The somatic genomic landscape of chromophobe renal cell carcinoma

Copy Number and Whole Exome Analysis

The Cancer Genome Atlas (TCGA) collected a total of 66 primary ChRCC specimens (Table S1) with matching normal tissue/blood, in order to better characterize the molecular basis of this cancer using multiple data platforms (Tables 1 and S1). Our comprehensive analysis of ChRCC involved a systematic examination by data type, including copy number and whole-exome sequencing (WES). By SNP array analysis, loss of one copy of the entire chromosome, for most or all of chromosomes 1, 2, 6, 10, 13, and 17, was seen in the majority of cases (86%, Figure 1A). Losses of chromosomes 3, 5, 8, 9, 11, 18, and 21 were also noted at significant frequencies (12-58%). There were no focal copy number events by GISTIC analysis (Mermel et al., 2011), suggestive of a simpler chromosomal landscape for ChRCC in comparison to that of other cancers, including the more common clear cell type RCC (ccRCC). We subdivided our ChRCC cases according to previously defined histologic categories of “classic” (n=47), which demonstrate the classical pale cytoplasmic features for which the disease was named, and “eosinophilic” (n=19), based on abundant, eosinophilic cytoplasm and densely packed mitochondria, by expert consensus pathology review (Brunelli et al., 2005). While all classic cases showed the characteristic ChRCC copy number pattern, only about half of the eosinophilic cases (10/19) showed the same, with four eosinophilic cases showing no copy number alterations. This suggests a degree of genomic heterogeneity that distinguishes the histopathology-based classifications.

WES of 66 ChRCC cases targeted ∼186,260 exons in ∼18,091 genes, achieving 90% target coverage at a minimum of 20X for both tumor and matched normal samples. Overall, ChRCC displayed a low median rate of exonic somatic mutations (∼0.4 per Mb) compared to most tumors (Alexandrov et al., 2013), approximately 3-fold less than the median number seen in ccRCC (which differences were also observable within strata defined by age or stage), with the one exception showing elevated somatic mutation rate (>10/Mb by WES) and mutation signature of DNA mismatch repair deficiency (Alexandrov et al., 2013). Using alternative sequencing instrumentation, we validated 60 somatic mutation events for a set of 30 genes both arising from WES and having inferred biologically relevance (Table S2). While our lower case numbers limited purely data-driven approaches to assigning statistical significance to infrequently mutated genes, we did have sufficient power to identify significant genes with a frequency of ∼10% (Lawrence et al., 2014). Only two significant genes were thus identified (MutSig q<0.1): TP53 and PTEN.

TP53 was frequently mutated in 32% of cases (21 of the 66 profiled), with mutations correlating with decreased expression of p53 transcriptional targets (Figures S1A-S1C). PTEN was the next most frequently mutated, with 9% (6 of 66) nonsilent mutations detected. No other genes were found to be mutated at a frequency higher than 5%, though mutations involving cancer-relevant genes were found at lower frequencies (Figure 1B). Mutations were seen in MTOR (2 cases), NRAS (1 activating mutation), and TSC1 or TSC2 (4 cases), and two homozygous deletions were seen in PTEN, indicating that genomic targeting of the mTOR pathway occurred overall in 15 (23%) of 66 ChRCC (Figure 1B). Biological significance could be ascribed to infrequently mutated genes, in terms of associated pathways, including the p53 and PTEN pathways (Table S2). The genetic diseases BHD and TSC both predispose to the development of ChRCC, and associated mutations converge in activation of the PTEN signaling pathway. Our study focused on sporadic disease, and a surprisingly high percentage (∼47%) of our core cases did not show alterations associated with either PTEN or p53 pathways. As no additional pathways involving sizeable numbers of cases could be implicated from the exome data, our search was extended to mtDNA and structural variant analysis, as described below.

DNA Methylation and RNA Analysis

TCGA data platforms allow for comparisons between tumor types (Cancer_Genome_Atlas_Research_Network et al., 2013). For example, we observed widespread differences in DNA methylation between ChRCC and ccRCC (Figure 2A), involving over 64K loci out of ∼450K profiled (p<0.001, t-test using logit-transformed data, beta value difference>0.1). ChRCC displayed more hypomethylation and fewer hypermethylation events compared to ccRCC. We also observed epigenetic silencing of CDKN2A/p16 in four ChRCC cases (Figure 2B). In principle, differential DNA methylation patterns could involve cancer-relevant pathways, but may also reflect cell of origin of the cancer (Shen and Laird, 2013). Based on immunohistochemical analyses (Prasad et al., 2007), ChRCC has been postulated to arise from intercalated cells in the distal convoluted tubule of the nephron, while ccRCC is thought to arise from cells in the proximal convoluted tubule; however, this issue has remained unresolved. The above DNA methylation patterns were consistent with distinct origins, leading us to further explore these origins using gene expression data.

