Oncogene (2013), 1–5 & 2013 Macmillan Publishers Limited All rights reserved 0950-9232/13 www.nature.com/onc
SHORT COMMUNICATION
Alterations in chromatin accessibility and DNA methylation in clear cell renal cell carcinoma MJ Buck1,2, LM Raaijmakers3, S Ramakrishnan4, D Wang5, S Valiyaparambil1, S Liu5, NJ Nowak1,2 and R Pili4 Recent studies have demonstrated that in clear cell renal cell carcinoma (ccRCC) several chromatin remodeling enzymes are genetically inactivated. Although, growing evidence in cancer models has demonstrated the importance of epigenetic changes, currently only changes in DNA methylation can be accurately determined from clinical samples. To address this limitation, we have applied formaldehyde-assisted isolation of regulatory elements (FAIREs) combined with next-generation sequencing (FAIRE-seq) to identify specific changes in chromatin accessibility in clinical samples of ccRCC. We modified the FAIRE procedure to allow us to examine chromatin accessibility for small samples of solid tumors. Our FAIRE results were compared with DNA-methylation analysis and show how chromatin accessibility decreases at many sites where DNA-methylation remains unchanged. In addition, our FAIREseq analysis allowed us to identify regulatory elements associated with both normal and tumor tissue. We have identified decreases in chromatin accessibility at key ccRCC-linked genes, including PBRM1, SETD2 and MLL2. Overall, our results demonstrate the power of examining multiple aspects of the epigenome. Oncogene advance online publication, 4 November 2013; doi:10.1038/onc.2013.455 Keywords: Epigenetics; epigenome; FAIRE; FAIRE-seq; renal cell carcinoma
INTRODUCTION Clear cell renal cell carcinoma (ccRCC) is the most prevalent adult malignant neoplasm of the kidney comprising 70–75% of adult renal malignancies.1 For several years, therapeutic strategies for advanced ccRCC have been focused on the role of hypoxia signaling in this tumor type in view of its genetic/ epigenetic silencing of the Von Hippel–Lindau (VHL) gene leading to the development of therapies targeting the vascular endothelial growth factor induced by overexpression of hypoxiainducible factors.2 These therapies have resulted in clinical benefit in metastatic ccRCC but leading inevitably to acquire resistance. Epigenetic modifications leading to gene silencing or inactivation have been shown to have a major role in the initiation and progression of cancer as well as impact on treatment. These modifications are often reflected as alterations in DNA methylation status and chromatin state regulated by enzyme-modulated histone acetylation and deacetylation. Several histone deacetylase inhibitors have been developed but the clinical effects as monotherapies in solid tumors, including ccRCC, have been limited. Rationale combination strategies with other anticancer therapies are currently being tested, including targeting hypoxia inducible factor (HIF) with histone deacetylase inhibitors in combination with either vascular endothelial growth factor (VEGF) or mammalian target of rapamycin (mTOR) inhibitors.3,4 These trials are timely because of recent discoveries identifying mutations of genes that function to regulate chromatin state and histones.5–7
The mutational spectrum of genes implicated in the initiation and progression ccRCC has grown significantly over the past couple of years with the application of next-generation sequencing technology opening up new potential targets for therapeutic intervention. Although mutations in the Vhl E3 ubiquitin protein ligase gene on chromosome 3 still represent the major genetic alteration with B90% of ccRCC harboring single-copy loss in these tumors, recent reports have identified mutations in chromatin remodeling genes (PBRM1 and SETD2) that are also located on the short arm of chromosome 3.6–8 In addition to genes that are coincidentally located on the same chromosome arm as VHL, inactivating mutations have also been identified in the histone-modifying genes JARID1C, UTX and MLL2.5,6 We have investigated with genome-wide approaches on chromatin and methylation status in a limited set of ccRCC. FAIRE-seq (formaldehyde assisted isolation of regulatory elements) was performed on matched pairs of tumor/normal ccRCC in order to identify alterations of the chromatin state reflected as changes in DNA accessibility. We identified decreases in chromatin accessibility at genes implicated in ccRCC, such as PBRM1, SETD2 and MLL2. We also performed array-based methylation analysis on this same set of tumors, integrated these results with FAIRE-seq analysis and determined that chromatin remodeling can occur in parallel with methylation or independently. Our findings of modifications to chromatin and methylation status provide further evidence that epigenetics has a major role in this type of cancer, and success in treatment may well lie in the manipulation of the epigenome. Fully understanding the basis of refractory disease
