

0226

up3.

3ch

r1:2

1482

6239

-214

8262

98CE

NPF

A_3

2_P1

5524

70.

0128

up3.

3ch

r19:

4946

9029

-494

6908

8FT

L

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

138

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

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oord

inat

esG

ene

sym

bol

A_2

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1457

10.

0492

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1125

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5699

4SP

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A_2

3_P1

0086

80.

0142

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3ch

r17:

3486

9010

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6895

1M

YO19

A_2

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7715

50.

0005

up3.

3ch

r3:1

4874

8450

-148

7483

91H

LTF

A_3

2_P6

172

0.01

12up

3.3

chr7

:152

1624

43-1

5216

2502

LOC1

0012

8822

A_2

3_P5

0504

0.01

18up

3.3

chr1

9:49

4687

31-4

9468

790

FTL

A_2

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3015

80.

0229

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4484

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4170

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70.

0002

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2ch

r2:1

6011

2861

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1128

02W

DSU

B1A

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P339

5146

0.03

26up

3.2

chr7

:296

8559

7-29

6855

38LO

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762

A_2

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0367

80.

0059

up3.

2ch

r11:

1080

0588

3-10

8005

942

ACAT

1A

_33_

P323

1297

0.03

70up

3.2

chr1

:167

5103

82-1

6751

0323

CREG

1A

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945

0.04

91up

3.2

chr1

8:13

6635

74-1

3663

515

C18o

rf19

A_2

3_P2

0031

00.

0441

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r1:6

8939

846-

6893

9812

DEP

DC1

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0465

10.

0261

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6484

5055

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4499

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9753

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4444

5525

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4558

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1881

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0482

up3.

1ch

r17:

7622

0720

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2077

9BI

RC5

A_2

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3303

0.01

30up

3.1

chr1

:242

0487

17-2

4204

8776

EXO

1A

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P322

354

0.02

42up

3.1

chr1

8:47

9198

99-4

7919

958

SKA

1A

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P149

494

0.00

94up

3.1

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0:25

2886

65-2

5288

606

ABH

D12

A_3

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2845

570.

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0ch

r2:1

7413

1452

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1315

11ZA

KA

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P329

065

0.00

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3-26

4148

02BT

N3A

1A

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168

0.01

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3.0

chr1

7:26

6549

24-2

6654

983

TMEM

97A

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P341

2722

0.01

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3.0

chr7

:229

7121

-229

7062

SNX

8

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

139

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

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s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

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ld c

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sym

bol

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2209

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0076

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8A

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0076

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7516

653-

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4-97

2182

43A

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5AA

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0.04

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chr1

7:73

2691

33-7

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074

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5A19

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10.

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5131

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6:11

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0.00

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2.9

chr1

8:21

7421

85-2

1742

126

OSB

PL1A

A_3

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0139

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3226

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0.04

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5610

9-26

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4048

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511

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5429

170

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5212

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7:76

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52-7

6170

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A_2

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7771

0.04

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2.9

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1CB

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9-62

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1AA

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2.8

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:263

7825

7-26

3783

16BT

N3A

2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

140

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

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ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

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ld c

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bol

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8093

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0003

8896

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5495

0471

CKS1

BA

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8612

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0:50

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8610

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2-94

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7364

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0:59

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3M

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60up

2.7

chr1

2:53

6867

48-5

3687

109

ESPL

1A

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3235

0.04

61up

2.7

chr8

:125

3184

97-1

2531

8438

LOC2

8605

2A

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P395

374

0.03

69up

2.7

chr6

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8901

1-26

1889

52H

IST1

H4D

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3848

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r6:1

5329

1957

-153

2918

98FB

XO5

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

141

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P5

6736

0.01

11up

2.7

chr2

:132

2404

30-1

3224

0489

TUBA

3DA

_23_

P429

491

0.04

05up

2.7

chr1

1:82

6453

51-8

2645

410

C11o

rf82

A_2

4_P9

4319

30.

0097

up2.

7ch

r2:4

4545

965-

4454

5906

PREP

LA

_23_

P215

790

0.03

35up

2.6

chr7

:552

7484

1-55

2749

00EG

FRA

_23_

P373

750.

0246

up2.

6ch

r14:

9133

8075

-913

3801

6RP

S6K

A5

A_3

3_P3

7679

270.

0463

up2.

6ch

r5:1

4266

0788

-142

6607

29N

R3C1

A_2

3_P4

5917

0.03

02up

2.6

chr1

:154

9515

27-1

5495

1586

CKS1

BA

_33_

P332

2589

0.04

62up

2.6

chr2

:583

8730

9-58

3872

50FA

NCL

A_2

4_P1

2662

80.

0109

up2.

6ch

r12:

5745

0095

-574

5003

6TM

EM19

4AA

_23_

P122

805

0.03

02up

2.6

chr7

:129

8050

35-1

2980

4976

TMEM

209

A_3

3_P3

2802

130.

0101

up2.

6ch

r20:

4452

3321

-445

2338

0CT

SAA

_33_

P327

2553

0.00

21up

2.6

chr2

2:50

9581

22-5

0958

181

NCA

PH2

A_3

3_P3

3251

310.

0306

up2.

6ch

r5:1

7913

7006

-179

1370

65CA

NX

A_2

3_P3

3460

80.

0132

up2.

6ch

r7:6

5425

765-

6542

5706

GU

SBA

_33_

P337

4205

0.04

75up

2.5

chr1

0:12

9913

252-

1299

1319

3M

KI6

7A

_23_

P122

947

0.03

02up

2.5

chr7

:326

1984

2-32

6204

30AV

L9A

_32_

P685

330.

0401

up2.

5ch

r2:6

2052

180-

6205

2121

FAM

161A

A_2

3_P5

9547

0.01

77up

2.5

chr7

:330

5425

5-33

0541

96N

T5C3

A_3

3_P3

4126

130.

0277

up2.

5ch

r12:

9894

2332

-989

4239

1TM

POA

_24_

P491

900.

0468

up2.

5ch

r17:

6598

7591

-659

8753

2C1

7orf

58A

_23_

P750

380.

0028

up2.

5ch

r10:

1155

9466

8-11

5594

609

DCL

RE1A

A_2

3_P2

5062

90.

0055

up2.

5ch

r6:3

2810

490-

3281

0023

PSM

B8

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

142

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P3

3699

20.

0180

up2.

5ch

r7:1

1973

49-1

1952

19ZF

AN

D2A

A_3

3_P3

2887

540.

0214

up2.

5ch

r19:

5130

1021

-513

0096

2C1

9orf

48A

_23_

P152

136

0.03

77up

2.5

chr1

6:58

4397

99-5

8439

858

GIN

S3A

_23_

P966

410.

0269

up2.

5ch

rX:1

2841

243-

1284

1302

PRPS

2A

_23_

P288

860.

0416

up2.

5ch

r20:

5096

102-

5095

957

PCN

AA

_33_

P332

4333

0.03

27up

2.5

chr1

8:14

1853

09-1

4185

368

AN

KRD

20A

5PA

_33_

P341

5663

0.03

33up

2.5

chr5

:897

5420

1-89

7541

42M

BLAC

2A

_23_

P159

671

0.04

92up

2.5

chrX

:189

1131

9-18

9112

60PH

KA

2A

_23_

P422

193

0.00

95up

2.5

chrX

:485

6688

7-48

5669

46SU

V39

H1

A_2

3_P7

976

0.00

96up

2.5

chr6

:261

5692

8-26

1569

87H

IST1

H1E

A_2

3_P1

594

0.03

29up

2.4

chr1

1:64

0061

11-6

4006

170

VEG

FBA

_23_

P370

989

0.02

82up

2.4

chr8

:488

8833

4-48

8883

93M

CM4

A_2

3_P2

5470

20.

0482

up2.

4ch

r6:1

8225

006-

1822

4948

DEK

A_2

3_P1

2492

70.

0347

up2.

4ch

r5:1

7679

8914

-176

7989

73RG

S14

A_3

3_P3

4202

540.

0034

up2.

4ch

r7:8

1972

1-81

9780

HEA

TR2

A_2

4_P1

6666

10.

0216

up2.

4ch

r7:7

7423

590-

7742

3531

TMEM

60A

_24_

P358

425

0.02

59up

2.4

chr2

:373

1690

7-37

3169

66C

CDC7

5A

_33_

P338

9188

0.03

39up

2.4

chr1

0:60

1479

60-6

0148

019

TFA

MA

_23_

P242

30.

0011

up2.

4ch

r12:

1075

8734

-107

5867

5M

AGO

HB

A_3

3_P3

2485

190.

0482

up2.

4ch

r3:1

6013

2206

-160

1322

65SM

C4A

_32_

P118

940.

0248

up2.

4ch

r12:

1237

3847

8-12

3741

393

C12o

rf65

A_3

3_P3

2160

080.

0335

up2.

4ch

r13:

2172

7794

-217

2773

5SK

A3

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

143

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P4

1942

0.04

58up

2.4

chr5

:897

8136

7-89

7814

26PO

LR3G

A_3

2_P8

7531

0.01

62up

2.4

chr1

:225

1564

79-2

2515

6538

DN

AH

14A

_23_

P252

855

0.03

06up

2.4

chr5

:349

2504

8-34

9253

57BR

IX1

A_2

3_P1

4846

30.

0247

up2.

4ch

rX:1

1965

9059

-119

6590

00CU

L4B

A_2

3_P6

3459

0.01

31up

2.3

chr1

:234

5195

91-2

3451

9650

C1or

f31

A_3

3_P3

3799

470.

0218

up2.

3ch

r6:3

1321

712-

3132

1653

HLA

-BA

_23_

P804

730.

0465

up2.

3ch

r3:1

2626

1750

-126

2618

09CH

ST13

A_2

4_P5

6317

0.03

71up

2.3

chr1

3:98

0462

26-9

8046

285

MBN

L2A

_33_

P333

9253

0.03

72up

2.3

chr9

:033

0196

59-0

3301

9600

APT

XA

_23_

P251

695

0.02

59up

2.3

chr2

0:23

3352

35-2

3335

294

NXT

1A

_33_

P325

2479

0.01

83up

2.3

chr1

:150

9177

12-1

5091

7771

SETD

B1A

_33_

P327

9708

0.01

39up

2.3

chr1

1:62

6091

62-6

2609

103

RNU

2-2

A_2

3_P2

4997

0.01

17up

2.3

chr1

2:58

1423

22-5

8142

263

CDK

4A

_33_

P327

2828

0.01

57up

2.3

chr5

:349

1485

9-34

9148

00RA

D1

A_2

3_P1

1409

50.

0123

up2.

3ch

rX:2

1900

749-

2190

0808

MBT

PS2

A_3

3_P3

2918

310.

0463

up2.

3ch

r10:

9527

9478

-952

7953

7CE

P55

A_2

3_P5

0180

50.

0278

up2.

3ch

r2:9

9779

128-

9977

9187

LIPT

1A

_23_

P210

330.

0143

up2.

3ch

r3:1

5565

4290

-155

6554

29G

MPS

A_2

3_P1

5474

00.

0347

up2.

3ch

r20:

2120

9737

-212

1341

4PL

K1S

1A

_23_

P626

590.

0271

up2.

3ch

r1:4

0538

584-

4053

8525

PPT1

A_2

3_P2

5228

30.

0431

up2.

3ch

r17:

2932

6723

-293

2678

2RN

F135

A_3

3_P3

3185

810.

0194

up2.

3ch

r3:1

4578

7511

-145

7874

52PL

OD

2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

144

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P1

3397

40.

0110

up2.

3ch

r6:3

4574

417-

3457

4358

C6or

f106

A_3

3_P3

2266

100.

0350

up2.

2ch

r9:1

5474

127-

1547

4068

PSIP

1A

_23_

P256

455

0.04

44up

2.2

chr7

:767

6682

-767

6623

RPA

3A

_33_

P330

9271

0.01

15up

2.2

chr6

:806

2828

-806

2769

MU

TED

A_2

3_P1

4196

50.

0385

up2.

2ch

r19:

1716

0709

-171

6065

0H

AUS8

A_3

3_P3

3090

340.

0297

up2.

2ch

r10:

3868

1035

-386

8097

6SE

PT7L

A_2

3_P1

3134

80.

0071

up2.

2ch

r2:3

9963

924-

3996

3865

THU

MPD

2A

_24_

P212

072

0.04

01up

2.2

chr5

:939

6630

0-93

9663

59A

NK

RD32

A_2

3_P4

0821

0.01

89up

2.2

chr3

:148

8902

46-1

4889

0304

HPS

3A

_33_

P335

7322

0.02

59up

2.2

chr9

:106

9036

24-1

0690

3683

SMC2

A_3

3_P3

2809

300.

0248

up2.

2ch

r8:6

7834

344-

6783

4285

SNH

G6

A_3

3_P3

3938

360.

0252

up2.

2ch

r7:3

3057

111-

3305

7052

NT5

C3A

_23_

P167

692

0.01

15up

2.2

chr5

:179

6697

26-1

7966

9667

MA

PK9

A_3

3_P3

2937

340.

0273

up2.

2ch

r5:0

9260

4130

-092

6041

89EN

ST00

0005

1221

0.4

A_3

2_P8

3118

10.

0021

up2.

2ch

r12:

1254

9712

2-12

5497

181

BRI3

BPA

_23_

P737

630.

0463

up2.

2ch

rX:1

5370

6252

-153

7061

93LA

GE3

A_3

2_P3

4206

40.

0139

up2.

2ch

r11:

6173

2096

-617

3203

8FT

H1

A_2

4_P3

9969

40.

0440

up2.

2ch

r20:

2806

11-2

8067

0ZC

CHC3

A_2

3_P7

1146

0.02

60up

2.2

chr7

:441

5682

4-44

1567

65PO

LD2

A_3

3_P3

2290

670.

0189

up2.