We examined our gene expression data in the context of an external gene expression dataset of normal tissue microdissected from various regions of the nephron (Cheval et al., 2012). Supervised analysis, globally comparing each TCGA ChRCC or ccRCC tumor expression profile (n=66 and n=417, respectively) to that of each sample in the nephron atlas, showed high mRNA expression correlations for ChRCC with distal regions of the nephron. ccRCC gene expression, however, was correlated with patterns associated with the proximal nephron (Figure 2C). These associations were also evident, when focusing on the subset of differential genes in ChRCC versus ccRCC associated with inverse DNA methylation changes (Figure 2D). These results put in context many of the widespread molecular differences between these two kidney cancer types, as well as suggesting that cancers may be defined in part by cell of origin in addition to genetic aberrations.

In addition to widespread differences in gene expression between ChRCC and ccRCC, and differences from normal kidney (Figure S2A and Table S3), unsupervised clustering of mRNA profiles indicated further molecular heterogeneity within ChRCC, with at least two subsets identified (Figure S2B) as defined by differential gene expression patterns. Cluster analysis of miRNA profiles also indicated heterogeneity (Figure S2C), and we could identify anti-correlations between miRNAs and their predicted mRNA targets (Table S4), including an anti-correlation (False Discovery Rate, or FDR<0.01) involving miR-145 (low in ChRCC versus normal) and the complex I-associated NDUFA4 gene (Figure S2D)(Kano et al., 2010). Molecular correlates of patient survival in ChRCC were identifiable at levels of mRNA, miRNA, and DNA methylation (Table S5); many of these correlates were shared with those previously observed for ccRCC (The_Cancer_Genome_Atlas_Research_Network, 2013) and included cell cycle genes, but not the ‘Warburg effect’-like patterns of aggressive ccRCC (The_Cancer_Genome_Atlas_Research_Network, 2013).

Pathway and Mitochondrial DNA Analysis

When viewed in the context of mitochondrial function, expression of nuclear-encoded genes in ChRCC, as compared to normal kidney, suggested increased utilization of the Krebs cycle and electron transport chain (ETC) for adenosine triphosphate (ATP) generation (Figures 3A, S3A, and S3B). In ChRCC, nearly all genes encoding enzymes in the Krebs cycle showed increased expression over normal, with the entry of pyruvate into the Krebs cycle via Acetyl CoA likely through the pyruvate dehydrogenase complex (PDC). Concordantly, all complexes of the ETC demonstrated mRNA increases in at least one gene. These patterns could reflect an increased level of mitochondrial biosynthesis, resulting in greater numbers of mitochondria within each tumor cell; this possibility is supported by both the increased expression of mitochondrial biogenesis regulator PPARGC1A (p<1E-5, t-test using log-transformed data, Table S3), and increased mitochondrial genome copy numbers (four times more on average in ChRCC versus normal kidney, Figures 3B and S3C). These findings interestingly parallel the eosinophilic histology observed in some ChRCC, corresponding to the high uptake of eosin by mitochondria. Eosinophilic ChRCC tumors share many features with the benign variant oncocytoma, which is also characterized by dense accumulations of mitochondria (Amin et al., 2008; Tickoo et al., 2000). Furthermore, the gene expression landscape appeared very different from that of ccRCC, where expression of genes involved in mitochondrial functions is strongly suppressed (Figure S3D) (The_Cancer_Genome_Atlas_Research_Network, 2013). These findings suggest that various bioenergetics strategies may support tumor growth, and that not all cancers necessarily seek to minimize their reliance upon oxidative phosphorylation (The_Cancer_Genome_Atlas_Research_Network, 2013).

Given the indicated prevalent role of mitochondria in ChRCC and the likelihood of rapid mitochondrial genome replication (Figure 3B), we sequenced mtDNA from 61 of our 66 ChRCC cases, using a Polymerase Chain Reaction (PCR)-based amplification approach (Table S6). In all, we identified 142 somatic mutation events (i.e. not present in the normal) at various levels of heteroplasmy (i.e. mixture with other variants), 75 of these residing within the commonly altered D-Loop non-coding region (Chatterjee et al., 2006). Thirty-five mutation events (involving 27 cases) were present in over 50% of mtDNA copies in the tumor (>50% heteroplasmy) (Figure 4A). Human mtDNA encodes 13 proteins involved in respiration and oxidative phosphorylation (Figure 3A), and we found 15 nonsilent mutations in 12 ChRCC cases involving these genes (>50% heteroplasmy), all of which validated using alternative strategies, including WGS-based analysis (Larman et al., 2012)(Table S6). Based on previous functional studies in oncocytoma (Gasparre et al., 2008; Mayr et al., 2008; Simonnet et al., 2003), and as many of our variants represented frameshift substitutions, these mtDNA mutations are thought, in general, to lead to inactivation, rather than activation, of the associated protein.