1 Department of Biochemistry, Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, NY, USA; 2Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY, USA; 3CanSys Program, Roswell Park Cancer Institute, Buffalo, NY, USA; 4Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA and 5 Biostatistics Roswell Park Cancer Institute, Buffalo, NY, USA. Correspondence: Dr MJ Buck or Professor NJ Nowak, Department of Biochemistry, State Center of Excellence in Bioinformatics and Life Sciences, State University of NY at Buffalo, Buffalo, NY, USA or Professor R Pili, Genitourinary Program, Roswell Park Cancer Institute, Buffalo, NY, USA. E-mail: [email protected] or [email protected] or [email protected] Received 2 May 2013; revised 28 August 2013; accepted 14 September 2013
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
2
Figure 1. Promoter hypermethylation in ccRCC. Serial collections of tumor samples and matched normal kidney tissue from patients undergoing surgical resection (radical nephrectomy) for kidney cancer were obtained upon institutional approval. De-identified samples were accessioned, snap frozen and stored at 80 1C. At the time of the study, five samples of ccRCC were randomly chosen and DNA was extracted. (a) Boxplot showing median methylation across all human promoters with CpG islands. DNA-methylation from sample isolated DNA was determined with Infinium HumanMethylation450 beadChIP using standard procedures.14 The raw intensity of Illumina Infinium HumanMethylation450 BeadChip was scanned and extracted using BeadScan in GenomeStudio module. The bead information was summarized into BeadStudio IDAT files and then processed by R package minfi. The resulted methylation level (b value) ranged between 0 and 1, with 0 absent methylation and 1 complete methylation. The swan normalization was further performed to correct design bias, and combat correction was performed to correct batch effects. Rigorous quality-control criteria were used for filtering at both locus and sample levels. Specifically, we removed the B10 k probes, which contain SNPs at/near target CpG site and B140 k probes ambiguously mapped to the human genome. (b) Boxplot showing median methylation across all human promoters without CpG islands. (c) Hierarchical clustering of 4898 differentially methylated loci. Differential methylation loci were selected, which had probes with |delta b|X0.2 between sample groups. The level of statistical significance was assessed using empirical Bayes moderated t-statistics. The Benjamini and Hochberg method was used to control the false discovery rate for the multiple testing. Significantly differential methylation was defined at false discovery rate-adjusted Po0.01. Hierarchical clustering algorithm based on average linkage and Pearson correlation metric was employed to cluster the probes that showed the most variable DNA methylation levels across the tumor panel.
a
b
d
5
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Oncogene (2013) 1 – 5
& 2013 Macmillan Publishers Limited
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
3 and the role of modifications to the epigenome in ccRCC has the promise to lead to new therapeutic approaches for these patients. RESULTS AND DISCUSSION To identify ccRCC-specific changes in the epigenome, we examined tumor and adjacent normal tissue from patients after
nephrectomy. The tumors were ccRCC with Furhman grade ranging from 2 to 4. Genome-wide changes in DNA methylation showed a significant increase in DNA methylation across the genome at promoters with and without CpG islands (Figures 1a and b). Consistent with previous findings we observed that RASSF1A and TWIST are hypermethylated in ccRCC (Supplementary Figure 1).9 To determine whether methylation state can define
Figure 3. Chromatin becomes more condensed in ccRCC. Overlapping peaks between experiments were identified with BedTools20 and patient-specific peaks were removed from further analysis, leaving 294 238 FAIRE accessible regions, which appear in multiple patients. Each FAIRE accessible peak was then classified as tumor, normal or both. A total of 242 501 sites was found to be overlapping in at least two normal and two tumor samples. Sites were defined as overlapping in case of an overlap of at least 1 bp. Of those sites, 77% was found to be in an active state. 17 830 sites are unique to tumor samples. These sites were found in at least two tumor samples, but no normal samples. Of these sites, 68% was found to be in an active state. 33 907 sites are unique to normal samples. These sites were found in at least two normal samples, but no tumor samples. Of these sites, 72% was found to be in an active state.