2ch

r6:2

7806

454-

2780

6513

HIS

T1H

2BN

A_2

3_P3

1116

0.01

20up

2.2

chr6

:247

0179

4-24

7018

53AC

OT1

3A

_33_

P326

2694

0.01

58up

2.2

chr1

6:03

0831

898-

0308

3183

9na

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

145

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P2

0259

40.

0482

up2.

1ch

r10:

1215

8953

9-12

1589

480

MCM

BPA

_24_

P363

477

0.01

39up

2.1

chr7

:227

4904

-227

4845

FTSJ

2A

_23_

P160

934

0.01

26up

2.1

chr1

:150

2041

24-1

5020

2979

AN

P32E

A_3

2_P2

0555

30.

0383

up2.

1ch

r5:1

7239

6613

-172

3966

72RP

L26L

1A

_33_

P341

2479

0.02

68up

2.1

chr1

0:10

0189

352-

1001

8929

3H

PS1

A_3

3_P3

2152

390.

0441

up2.

1ch

r10:

9152

2529

-915

2258

8K

IF20

BA

_33_

P324

2388

0.01

12up

2.1

chr3

:196

4627

96-1

9646

2855

PIG

XA

_23_

P798

180.

0148

up2.

1ch

r20:

4282

5757

-428

2569

8C2

0orf

111

A_3

2_P2

3330

40.

0072

up2.

1ch

r1:2

2641

9007

-226

4189

48LI

N9

A_2

3_P2

0953

80.

0137

up2.

1ch

r2:9

7260

032-

9725

9973

KIA

A13

10A

_23_

P209

200

0.02

28up

2.1

chr1

9:30

3151

06-3

0315

165

CCN

E1A

_23_

P594

50.

0341

up2.

1ch

r20:

3766

8102

-376

6816

1D

HX

35A

_32_

P252

730.

0339

up2.

1ch

r2:1

9835

1440

-198

3513

81H

SPD

1A

_23_

P565

670.

0180

up2.

1ch

r2:3

9008

980-

3900

9039

GEM

IN6

A_2

3_P3

2011

30.

0219

up2.

1ch

r20:

6276

22-6

2756

3SR

XN

1A

_33_

P325

3975

0.03

93up

2.1

chr1

1:10

8012

358-

1080

1241

7AC

AT1

A_3

3_P3

4363

160.

0093

up2.

1ch

r20:

3095

6857

-309

5691

6A

SXL1

A_3

3_P3

3338

630.

0265

up2.

1ch

r7:5

9423

15-5

9423

74C

CZ1

A_2

3_P2

5868

90.

0093

up2.

1ch

r7:8

2544

8-82

5507

HEA

TR2

A_2

3_P1

5258

30.

0404

up2.

1ch

r17:

7708

4403

-770

8446

2EN

GA

SEA

_23_

P794

10.

0075

up2.

1ch

r6:4

3022

085-

4302

2026

MRP

L2A

_23_

P404

091

0.02

78up

2.1

chr5

:148

7335

95-1

4873

3654

GRP

EL2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

146

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

4_P1

4621

10.

0382

up2.

0ch

r6:2

6158

474-

2615

8533

HIS

T1H

2BD

A_3

3_P3

2870

280.

0273

up2.

0ch

r7:3

5912

316-

3591

2375

SEPT

7A

_24_

P278

299

0.03

55up

2.0

chr1

0:56

8108

8-56

8102

9A

SB13

A_2

3_P3

7009

70.

0382

up2.

0ch

r2:2

0248

8995

-202

4889

36TM

EM23

7A

_33_

P342

4803

0.03

02up

2.0

chr6

:031

3217

03-0

3132

1644

HLA

-CA

_33_

P324

4872

0.00

09up

2.0

chr1

:180

0531

81-1

8005

3240

CEP3

50A

_33_

P325

3501

0.03

11up

2.0

chr1

:149

7836

17-1

4978

3558

HIS

T2H

2BF

A_3

3_P3

2961

980.

0183

up2.

0ch

r5:1

2638

0533

-126

3804

74C5

orf6

3A

_24_

P551

480.

0303

up2.

0ch

r6:2

7100

377-

2710

0318

HIS

T1H

2BJ

A_2

3_P2

5550

30.

0381

dow

n2.

0ch

r11:

1170

3850

0-11

7038

559

PAFA

H1B

2A

_23_

P257

743

0.04

90do

wn

2.0

chr9

:379

1995

1-37

9198

92SH

BA

_33_

P322

8385

0.03

78do

wn

2.0

chr1

:285

6290

3-28

5629

62AT

PIF1

A_2

3_P7

9426

0.02

71do

wn

2.0

chr2

:231

6849

57-2

3168

5016

CAB3

9A

_33_

P328

1930

0.03

94do

wn

2.0

chr3

:370

9527

3-37

0952

14LR

RFIP

2A

_24_

P926

760

0.01

91do

wn

2.0

chr3

:426

7204

7-42

6727

01N

KTR

A_2

3_P2

1200

20.

0193

dow

n2.

0ch

r3:4

2689

920-

4268

9979

NK

TRA

_32_

P968

070.

0146

dow

n2.

0ch

r1:1

7390

0682

-173

9006

23RC

3H1

A_2

3_P1

4398

70.

0078

dow

n2.

0ch

r3:1

1468

321-

1146

8380

ATG

7A

_33_

P325

1054

0.04

35do

wn

2.0

chr9

:363

3666

7-36

3366

08RN

F38

A_3

3_P3

3178

150.

0463

dow

n2.

0ch

r12:

2537

8608

-253

7854

9K

RAS

A_3

3_P3

2125

750.

0373

dow

n2.

0ch

r20:

3615

1971

-361

5203

0N

NAT

A_2

3_P2

1181

40.

0074

dow

n2.

0ch

r3:4

7893

446-

4789

3387

MA

P4

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

147

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P1

7870

0.01

35do

wn

2.0

chr2

2:38

6154

32-3

8615

373

TMEM

184B

A_2

3_P1

0419

90.

0162

dow

n2.

0ch

r10:

3320

0566

-332

0050

7IT

GB1

A_2

3_P2

0926

90.

0183

dow

n2.

0ch

r2:4

4458

389-

4445

8448

PPM

1BA

_23_

P150

876

0.02

55do

wn

2.0

chr1

2:12

3350

139-

1233

5008

0V

PS37

BA

_33_

P321

5575

0.04

69do

wn

2.0

chr1

:180

2408

5-18

0241

44A

RHG

EF10

LA

_23_

P553

190.

0297

dow

n2.

0ch

r17:

2720

6782

-272

0672

3FL

OT2

A_3

3_P3

2796

200.

0364

dow

n2.

1ch

r22:

2263

0310

-226

3025

1BC

RP4

A_3

3_P3

8666

310.

0452

dow

n2.

1ch

r11:

4388

1253

-438

8131

2D

KFZ

P564

C152

A_2

3_P1

0320

10.

0192

dow

n2.

1ch

r1:2

4289

291-

2428

9349

PNRC

2A

_23_

P204

609

0.02

10do

wn

2.1

chr1

2:95

6947

37-9

5694

796

VEZ

TA

_33_

P333

4575

0.04

50do

wn

2.1

chr3

:172

1433

66-1

7214

3307

TCO

NS_

l2_0

0019

618

A_2

3_P1

8276

0.04

81do

wn

2.1

chr3

:501

5613

7-50

1561

96RB

M5

A_2

3_P1

2386

60.

0191

dow

n2.

1ch

r9:3

4251

982-

3425

2041

UBA

P1A

_23_

P151

90.

0399

dow

n2.

1ch

r11:

6726

1724

-672

6149

2PI

TPN

M1

A_3

3_P3

2771

400.

0232

dow

n2.

1ch

r1:1

9950

017-

1995

0076

C1or

f151

A_3

3_P3

3604

260.

0109

dow

n2.

1ch

r4:1

0099

497-

1009

9438

WD

R1A

_23_

P217

098

0.03

66do

wn

2.1

chr9

:800

3212

7-80

0321

86V

PS13

AA

_23_

P129

556

0.01

49do

wn

2.1

chr1

6:27

3759

99-2

7376

058

IL4R

A_2

3_P1

4651

20.

0443

dow

n2.

1ch

r9:8

8641

621-

8864

1562

GO

LM1

A_2

3_P3

1949

20.

0229

dow

n2.

1ch

r11:

1261

3209

2-12

6132

151

FAM

118B

A_2

4_P7

1153

0.00

09do

wn

2.1

chr1

:262

8693

3-26

2868

74PA

FAH

2A

_24_

P131

222

0.04

33do

wn

2.1

chr1

:173

1340

5-17

3133

46AT

P13A

2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

148

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

4_P1

9106

70.

0278

dow

n2.

1ch

r1:9

7893

83-9

7893

24CL

STN

1A

_23_

P146

644

0.03

12do

wn

2.1

chr1

5:60

6413

24-6

0639

880

AN

XA

2A

_24_

P269

814

0.01

57do

wn

2.1

chr1

0:12

4191

798-

1241

9185

7PL

EKH

A1

A_2

4_P1

0965

20.

0347

dow

n2.

2ch

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7740

0666

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0060

7PE

AK

1A

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730.

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dow

n2.

2ch

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0995

798-

2099

5857

KIA

A17

97A

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P329

6499

0.00

27do

wn

2.2

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:128

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41-1

2884

1482

PTPR

KA

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910.

0146

dow

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2ch

r4:5

6284

050-

5628

4109

TMEM

165

A_3

3_P3

3821

570.

0111

dow

n2.

2ch

r11:

8566

8853

-856

6879

4PI

CALM

A_3

3_P3

3207

620.

0435

dow

n2.

2ch

r14:

9252

5010

-925

2495

1AT

XN

3A

_33_

P322

2917

0.03

23do

wn

2.2

chr1

5:74

0067

91-7

4006

850

CD27

6A

_23_

P140

907

0.03

06do

wn

2.2

chr1

6:42

2210

-422

151

TMEM

8AA

_23_

P200

560

0.01

12do

wn

2.2

chr1

:224

1926

9-22

4193

28CD

C42

A_2

4_P4

7182

0.01

10do

wn

2.2

chr1

0:75

8792

27-7

5879

286

VCL

A_2

3_P3

0024

0.01

12do

wn

2.2

chr4

:103

5378

56-1

0353

7915

NFK

B1A

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P277

295

0.00

84do

wn

2.2

chr3

:128

8065

80-1

2880

6521

RAB4

3A

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P337

1224

0.01

46do

wn

2.2

chr3

:434

0787

8-43

4078

19A

NO

10A

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P321

5123

0.00

71do

wn

2.2

chr4

:993

6317

2-99

3632

31RA

P1G

DS1

A_2

3_P1

2671

60.

0137

dow

n2.

2ch

r1:2

8564

499-

2856

4558

ATPI

F1A

_33_

P325

1148

0.02

08do

wn

2.2

chr2

2:43

5591

57-4

3559

216

TSPO

A_3

2_P6

6974

0.02

16do

wn

2.2

chr1

1:11

7076

257-

1170

7619

8PC

SK7

A_2

3_P2

5488

80.

0196

dow

n2.

2ch

r7:1

4308

7000

-143

0870

59ZY

XA

_23_

P435

30.

0306

dow

n2.

2ch

r17:

2563

9668

-256

3972

7W

SB1

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

149

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P2

5918

90.

0414

dow

n2.

2ch

r1:2

5166

518-

2516

7308

CLIC

4A

_33_

P332

8426

0.00

65do

wn

2.3

chr3

:434

0792

0-43

4078

61A

NO

10A

_23_

P358

009

0.01

46do

wn

2.3

chr1

:158

9764

9-15

8977

08D

NA

JC16

A_3

3_P3

5935

460.

0112

dow

n2.

3ch

r14:

1039

6952

2-10

3969

581

MA

RK3

A_3

3_P3

2850

770.

0139

dow

n2.

3ch

r4:4

7455

136-

4745

5077

CO

MM

D8

A_3

3_P3

7999

360.

0370

dow

n2.

3ch

r1:1

8024

301-

1802

4360

ARH

GEF

10L

A_2

3_P7

7073

0.01

28do

wn

2.3

chr1

5:51

0143

68-5

1012

286

SPPL

2AA

_23_

P140

648

0.00

22do

wn

2.3

chr1

5:23

0033

25-2

3003

384

CYFI

P1A

_23_

P380

766

0.01

83do

wn

2.3

chr1

4:10

3398

858-

1033

9879

9CD

C42B

PBA

_23_

P126

241

0.04

68do

wn

2.3

chr1

:211

3399

2-21

1339

33EI

F4G

3A

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P323

5568

0.04

63do

wn

2.3

chr1

:196

6562

3-19

6655

64CA

PZB

A_2

3_P8

7500

0.03

07do

wn

2.3

chr1

2:56

2141

37-5

6214

196

ORM

DL2

A_2

3_P2

2926

0.00

62do

wn

2.3

chr1

:171

7507

-171

7448

GN

B1A

_33_

P321

8138

0.04

68do

wn

2.3

chr1

7:74

1783

8-74

1789

7PO

LR2A

A_3

3_P3

3127

900.

0485

dow

n2.

3ch

r15:

6620

6222

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0616

3M

EGF1

1A

_23_

P946

360.

0065

dow

n2.

3ch

r9:1

2561

6832

-125

6165

09RC

3H2

A_3

3_P3

6414

270.

0075

dow

n2.

3ch

r15:

4843

4932

-484

3487

3M

YEF2

A_2

3_P4

8886

0.03

15do

wn

2.3

chr1

5:58

8893

14-5

8889

255

AD

AM

10A

_23_

P327

069

0.03

06do

wn

2.3

chr4

:688

4726

-688

4785

KIA

A02

32A

_23_

P257

895

0.04

01do

wn

2.3

chr2

2:22

1236

03-2

2123

544

MA

PK1

A_2

3_P5

1187

0.02

17do

wn

2.3

chr1

:211

6703

-211

6762

PRKC

ZA

_23_

P166

807

0.01

46do

wn

2.3

chr3

:519

9155

1-51

9914

92PC

BP4

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

150

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P1

2126

50.