Electron transport chain Complex I genes were altered in 18% of cases (n=11, Figures 3A and 1B and Table S3); the most frequently altered gene was MT-ND5, in six cases (all with >70% heteroplasmy), with five of these being histologically classified as eosinophilic ChRCC (p<0.01, one-sided Fisher's exact test), and three showing no copy number abnormalities (p<0.002). MT-ND5 is essential for the activity of complex I (Chomyn, 2001), which is responsible for the transfer of electrons from NADH to ubiquinone. One ChRCC case had a single base insertion at position 12417 that changes the length of an 8-bp homopolymer tract in MT-ND5, which has been observed previously in several other cancer types (Larman et al., 2012); another case had insertion at 12384, at which position a mutation was found elsewhere in oncocytoma and associated with loss of complex I activity (Mayr et al., 2008). Two ChRCC cases each had single base deletions at position 13230 of MT-ND5, but no other mtDNA mutations were recurrent in our cases. We also found MT-ND5-mutated ChRCC cases to have a distinct gene transcription signature (Figures 4B, S4A, and S4B, 719 genes with p<0.001 by t-test, FDR<0.05), which was shared by other eosinophilic cases and were not limited to genes in regions of recurrent copy number abnormality (Figure S4C). Genes with high expression in MT-ND5-mutated cases were enriched for those associated with mitochondria (43 with Gene Ontology term “mitochondrion”, p<5E-6, one-sided Fisher's exact test), including several with roles in ETC (SDHB, NDUFS1, ATP5F1, COX10, COX11, Table S3). Notably, mutations in complex I did not result in expression patterns associated with loss of oxidative phosphorylation (Figure 4C), as might be assumed (Larman et al., 2012), suggesting possible alternative roles for complex I alteration in cancer-associated metabolic activity (Figure S4D). The associations made here, involving mtDNA mutations with mitochondrial abundance and differential gene expression patterns (which may be unique to ChRCC and related cancers), could perhaps suggest either a compensatory role for loss of complex I function, or selective pressures operating to promote alternative pathways.

Whole Genome Analysis

WGS for 50 of our 66 ChRCC cases was performed (60X and 30X coverage for paired tumor and normal, respectively). Meerkat algorithm (Yang et al., 2013) was applied to detect genomic rearrangements, with an average of 16 found per case (range 0-207, Figure S5A), but without involving recurrent gene-gene fusions. By WGS analysis, a subset of ChRCC manifested kataegis (Figures 5A and S5B), a phenomenon involving highly localized substitution mutations (C>T or C>G). Consistent with observations in other cancers (Alexandrov et al., 2013; Nik-Zainal et al., 2012), we found that regions of kataegis in ChRCC were found in the vicinity of genomic rearrangements (Figures 5A and S5B, average of 150 rearrangements by pter/qter region). Three ChRCC WGS profiles showed particularly strong patterns involving chromosomal regions 3p, 5p, 5q, 8q, 13q, or 15q (Figure 5B). A mutation signature consistent with APOBEC cytidine deaminase activity (Alexandrov et al., 2013; Roberts et al., 2013) was significantly enriched in kataegis regions as well as in tightly spaced mutation clusters forming kataegis events (Figures S5C-S5F, Table S7). While not detectable in ChRCC WES data (Alexandrov et al., 2013), WGS mutation spectra of six ChRCC cases, including the three with strong kataegis patterns, showed statistically significant (albeit moderate) APOBEC-patterned mutagenesis across the entire genome (Figure S5C). APOBEC3B mRNA expression was also elevated in ChRCC compared to normal kidney (Figure S5G).

We compared gene expression profiles between ChRCC cases with and without a strong kataegis pattern (n=3 and n=47, respectively), and identified 29 differentially expressed genes (FDR<0.05) including TERT (p<1E-10, t-test, FDR<1E-6, Figure 5C). The TERT gene itself showed a wide range of expression levels across ChRCC, from undetectable to hundreds of units by RNA-seq. Focusing our attention on TERT, we sequenced the promoter region for recently identified mutations (C228T and C250T) (Huang et al., 2013); three cases harbored C228T mutations, but were associated with only marginal TERT expression levels (average expression ∼1 unit). WGS analysis of DNA copy within the TERT region identified some copy number variation, but not at levels that would account for the extent of deregulated expression. However, multiple cases did show abrupt changes in copy number, at points that fell within the region 10 kb upstream of the TERT transcription start site (Figure 5D). This observation suggested the existence of structural breakpoints, leading us to reexamine our Meerkat-generated results with greater scrutiny.