Figure 2. FAIRE enriches for active regulatory regions. FAIRE-seq was performed as previously described with slight modifications.12 Briefly, 5 mg of frozen normal and tumorigenic tissue was ground in liquid nitrogen, suspended in 1 PBS and crosslinked with 1% formaldehyde at 25 1C for 7 min. After quenching with glycine, pelleted cells were resuspend in 300 ml lysis buffer (2% Triton X-100, 1% SDS, 100 mM NaCl, 10 mM Tris-Cl pH 8.0, 1 mM EDTA), and sonicated in a Bioruptor (high 10 min, 30 s ON, 30 s OFF). The cleared supernatant was phenol/chloroform extracted twice and chloroform-isomyl alcohol extracted, and the FAIRE-isolated DNA was precipitated with ethanol. The precipitated DNA was further cleaned up by treatment with RNase A and Qiagen MiniElute column purified. Illumina sequencing libraries were prepared using the standard Illumina ChIP-seq protocol and 50 bp single-end sequenced in an individual flow cell lane of a HiSeq2000. (a) Average log2 FAIREseq signal measured at 41 317 transcriptional start sites (TSSs) as defined by University of California, San Francisco (UCSC) genes was determined by ArchTeX with standardized, 120 bp extension, in log2 ratio space.15 (b) Average log2 FAIRE-seq signal measured at 24 725 ubiquitously bound CTCF sites identified in 10 cell lines.16 (c) FAIRE peaks are more likely located at active regulatory regions (72–80%) compared with 42% of genome, which are active. Significantly accessible regions were identified from the aligned data sets with MACS2 (Pvalue ¼ 0.005, broad peaks, shift size ¼ 75 and MFOLD ¼ 1030).17 Functionality was assigned to each FAIRE peak using the 15 chromatin state assignments from Ernst and Kellis from nine cell lines.18 To do this, each peak was first assigned an active (active, poised or weak promoter, strong or weak enhancer, insulator, weak transcribed, transcriptional transition or transcriptional elongation) or inactive (polycomb repressed, heterochromatin or repetitive) state. If a peak was located in an active state in any cell line, then it was assigned the active category. In the active group, a peak was then classified by the state found in the majority of cell lines. (d) Hierarchical clustering of 2727 FAIRE-peak locations that is differentially accessible (delta FAIRE signal 40.75; see Supplementary Table 2). Hierarchical clustering was performed on log2 standardized data with Gene Cluster with Pearson correlation as the similarity metric.19 & 2013 Macmillan Publishers Limited
Oncogene (2013) 1 – 5
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
4 ccRCC, we clustered the differential methylated sites (Figure 1c). As shown in Figure 1c, the CpG methylation status is very informative in its ability to classify ccRCCs from normal tissue. All samples except 162-T cluster within the correct tumor/normal grouping. Overall, these results show widespread promoter hypermethylation in ccRCC consistent with promoter gene silencing. To further examine changes in the epigenome during ccRCC, we measured chromatin accessibility with FAIRE-seq.10,11 To apply FAIRE-seq to our clinical samples, we made modifications to the procedure to allow us to measure chromatin accessibility from limited tissue samples. For each sample, we obtained 90–138 million aligned sequence reads for a total 45 Gb of sequence (Supplementary Table 1). To ensure the quality of each FAIRE experiment, we analyzed transcriptional start sites (TSSs) and CCCTC-binding factor (CTCF)binding site enrichment (Figure 2). FAIRE has been shown to significantly enrich for TSSs and CTCF-binding locations.12 As can be seen in Figures 2a and b, a peak of enrichment is observed at the TSS and CTCF in both normal and ccRCC samples. The FAIRE experiments for patient 171 did not show a significant peak of enrichment at TSSs and was removed from further analysis. For the remaining eight samples, we identified 238 544–94 525 regions of accessibility (see Supplementary Table 1). These accessible locations occurred in similar locations across all experiments with over 70% of FAIRE peaks localizing at functionally active genomic elements (Figure 2c). This is a
significant enrichment compared with the 41% of active regions seen in the genome. To determine whether changes in chromatin accessibility are characteristic of ccRCC, we clustered differential FAIRE peaks (see Supplementary Table 2). As can be seen in Figure 2d all samples except 162-T group with the appropriate sample type. In addition, it is clear that clustering is being driven by a decrease in FAIRE-seq signal at the majority of sites in ccRCC samples. These results are in concordance with our methylation assays and suggest that chromatin is becoming inactive across the genome in ccRCC. We next examined all non-patient-specific FAIRE peaks (Figure 3). The majority (242 501) of the total peaks (294 238) are shared between the tumor and normal samples. These peaks are predominately functional active (77%) and are associated with weak transcription (48%), enhancers (26%), insulators (8%), transcription elongation (8%) and promoters (8%). In all, 33 907 accessible locations are only identified in normal samples, whereas 17 830 peaks are found only in tumors. This roughly 2 to 1 difference in normal specific accessible locations compared with tumors further demonstrates that the majority of chromatin events are a loss of accessibility in ccRCC. The normal and tumor only peaks are distributed across the genome in a similar manner which is almost identical to the unchanged locations. In addition, our results at promoters are symptomatic of changes in downstream gene expression, where we found that upregulated genes during
Figure 4. FAIRE identified clusters of open regulatory elements (COREs) are associated with ccRCC genes. (a) We defined normal COREs as three or more normal-specific FAIRE sites separated by o20 kb.13 Tumor COREs are defined as three or more tumor-specific FAIRE sites separated by less than o20 kb. (b) Two normal COREs associated with SETD2. (c) GO biological process significantly associated with normal or tumor COREs with the corresponding hypergeometric P value. (d) Normal and tumor COREs are strongly enriched for known ccRCC mutated genes. Genes that have been previously identified as mutated in RCC are shown, with the type mutation N (nonsense), M (missense), B (nonsense and missense). The percentage of patients with tumor/normal-matched pairs in the TCGA project with that gene downregulated (% down) is shown.21 The ratio of tumor to normal for the number of FAIRE-seq tags across each COREs is tabulated for each sample. Oncogene (2013) 1 – 5