0378

dow

n2.

4ch

r3:5

2264

904-

5226

4046

TWF2

A_2

3_P6

9493

0.03

63do

wn

2.4

chr3

:493

9727

1-49

3972

12RH

OA

A_3

3_P3

3128

770.

0462

dow

n2.

4ch

r9:3

5661

115-

3566

1174

CCD

C107

A_3

2_P8

5813

0.00

76do

wn

2.4

chr4

:993

6472

5-99

3647

84RA

P1G

DS1

A_2

3_P9

0311

0.04

61do

wn

2.4

chr1

9:48

1605

4-48

1599

5TI

CAM

1A

_23_

P142

389

0.02

46do

wn

2.4

chr1

9:35

7584

34-3

5758

493

LSR

A_2

3_P3

7598

0.00

05do

wn

2.4

chr1

5:73

8533

76-7

3853

317

NPT

NA

_23_

P391

607

0.01

94do

wn

2.4

chr9

:140

5093

91-1

4050

9450

ARR

DC1

A_2

3_P1

6642

10.

0069

dow

n2.

4ch

r22:

3068

8234

-306

8817

5TB

C1D

10A

A_2

3_P1

2675

20.

0126

dow

n2.

4ch

r1:1

9670

902-

1966

6103

CAPZ

BA

_23_

P195

900.

0390

dow

n2.

4ch

r6:1

5918

7729

-159

1876

70EZ

RA

_23_

P420

361

0.03

73do

wn

2.4

chr3

:101

6850

6-10

1685

65BR

K1

A_2

3_P1

0903

40.

0492

dow

n2.

4ch

r20:

4395

4637

-439

5457

8SD

C4A

_32_

P505

220.

0359

dow

n2.

4ch

r20:

1349

950-

1349

891

FKBP

1AA

_23_

P406

424

0.00

92do

wn

2.4

chr1

:113

2453

13-1

1324

5254

RHO

CA

_33_

P329

1294

0.00

58do

wn

2.4

chr8

:286

1111

7-28

6111

76EX

TL3

A_3

3_P3

4070

420.

0250

dow

n2.

4ch

r1:1

1703

09-1

1703

68B3

GA

LT6

A_2

3_P5

6228

0.01

80do

wn

2.4

chr1

9:19

7405

35-1

9740

476

GM

IPA

_23_

P201

939

0.04

92do

wn

2.4

chr1

:113

2531

52-1

1325

3093

PPM

1JA

_24_

P204

244

0.03

39do

wn

2.4

chr4

:154

2286

42-1

5422

8621

AN

XA

2P1

A_3

2_P2

2401

0.00

35do

wn

2.5

chr1

:366

4615

9-36

6462

18M

AP7

D1

A_2

3_P1

2638

80.

0271

dow

n2.

5ch

r1:2

6607

808-

2660

7867

SH3B

GRL

3

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

151

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P3

4341

10.

0041

dow

n2.

5ch

r1:9

9118

6-99

1245

AGRN

A_2

4_P4

1437

60.

0249

dow

n2.

5ch

r4:3

8702

518-

3870

2577

KLF

3A

_23_

P771

450.

0042

dow

n2.

5ch

r15:

6618

1162

-661

8122

1RA

B11A

A_2

3_P1

3549

90.

0110

dow

n2.

5ch

r1:2

5170

053-

2517

0112

CLIC

4A

_32_

P128

701

0.04

50do

wn

2.5

chr4

:120

2163

31-1

2021

6390

USP

53A

_33_

P338

0101

0.00

74do

wn

2.5

chr1

:366

4593

7-36

6459

96M

AP7

D1

A_3

3_P3

3495

970.

0056

dow

n2.

5na

naA

_23_

P145

485

0.03

02do

wn

2.5

chr6

:150

2677

14-1

5026

7773

ULB

P2A

_32_

P155

506

0.01

28do

wn

2.5

chr3

:236

3161

1-23

6316

70U

BE2E

2A

_24_

P150

430.

0377

dow

n2.

5ch

r3:4

7387

924-

4738

7983

KLH

L18

A_2

3_P5

4376

0.02

60do

wn

2.5

chr1

5:74

2756

22-7

4275

563

STO

ML1

A_2

4_P4

0562

10.

0347

dow

n2.

5ch

r3:5

2526

378-

5252

6437

NIS

CHA

_23_

P442

570.

0096

dow

n2.

5ch

r4:4

7453

025-

4745

2966

CO

MM

D8

A_3

3_P3

2147

200.

0458

dow

n2.

5ch

r1:3

7949

708-

3794

9767

ZC3H

12A

A_2

4_P5

6130

0.02

05do

wn

2.6

chr1

2:56

5535

03-5

6553

806

MYL

6A

_33_

P323

0709

0.00

60do

wn

2.6

chr1

7:49

2793

6-49

2799

5K

IF1C

A_3

3_P3

2248

190.

0183

dow

n2.

6ch

r8:1

4222

0970

-142

2209

11SL

C45A

4A

_23_

P211

806

0.00

20do

wn

2.6

chr3

:370

9485

5-37

0947

96LR

RFIP

2A

_23_

P423

389

0.02

16do

wn

2.6

chr9

:357

3344

4-35

7351

26CR

EB3

A_2

4_P6

7810

40.

0379

dow

n2.

6ch

r20:

6227

2003

-622

7194

4ST

MN

3A

_32_

P186

138

0.03

58do

wn

2.6

chr6

:999

7962

6-99

9796

85LO

C100

1308

90A

_33_

P321

7704

0.00

49do

wn

2.6

chr9

:351

0425

4-35

1041

95K

IAA

1539

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

152

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

4_P4

6637

40.

0302

dow

n2.

6ch

r1:2

8297

293-

2829

7234

EYA

3A

_24_

P986

130.

0219

dow

n2.

6ch

r10:

8227

9096

-822

7918

4TS

PAN

14A

_23_

P371

787

0.04

76do

wn

2.6

chr1

4:70

1815

78-7

0181

637

KIA

A02

47A

_33_

P358

5268

0.04

26do

wn

2.6

chr3

:502

9625

8-50

2963

17G

NA

I2A

_33_

P341

5923

0.03

10do

wn

2.7

chr1

2:50

0388

14-5

0038

755

FMN

L3A

_33_

P329

4002

0.03

76do

wn

2.7

chr2

2:43

0881

87-4

3088

128

A4G

ALT

A_3

2_P1

0168

90.

0039

dow

n2.

7ch

r7:1

2098

9898

-120

9898

39FA

M3C

A_3

3_P3

3673

320.

0216

dow

n2.

7ch

r1:2

5227

99-2

5228

57C1

orf9

3A

_24_

P577

300.

0118

dow

n2.

7ch

r14:

2330

4096

-233

0415

5M

RPL5

2A

_24_

P683

110.

0492

dow

n2.

7ch

r14:

6441

6714

-644

2148

5SY

NE2

A_3

3_P3

2368

810.

0367

dow

n2.

7ch

r1:1

9984

882-

1998

4941

C1or

f151

-NBL

1A

_24_

P811

704

0.00

93do

wn

2.7

chr1

2:27

8029

48-2

7803

007

PPFI

BP1

A_3

2_P1

1450

0.00

50do

wn

2.7

chr1

0:43

3186

87-4

3319

089

BMS1

A_2

3_P3

6345

0.03

06do

wn

2.7

chr1

1:72

4688

22-7

2466

750

STA

RD10

A_2

4_P3

1643

00.

0180

dow

n2.

7ch

r6:8

6176

992-

8618

1005

NT5

EA

_23_

P720

250.

0235

dow

n2.

7ch

r3:4

8895

022-

4889

4963

SLC2

5A20

A_3

3_P3

3338

260.

0255

dow

n2.

7ch

r15:

7619

3256

-761

9331

5U

BE2Q

2A

_33_

P332

4884

0.00

14do

wn

2.8

chr6

:109

7683

97-1

0976

8338

MIC

AL1

A_2

3_P1

4388

50.

0003

dow

n2.

8ch

r3:5

6762

170-

5676

2111

ARH

GEF

3A

_23_

P373

724

0.00

44do

wn

2.8

chr1

2:27

8483

53-2

7848

412

PPFI

BP1

A_2

3_P2

2119

0.00

71do

wn

2.8

chr8

:144

9896

80-1

4498

9621

PLEC

A_3

3_P3

2828

400.

0331

dow

n2.

8ch

r14:

5004

4133

-500

4407

4RP

S29

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

153

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P1

4446

50.

0180

dow

n2.

8ch

r4:1

0855

2893

-108

5528

34PA

PSS1

A_3

3_P3

2600

530.

0409

dow

n2.

8ch

r9:1

3399

5766

-133

9958

25A

IF1L

A_2

4_P2

5734

80.

0094

dow

n2.

8ch

r3:6

9154

416-

6915

4475

ARL

6IP5

A_3

2_P1

6308

90.

0131

dow

n2.

9ch

r12:

1057

6044

0-10

5761

274

C12o

rf75

A_2

3_P3

1412

00.

0377

dow

n2.

9ch

r22:

5101

8626

-510

1846

0CH

KB

A_3

3_P3

3300

990.

0278

dow

n2.

9ch

rX:2

8220

73-2

8220

14A

RSD

A_2

3_P9

465

0.00

34do

wn

2.9

chr9

:130

5759

24-1

3057

5983

FPG

SA

_23_

P153

529

0.04

63do

wn

2.9

chr1

9:49

7147

60-4

9714

819

TRPM

4A

_24_

P975

260.

0158

dow

n2.

9ch

r3:3

2523

335-

3252

3276

CMTM

6A

_23_

P207

220.

0076

dow

n2.

9ch

r9:1

3927

0092

-139

2700

33SN

APC

4A

_23_

P138

760

0.04

17do

wn

2.9

chr1

1:67

1321

22-6

7132

063

CLCF

1A

_24_

P203

830.

0337

dow

n2.

9ch

r3:9

8455

71-9

8456

30A

RPC4

A_2

3_P3

3791

70.

0392

dow

n2.

9ch

r12:

2773

1136

-277

8631

7PP

FIBP

1A

_33_

P321

0278

0.03

91do

wn

2.9

chr1

4:64

6930

75-6

4693

134

SYN

E2A

_23_

P144

054

0.01

21do

wn

2.9

chr3

:532

2622

2-53

2262

81PR

KCD

A_2

3_P1

3883

50.

0243

dow

n2.

9ch

r11:

6497

7906

-649

7832

6CA

PN1

A_2

3_P1

5085

20.

0154

dow

n3.

0ch

r12:

5653

7790

-565

3784

9ES

YT1

A_3

3_P3

3733

640.

0068

dow

n3.

0ch

r1:2

5167

306-

2516

7365

CLIC

4A

_23_

P118

430.

0302

dow

n3.

0ch

r1:2

0458

6412

-204

5863

53LR

RN2

A_3

3_P3

4036

150.

0369

dow

n3.

0ch

r20:

1350

005-

1349

946

FKBP

1AA

_33_

P336

3560

0.03

59do

wn

3.0

chr1

:155

4677

6-15

5468

35TM

EM51

A_2

3_P1

0620

40.

0149

dow

n3.

0ch

r14:

7779

7434

-777

9749

3G

STZ1

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

154

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

3367

000.

0243

dow

n3.

0ch

r4:7

7704

264-

7770

4323

SHRO

OM

3A

_23_

P485

500.

0207

dow

n3.

0ch

r14:

1053

6292

6-10

5362

985

KIA

A02

84A

_24_

P137

522

0.01

12do

wn

3.1

chr4

:120

2152

56-1

2021

5315

USP

53A

_24_

P283

341

0.00

36do

wn

3.1

chr6

:109

7661

94-1

0976

6135

MIC

AL1

A_3

3_P3

3274

790.

0080

dow

n3.

1ch

r3:4

4956

813-

4495

6754

ZDH

HC3

A_3

3_P3

3653

570.

0045

dow

n3.

1ch

r3:5

0355

325-

5035

5266

HYA

L2A

_23_

P205

336

0.03

59do

wn

3.1

chr1

4:96

8533

68-9

6853

427

C14o

rf12

9A

_23_

P509

070.

0255

dow

n3.

1ch

r2:1

8754

5295

-187

5453

54IT

GAV

A_2

3_P2

7724

0.03

82do

wn

3.2

chr1

9:48

2878

27-4

8287

886

SEPW

1A

_23_

P205

228

0.04

03do

wn

3.2

chr1

3:52

5070

94-5

2507

035

ATP7

BA

_23_

P557

060.

0271

dow

n3.

2ch

r19:

4554

1385

-455

4144

4RE

LBA

_33_

P327

8573

0.02

52do

wn

3.2

chrX

:490

2290

8-49

0229

67M

AGIX

A_2

3_P5

7961

0.00

33do

wn

3.2

chr3

:484

5073

7-48

4484

40PL

XN

B1A

_23_

P115

430.

0376

dow

n3.

2ch

r1:2

4171

909-

2417

1850

FUCA

1A

_23_

P287

30.

0169

dow

n3.

2ch

r14:

1041

4208

4-10

4143

801

KLC

1A

_24_

P921

321

0.00

81do

wn

3.2

chr1

1:48

1920

48-4

8192

107

PTPR

JA

_24_

P992

160.

0294

dow

n3.

3ch

r14:

2334

5985

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4616

5LR

P10

A_2

3_P7

6969

0.00

41do

wn

3.3

chr1

4:72

2059

22-7

2205

981

SIPA

1L1

A_3

3_P3

3264

320.

0427

dow

n3.

3ch

r19:

4828

4568

-482

8462

7SE

PW1

A_2

4_P9

2896

90.

0004

dow

n3.

3ch

r9:1

1213

8132

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1380

73PT

PN3

A_3

3_P3

2473

920.

0071

dow

n3.