Subsequent WGS analysis identified genomic rearrangements involving the TERT promoter region, leading to breakpoints within the region in six out of 50 ChRCC cases (Figure 5D and Table 2); these cases also had the highest levels of TERT expression (average>500 units, p<1E-20, t-test; Table 2 and Figure 5E), even compared to cases with 228T mutation, and three showed the strongest manifestation of kataegis (p=0.001, one-sided Fisher's exact). In five ChRCC cases, the TERT-associated rearrangements were intrachromosomal (one involving part of PDCD6), while the sixth case involved NEK5 on chromosome 13. When considering intra-tumor heterogeneity, in most cases these variants were estimated to reside in nearly all of the cells (when counting the numbers of concordant versus discordant read pairs), which would indicate that the TERT-associated rearrangements represent early events and therefore possible drivers. Of the seven rearrangements identified by WGS, we confirmed six (involving six cases) by PCR, by designing primers that spanned both sides of the breakpoint junction (Figure 6A and Table S8), allowing for amplification of DNA spanning the breakpoint region in the tumor sample (Figures 6B and S6); subsequent sequencing of the PCR product independently confirmed the junction in each case (Figure 6C). While point mutations in the TERT promoter, leading to up-regulation of TERT itself, have been recently reported in cancers such as melanoma (Heidenreich et al., 2013; Huang et al., 2013), our results represent another phenomenon, of recurrent genomic rearrangement breakpoints in the TERT promoter being associated with elevated TERT expression in cancer. A precise mechanism remains to be elucidated, though, as a result of rearrangement, a number of cis-regulatory elements were found to be placed in close proximity to the core promoter of TERT (Figure S7).

Patient and Sample Characteristics

With informed consent, biospecimens were collected from newly diagnosed patients with ChRCC undergoing surgical resection and who had received no prior treatment for their disease. Samples were obtained with approval from institutional review boards at Brigham and Women's Hospital, Memorial Sloan-Kettering Cancer Center, National Cancer Institute, and The University of Texas M.D. Anderson Cancer Center. Using a co-isolation protocol, DNA and RNA were purified. Details of sample preparation are described in the Supplemental Experimental Procedures.

Data Generation

In total, 66 ChRCC cases were assayed on at least one molecular profiling platform (Table 1), which platforms included: (1) RNA sequencing; (2) DNA methylation arrays; (3) miRNA sequencing; (4) Affymetrix SNP arrays; (5) whole exome sequencing; (6) whole genome sequencing; and (7) mtDNA sequencing (using long-range PCR to amplify mtDNA). As described above and in the Supplemental Experimental Procedures, both single platform analyses and integrated cross-platform analyses were performed. Sequence files are available from CGHub (https://cghub.ucsc.edu/). All other molecular, clinical and pathological data are available through the TCGA Data Portal (https://tcga-data.nci.nih.gov/tcga/).

Whole-Genome and Exome Sequencing Analysis

Massively Parallel Sequencing Exome capture was performed using NimbleGen (custom designed) VCRome 2.1 (42MB) according the manufacturer's instructions. All exome and whole-genome sequencing was performed on the Illumina HiSeq platforms. Basic alignment and initial sequence analysis were carried out using the Mercury analysis pipeline (Reid et al., 2014).

MtDNA Sequencing Analysis

MtDNA was isolated from tissue samples using long-range PCR methods. Amplified mtDNA PCR products were constructed into Illumina paired-end libraries, and raw sequence data were pre-processed and aligned using the Mercury pipeline.

RNA Sequencing Analysis

Both mRNA and miRNA libraries were separately generated from total RNA and constructed using manufacturer protocols. Sequencing was done on the Illumina HiSeq platform. Read mapping and downstream data analysis were performed as described in the Supplemental Experimental Procedures.

Array Data Analysis

DNA was hybridized to Affymetrix SNP 6.0 arrays and Illumina Infinium HumanMethylation450 (HM450) BeadChip arrays, according to manufacturer protocols.

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Article Highlights: CANCER-CELL-D-14-00457.

• Comprehensive molecular analysis of 66 kidney chromophobe cases

• Global molecular patterns provide clues as to this cancer's cell of origin

• mtDNA sequencing reveals loss-of-function mutations in NADH dehydrogenase subunits

• Genomic structural rearrangements involving TERT promoter region

Significance.