& 2013 Macmillan Publishers Limited
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
5 ccRCC become more accessible and the promoters of downregulated genes become less accessible (Supplementary Figure 2). We next sought to link tumor- and normal-specific elements to specific genes. We noticed that open chromatin locations were not evenly distributed throughout the genome but tend to cluster. We identified clusters of regulatory elements (COREs), within 20 kb, for our normal and tumor unique groupings as previously described13 (Figures 4a and b). There were 2131 normal COREs and 755 tumor COREs, which are associated with relevant biological processes (Figure 4c and Supplementary Tables 3 and 4). Normal COREs also enrich for the KLF family of transcription factors (P ¼ 5 10 7; KLF2, KLF3, KLF4, KLF5, KLF6, KLF7, KLF9, KLF10, KLF12, KL13). Our findings are further supported by the association of normal and tumor COREs with genes commonly mutated in ccRCC (P ¼ 1.48 10 6; Figure 4d). Normal COREs are associated with PBRM1, SETD2, TSC1, SHANK1, RYR1, CARD11, MLL2 and WRN. Tumor COREs are associated with AKAP13, ZNF804A, LRP1B, NAV3 and LRRK2. The genes associated with normal COREs and thus to more condensed chromatin state in tumors are more likely to be classified as nonsense mutations and downregulated in tumor (Figure 4d). On the other hand, the five genes associated with tumor COREs are classified predominately as missense mutations. These findings suggest that multiple mechanisms exist for gene inactivation including a nonsense mutation or by inactivation through a chromatin-mediated method. We do recognize that tissue heterogeneity remains a major challenge in genetic and epigenetic studies. The limited tissue availability did not allow us to have multiple representative samples from the original tumor and matched normal kidney. Future studies will need to utilize multiple tissue samples from the same patient to have more representative genetic and epigenetic information. In summary, we identified changes in chromatin accessibility in ccRCC and the majority of these events were not associated with changes in DNA methylation (Supplementary Figure 3). Thus, FAIRE-seq provides a unique view of a cancer cell’s chromatin state and is ideally suited for the clinic. FAIRE-seq provides a comprehensive picture of a cells chromatin from a small number of cells and will be invaluable tool in monitoring response to epigenetic treatments. Our results suggest that normal COREs are significantly associated with genes with missense mutations in ccRCC. This finding also provides evidence that FAIRE can be used in concert with whole-genome sequencing to identify functionally important genetic alterations. In the future, further development of technical controls and computational methods will allow FAIRE to be applied to various tumor types in a quick and effective manner.
CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS This research was supported in part by the National Cancer Institute at the National Institutes of Health (P30CA016056; to RP), a grant from the RPCI -Alliance Foundation (to RP) and a donation from the, The Bruce Cuvelier Family Trust (to RP).
REFERENCES 1 Henrique R, Luis AS, Jeronimo C. The epigenetics of renal cell tumors: from biology to biomarkers. Front Genet 2012; 3: 94. 2 Sonpavde G, Bellmunt J, Schutz F, Choueiri TK. The double edged sword of bleeding and clotting from VEGF inhibition in renal cancer patients. Curr Oncol Rep 2012; 14: 295–306. 3 Ververis K, Hiong A, Karagiannis TC, Licciardi PV. Histone deacetylase inhibitors (HDACIs): multitargeted anticancer agents. Biologics 2013; 7: 47–60. 4 O’Rourke CJ, Knabben V, Bolton E, Moran D, Lynch T, Hollywood D et al. Manipulating the epigenome for the treatment of urological malignancies. Pharmacol Ther 2013; 138: 185–196. 5 van Haaften G, Dalgliesh GL, Davies H, Chen L, Bignell G, Greenman C et al. Somatic mutations of the histone H3K27 demethylase gene UTX in human cancer. Nat Genet 2009; 41: 521–523. 6 Dalgliesh GL, Furge K, Greenman C, Chen L, Bignell G, Butler A et al. Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 2010; 463: 360–363. 7 Varela I, Tarpey P, Raine K, Huang D, Ong CK, Stephens P et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 2011; 469: 539–542. 8 Pena-Llopis S, Vega-Rubin-de-Celis S, Liao A, Leng N, Pavia-Jimenez A, Wang S et al. BAP1 loss defines a new class of renal cell carcinoma. Nat Genet 2012; 44: 751–759. 9 Cairns P. Renal cell carcinoma. Cancer Biomark 2010; 9: 461–473. 10 Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res 2007; 17: 877–885. 11 Rizzo JM, Buck MJ. Key Principles and Clinical Applications of "Next-Generation" DNA Sequencing. Cancer Prev Res (Phila) 2012; 5: 887–900. 12 Simon JM, Giresi PG, Davis IJ, Lieb JD. Using formaldehyde-assisted isolation of regulatory elements (FAIRE) to isolate active regulatory DNA. Nat Protoc 2012; 7: 256–267. 13 Gaulton KJ, Nammo T, Pasquali L, Simon JM, Giresi PG, Fogarty MP et al. A map of open chromatin in human pancreatic islets. Nat Genet 2010; 42: 255–259. 14 Bibikova M, Barnes B, Tsan C, Ho V, Klotzle B, Le JM et al. High density DNA methylation array with single CpG site resolution. Genomics 2011; 98: 288–295. 15 Lai WK, Bard JE, Buck MJ. ArchTEx: accurate extraction and visualization of nextgeneration sequence data. Bioinformatics 2012; 28: 1021–1023. 16 Wang H, Maurano MT, Qu H, Varley KE, Gertz J, Pauli F et al. Widespread plasticity in CTCF occupancy linked to DNA methylation. Genome Res 2012; 22: 1680–1688. 17 Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE et al. Modelbased analysis of ChIP-Seq (MACS). Genome Biol 2008; 9: R137. 18 Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, Ward LD, Epstein CB et al. Mapping and analysis of chromatin state dynamics in nine human cell types. Nature 2011; 473: 43–49. 19 Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998; 95: 14863–14868. 20 Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010; 26: 841–842. 21 Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013; 499: 43–49.