3ch

r21:

1095

1365

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5130

6TP

TEA

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P337

9463

0.00

15do

wn

3.3

chr1

:241

2184

8-24

1219

07LY

PLA

2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

155

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

2_P1

4812

20.

0078

dow

n3.

3ch

r2:8

9953

072-

8995

3131

naA

_33_

P328

6481

0.01

16do

wn

3.3

chr6

:052

5223

93-0

5252

2452

RP1-

152L

7.9

A_2

3_P6

0296

0.04

63do

wn

3.3

chr9

:777

6167

5-77

7617

34O

STF1

A_3

3_P3

3650

020.

0278

dow

n3.

4ch

r6:3

1549

64-3

1549

05TU

BB2A

A_3

3_P3

2206

430.

0052

dow

n3.

4ch

r9:1

3047

6294

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4762

35PT

RH1

A_3

3_P3

2213

030.

0430

dow

n3.

4ch

r17:

4083

1479

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3142

0C

CR10

A_2

3_P1

0600

20.

0191

dow

n3.

4ch

r14:

3587

0847

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7078

8N

FKBI

AA

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090.

0090

dow

n3.

4ch

r3:1

1183

6665

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8367

24C3

orf5

2A

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8510

0.00

19do

wn

3.4

chr1

:241

2117

6-24

1212

35LY

PLA

2A

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P324

4669

0.00

81do

wn

3.5

chr6

:759

6345

2-75

9633

93TM

EM30

AA

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P112

623

0.04

12do

wn

3.5

chr9

:677

8497

1-67

7849

12FA

M27

E3A

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P329

8024

0.04

76do

wn

3.5

chr1

7:48

7452

20-4

8745

279

ABC

C3A

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370.

0303

dow

n3.

5ch

r12:

5163

9735

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3967

6SM

AGP

A_3

3_P3

3186

710.

0011

dow

n3.

5ch

r1:2

4121

957-

2412

2016

LYPL

A2

A_2

3_P1

2384

80.

0429

dow

n3.

5ch

r9:1

2454

7348

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5474

07D

AB2

IPA

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P693

390.

0095

dow

n3.

6ch

r3:3

8164

415-

3816

4356

ACA

A1

A_2

4_P1

6644

30.

0282

dow

n3.

6ch

r6:3

3052

981-

3305

3587

HLA

-DPB

1A

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P250

619

0.04

87do

wn

3.6

chr6

:158

0944

69-1

5809

4528

ZDH

HC1

4A

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P326

4926

0.04

19do

wn

3.6

chr1

4:55

2512

76-5

5251

335

SAM

D4A

A_2

4_P1

5792

60.

0039

dow

n3.

6ch

r6:1

3820

3679

-138

2037

38TN

FAIP

3A

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P323

8215

0.02

71do

wn

3.6

chr2

:165

5414

28-1

6554

1369

CO

BLL1

A_2

3_P9

2202

0.00

76do

wn

3.7

chr3

:497

6010

1-49

7600

42G

MPP

B

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

156

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

4_P1

3743

40.

0431

dow

n3.

7ch

r3:9

8515

261-

9851

5202

DCB

LD2

A_2

3_P9

7296

0.00

11do

wn

3.7

chr1

:167

8637

0-16

7864

29N

ECA

P2A

_23_

P314

530.

0278

dow

n3.

7ch

r7:8

9794

004-

8979

4063

STEA

P1A

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P356

616

0.04

03do

wn

3.8

chr1

1:34

1728

01-3

4172

742

ABT

B2A

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P324

3399

0.01

18do

wn

3.8

chr3

:578

7687

4-57

8769

33SL

MA

PA

_23_

P210

763

0.02

50do

wn

3.8

chr2

0:10

6191

20-1

0619

061

JAG

1A

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P327

1635

0.03

20do

wn

3.8

chr6

:330

4848

9-33

0485

37H

LA-D

PB1

A_2

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9303

40.

0181

dow

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9ch

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6915

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5142

8H

AS3

A_3

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3813

050.

0076

dow

n3.

9ch

r14:

1046

7084

7-10

4670

906

JD53

2100

A_2

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2207

0.02

71do

wn

3.9

chr1

0:28

9715

51-2

8971

610

BAM

BIA

_32_

P703

0.02

47do

wn

3.9

chr1

:857

4339

6-85

7434

55LO

C646

626

A_2

4_P2

7649

00.

0026

dow

n3.

9ch

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4120

413-

2412

0604

LYPL

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A_3

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470.

0064

dow

n4.

0ch

r2:1

7336

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3692

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GA

6A

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334

0.00

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wn

4.0

chr7

:897

9053

1-89

7905

90ST

EAP1

A_2

3_P3

1458

40.

0093

dow

n4.

0ch

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364-

5068

6423

MA

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PK3

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0016

20.

0312

dow

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2826

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4282

6745

HIG

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2716

510.

0150

dow

n4.

1ch

r6:3

3052

784-

3305

2843

HLA

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1A

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P337

1718

0.02

71do

wn

4.2

chrX

:238

0386

3-23

8039

22SA

T1A

_23_

P160

546

0.02

19do

wn

4.2

chr1

:150

9693

78-1

5096

9319

FAM

63A

A_2

3_P1

3701

60.

0224

dow

n4.

3ch

rX:2

3804

055-

2380

4114

SAT1

A_2

4_P4

1696

10.

0405

dow

n4.

3ch

r22:

1995

7509

-199

5745

0A

RVCF

A_2

4_P3

5471

50.

0126

dow

n4.

3ch

r6:8

6204

891-

8620

4950

NT5

E

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

157

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P9

293

0.02

29do

wn

4.3

chr9

:718

6944

4-71

8695

03TJ

P2A

_33_

P333

4102

0.03

12do

wn

4.4

chr1

7:43

5067

78-4

3506

719

ARH

GA

P27

A_2

3_P6

771

0.04

82do

wn

4.4

chr3

:860

9688

-860

9747

LMCD

1A

_33_

P353

8279

0.00

42do

wn

4.4

chr9

:341

8785

6-34

1879

15PR

O28

52A

_23_

P178

110.

0021

dow

n4.

5ch

r22:

3081

8809

-308

1886

8SE

C14L

2A

_23_

P210

176

0.02

63do

wn

4.6

chr2

:173

3705

56-1

7337

0615

ITG

A6

A_3

3_P3

3603

410.

0125

dow

n4.

6ch

r10:

8117

072-

8117

131

GAT

A3

A_2

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2684

40.

0150

dow

n4.

6ch

r1:6

5213

04-6

5212

45TN

FRSF

25A

_23_

P769

010.

0056

dow

n4.

7ch

r14:

6521

0966

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1102

5PL

EKH

G3

A_3

3_P3

3042

120.

0058

dow

n4.

8ch

r14:

6521

0943

-652

1100

2PL

EKH

G3

A_2

3_P1

1800

0.02

08do

wn

4.8

chr1

:208

0946

0-20

8094

01CA

MK

2N1

A_2

3_P9

1829

0.01

35do

wn

4.9

chr3

:985

1768

5-98

5176

26D

CBLD

2A

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470.

0329

dow

n4.

9ch

r14:

2476

0771

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6037

6D

HRS

1A

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8303

0.00

06do

wn

4.9

chr2

1:03

9618

944-

0396

1888

5KC

NJ1

5A

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P200

670

0.00

42do

wn

5.0

chr1

:673

0327

7-67

3032

66W

DR7

8A

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P396

375

0.01

02do

wn

5.1

chr1

:215

4471

8-21

5446

59EC

E1A

_24_

P383

523

0.02

31do

wn

5.1

chr1

4:55

2557

24-5

5255

783

SAM

D4A

A_2

3_P4

0174

0.00

58do

wn

5.3

chr2

0:44

6451

21-4

4645

180

MM

P9A

_23_

P211

039

0.04

41do

wn

5.3

chr2

1:28

2097

68-2

8209

709

AD

AM

TS1

A_3

3_P3

3660

530.

0116

dow

n5.

4ch

r3:1

1930

8728

-119

3087

87A

DPR

HA

_23_

P807

390.

0031

dow

n5.

4ch

r3:3

8049

181-

3804

9122

PLCD

1A

_23_

P110

571

0.03

15do

wn

5.6

chr5

:664

6280

8-66

4628

67M

AST

4

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

158

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

2270

790.

0097

dow

n5.

6ch

r9:1

3416

6773

-134

1668

32PP

APD

C3A

_33_

P341

1477

0.02

46do

wn

5.6

chr1

9:39

6924

61-3

9692

520

NC

CRP1

A_2

3_P5

0919

0.03

64do

wn

5.7

chr2

:224

8422

95-2

2484

0597

SERP

INE2

A_2

4_P3

7633

90.

0319

dow

n5.

7ch

r1:1

3277

68-1

3277

09C

CNL2

A_2

3_P7

7048

0.02

29do

wn

5.7

chr1

4:10

0757

547-

1007

5748

8SL

C25A

29A

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893

0.02

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wn

5.9

chrX

:109

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46-1

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9187

CHRD

L1A

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033

0.02

47do

wn

6.0

chr1

1:11

8097

483-

1180

9742

4M

PZL3

A_3

3_P3

2583

240.

0017

dow

n6.

0ch

r19:

0159

6286

2-01

5962

803

AC0

0479

1.2

A_3

3_P3

2888

390.

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dow

n6.

1ch

r14:

5847

0872

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7081

3C1

4orf

37A

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P396

858

0.04

32do

wn

6.1

chr1

0:35

9274

37-3

5927

378

FZD

8A

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P181

295

0.03

73do

wn

6.1

chr1

4:58

5983

13-5

8598

254

C14o

rf37

A_2

3_P1

6861

00.

0157

dow

n6.

2ch

r7:1

6818

689-

1682

3048

TSPA

N13

A_2

3_P3

8505

0.02

18do

wn

6.2

chr1

7:46

3761

9-46

3756

0CX

CL16

A_2

3_P1

4962

60.

0121

dow

n6.

4ch

r1:6

5272

81-6

5272

22PL

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G5

A_2

4_P8

109

0.04

48do

wn

6.4

chr1

1:41

8916

-418

783

AN

O9

A_2

4_P1

4879

60.

0216

dow

n6.

4ch

r3:4

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610-

4972

1551

MST

1A

_23_

P353

490.

0271

dow

n6.

8ch

r10:

2974

7064

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4700

5SV

ILA

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P326

6744

0.03

70do

wn

6.9

chr1

:276

8036

2-27

6804

21SY

TL1

A_3

3_P3

2293

700.

0418

dow

n6.

9ch

r6:1

9840

841-

1984

0900

ID4

A_2

3_P1

9673

0.00

21do

wn

7.0

chr6

:134

4907

15-1

3449

0656

SGK

1A

_23_

P258

769

0.03

86do

wn

7.0

chr6

:330

5435

9-33

0544

18H

LA-D

PB1

A_3

3_P3

2618

690.

0006

dow

n7.

1ch

r8:2

1894

337-

2189

4396

NPM

2

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

159

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

3576

580.

0353

dow

n7.

2ch

r12:

6630

9240

-663

0929

9H

MG

A2

A_3

3_P3

2455

170.

0278

dow

n7.

3ch

r10:

4282

7945

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2788

6LO

C441

666

A_2

3_P1

2082

20.

0363

dow

n7.

5ch

r22:

2492

1819

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2187

8U

PB1

A_2

4_P2

5741

60.

0271

dow

n7.

6ch

r4:7

4964

401-

7496

4342

CXCL

2A

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P339

6635

0.02

33do

wn

7.7

chrX

:482

0618

0-48

2061

21SS

X3

A_3

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3632

600.

0044

dow

n7.

7ch

r11:

7404

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2PG

M2L

1A

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930.

0004

dow

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9000

2437

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0249

6TU

BB3

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3357

10.

0455

dow

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9ch

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4667

593-

9466

7534

ARH

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5421

70.

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6096

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9583

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A_2

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5265

50.

0039

dow

n8.

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r17:

6208

0019

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7996

0IC

AM

2A

_23_

P472

820.

0003

dow

n8.

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1300

7981

5-13

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874

ST14

A_3

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2565

100.

0253

dow

n8.

3ch

r2:4

7747

995-

4774

7936

KCN

K12

A_2

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4317

10.

0255

dow

n8.

5ch

rX:3

4646

240-

3464

6181

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47A

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9827

0.02

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wn

8.7

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5600

2-95

9560

61PR

OM

2A

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6885

0.00

19do

wn

8.7

chr1

9:35

9942

72-3

5994

213

DM

KN

A_2

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0791

10.

0171

dow

n9.

3ch

r17:

1634

0226

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4028

5TR

PV2

A_3

3_P3

2789

410.

0095

dow

n9.

8ch

r14:

2464

9400

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4945

9RE

C8A

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253

0.03

47do

wn

9.9

chr1

2:88

8867

12-8

8886

653

KIT

LGA

_33_

P332

1657

0.03

72do

wn

10.0

chr1

:221

4879

9-22

1487

40H

SPG

2A

_33_

P330

1469

0.00

91do

wn

10.1

chr2

:132

9052

24-1

3290

5165

AN

KRD

30BL

A_2

3_P2

0650

10.

0038

dow

n10

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r16:

7444

2597

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4253

8CL

EC18

BA

_23_

P789

800.

0493

dow

n11

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r19:

1792

3824

-179

2388

3B3

GN

T3

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

160

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_2

3_P2

1195

70.

0008

dow

n11

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r3:3

0735

163-

3073

5222

TGFB

R2A

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P341

0806

0.00

59do

wn

11.7

chr1

3:96

2318

47-9

6231

906

CLD

N10

A_2

3_P5

0182

20.

0278

dow

n11

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r17:

3991

1209

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1115

0JU

PA

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P215

744

0.01

67do

wn

12.5

chr7

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3511

42-1

1735

1083

CTTN

BP2

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3950

280.

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dow

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6576

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225

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12.6

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2-27

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290.