Rare diseases can provide insights into the biology of more common pathologies. Using diverse molecular platforms, we deconstructed ChRCC, a tumor characterized by slow but persistent growth and high resistance to conventional cancer therapies. Global molecular patterns provide clues as to this cancer's cell of origin. MtDNA alterations represent an integral component of the molecular portrait of ChRCC. The observed TERT promoter rearrangements may result from genomic instability in precancerous cells undergoing the crisis stage of immortalization, leading to activated telomerase. These data will facilitate further discovery of driver alterations extending beyond the exome as well as the generation of hypotheses that can advance our molecular understanding of this and other cancers.

Analysis Working Group

Baylor College of Medicine – Chad J. Creighton(1, 2), Caleb F. Davis(1), Margaret Morgan(1), Preethi H. Gunaratne(1, 3), Lawrence A. Donehower(1), Benny A. Kaipparettu(2, 4), David A. Wheeler(1), Richard A. Gibbs(1), Brigham and Women's Hospital – Sabina Signoretti(5, 6), Broad Institute – Andrew D. Cherniack(7), Canada's Michael Smith Genome Sciences Centre BC Cancer Agency – A. Gordon Robertson(8), Andy Chu(8), Dana Farber Cancer Institute - Toni K. Choueiri(6, 9), Elizabeth P. Henske(6, 7, 9), David J. Kwiatkowski(6, 7, 9), Memorial Sloan-Kettering Cancer Center – Victor Reuter(10), James J. Hsieh(11, 12), A. Ari Hakimi(13), Satish K. Tickoo(10), National Cancer Institute -Christopher Ricketts(14), W. Marston Linehan(14), Laura S. Schmidt(14, 15), National Institute of Environmental Health Sciences – Dmitry A. Gordenin(16), Rutgers University – Gyan Bhanot(17, 18), Michael Seiler(19), The University of Texas M.D. Anderson Cancer Center - Pheroze Tamboli(20), University of North Carolina, Chapel Hill - W. Kimryn Rathmell(21-23), Catherine C. Fahey(24), Kathryn E. Hacker(24), Angela B. Smith(23), Eric M. Wallen(23), University of Southern California – Hui Shen(25), Peter W. Laird(25), Yale University – Brian Shuch(26).

Genome Sequencing Center

Baylor College of Medicine - Donna Muzny(1), Caleb F. Davis(1), Christian Buhay(1), Min Wang(1), Margaret Morgan(1), Lawrence A. Donehower(1), Hsu Chao(1), Mike Dahdouli(1), Liu Xi(1), Nipun Kakkar(1), Jeffrey G. Reid(1), Brittany Downs(1), Jennifer Drummond(1), Donna Morton(1), Harsha Doddapaneni(1), Lora Lewis(1), Adam English(1), Qingchang Meng(1), Christie Kovar(1), Qiaoyan Wang(1), Walker Hale(1), Alicia Hawes(1), Divya Kalra(1), Kimberly Walker(1), Chad J. Creighton(1, 2), David A. Wheeler(1), Richard A. Gibbs(1).

Genome Characterization Centers

Broad Institute – Andrew D. Cherniack(7), Bradley A. Murray(7), Carrie Sougnez (7), Gordon Saksena(7), Scott L. Carter(7), Steven E. Schumacher(7, 27), Barbara Tabak(7, 27), Travis I. Zack(7, 27), Gad Getz(7, 28), Rameen Beroukhim(6, 7, 9, 27), Stacey B. Gabriel(7), Matthew Meyerson(6, 7, 29), Canada's Michael Smith Genome Sciences Center, BC Cancer Agency – Adrian Ally(8), Miruna Balasundaram(8), Inanc Birol(8), Denise Brooks(8), Yaron S.N. Butterfield(8), Andy Chu(8), Eric Chuah(8), Amanda Clarke(8), Noreen Dhalla(8), Ranabir Guin(8), Robert A. Holt(8), Katayoon Kasaian(8), Darlene Lee(8), Haiyan I. Li(8), Emilia Lim(8), Yussanne Ma(8), Michael Mayo(8), Richard A. Moore(8), Andrew J. Mungall(8), A. Gordon Robertson(8), Jacqueline E. Schein(8), Payal Sipahimalani(8), Angela Tam(8), Nina Thiessen(8), Tina Wong(8), Steven J.M. Jones(8), Marco A. Marra(8), University of North Carolina, Chapel Hill - J. Todd Auman(30), Donghui Tan(21), Shaowu Meng(31), Corbin D. Jones(32, 33), Katherine A. Hoadley(31), Piotr A. Mieczkowski(21), Lisle E. Mose(31), Stuart R. Jefferys(31), Jeffrey Roach(34), Umadevi Veluvolu(21), Matthew D. Wilkerson (31), Scot Waring(21), Elizabeth Buda(33), Junyuan Wu(31), Tom Bodenheimer(31), Alan P. Hoyle(31), Janae V. Simons(31), Mathew G. Soloway(31), Saianand Balu(31), Joel S. Parker(21, 31), D. Neil Hayes(31, 35), Charles M. Perou(21, 31, 36), University of Southern California & Johns Hopkins – Hui Shen(25), Daniel J. Weisenberger(25), Moiz S. Bootwalla(25), Timothy Triche Jr. (25), Phillip H. Lai(25), David J. Van Den Berg(25), Stephen B. Baylin(37), Peter W. Laird(25).