Supplementary Information accompanies this paper on the Oncogene website (http://www.nature.com/onc)
& 2013 Macmillan Publishers Limited
Oncogene (2013) 1 – 5
SHORT COMMUNICATION
Alterations in chromatin accessibility and DNA methylation in clear cell renal cell carcinoma MJ Buck1,2, LM Raaijmakers3, S Ramakrishnan4, D Wang5, S Valiyaparambil1, S Liu5, NJ Nowak1,2 and R Pili4 Recent studies have demonstrated that in clear cell renal cell carcinoma (ccRCC) several chromatin remodeling enzymes are genetically inactivated. Although, growing evidence in cancer models has demonstrated the importance of epigenetic changes, currently only changes in DNA methylation can be accurately determined from clinical samples. To address this limitation, we have applied formaldehyde-assisted isolation of regulatory elements (FAIREs) combined with next-generation sequencing (FAIRE-seq) to identify specific changes in chromatin accessibility in clinical samples of ccRCC. We modified the FAIRE procedure to allow us to examine chromatin accessibility for small samples of solid tumors. Our FAIRE results were compared with DNA-methylation analysis and show how chromatin accessibility decreases at many sites where DNA-methylation remains unchanged. In addition, our FAIREseq analysis allowed us to identify regulatory elements associated with both normal and tumor tissue. We have identified decreases in chromatin accessibility at key ccRCC-linked genes, including PBRM1, SETD2 and MLL2. Overall, our results demonstrate the power of examining multiple aspects of the epigenome. Oncogene advance online publication, 4 November 2013; doi:10.1038/onc.2013.455 Keywords: Epigenetics; epigenome; FAIRE; FAIRE-seq; renal cell carcinoma
INTRODUCTION Clear cell renal cell carcinoma (ccRCC) is the most prevalent adult malignant neoplasm of the kidney comprising 70–75% of adult renal malignancies.1 For several years, therapeutic strategies for advanced ccRCC have been focused on the role of hypoxia signaling in this tumor type in view of its genetic/ epigenetic silencing of the Von Hippel–Lindau (VHL) gene leading to the development of therapies targeting the vascular endothelial growth factor induced by overexpression of hypoxiainducible factors.2 These therapies have resulted in clinical benefit in metastatic ccRCC but leading inevitably to acquire resistance. Epigenetic modifications leading to gene silencing or inactivation have been shown to have a major role in the initiation and progression of cancer as well as impact on treatment. These modifications are often reflected as alterations in DNA methylation status and chromatin state regulated by enzyme-modulated histone acetylation and deacetylation. Several histone deacetylase inhibitors have been developed but the clinical effects as monotherapies in solid tumors, including ccRCC, have been limited. Rationale combination strategies with other anticancer therapies are currently being tested, including targeting hypoxia inducible factor (HIF) with histone deacetylase inhibitors in combination with either vascular endothelial growth factor (VEGF) or mammalian target of rapamycin (mTOR) inhibitors.3,4 These trials are timely because of recent discoveries identifying mutations of genes that function to regulate chromatin state and histones.5–7
The mutational spectrum of genes implicated in the initiation and progression ccRCC has grown significantly over the past couple of years with the application of next-generation sequencing technology opening up new potential targets for therapeutic intervention. Although mutations in the Vhl E3 ubiquitin protein ligase gene on chromosome 3 still represent the major genetic alteration with B90% of ccRCC harboring single-copy loss in these tumors, recent reports have identified mutations in chromatin remodeling genes (PBRM1 and SETD2) that are also located on the short arm of chromosome 3.6–8 In addition to genes that are coincidentally located on the same chromosome arm as VHL, inactivating mutations have also been identified in the histone-modifying genes JARID1C, UTX and MLL2.5,6 We have investigated with genome-wide approaches on chromatin and methylation status in a limited set of ccRCC. FAIRE-seq (formaldehyde assisted isolation of regulatory elements) was performed on matched pairs of tumor/normal ccRCC in order to identify alterations of the chromatin state reflected as changes in DNA accessibility. We identified decreases in chromatin accessibility at genes implicated in ccRCC, such as PBRM1, SETD2 and MLL2. We also performed array-based methylation analysis on this same set of tumors, integrated these results with FAIRE-seq analysis and determined that chromatin remodeling can occur in parallel with methylation or independently. Our findings of modifications to chromatin and methylation status provide further evidence that epigenetics has a major role in this type of cancer, and success in treatment may well lie in the manipulation of the epigenome. Fully understanding the basis of refractory disease