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dow

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r3:1

6880

1415

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8013

56M

ECO

MA

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230

0.04

49do

wn

12.8

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4-41

3267

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dow

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5744

9888

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500.

0329

dow

n13

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r20:

6760

810-

6760

869

BMP2

A_2

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9067

0.04

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9:50

9320

37-5

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096

SPIB

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5588

40.

0000

dow

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0.00

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wn

13.3

chr1

2:52

6425

90-5

2642

649

KRT

7A

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8402

0.00

13do

wn

14.0

chrX

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0967

26-0

7309

6785

CHIC

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501

0.02

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wn

14.2

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4:24

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00-2

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DH

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60.

0451

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6232

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TNFR

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orf1

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0.00

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15.3

chr8

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6997

20-1

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9661

NCA

LDA

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P129

157

0.01

43do

wn

15.3

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5:75

6473

73-7

5647

432

NEI

L1A

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9279

0.02

04do

wn

15.9

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4:24

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56-2

4037

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Supp

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tinue

d)

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161

Supp

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List

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sign

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sym

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3812

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3521

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dow

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4667

3252

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16.3

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5424

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16.4

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4763

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dow

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2556

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17.5

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6727

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AQP7

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6095

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1609

6931

FBLI

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7048

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7290

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2409

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dow

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1968

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8039

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5575

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dow

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4703

368-

7470

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18.7

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7-74

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3812

2104

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2216

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M2

A_2

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7661

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19.5

chr1

9:36

6424

37-3

6642

378

CO

X7A

1

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

162

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

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at l

east

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ld d

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entia

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l lin

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vs P

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2615

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640.

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0.00

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19.8

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19.8

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9-20

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9617

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21.0

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1:52

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1975

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21.8

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1253

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Supp

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d)

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163

Supp

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List

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23.6

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24.0

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25.5

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1

Supp

lem

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ry T

able

S2.

(con

tinue

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Chapter 5

164

Supp

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List

of p

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t are

sign

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Supp

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165

Supp

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List

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8:47

1183

60-4

7118

419

LIPG

A_3

3_P3

3144

010.

0039

dow

n47

.9ch

r3:1

9012

8216

-190

1282

75CL

DN

16A

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P320

261

0.02

35do

wn

48.9

chr1

9:35

9881

94-3

5988

135

DM

KN

A_2

3_P1

6308

70.

0399

dow

n49

.0ch

r14:

5247

1790

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7173

1N

ID2

A_3

3_P3

3587

310.

0005

dow

n49

.1ch

r9:7

8789

952-

7879

0011

PCSK

5A

_33_

P338

8391

0.00

96do

wn

49.7

chr1

:352

2785

3-35

2279

12G

JB4

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

166

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

3290

880.

0490

dow

n49

.9ch

r16:

3114

2813

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4275

4PR

SS8

A_2

3_P3

6934

30.

0157

dow

n51

.3ch

r19:

5149

9375

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9931

6K

LK8

A_3

3_P3

3924

050.

0001

dow

n51

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8594

4973

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4503

2C1

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99A

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560.

0000

dow

n52

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r19:

5148

0736

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8067

7K

LK7

A_2

3_P4

3898

0.00

01do

wn

52.4

chr1

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1813

0-92

5181

89EP

HX

4A

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P121

657

0.00

52do

wn

53.0

chr4

:114

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9-11

4006

70H

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1A

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340.

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dow

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2496

0BN

C1A

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20.

0000

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1297

2894

6-12

9729

005

TMEM

45B

A_2

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0448

0.00

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54.0

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1741

0-26

0173

51H

IST1

H1A

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30.

0000

dow

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7705

5372

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0554

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D1

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0000

dow

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r9:1

3169

6085

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6962

97PH

YHD

1A

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8918

0.02

45do

wn

56.1

chr1

1:18

2668

50-1

8266

791

SAA

2A

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9107

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wn

58. 5

chr1

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6057

92-2

0960

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LOC6

4258

7A

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541

0.04

24do

wn

59.9

chrX

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8150

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5381

5067

CTAG

1AA

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2611

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61.1

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5841

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dow

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1386

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WN

T7A

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100.

0485

dow

n62

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0135

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3503

42LA

D1

A_2

3_P2

4104

0.04

82do

wn

63.4

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0:75

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50-7

5677

109

PLAU

A_3

3_P3

3061

460.

0234

dow

n64

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7567

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7467

3PL

AUA

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P337

2099

0.00

17do

wn

68.5

chr4

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1071

39-1

0110

7080

DD

IT4L

A_2

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1946

0.00

00do

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69.3

chr9

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48-1

1613

2607

BSPR

YA

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040.

0163

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1084

1931

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8418

72M

ALL

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

167

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

2752

200.

0112

dow

n72

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rX:1

0520

2523

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2025

82N

RKA

_23_

P301

260.

0020

dow

n74

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5937

391-

1593

7332

FGFB

P1A

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P374

844

0.01

39do

wn

75.3

chr1

1:68

4585

33-6

8458

592

GA

LA

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P325

5434

0.00

00do

wn

76.5

chr1

4:10

1327

286-

1013

2734

5M

EG3

A_2

3_P5

301

0.00

16do

wn

78.5

chr2

:121

9786

24-1

2197

8565

TFCP

2L1

A_2

3_P4

9060

0.00

00do

wn

81.7

chr1

5:41

1493

16-4

1149

375

SPIN

T1A

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8343

0.02

96do

wn

83.4

chrX

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77-1

5388

0418

CTAG

2A

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P253

350

0.01

37do

wn

83.4

chr8

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1196

7-40

0120

26C8

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A_2

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6428

40.

0000

dow

n85

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r17:

7163

986-

7163

822

CLD

N7

A_3

2_P8

5999

0.00

42do

wn

86.8

chr1

6:83

8300

27-8

3830

086

CDH

13A

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P321

5948

0.02

18do

wn

87.0

chr1

1:11

8124

256-

1181

2419

7M

PZL2

A_2

3_P7

1530

0.02

73do

wn

87.5

chr8

:119

9367

79-1

1993

6720

TNFR

SF11

BA

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P302

672

0.00

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wn

88.5

chr4

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1072

95-1

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7236

DD

IT4L

A_3

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3362

570.

0000

dow

n93

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6014

38-3

6014

97IR

X1

A_3

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2482

650.

0000

dow

n93

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r6:3

1548

394-

3154

8335

LTB

A_2

3_P1

0121

0.00

00do

wn

102

chr8

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2022

0-41

1201

61SF

RP1

A_3

3_P3

4116

280.

0061

dow

n10

2ch

r9:2

1970

960-

2197

0901

CDK

N2A

A_2

3_P1

3996

50.

0000

dow

n10

2ch

r13:

4497

1760

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7182

0SE

RP2

A_2

3_P8

8626

0.00

41do

wn

107

chr1

5:90

3342

36-9

0333

766

AN

PEP

A_3

3_P3

4222

980.

0216

dow

n11

2ch

r22:

2326

1765

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6182

4na

A_2

3_P1

4694

60.

0009

dow

n11

2ch

r11:

6578

0838

-657

8089

7CS

T6A

_33_

P339

5675

0.02

35do

wn

115

chr2

2:23

2433

36-2

3243

395

na

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

Chapter 5

168

Supp

lem

enta

ry T

able

S2.

List

of p

rote

in-c

odin

g ge

ne p

robe

s tha

t are

sign

ifica

ntly

and

at l

east

2 fo

ld d

iffer

entia

lly e

xpre

ssed

bet

wee

n cc

RCC

cel

l lin

es

and

PTEC

s.

Prob

e na

me

P (C

orr)

cc

RCC

vs P

TEC

sFo

ld c

hang

eG

enom

ic c

oord

inat

esG

ene

sym

bol

A_3

3_P3

3171

630.

0000

dow

n11

5ch

r6:5

0815

178-

5081

5237

TFA

P2B

A_2

3_P2

1426

70.

0056

dow

n11

5ch

r6:4

6967

904-

4696

7845

GPR

110

A_2

3_P2

5700

30.

0072

dow

n11

8ch

r9:7

8808

212-

7880

8271

PCSK

5A

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P169

150.

0006

dow

n12

3ch

r2:3

7599

594-

3759

9862

QPC

TA

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636

0.01

63do

wn

134

chr1

:183

2136

10-1

8321

3669

LAM

C2A

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P376

488

0.00

00do

wn

142

chr6

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4583

7-31

5458

96TN

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P160

968

0.00

58do

wn

143

chr1

:183

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01-1

8320

9453

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C2A

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P337

9039

0.02

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143

chr2

2:23

2376

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3237

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IGLL

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680.

0006

dow

n19

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7600

060-

3760

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TA

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8156

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wn

205

chr2

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9703

1-10

1969

72CY

S1A

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P337

934

0.00

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wn

216

chr1

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1268

5-16

1127

44FB

LIM

1A

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750.

0000

dow

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5372

791-

5537

2850

SOX

17A

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610.

0079

dow

n33

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1023

9408

8-10

2394

029

MM

P7A

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5940

0.00

00do

wn

339

chr1

9:51

4619

47-5

1461

888

KLK

6A

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8192

0.02

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340

chr1

2:54

8498

04-5

4849

745

GTS

F1A

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P491

550.

0000

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r16:

6873

2812

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3287

1CD

H3

A_2

3_P1

6143

90.

0000

dow

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8ch

r10:

8873

0308

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3036

7C1

0orf

116

A_2

3_P1

4952

90.

0131

dow

n64

5ch

r1:5

9041

468-

5904

1409

TACS

TD2

A_3

2_P1

3307

20.

0000

dow

n76

8ch

r11:

1428

9180

-142

8923

9SP

ON

1A

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P500

000

0.00

00do

wn

848

chr1

3:78

2185

64-7

8218

623

SCEL

A_2

3_P6

4873

0.00

00do

wn

865

chr1

2:91

5398

93-9

1539

834

DCN

A p

robe

s hi

ghlig

hted

in g

ray

corr

espo

nd to

lncR

NA

s an

d no

t to

prot

ein

codi

ng g

enes

. Thes

e pr

obes

wer

e in

clud

ed in

iden

tifica

tion

of c

is-a

ctin

g ln

cRN

A a

nd p

rote

in

codi

ng g

ene

pair

s.

Supp

lem

enta

ry T

able

S2.

(con

tinue

d)

lncRnAs in ccRcc

5

169

Supp

lem

enta

ry T

able

S3.

List

of l

ncRN

A p

robe

s tha

t are

sign

ifica

ntly

and

at le

ast 2

fold

diff

eren

tially

expr

esse

d be

twee

n SE

TD2-

KD

and

WT/

NT-

PTEC

s.

Prob

e na

me

p (c

orr)

K

D v

s WT

Fold

cha

nge

Prob

e co

ordi

nate

sLi

ncip

edia

nam

e

PVD

_LN

CIP

EDIA

_201

3_34

060.

0216

up10

.1ch

r14:

2081

1544

-208

1160

3ln

c-C

CNB1

IP1-

1PV

D_2

013_

lncr

nadb

_11

0.00

61up

3.5

chr1

0:12

7,70

1,14

7-12

7,70

1,20

6ln

c-FA

NK

1-1

PVD

_201

3_ln

crna

db_1

20.

0030

up3.

5ch

r10:

127,

700,

988-

127,

701,

047

lnc-

FAN

K1-

2PV

D_2

013_

lncr

nadb

_10

0.00

40up

3.4

chr1

0:12

7,70

0,99

6-12

7,70

1,05

5ln

c-FA

NK

1-1

PVD

_201

3_ln

crna

db_1

30.

0030

up3.

4ch

r10:

127,

700,

989-

127,

701,

048

lnc-

FAN

K1-

2PV

D_L

NC

IPED

IA_2

013_

1322

30.

0022

up2.

3ch

r8:3

7330

595-

3733

0654

lnc-

RP11

-150

O12

.6.1

-1PV

D_L

NC

IPED

IA_2

013_

2411

30.

0022

up2.

1ch

r10:

4297

2830

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7288

9ln

c-BM

S1-3

PVD

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CIP

EDIA

_201

3_11

846

0.00

33up

2.1

chr1

6:68

2610

78-6

8261

137

lnc-

PLA

2G15

-1PV

D_L

NC

IPED

IA_2

013_

1426

30.

0100

up2.

1ch

r16:

7061

1511

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1157

0ln

c-SF

3B3-

1PV

D_L

NC

IPED

IA_2

013_

6411

0.04

03do

wn

2.0

chr1

:591

8125

3-59

1813

12ln

c-FG

GY-

6PV

D_L

NC

IPED

IA_2

013_

1879

0.03

08do

wn

2.0

chr1

6:30

9309

41-3

0931

000

lnc-

BCL7

C-2

PVD

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CIP

EDIA

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3_14

573

0.00

22do

wn

2.0

chr1

4:10

0757

694-

1007

5775

3ln

c-SL

C25

A29

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D_L

NC

IPED

IA_2

013_

2045

20.

0097

dow

n2.

1ch

r3:4

7926

39-4

7926

98ln

c-AC

0188

16.3

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PVD

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CIP

EDIA

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3_11

870.

0022

dow

n2.

1ch

r11:

4634

54-4

6351

3ln

c-A

NO

9-1

PVD

_201

3_ln

crna

db_5

90.

0022

dow

n2.

1ch

r6:5

4,63

5,82

1-54

,635

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lnc-

FAM

83B-

2PV

D_L

NC

IPED

IA_2

013_

1023

50.

0353

dow

n2.

1ch

r2:1

6573

534-

1657

3593

lnc-

MYC

N-5

PVD

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CIP

EDIA

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3_21

040

0.01

13do

wn

2.1

chr1

7:43

8367

4-43

8373

3ln

c-M

YBBP

1A-2

PVD

_LN

CIP

EDIA

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3_90

420.

0113

dow

n2.