Genome Data Analysis

Baylor College of Medicine – Chad J. Creighton(1, 2), Fengju Chen(2), Cristian Coarfa(2, 38), Caleb F. Davis(1), Lawrence A. Donehower(1), Preethi H. Gunaratne(1, 3), David A. Wheeler(1), Broad Institute - Gad Getz(7, 28), Michael S. Noble(7), Daniel DiCara(7), Hailei Zhang(7), Juok Cho(7), David I. Heiman(7), Nils Gehlenborg(7), Gordon Saksena(7), Doug Voet(7), Pei Lin(7), Scott Frazer(7), Petar Stojanov(6, 7), Yingchun Liu(7), Lihua Zou(7), Jaegil Kim(7), Michael S. Lawrence(7), Lynda Chin(7, 39), Harvard Medical School/The University of Texas M.D. Anderson Cancer Center - Lixing Yang(40), Sahil Seth(39), Christopher A. Bristow(39), Alexei Protopopov(39), Xingzhi Song(39), Jianhua Zhang(39), Angeliki Pantazi(41), Angela Hadjipanayis(41), Eunjung Lee(40), Lovelace J. Luquette(40), Semin Lee(40), Michael Parfenov(41), Netty Santoso(41), Jonathan Seidman(41), Andrew W. Xu(40), Lynda Chin(7, 39), Raju Kucherlapati(41, 42), Peter J. Park(40, 41), Korea Institute of Science and Technology Information – Hyojin Kang(43), Junehawk Lee(43, 44), National Institute of Environmental Health Sciences– Dmitry A. Gordenin(16), Steven A. Roberts(16), Leszek J. Klimczak(16), David Fargo(16), National Institutes of Health – Martin Lang(14), Rutgers University – Gyan Bhanot(17, 18), Michael Seiler(19), Samsung Genome Institute – Yoon-La Choi(45), Sang Cheol Kim(45), June-Koo Lee(46), Woong-Yang Park(45, 47), The University of Texas M.D. Anderson Cancer Center – Wenyi Wang(48), Yu Fan(48), Jaeil Ahn(48), Rehan Akbani(48), John N. Weinstein(48), UC Santa Cruz -David Haussler(49, 50), Singer Ma(50), Amie Radenbaugh(50), Jingchun Zhu(50), University of Groningen – Michael Biehl(51), University of Houston – Preethi H. Gunaratne(1, 3).

Biospecimen Core Resource

The Research Institute at Nationwide Children's Hospital - Tara M. Lichtenberg(52), Erik Zmuda(52), Aaron D. Black(52), Benjamin Hanf(52), Nilsa C. Ramirez(52, 53), Lisa Wise(52), Jay Bowen(52), Kristen M. Leraas(52), Tracy M. Hall(52), Julie M. Gastier-Foster(52, 53).

Tissue Source Sites

Brigham and Women's Hospital - Sabina Signoretti(5, 6), Dana-Farber Cancer Institute – William G. Kaelin(6, 9), Toni K. Choueiri(6, 9), Memorial Sloan-Kettering Cancer Center - Satish K. Tickoo(10), Victor Reuter(10), A. Ari Hakimi(13), University of North Carolina at Chapel Hill - W. Kimryn Rathmell(21-23), Leigh Thorne(31), Lori Boice(31), Mei Huang(31), National Cancer Institute - W. Marston Linehan(14), Cathy Vocke(14), James Peterson(14), Robert Worrell(14), Maria Merino(14), Laura S. Schmidt(14, 15), The University of Texas M.D. Anderson Cancer Center – Pheroze Tamboli(20), Bogdan A. Czerniak(20), Kenneth D. Aldape(20), Christopher G. Wood(54).