1 Department of Biochemistry, Center of Excellence in Bioinformatics and Life Sciences, State University of New York at Buffalo, Buffalo, NY, USA; 2Cancer Genetics, Roswell Park Cancer Institute, Buffalo, NY, USA; 3CanSys Program, Roswell Park Cancer Institute, Buffalo, NY, USA; 4Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY, USA and 5 Biostatistics Roswell Park Cancer Institute, Buffalo, NY, USA. Correspondence: Dr MJ Buck or Professor NJ Nowak, Department of Biochemistry, State Center of Excellence in Bioinformatics and Life Sciences, State University of NY at Buffalo, Buffalo, NY, USA or Professor R Pili, Genitourinary Program, Roswell Park Cancer Institute, Buffalo, NY, USA. E-mail: [email protected] or [email protected] or [email protected] Received 2 May 2013; revised 28 August 2013; accepted 14 September 2013
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
2
Figure 1. Promoter hypermethylation in ccRCC. Serial collections of tumor samples and matched normal kidney tissue from patients undergoing surgical resection (radical nephrectomy) for kidney cancer were obtained upon institutional approval. De-identified samples were accessioned, snap frozen and stored at 80 1C. At the time of the study, five samples of ccRCC were randomly chosen and DNA was extracted. (a) Boxplot showing median methylation across all human promoters with CpG islands. DNA-methylation from sample isolated DNA was determined with Infinium HumanMethylation450 beadChIP using standard procedures.14 The raw intensity of Illumina Infinium HumanMethylation450 BeadChip was scanned and extracted using BeadScan in GenomeStudio module. The bead information was summarized into BeadStudio IDAT files and then processed by R package minfi. The resulted methylation level (b value) ranged between 0 and 1, with 0 absent methylation and 1 complete methylation. The swan normalization was further performed to correct design bias, and combat correction was performed to correct batch effects. Rigorous quality-control criteria were used for filtering at both locus and sample levels. Specifically, we removed the B10 k probes, which contain SNPs at/near target CpG site and B140 k probes ambiguously mapped to the human genome. (b) Boxplot showing median methylation across all human promoters without CpG islands. (c) Hierarchical clustering of 4898 differentially methylated loci. Differential methylation loci were selected, which had probes with |delta b|X0.2 between sample groups. The level of statistical significance was assessed using empirical Bayes moderated t-statistics. The Benjamini and Hochberg method was used to control the false discovery rate for the multiple testing. Significantly differential methylation was defined at false discovery rate-adjusted Po0.01. Hierarchical clustering algorithm based on average linkage and Pearson correlation metric was employed to cluster the probes that showed the most variable DNA methylation levels across the tumor panel.
a
b
d
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Oncogene (2013) 1 – 5
& 2013 Macmillan Publishers Limited
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
3 and the role of modifications to the epigenome in ccRCC has the promise to lead to new therapeutic approaches for these patients. RESULTS AND DISCUSSION To identify ccRCC-specific changes in the epigenome, we examined tumor and adjacent normal tissue from patients after
nephrectomy. The tumors were ccRCC with Furhman grade ranging from 2 to 4. Genome-wide changes in DNA methylation showed a significant increase in DNA methylation across the genome at promoters with and without CpG islands (Figures 1a and b). Consistent with previous findings we observed that RASSF1A and TWIST are hypermethylated in ccRCC (Supplementary Figure 1).9 To determine whether methylation state can define
Figure 3. Chromatin becomes more condensed in ccRCC. Overlapping peaks between experiments were identified with BedTools20 and patient-specific peaks were removed from further analysis, leaving 294 238 FAIRE accessible regions, which appear in multiple patients. Each FAIRE accessible peak was then classified as tumor, normal or both. A total of 242 501 sites was found to be overlapping in at least two normal and two tumor samples. Sites were defined as overlapping in case of an overlap of at least 1 bp. Of those sites, 77% was found to be in an active state. 17 830 sites are unique to tumor samples. These sites were found in at least two tumor samples, but no normal samples. Of these sites, 68% was found to be in an active state. 33 907 sites are unique to normal samples. These sites were found in at least two normal samples, but no tumor samples. Of these sites, 72% was found to be in an active state.