1ch

r1:2

0196

9343

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9694

02ln

c-LM

OD

1-1

PVD

_LN

CIP

EDIA

_201

3_14

585

0.04

03do

wn

2.1

chr1

7:42

3847

23-4

2384

782

lnc-

SLC2

5A39

-1PV

D_L

NC

IPED

IA_2

013_

5395

0.04

34do

wn

2.2

chr1

0:13

2001

257-

1320

0131

6ln

c-EB

F3-3

PVD

_LN

CIP

EDIA

_201

3_33

090.

0043

dow

n2.

2ch

r15:

7465

8253

-746

5831

2ln

c-C

CDC3

3-2

Chapter 5

170

Supp

lem

enta

ry T

able

S3.

List

of l

ncRN

A p

robe

s tha

t are

sign

ifica

ntly

and

at le

ast 2

fold

diff

eren

tially

expr

esse

d be

twee

n SE

TD2-

KD

and

WT/

NT-

PTEC

s.

Prob

e na

me

p (c

orr)

K

D v

s WT

Fold

cha

nge

Prob

e co

ordi

nate

sLi

ncip

edia

nam

e

PVD

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CIP

EDIA

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3_26

810.

0433

dow

n2.

2ch

r2:7

0351

450-

7035

1509

lnc-

C2or

f42-

1PV

D_L

NC

IPED

IA_2

013_

4272

0.02

88do

wn

2.2

chr8

:120

2557

02-1

2025

5761

lnc-

CO

LEC1

0-1

PVD

_LN

CIP

EDIA

_201

3_18

101

0.04

72do

wn

2.2

chrX

:457

0754

1-45

7076

00ln

c-ZN

F674

-3PV

D_L

NC

IPED

IA_2

013_

8905

0.01

89do

wn

2.2

chr2

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5812

9-31

0581

88ln

c-LC

LAT1

-2PV

D_L

NC

IPED

IA_2

013_

1525

60.

0102

dow

n2.

3ch

r16:

3075

3169

-307

5322

8ln

c-SR

CA

P-1

PVD

_LN

CIP

EDIA

_201

3_13

859

0.00

41do

wn

2.3

chr6

:107

1949

89-1

0719

5048

lnc-

RTN

4IP1

-2PV

D_L

NC

IPED

IA_2

013_

6426

0.00

38do

wn

2.3

chr1

1:64

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d)

lncRnAs in ccRcc

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171

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Chapter 5

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SuMMaRy, DiScuSSion anD fuTuRE PERSPEcTivES

c h a P T E R 6

Summary, diScuSSion and future perSpective

6

177

In this thesis, we studied the tumor suppressive functions of SETD2 and PBRM1 in ccRCC development. In chapter 2, we comprehensively reviewed the literature concerning SETD2, from its basic biological functions to clinic relevance, especially for ccRCC tumors. In chapters 3 and 4, we investigated the consequences of SETD2 and PBRM1 loss in primary tubular epithelial cells (PTECs), the proposed normal counterparts of ccRCC tumor cells. In chapter 5, we broadened our study to long non-coding RNAs (lncRNAs) in an attempt to identify lncRNAs involved in ccRCC development. Here, I summarize our findings, discuss the results in a broader view, and propose some (near-) future perspectives.

SETD2 loss in PTECsIn mammalian cells, SETD2 is the sole protein responsible for the trimethylation of histone H3 at lysine 36 (H3K36me3). The H3K36me3 histone mark is linked to actively transcribed regions. Loss of SETD2 results in loss of H3K36me3, which prohibits binding of H3K36me3 reader proteins to carry out their functions. Consequently, SETD2 deficient cells showed defects in facilitating transcription elongation, preventing spurious transcription initiation, RNA processing, DNA mismatch repair (MMR), and homologous recombination (HR) repair. These defects increase the risk of transformation of SETD2 deficient cells (chapter 2). SETD2-loss may also abolish its direct interaction with other proteins, e.g. TP53 (Xie et al., 2008). Our current knowledge on the direct binding partners of SETD2 is still limited, which calls for further investigations.

Inactivation of SETD2 prevented PTECs from senescence-induced growth arrest (chapter 3), an observation that has not been described before. Interestingly, SETD2-knockdown(KD) PTECs retained expression of G2M check-point genes and E2F target genes at a level similar to wild type PTECs at day 6. In contrast, day 16 WT PTECs showed a significant downregulation of these gene sets. Subsequent RT-qPCR showed that the CDKN2A-E2F axis was inhibited in SETD2-KD PTECs. In addition, SETD2-loss conveyed PTECs with additional oncogenic expression signatures, e.g. genes related to Epithelial-Mesenchymal Transition (EMT). The expression of several lncRNAs was downregulated upon SETD2-KD. These downregulated lncRNAs showed further decreased levels in ccRCC cell lines (chapter 5). Similarly we also observed a further downregulation of the protein coding gene expression levels in the ccRCC cell lines (chapter 5).

The SETD2-KD PTECs were insensitive to the normal senescence barrier, a known tumor suppressive mechanism. To our knowledge, this is the first functional study that clarifies how SETD2-loss contributes to ccRCC initiation. The inhibition of the CDKN2A-E2F axis in SETD2-KD PTECs caused this senescence resistance. Previously, Xie et al (2008) showed that SETD2 could directly interact with TP53 to modulate a specific set of TP53 downstream genes. Interestingly, we observed an increased expression of CDKN1A, the TP53 downstream gene during senescence

Chapter 6

178

induction, in SETD2-KD PTECs. Apparently, the activated TP53-CDKN1A axis cannot efficiently establish senescence in SETD2-KD cells. After finishing the work reported in the thesis chapters, as a first step to further explore this, we carried out a growth competition assay using lentiviral shRNA constructs against SETD2 and against TP53. This resulted in a mixed culture of untransduced, SETD2-KD, TP53-KD and double-KD PTECs. We followed the relative abundance of these cells over time. The TP53-KD PTECs gradually decreased in abundance much alike the WT-PTECs. This indicates that TP53-KD alone does not prevent PTECs from going into senescence. However, the double-KD PTECs showed an evident proliferative advantage over SETD2-KD PTECs (Figure 1). This demonstrates that although TP53-loss alone cannot prevent PTECs from going into senescence, it does promote the proliferation of SETD2-deficient cells. These observations are consistent with a study on fibroblasts by Beauséjour et al. (2003) who showed that CDKN2A is the second dominant and irreversible factor to establish the senescence barrier after the TP53-CDKN1A axis, and TP53-loss could only induce a robust proliferation in the cells with a low expression of CDKN2A. However, It is still not clear how SETD2 mediated H3K36me3 modulates the expression of CDKN2A during senescence induction. Several factors could contribute to the decreased expression levels of CDKN2A upon loss of H3K36me3, i.e. gene body methylation, which is co-localized with H3K36me3 marked regions, and positively associates with gene expression levels (Morselli et

Figure 1. Growth competition data of SETD2-KD (A) or TP53-KD (B) PTECs with SETD2&TP53-KD PTEC. PTECs at passage 2 (day 0) were transduced with GFP labeled shRNA against SETD2 and RFP labeled shNRA against TP53 at low MOI. The percentage of positive fluorescence cells was determined by FACS measurement at indicated time points after transduction. The bars indicate the percentage of positive cells for each cell type. In panel A, the total number of SETD2-KD cells is set at 100% for each measurement. In panel B, the total number of TP53-KD cells is set at 100% for each measurement. The red bar indicates the percentage of SETD2/TP53 double knock-down cells at each time point. Data are shown for three independent experiments using three different PTEC cultures. Panel A shows that the double knock-down cells proliferate faster than the SETD2 knock-down cells. Panel B shows that the TP53 single knock-down cells have almost disappeared after 22 days.

A B


6

179

al., 2015). In addition, the DNA methyltransferases DNMT3 A/B can recognize the H3K36me3 signal through their PWWP domain for DNA methylation (Dhayalan et al., 2010). Thus the absence of H3K36me3 and the subsequent loss of gene body methylation loss may lead to altered chromatin structure of CDKN2A gene body, and its decreased expression.

Besides irreversible growth arrest, senescent cells are also characterized by the senescence-associated secretory phenotype (SASP), the secretion of various pro-inflammatory cytokines, chemokines, growth factors and proteases (Campisi J., 2013). Some of the SASP factors are able to activate the immune system to clear senescent cells; while some others promote cell proliferation, angiogenesis and EMT transition. Depending on the context, SASP can be either beneficial or harmful for cancer cells (Campisi J., 2013). We noticed that some of the expression signatures that were enriched in senescent and SETD2-KD PTECs, i.e. TNFA_SIGNALING_VIA_NFκB, IL6_JAK_STAT3_SIGNALING and INFLAMMATORY_RESPONSE (chapter 3), might be related to SASP. The effect of SETD2-KD PTECs on SASP should be further validated at protein level.

H3K36me3 is also present at the body of lncRNA genes. Indeed, H3K36me3 ChIPseq was used to find new lncRNA transcripts (Derrien et al., 2012). It is thus not surprising that SETD2-KD PTECs also showed significant changes in the expression levels of lncRNA. Importantly, the downregulated lncRNAs upon SETD2-KD were further decreased in ccRCC tumors (chapter 5), suggesting that SETD2-loss might also contribute to ccRCC development through changes in lncRNA expression.

SETD2 inactivating mutations are detected in a wide spectrum of tumors, albeit with low frequency. In breast cancer, SETD2 inactivation has been suggested to be one of the driver mutations (Stephens et al., 2012). Our new preliminary data indicate that SETD2 plays a role in the senescence barrier establishment in breast epithelial cells (data not shown). We need to confirm this and it will be attractive to investigate if SETD2 loss will also influence senescence in other primary epithelial cells. To this end, we could perform a stable SETD2-KD in a panel of primary cells, especially including the ones assumed to be the normal counterparts of different types of tumors. Studying the growth characteristics of these cells and determine presence of senescence by measuring β-gal activity will indicate whether SETD2 has similar roles in other epithelial cell types. Expression analysis of CDKN2A in SETD2-KD cells will clarify if SETD2/H3K36 trimethylation is a general mechanism in controlling cellular senescence. It is also worth investigating if SETD2 loss results in alterations of the methylation status of CDKN2A gene body. To answer this question, we could perform bisulfite sequencing of the gene body of CDKN2A in SETD2-KD PTECs. The non-senescent and senescent PTECs, could be included as controls respectively.

The custom designed microarray used in our study also contains both lncRNA probes and protein coding gene probes. This enables us to further identify putative senescence-associated lncRNAs. The lncRNAs that show altered expressions in

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senescent PTECs, but remain stable in SETD2-KD PTECs, as compared to non-senescent PTECs, are the first candidates to functionally explore. Next, we could determine their abundance in the nuclear and cytoplasmic fractions of the cells respectively. The lncRNAs that are abundant in the nuclear fraction might be relevant for gene expression regulation. Potential cis-regulated target genes could be identified by combining the expression data of lncRNAs and protein coding genes. Through this step-by-step filtering, the number of candidate lncRNAs will be reduced and for this smaller set of candidates, knockdown or overexpression studies could be performed to confirm its function in senescence.

PBRM1 loss in PTECsIn PBRM1-KD PTECs we did not observe evident changes in cellular proliferation, or in the process of senescence (chapter 4). We did observe significant expression changes (>2 fold) in both protein-coding genes (130 up/155 down) and lncRNAs (9 up/25 down)(chapters 4 and 5). For protein-coding genes, the most striking changes for both up and downregulated genes, were related to the IFN-α and IFN-γ responsive gene sets. Both protein-coding genes and lncRNAs with significantly lower expression levels in the PBRM1-KD PTECs showed an even lower expression levels in the ccRCC cell lines. These downregulated genes were enriched in gene ontology annotations related to cell differentiation, synapse organization and cytoskeleton organization.

Previous studies on ccRCC cell lines revealed that PBRM1-loss promoted the cellular proliferation, migration, and colony formation (Varela et al., 2011). These changes were not observed upon PBRM1-KD in PTECs, which are the presumed normal counterparts of ccRCC. This difference might indicate that inactivation of PBRM1 has different roles in ccRCC initiation and progression. Recently, Benusiglio et al (2015) reported an inactivating PBRM1 germ line mutation in a ccRCC family, of which all identified mutation carriers developed ccRCC tumors. Loss of WT PBRM1 was observed in the tumors. This reinforces the importance of PBRM1 loss as a driver of ccRCC development.

PBRM1 is one of the subunits specific for the PBAF subgroup of SWI/SNF complexes. The bromodomains of PBRM1 read histones with H3K4 acetylation (H3K4ac), a histone mark enriched at the promoter regions of actively transcribed genes (Wang et al., 2008). In this way, PBRM1 targets the PBAF complex to specific genomic segments to alter the local accessibility of the chromatin. The altered chromatin accessibility subsequently influences expression of the downstream target gene. Thirty-one genes that were differentially expressed upon PBRM1-KD also showed altered expression in ccRCC cell lines. Twenty five out of these 31 genes are linked to known biological processes and molecular functions, some of these 25 to multiple processes and functions. Gene ontology annotation revealed presence of 10 genes related to immune response (hormone stimulus response), 6 genes related to chromatin organization and transcription; 6 to cell adhesion; 11 to cellular proliferation and apoptosis.


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Aberrant expression of immune response genes upon PBMR1-KD of PTECs could contribute to tumor development by facilitating escape from anti-tumor responses (Crusz and Balkwill 2015; Giraldo et al., 2015). Gene Set Enrichment Analysis (GSEA) confirmed involvement of IFN-α and IFN-γ responsive gene sets upon PBRM1-KD. Previous studies have already shown that the SWI/SNF complexes are responsible for the expression of IFN responsive genes (Lemon et al., 2001; Liu et al., 2001 and 2002; Huang et al.,2002; Cui et al., 2004; Wang et al., 2004). However these studies did not always pinpoint the precise complex responsible for their findings as they focused on one of the subunits present in all complexes. Our data clearly demonstrate the effect of loss of the PBAF complex on the basal expression of IFN-α/γ responsive genes.