Disease Working Group

Baylor College of Medicine – Richard A. Gibbs(1), Margaret Morgan(1), David A. Wheeler(1), Brigham and Women's Hospital - Sabina Signoretti(5, 6), Oregon Health and Sciences University – Paul T. Spellman(55), Dana-Farber Cancer Institute - Toni K. Choueiri (6, 9), William G. Kaelin(6, 9), Georgetown University - Michael B. Atkins(56), Memorial Sloan-Kettering Cancer Center - Satish K. Tickoo(10), Victor Reuter(10), National Cancer Institute - W. Marston Linehan(14), Cathy Vocke(14), Maria Merino(14), Laura S. Schmidt(14, 15), The University of Texas M.D. Anderson Cancer Center -Pheroze Tamboli(20), Christopher G. Wood(54), Kenneth D. Aldape(20), Mayo Clinic – John Cheville(57), R. Houston Thompson(57), University of North Carolina, Chapel Hill - W. Kimryn Rathmell(21-23), Broad Institute – Rameen Beroukhim(6, 7, 9, 27).

Data Coordination Center

SRA International - Mark A. Jensen(58), Todd Pihl(58), Yunhu Wan(58), Brenda Ayala(58), Julien Baboud(58), Sudhakar Velaga(58), Jessica Walton(58), Yunhu Wan(58), SAIC-Frederick Inc. – Jia Liu(59), Sudha Chudamani(59), Ye Wu(59).

Project Team

National Cancer Institute - Margi Sheth(60), Kenna R. Mills Shaw(60, 61), John A. Demchok(60), Tanja Davidsen(60), Liming Yang(60), Zhining Wang(60), Roy W. Tarnuzzer(60), Jiashan Zhang(60), Greg Eley(60), Ina Felau (49), Jean Claude Zenklusen(60), National Human Genome Research Institute -Carolyn M. Hutter(62), Mark S. Guyer(62), Bradley A. Ozenberger(62), Heidi J. Sofia(62), Scimentis, LLC - Greg Eley(63).

1. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030.

2. Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030.

3. Department of Biology & Biochemistry, University of Houston, 4800 Calhoun Road, Houston, TX 77204.

4. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.

5. Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston MA 02115.

6. Department of Medical Oncology, Dana-Farber Cancer Institute, 450 Brookline Ave, Boston, MA 02215.

7. The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142.

8. Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, BC V5Z 4S6.

9. Department of Medicine, Harvard Medical School, Boston, MA 02215.

10. Department of Pathology, Memorial Sloan-Kettering Cancer, 1275 York Avenue, New York, NY 10065.

11. Department of Medicine, Weill-Cornell Medical College, New York, NY 10021.

12. Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, NY 10065.

13. Department of Surgery, Urology Service Memorial Sloan Kettering Cancer Center, New York, NY 10065.

14. Urologic Oncology Branch, Center for Cancer Research, National Cancer Institute CRC Room 1W-5940 Bethesda, MD 20892.

15. Leidos Biomedical Research, Inc., Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702.

16. National Institute of Environmental Health Sciences, 111 T.W. Alexander Drive, Research Triangle Park, NC 27709.

17. Department of Molecular Biology and Biochemistry, Rutgers University, Busch Campus, Piscataway, NJ 08854.

18. Cancer Institute of New Jersey, 195 Little Albany Street, New Brunswick, NJ 08903.

19. Bioinformatics Program, Boston University, 24 Cummington St, Boston, MA 02215.

20. Department of Pathology, The University of Texas, M.D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030.

21. Department of Genetics, University of North Carolina at Chapel Hill, NC 27599.

22. Department of Medicine, Division of Hematology and Oncology, University of North Carolina, Chapel Hill, NC 27599.

23. Department of Urology, University of North Carolina, Chapel Hill, NC 27599.

24. Curriculum in Genetics and Molecular Biology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599.

25. USC Epigenome Center, University of Southern California, Los Angeles, CA 90033.

26. Department of Urology, Yale School of Medicine, New Haven, CT 06520.

27. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215.

28. Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114.

29. Department of Pathology, Harvard Medical School, Boston, MA 02215.

30. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

31. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

32. Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

33. Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

34. Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.

35. Department of Internal Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599

36. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, NC 27599.

37. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21287.

38. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030.

39. Institute for Applied Cancer Science, Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.

40. Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115.

41. Department of Genetics, Harvard Medical School, Boston, MA 02115.

42. Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115.

43. National Institute of Supercomputing and Networking, Korea Institute of Science and Technology Information, Daejeon, Korea.

44. Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.

45. Samsung Genome Institute, Samsung Medical Center, Seoul, Korea.

46. Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.

47. Sungkyunkwan University School of Medicine, Seoul, Korea.

48. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.

49. Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064.

50. Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064.

51. University of Groningen, Johann Bernoulli Institute for Mathematics and Computer Science, Intelligent Systems Group, P.O. Box 407, 9700 AK Groningen.