Figure 2. FAIRE enriches for active regulatory regions. FAIRE-seq was performed as previously described with slight modifications.12 Briefly, 5 mg of frozen normal and tumorigenic tissue was ground in liquid nitrogen, suspended in 1 PBS and crosslinked with 1% formaldehyde at 25 1C for 7 min. After quenching with glycine, pelleted cells were resuspend in 300 ml lysis buffer (2% Triton X-100, 1% SDS, 100 mM NaCl, 10 mM Tris-Cl pH 8.0, 1 mM EDTA), and sonicated in a Bioruptor (high 10 min, 30 s ON, 30 s OFF). The cleared supernatant was phenol/chloroform extracted twice and chloroform-isomyl alcohol extracted, and the FAIRE-isolated DNA was precipitated with ethanol. The precipitated DNA was further cleaned up by treatment with RNase A and Qiagen MiniElute column purified. Illumina sequencing libraries were prepared using the standard Illumina ChIP-seq protocol and 50 bp single-end sequenced in an individual flow cell lane of a HiSeq2000. (a) Average log2 FAIREseq signal measured at 41 317 transcriptional start sites (TSSs) as defined by University of California, San Francisco (UCSC) genes was determined by ArchTeX with standardized, 120 bp extension, in log2 ratio space.15 (b) Average log2 FAIRE-seq signal measured at 24 725 ubiquitously bound CTCF sites identified in 10 cell lines.16 (c) FAIRE peaks are more likely located at active regulatory regions (72–80%) compared with 42% of genome, which are active. Significantly accessible regions were identified from the aligned data sets with MACS2 (Pvalue ¼ 0.005, broad peaks, shift size ¼ 75 and MFOLD ¼ 1030).17 Functionality was assigned to each FAIRE peak using the 15 chromatin state assignments from Ernst and Kellis from nine cell lines.18 To do this, each peak was first assigned an active (active, poised or weak promoter, strong or weak enhancer, insulator, weak transcribed, transcriptional transition or transcriptional elongation) or inactive (polycomb repressed, heterochromatin or repetitive) state. If a peak was located in an active state in any cell line, then it was assigned the active category. In the active group, a peak was then classified by the state found in the majority of cell lines. (d) Hierarchical clustering of 2727 FAIRE-peak locations that is differentially accessible (delta FAIRE signal 40.75; see Supplementary Table 2). Hierarchical clustering was performed on log2 standardized data with Gene Cluster with Pearson correlation as the similarity metric.19 & 2013 Macmillan Publishers Limited
Oncogene (2013) 1 – 5
Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
4 ccRCC, we clustered the differential methylated sites (Figure 1c). As shown in Figure 1c, the CpG methylation status is very informative in its ability to classify ccRCCs from normal tissue. All samples except 162-T cluster within the correct tumor/normal grouping. Overall, these results show widespread promoter hypermethylation in ccRCC consistent with promoter gene silencing. To further examine changes in the epigenome during ccRCC, we measured chromatin accessibility with FAIRE-seq.10,11 To apply FAIRE-seq to our clinical samples, we made modifications to the procedure to allow us to measure chromatin accessibility from limited tissue samples. For each sample, we obtained 90–138 million aligned sequence reads for a total 45 Gb of sequence (Supplementary Table 1). To ensure the quality of each FAIRE experiment, we analyzed transcriptional start sites (TSSs) and CCCTC-binding factor (CTCF)binding site enrichment (Figure 2). FAIRE has been shown to significantly enrich for TSSs and CTCF-binding locations.12 As can be seen in Figures 2a and b, a peak of enrichment is observed at the TSS and CTCF in both normal and ccRCC samples. The FAIRE experiments for patient 171 did not show a significant peak of enrichment at TSSs and was removed from further analysis. For the remaining eight samples, we identified 238 544–94 525 regions of accessibility (see Supplementary Table 1). These accessible locations occurred in similar locations across all experiments with over 70% of FAIRE peaks localizing at functionally active genomic elements (Figure 2c). This is a
significant enrichment compared with the 41% of active regions seen in the genome. To determine whether changes in chromatin accessibility are characteristic of ccRCC, we clustered differential FAIRE peaks (see Supplementary Table 2). As can be seen in Figure 2d all samples except 162-T group with the appropriate sample type. In addition, it is clear that clustering is being driven by a decrease in FAIRE-seq signal at the majority of sites in ccRCC samples. These results are in concordance with our methylation assays and suggest that chromatin is becoming inactive across the genome in ccRCC. We next examined all non-patient-specific FAIRE peaks (Figure 3). The majority (242 501) of the total peaks (294 238) are shared between the tumor and normal samples. These peaks are predominately functional active (77%) and are associated with weak transcription (48%), enhancers (26%), insulators (8%), transcription elongation (8%) and promoters (8%). In all, 33 907 accessible locations are only identified in normal samples, whereas 17 830 peaks are found only in tumors. This roughly 2 to 1 difference in normal specific accessible locations compared with tumors further demonstrates that the majority of chromatin events are a loss of accessibility in ccRCC. The normal and tumor only peaks are distributed across the genome in a similar manner which is almost identical to the unchanged locations. In addition, our results at promoters are symptomatic of changes in downstream gene expression, where we found that upregulated genes during