The preliminary data that we collected so far do not fully explain how loss of PBRM1 functionality can be an initiating event in the development of ccRCC. To identify the direct target genes of PBRM1, a chromatin immunoprecipitation (ChIP) sequencing experiment using an antibody against PBRM1 could be considered. Overlapping the ChiP-seq data with the expression data will indicate which genes are the direct PBRM1-KD targets. In addition, an assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) can also be used to identify the accessible DNA regions before and after PBRM1-loss.

Our data show that the PBAF complex regulates the basal expression of IFN-α and IFN-γ responsive genes. It will be interesting to investigate if PBRM1 depleted PTECs show different expression of those genes upon IFN-α and/or IFN-γ treatment as compared to their wild-type counterparts. This regulation was investigated in HELA cells in several studies (Lemon et al., 2001; Liu et al., 2001 and 2002; Huang et al.,2002; Cui et al., 2004; Wang et al., 2004). These studies showed that expression of PBRM1 was essential for the expression of IFN responsive genes. Specifically, we could investigate if PBRM1 depleted ccRCC cells show differences in expression levels upon IFN treatment. Subsequently, we should determine if these expression changes are associated with the proliferation status of ccRCC. These results will help us to understand if PBRM1 negative ccRCC cells behave differently from PBRM1 positive ccRCC upon IFN treatment. In addition, these investigations may give clues for understanding why only part of the ccRCC patients respond to immuno-therapeutics, such as interferons (Leibovich et al. 2003, McDermott et al. 2005, Motzer and Molina 2009), and many of these patients developed therapy-resistant tumors after treatment (Sankin et al., 2015). In addition, it will be interesting to investigate if the immuno-treatment resistance is associated with the PBRM1 mutation status. If so, this could eventually make PBRM1 mutation status an important therapy-related biomarker.

CcRCC associated lncRNAsWe identified 89 lncRNAs that were significantly differentially (>2 fold) expressed in ccRCC cell lines as compared to PTECs (Chapter 5). Several of them also showed altered expression upon SETD2-KD and PBRM1-KD in PTECs. The downregulated

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lncRNAs upon SETD2-KD and PBRM1-KD showed further decreased expressions in ccRCC derived cell lines. A total of 39 putative lncRNA-protein coding RNA pairs were identified in ccRCC cell lines, 7 pairs in SETD2-KD PTECs, and 3 pairs in PBRM1-KD PTECs.

Several lncRNAs were reported to be dysregulated in ccRCC tumors, as compared to the non-tumorous tissues (reviewed by Seles et al. 2016). We could only confirm MEG3 significantly decreased expression in ccRCC cell lines compared to PTECs. Comparisons between previously published microarray data did not show a lot overlap either. This disconcordance is probably caused by the heterogeneous nature of the samples. First, tissue sections are always a mixture of cell populations, containing both tumor cells and other normal cell types. A second reason might be the intra-tumor heterogeneity of the ccRCC tumor itself (Gerlinger et al., 2012). This notion is supported by a study of Malouf et al., who categorized ccRCC tumors into 4 different groups based on their distinct lncRNA expression patterns (Malouf et al., 2015). Probably only a small number of lncRNAs are consistently differentially expressed in ccRCC tumors and cell lines, compared to their normal counterparts.

MEG3 (also known as GTL2) was first identified as an imprinted gene located at human 14q (Miyoshi et al., 2000). In a mouse model, MEG3 has been shown with a dynamic expression pattern during central neural system development (McLaughlin et al., 2006). Cyclic-AMP (cAMP) could facilitate the binding of CREB transcription factors to the promoter region of MEG3 to regulate its expression, and the methylation of the promoter region could abolish this binding. Decreased expression of MEG3 was also reported for non-small cell lung cancer. In these cells MEG3 functions as an inhibitor of proliferation and inducer of apoptosis by upregulating the TP53 level (Lu et al., 2013). Wang et al. (2015) observed decreased expression of MEG3 in ccRCC tumors, and its overexpression significantly induced the apoptosis rate in a ccRCC cell line. Both our data and results from other studies indicate that MEG3 is a tumor suppressive lncRNA that is significantly downregulated in ccRCC tumors.

It is important to further validate the expression levels of the ccRCC-associated lncRNAs that we identified in these cell lines in a panel of tumor samples. To reduce the bias caused by the heterogeneous nature of the tumors, laser microdissection could be used to harvest a homogeneous tumor cell population. Alternatively, RNA fluorescence in situ hybridization can also be used to detect lncRNA molecules in complex tissue samples and identify lncRNA expression directly. To study the functions of selected lncRNAs we could carry out knock-down and knock-in experiments in ccRCC cell lines, followed by monitoring the changes in proliferation, apopotosis and colony formation. These results will help us to understand how lncRNA contributes to ccRCC development.

In addition, the putative cis-acting lncRNA-protein coding gene pairs identified in our study also need further confirmation. This can help us to understand the interactions between lncRNAs and their nearby protein coding genes.


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aDDiTional fuTuRE PERSPEcTivESComprehensive understanding of ccRCC initiationIn this thesis, we studied the consequences of SETD2 and PBRM1 loss in PTECs separately, whereas the development of ccRCC is a combination of multiple aberrations. For a comprehensive understanding of ccRCC initiation, we need to study different inactivating combinations in PTECs. The shRNA based approach is limited due to the availability of a limited number of fluorescent detectors. Combination of different inactivating events in a single cell can be achieved by first inducing loss of SETD2, which will allow prolonging culture of these cells and next generate stable knock-out cells by using a clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system. The CRISPR-Cas9 has been shown capable of targeting different genomic loci by delivering a single Cas9 enzyme with two or more single guide RNAs (sgRNAs) for DNA cleavage (Kabadi et al., 2014).

CcRCC tumors are characterized by loss of the entire p-arm of chromosome 3. So in order to study the development of ccRCC it would be interesting to mimic this structural aberration in PTECs. To overcome the limited proliferative capacity of primary PTECs, we could first use exogenous hTERT to immortalize these cells. As an alternative to inducing a complete loss of 3p, it might be more feasible to specifically deplete the 3p21 region including the PBRM1, SETD2 and BAP1 loci within a 5MB region. He et al. (2015) showed the feasibility of this approach by delivering two sgRNAs that target different genomic sites. The resulting double strand DNA breaks causes a genomic deletion of the region flanked by the sgRNAs. With a hemizygous 3p background, introduction of point mutations to the ccRCC tumor suppressor genes can more closely mimic the genetic aberrations occurring in ccRCC tumors. In addition the CRISPR-Cas9 system-mediated genome editing is at the DNA level, which results in a more efficient knockdown. The CRISPR-Cas9 system can also be used for correcting disease-associated genetic aberrations. A relative easy approach may be to repair the mutations in ccRCC cell lines using the CRISPR-Cas9 and monitor phenotypes of the cells. Moving from studies in cell lines to that in animals could help close the gap between observing changes in cell lines that are speculated to lead to cancer at the tissue level and actual ccRCC development. Unfortunately, previous attempts to study ccRCC development in SETD2 and PBRM1 knockout mice were unsuccessful (Hu et al., 2010; Zhang et al., 2014; Wang et al., 2004). Both knockouts lead to embryonic lethality, caused by angiogenesis defects (SETD2-/-) or cardiac chamber development defects (PBRM1-/-). Using a tissue specific promoter, in combination with the Cre/loxP or tetracycline-inducible systems to create inducible kidney epithelial cell specific SETD2-KO mice, might overcome this lethal phenotype.

SETD2/H3K36me3 deficient tumor cells might be sensitive to specific treatment approaches. Pfister et al (Pfister et al. 2015) demonstrated that the WEE1 tyrosine kinase inhibitor AZD1775 promotes degradation of ribonucleotide reductase subunit

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RRM2 through activation of CDK. The degradation of RRM2 leads to dNTP starvation and subsequent cell death. H3K36me3 facilitates RRM2 transcription, which implicates that loss of SETD2 dependent H3K36me3 will result in decreased RRM2 transcription levels. AZD1775 treatment of H3K36me3-deficient tumor cells is therefore expected to result in extremely low levels of RRM2 and subsequently lead to dNTP starvation, S-phase arrest, and apoptosis. Currently, there are more than 20 clinic trials at different phases registered in ClinicalTrails (https://clinicaltrials.gov/) to test AZD1775 efficacy in various tumors.

The SWI/SNF complex is also a promising target for tumor therapy using synthetic-lethal genetic interactions (reviewed by Kaelin (2005)). Synthetic lethality means that an additional loss of function mutation in a gene can specifically kill tumor cells with a specific mutational background. Acute leukemias show defects in transcriptional regulators, i.e. mutations in transcription factors, DNA methylation machinery and so on, but mutations in SWI/SNF subunits are rarely detected. Thus the SWI/SNF complex appears to be important in maintaining the transcriptional program in these cancer cells. It has been shown that loss of BRG1 (a core component of the SWI/SNF complex) could increase apoptosis of leukemia cells, and block cellular differentiation. Meanwhile, BRG1-loss neither influenced the proliferation, nor the viability, of other cancer cells and fibroblasts (Shi et al., 2013), indicating the effect is cell-type specific. Therefore, targeting the SWI/SNF subunits in the tumors with other genetic aberrations may be a possible novel strategy for targeting SWI/SNF mutated tumor samples therapy.


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Table 1. Gene ontology analysis.

Gene symbol GO TERM

BSPRY ion transport and bindingCCR10 cellular ion homeostasis, chemokine bindingCDA regulation of cell growth, regulation of nucleotide metabolic processCFTR cholesterol metabolic process, response to hormone stimulusCTSA intracellular protein transport, peptidase activityGJB4 gap junction channel activity, channel activityHIST1H2BD chromatin organization, DNA bindingIGFBP2 regulation of cell growth, response to hormone stimulusIL23A immune response, cell proliferationITGB6 inflammatory response, cell-matrix adhesionKRAS negative regulation of apoptosis, response to hormone stimulus, positive

regulation of NF-kappa B transcription factor activity, positive regulation of MAP kinase activity, Ras protein signal transduction

LOC646626 positive regulation of NF-kappa B transcription factor activity, negative regulation of apoptosis

MMP7 proteolysis, regulation of cell proliferationNNAT response to glucose stimulus, regulation of hormone secretionNT5C3 nucleoside metabolic processPAPSS1 nucleobase, nucleoside and nucleotide biosynthetic processPIR TranscriptionPROM1 sensory perceptionPRSS8 proteolysis, response to hormone stimulusPSMB8 mitotic cell cycle, immune responseRMI2 DNA metabolic process, DNA replicationS100A4 epithelial to mesenchymal transition, calcium-dependent protein bindingSAT1 N-acetyltransferase activityTNC cell adhesionTNF immune response, positive regulation of NF-kappaB transcription factor

activity, cell adhesion, negative regulation of apoptosis

Note: 31 genes, differentially expressed in both PBRM1-KD PTECs and ccRCC cell lines as compared to WT PTECs, were included in the analysis. The genes annotated in the DAVID resource (see chapter 4) with the GO terms ‘biological process’ (GOTERM_BP_FAT) and ‘molecular function’ (GOTERM_MF_FAT) are presented in this table.

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nEDERlanDSE SaMEnvaTTinGHet onderzoek in dit proefschrift heeft zich gericht op de meest voorkomende vorm van nierkanker, het heldercellige type, dat meestal optreedt tussen het 50e en 70e levensjaar en waaraan wereldwijd meer dan 100.000 patiënten per jaar overlijden. Er bestaan inmiddels verschillende soorten behandeling voor dit type kanker, maar alleen als de tumor beperkt is gebleven tot de nier zijn de vooruitzichten relatief goed. Vijf jaar na diagnose is in dat geval 80 tot 90% van de patiënten in leven. Voor de andere patiënten zijn de vooruitzichten helaas veel slechter. Een derde van de patiënten heeft op het moment van het stellen van de diagnose al een gevorderde ziekte en is dan meestal niet meer te genezen.

Het kwaadaardige gedrag van kankercellen wordt veroorzaakt door afwijkingen in hun DNA. De hoop is dat door het begrijpen van die DNA veranderingen een betere behandeling van kanker bedacht kan worden. Afgebakende delen van ons DNA, de duizenden zogenaamde “genen” die we hebben, coderen voor eiwitten of voor soorten RNA moleculen die de activiteit van genen beïnvloeden. In heldercellig type nierkanker worden vaak veranderingen in de SETD2 en PBRM1 genen gevonden, ze lijken dus belangrijk te zijn, maar hun rol in het ontstaan of verdere beloop van nierkanker is nog niet duidelijk. Dat was de aanleiding voor dit promotieonderzoek.

In hoofdstuk 1, wordt een samenvatting gegeven van wat bekend is over de epidemiologische, medisch praktische en genetische aspecten van heldercellig type nierkanker.

In Hoofdstuk 2 wordt uitgebreid besproken wat er nu bekend is over de functie van het SETD2 eiwit. In de celkern zit het DNA om kleine eiwitbolletjes gewonden, zogenaamde nucleosomen. . Deze nucleosomen bestaan uit 4 histonen die op verschillende manieren een klein beetje veranderd kunnen worden. Deze aanpassingen zijn belangrijk voor het goed functioneren van ons DNA. SETD2 is verantwoordelijk voor een bepaalde verandering van histon-3 die cruciaal is voor belangrijke processen zoals het correct aflezen en het repareren van DNA. SETD2 speelt daarmee een belangrijke rol in de moleculaire ‘huishouding’ rond de histonen. Veranderingen in SETD2 kunnen deze processen verstoren en in bepaalde gevallen kanker veroorzaken.

Heldercellig type nierkanker ontstaat in het lichaam vanuit normale cellen die een deel van de binnenbekleding van de nier vormen, we noemen ze in het Engels proximal tubular epithelial cells, afgekort PTECs. Veranderingen in het DNA van die cellen zouden PTECs kunnen doen veranderen in nierkankercellen. Mogelijk zijn ook fouten in het SETD2 gen bij het ontstaan van nierkanker betrokken. In hoofdstuk 3 wordt ons onderzoek hiernaar beschreven. In kweken van PTECs werd via een technische ingreep het SETD2 gen uitgeschakeld. Vervolgens werd gekeken wat dit voor effect had op de groei van die cellen. De celgroei van normale PTECs komt altijd na een beperkt aantal kweekdagen tot stilstand, maar na het uitschakelen van SETD2 was dat niet meer het geval. Ook werd gezien dat in PTECs waarin SETD2 was uitgeschakeld er een

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abnormale activiteit was van genen die bij de ontwikkeling van kanker betrokken zijn. Dit alles wijst er op de afwijkingen in SETD2 een rol kunnen spelen bij het ontstaan van heldercellig type nierkanker.

In hoofdstuk 4 wordt een soortgelijk experiment met het PBRM1 gen beschreven. Het PBRM1 gen is een belangrijk onderdeel van een eiwit complex dat verantwoordelijk is voor de precieze positionering van de nucleosomen op ons DNA. Daarbij is PBRM1 mede verantwoordelijk voor de interactie van dit complex met het DNA. In dit onderzoek werd een heel ander beeld gezien. Uitschakeling van dit gen leidde niet tot het verdwijnen van de normale celdood na een paar dagen kweken. Er werd echter wel een verandering waargenomen in de activiteit van bepaalde genen die bij de werking van ons afweersysteem een rol spelen (interferon gevoelige genen). In versterkte mate werden die veranderingen ook gezien in een serie kweken van heldercellig type nierkankercellen. Verder onderzoek moet over de betekenis hiervan duidelijkheid geven.

Niet alleen eiwitcoderende genen zijn belangrijk in ons lichaam maar ook genen die niet het maken van eiwit als einddoel hebben maar die coderen voor RNA moleculen die een rol spelen bij het regelen van genactiviteit en daarmee indirect de productie van eiwitten kunnen beïnvloeden. Een belangrijke klasse van die regulerende RNA moleculen wordt long noncoding RNAs (lncRNAs) genoemd. Afwijkende lncRNAs zijn al in allerlei soorten kanker aangetroffen, maar onderzoek hiernaar in nierkanker en PTECs is nog schaars. In hoofdstuk 5 wordt onderzoek beschreven naar het voorkomen van lncRNAs waarbij gekweekte normale PTECs, PTECs met uitgeschakelde SETD2 of PBRM1 genen en verschillende heldercellig type nierkankercellen werden vergeleken. Er waren duidelijke verschillen te zien in de hoeveelheid van de individuele lncRNAs tussen normale (PTECs) cellen en nierkankercellen. Het uitgeschakelen van SETD2 of PBRM1 in PTECS veroorzaakte al veranderingen die in versterkte mate in de nierkankercellen werden gevonden. Dit wijst er op dat afwijkende lncRNA profielen een kenmerk zijn van heldercellig type nierkanker en dat in PTECs alleen al door uitschakeling van SETD2 of PBRM1 veranderingen in die profielen in gang lijken te worden gezet.

In hoofdstuk 6 worden alle bevindingen samengevat en bediscussieerd. Het verrichte onderzoek heeft bijgedragen aan het begrijpen van de rol van SETD2 en PBRM1 in heldercellig type nierkanker. De gevonden afwijkende activiteiten van de eiwitcoderende en niet-eiwitcoderende genen in de nierkankercellen en in de PTECs met uitgeschakeld SETD2 en PBRM1 genen zouden behulpzaam kunnen zijn bij het identificeren van nieuwe aangrijpingspunten voor therapie en ontwikkelen van verbeterde diagnostiek voor heldercellig type nierkanker.

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acknowlEDGEMEnTS/DankwooRD“Although there is a lot to be concerned about in our life, I saw far, far more goodness over the past years. Sometimes it is overshadowed, but every cloud has a silver lining.” This is the journey of PhD study, an odyssey to explore the unknown. I would like to thank those who offered their generous help and accompanied me during these years. They always give me strength and encouragement to reach beyond the break.

First and foremost, I would like to thank my family for their support, understanding and respect to my choice. Because of the long trek, unfortunately they cannot attend the defence. In these years, I did not fulfill my responsibilities as a son, as a cousin, as an uncle. I missed chenchen’s birth, shasha’s wedding ceremony, and nannan’s promotion, here I would like to give all my best wishes to your domestic life and brilliant career in the future. As a son, I feel deeply apologetic to my parents. The longest companionship is the deepest love, but 5 years’ separation by the Eurasian Plate is not short. I hope in the rest of my life, I could always be available by your side.

I would also like to appreciate the financial support from China Scholarship Council, which gave me the chance to experience this painful but wonderful scientific journey abroad. There are a lot of twists and turns on this road, but here I see the most beautiful scenery. I clearly remember how awkward I was at the beginning of this PhD journey, now I am almost at the end point. For this achievement, I want to give my heartfelt thanks to Rolf, Anke and Klaas. They are my solid support when I am frustrated, a light of beacon when I am lost.

Dear Rolf, thanks for your positive attitude to me and to my work, coordination of different opinions, constant encouragement and support to my research and living. Without your support, I would have given up my PhD and left back to China. I can always see your big smile, which gives me confidence and relieves all my worries and nervous. I am highly impressed by the “elevator pitch” to present my SETD2 study, and your patient explanation on my writing on phone at 9:00 in the evening. I see the qualities of being a leader from you, which I will learn and practice in the future.

Dear Anke, you are an esteemed and beloved supervisor, medical biologist, and a brilliant scientist. As a scientist, you set up an example by yourself to show me how to behave like a researcher; as a teacher, you are strict to my work but always offer generous help and instructions. I feel very sorry that sometimes, hopefully not every time, I cannot fully follow your instructions, which makes you unhappy. There is the saying that “April showers bring May flowers”. Through answering your questions, I learned how to do research, from understanding the techniques to questioning the logics of my data. As long as the experiments are finished, I should first ask myself 100 times if the result is correct. It is more important to investigate the reasons to explain why the result is not as what we expected. These are what I learned: 1. well begun is half done, a good study design should be based on broad investigations of the known knowledge. 2. we have to sharpen our edge to succeed. The prerequisite to correctly use

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a technique and interpret the result is the understanding of its underlying mechanism. 3. The essence of scientific writing is a logic, brief and clear description of what you did and what you observed, followed by a broad discussion of the implications. I know what I learned are more than that, and to use them in my future studies will be the best return to your instructions.

Dear Klaas, you are the one that deserves the most warm applause here. I have to admit that a lot of your hypothesis on SETD2-loss were proved in recent years, splicing variants, DNA damage repair deficiency, interaction between SETD2 and TP53 etc., unfortunately not by me. If I was a more experienced students, and capable of performing all these different assays, we would have much more fruitful outcomes. I used to ask myself what is the most important thing in doing research? From your side, I think it is the insight into the study subject. This is simply referred as an “idea”, but now I gradually understand it does not come out of thin air, it comes from decades of academic accumulation in this field. I will “calm down and carry on”. When I wrote the future perspectives of in this thesis book, I realized my lack of ideas and narrowness of outlook. Thank you for introducing me to the world of SETD2, resulting two publications during my PhD study. To further explore the function of SETD2 mediated H3K36me3 in different biological contexts is my current interest, and probably in the near future also. Thank your for your earnest suggestions for my postdoc research application and scientific career. I wish you enjoy your research, and a better work-life balance in the future.

Jan, you are my best friend and strongest support in the lab. We not only have pleasant cooperation in different projects, but also build a harmonious personal friendship. In China we call this “忘年交” (A friendship bridging the age gap). I am so happy to meet your family members, and grateful to their kindness. There are a lot of memorable moments, the tour to the old dike, dinners, ice skating, and so on. I have to admit that your wife can make the most delicious ice cream in the world. I wish you and your family a joyful and healthy life.

Dear Helga, you are a good time manager. “Reversing time would be an Einstein challenge”, but managing time is the tea in you cup. Thank you for helping me with the planning and finishing my PhD thesis on time. Joost, thank you for helping me in microarray data analysis. I can always learn something new after each discussion. Thanks to Maaike B van Werkhoven and Marc A. Seelen, who offered the precious PTECs for my experiments. I would also like to say thanks to all the other colleagues in our group: Alain, Joost, Ferronica and Jia cong, as well as the students in our group Maria, Melterm, Gellert, and Eva. Your questions and discussions on our onco-meeting can always bring me some fresh air to interpret my result from different perspectives. Alain, we had a lot of deep talk on our personal life, the attitude to research and clinic work. I wish you enjoy a healthy life and a successful career in the future. Joost, it is very interesting to have discussions with you on data analysis, and playing table tennis. Also thanks for being the paranymph for my ceremony. During my PhD studies, I

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got a lot of help from colleagues in our department and other department. Iris, you have such broad knowledge on the complicated process of transcription, thanks for sharing your knowledge. Michiel, Mathieu, Rutger, Ludolf and Astrid, thanks for your tolerance to the troubles I made in the lab. Martijn, thank you for helping me with the learning of analysing sequencing data. I would like to thank Debora for performing the microarray experiment, thank Jasper for cell line authentication, thank Klaas (microscopy center) for helping me with images capturing and processing. Thanks for the group of sequencing facility, Pieter, Cleo, Bahram and Jelkie, with my RNA-seq experiment. Jackie, thank you for the editing of my manuscript. I know my Chinglish can bother you a lot. I also want to give my thanks to Jingyuan, who encouraged me to continue doing research like an elder sister. Also Yang Li and Chengjian Xu, thanks for your advice on RNA-seq and statistical analysis. Last but not least, I would like to thank Cisca, as the head of our department, for her inspirations and encouragement. “Aiming high and working hard!” Thank you, this will be my pursue in the future.

Of course, I want to say thanks to my small Chinese “community” in Groningen. We witnessed the growth and development of each other, not as passengers, but as participants. Since there are more than 60 names, I will not list all of them. But there is a special group named as “Bing Qi Jun studio” that I have to mention. Three members in this group are Bing Han and Qi Cao and myself. We had a lot of fun together: playing table tennis, watching movies, cooking, traveling etc. Those happy moments are too numerous to numerate. Although I am leaving, I wish both of you enjoy a happy life in the Netherlands. Far away from Europe, I want to say thanks to Han Xiao and Jia Liu in China for their support and encouragement. We have been known each other for almost 20 years.

To all of you, I will never forget the good things we have been through. This experience will become a source of my strength, the energy of my soul and the warmth of my blood! In the end I want to end up with a song: we’ve come a long way from where we began. I will tell you all about it when I see you again…

Jun LiThe department of Genetics

University Medical Center GroningenGroningen, the Netherlands

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liST of aBBREviaTionSALL acute lymphcytic leukemia AML acute myeloid leukemia FWER Bonferroni family-wise error rate BAP1 BRCA1 associated protein 1 BAF BRM-associated factors BRPF1 Bromodomain And PHD Finger Containing 1 ccRCC Clear cell RCC CRISPR clustered regularly interspaced short palindromic repeats CGH comparative genomic hybridizationCTD C-terminal domain CDKN1A cyclin-dependent kinase inhibitor 1A CDKN2A cyclin-dependent kinase inhibitor 2A CK 8/18 cytokeratin 8/18 CK AE1/3 cytokeratin clone AE1/3 DNMT3A/B DNA (cytosine-5)- methyltransferase 3A/B DSBs DNA double strand breaks MMR DNA Mismatch Repair DRB D-ribofuranosyl- benzimidazole EGF epidermal growth factor EGF Epidermal growth factor EMA epithelial membrane antigen EMT Epithelial-Mesenchymal-Transition FACT Facilitates Chromatin Transcription complex FWER Family-wise error rate FBS fetal bovine serum FBS fetal bovine serum FGFR2 fibroblast growth factor receptor 2 GINI Gene Identification by Nonsense-mediated mRNA decay Inhibition GO Gene Ontology GSEA gene set enrichment analysis GFP Green fluorescent protein H3K4ac H3K4 acetylation hnRNPL Heterogeneous Nuclear Ribonucleoprotein L H3K36me3 histone H3 lysine-36 trimethylation HR homologous recombination HOTAIR HOX transcript antisense RNA HYPB Huntingtin Yeast Partner B HD Huntington’s disease

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HIFα hypoxia inducible factor α ITS Insulin-Transferrin-Selenium IFN-α Interferon-alpha IFN-α/γ Interferon-α/γ LEDGF Lens Epithelium-Derived Growth Factor L-FABP liver-type fatty acid–binding protein 1 lnc-RNAs long non-coding RNAs KDM4A lysine(K)-specific demethylase 4A MORF4L1 Mortality Factor 4 like 1 MOI multiplicity of infection ncRNAs non-coding RNAs NSD1 Nuclear Receptor Binding SET Domain Protein 1 ANOVA One-way analysis of variance PBAF polybromo-associated BAF PRC2 Polycomb repressive complex 2 PTBP1 polypyrimidine tract binding protein 1 PTECs primary tubular epithelial cells PCA Principal component analysis PSIP1 PWWP domain of PC4 And SFRS1 Interacting Protein 1 RCC Renal cell cancer RNA Pol II RNA polymerase II SASP senescence-associated secretory phenotype SETD2 Set domain containing 2 SRI domain Set2 Rpb1 interacting domain SAP130 Sin3A- associated protein sgRNAs single guide RNAs SSA spliceostatinA SWI/SNF complexes SWItch/Sucrose Non-Fermenting complexes TCGA The Cancer Genome Atlas TSS transcriptional start sites TP53 tumor protein 53 TSG tumor suppressor gene SPT16H Ty16 Homolog Vim vimentin VHL Von Hippel–Lindau α-SMA α smooth muscle actin β-gal β-galactosidase

university of groningen setd2 and pbrm1 inactivation in the ...(peña-llopis et al., 2012), whereas...

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