Figure 4. FAIRE identified clusters of open regulatory elements (COREs) are associated with ccRCC genes. (a) We defined normal COREs as three or more normal-specific FAIRE sites separated by o20 kb.13 Tumor COREs are defined as three or more tumor-specific FAIRE sites separated by less than o20 kb. (b) Two normal COREs associated with SETD2. (c) GO biological process significantly associated with normal or tumor COREs with the corresponding hypergeometric P value. (d) Normal and tumor COREs are strongly enriched for known ccRCC mutated genes. Genes that have been previously identified as mutated in RCC are shown, with the type mutation N (nonsense), M (missense), B (nonsense and missense). The percentage of patients with tumor/normal-matched pairs in the TCGA project with that gene downregulated (% down) is shown.21 The ratio of tumor to normal for the number of FAIRE-seq tags across each COREs is tabulated for each sample. Oncogene (2013) 1 – 5
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Chromatin accessibility in clear cell renal cell carcinoma MJ Buck et al
5 ccRCC become more accessible and the promoters of downregulated genes become less accessible (Supplementary Figure 2). We next sought to link tumor- and normal-specific elements to specific genes. We noticed that open chromatin locations were not evenly distributed throughout the genome but tend to cluster. We identified clusters of regulatory elements (COREs), within 20 kb, for our normal and tumor unique groupings as previously described13 (Figures 4a and b). There were 2131 normal COREs and 755 tumor COREs, which are associated with relevant biological processes (Figure 4c and Supplementary Tables 3 and 4). Normal COREs also enrich for the KLF family of transcription factors (P ¼ 5 10 7; KLF2, KLF3, KLF4, KLF5, KLF6, KLF7, KLF9, KLF10, KLF12, KL13). Our findings are further supported by the association of normal and tumor COREs with genes commonly mutated in ccRCC (P ¼ 1.48 10 6; Figure 4d). Normal COREs are associated with PBRM1, SETD2, TSC1, SHANK1, RYR1, CARD11, MLL2 and WRN. Tumor COREs are associated with AKAP13, ZNF804A, LRP1B, NAV3 and LRRK2. The genes associated with normal COREs and thus to more condensed chromatin state in tumors are more likely to be classified as nonsense mutations and downregulated in tumor (Figure 4d). On the other hand, the five genes associated with tumor COREs are classified predominately as missense mutations. These findings suggest that multiple mechanisms exist for gene inactivation including a nonsense mutation or by inactivation through a chromatin-mediated method. We do recognize that tissue heterogeneity remains a major challenge in genetic and epigenetic studies. The limited tissue availability did not allow us to have multiple representative samples from the original tumor and matched normal kidney. Future studies will need to utilize multiple tissue samples from the same patient to have more representative genetic and epigenetic information. In summary, we identified changes in chromatin accessibility in ccRCC and the majority of these events were not associated with changes in DNA methylation (Supplementary Figure 3). Thus, FAIRE-seq provides a unique view of a cancer cell’s chromatin state and is ideally suited for the clinic. FAIRE-seq provides a comprehensive picture of a cells chromatin from a small number of cells and will be invaluable tool in monitoring response to epigenetic treatments. Our results suggest that normal COREs are significantly associated with genes with missense mutations in ccRCC. This finding also provides evidence that FAIRE can be used in concert with whole-genome sequencing to identify functionally important genetic alterations. In the future, further development of technical controls and computational methods will allow FAIRE to be applied to various tumor types in a quick and effective manner.
CONFLICT OF INTEREST The authors declare no conflict of interest.
ACKNOWLEDGEMENTS This research was supported in part by the National Cancer Institute at the National Institutes of Health (P30CA016056; to RP), a grant from the RPCI -Alliance Foundation (to RP) and a donation from the, The Bruce Cuvelier Family Trust (to RP).
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Supplementary Information accompanies this paper on the Oncogene website (http://www.nature.com/onc)
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