52. The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205.

53. The Ohio State University, Columbus, OH 43210.

54. Department of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030.

55. Oregon Health and Science University, Department of Molecular and Medical Genetics, Portland, OR 97239.

56. Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University, Washington D.C. 20057.

57. Mayo Clinic, Rochester, MN 55905.

58. SRA International, Inc., 4300 Fair Lakes Court, Fairfax, VA 22033.

59. SAIC-Frederick, Inc., 1050 Boyles Street, Frederick, MD 21702.

60. National Cancer Institute, 31 Center Dr, 3A20, Bethesda, MD 20892.

61. The MD Anderson Cancer Center Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (IPCT) 1400 Holcombe Blvd, FC12.2002 Houston, TX 77030.

62. National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, MD 20892.

63. Scimentis, LLC, Atlanta, GA 30666.

1. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–421. doi: 10.1038/nature12477. [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Amin MB, Amin MB, Tamboli P, Javidan J, Stricker H, de-Peralta Venturina M, Deshpande A, Menon M. Prognostic impact of histologic subtyping of adult renal epithelial neoplasms: an experience of 405 cases. Am J Surg Pathol. 2002;26:281–291. doi: 10.1097/00000478-200203000-00001. [DOI] [PubMed] [Google Scholar]
3. Amin MB, Paner GP, Alvarado-Cabrero I, Young AN, Stricker HJ, Lyles RH, Moch H. Chromophobe renal cell carcinoma: histomorphologic characteristics and evaluation of conventional pathologic prognostic parameters in 145 cases. Am J Surg Pathol. 2008;32:1822–1834. doi: 10.1097/PAS.0b013e3181831e68. [DOI] [PubMed] [Google Scholar]
4. Brunelli M, Eble JN, Zhang S, Martignoni G, Delahunt B, Cheng L. Eosinophilic and classic chromophobe renal cell carcinomas have similar frequent losses of multiple chromosomes from among chromosomes 1, 2, 6, 10, and 17, and this pattern of genetic abnormality is not present in renal oncocytoma. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2005;18:161–169. doi: 10.1038/modpathol.3800286. [DOI] [PubMed] [Google Scholar]
5. Cancer_Genome_Atlas_Research_Network. Weinstein J, Collisson E, Mills G, Shaw K, Ozenberger B, Ellrott K, Shmulevich I, Sander C, Stuart J. The Cancer Genome Atlas Pan-Cancer analysis project. Nature genetics. 2013;45:1113–1120. doi: 10.1038/ng.2764. [DOI] [PMC free article] [PubMed] [Google Scholar]
6. Chatterjee A, Mambo E, Sidransky D. Mitochondrial DNA mutations in human cancer. Oncogene. 2006;25:4663–4674. doi: 10.1038/sj.onc.1209604. [DOI] [PubMed] [Google Scholar]
7. Cheval L, Pierrat F, Rajerison R, Piquemal D, Doucet A. Of mice and men: divergence of gene expression patterns in kidney. PloS one. 2012;7:e46876. doi: 10.1371/journal.pone.0046876. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. Chomyn A. Mitochondrial genetic control of assembly and function of complex I in mammalian cells. J Bioenerg Biomembr. 2001;33:251–257. doi: 10.1023/a:1010791204961. [DOI] [PubMed] [Google Scholar]
9. Currie E, Schulze A, Zechner R, Walther TC, Farese RVJ. Cellular fatty acid metabolism and cancer. Cell Metab. 2013;18:153–161. doi: 10.1016/j.cmet.2013.05.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Gasparre G, Hervouet E, de Laplanche E, Demont J, Pennisi LF, Colombel M, Mege-Lechevallier F, Scoazec JY, Bonora E, Smeets R, et al. Clonal expansion of mutated mitochondrial DNA is associated with tumor formation and complex I deficiency in the benign renal oncocytoma. Hum Mol Genet. 2008;17:986–995. doi: 10.1093/hmg/ddm371. [DOI] [PubMed] [Google Scholar]
11. Gerlinger M, Rowan A, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–892. doi: 10.1056/NEJMoa1113205. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Heidenreich B, Rachakonda P, Hemminki K, Kumar R. TERT promoter mutations in cancer development. Curr Opin Genet Dev. 2013;24C:30–37. doi: 10.1016/j.gde.2013.11.005. [DOI] [PubMed] [Google Scholar]
13. Ho C, Chen S, Ho K, Chan W, Leung Y, Cheng K, Wong K, Cheung M, Wong K. Dual-tracer PET/CT in renal angiomyolipoma and subtypes of renal cell carcinoma. Clin Nucl Med. 2012;37:1075–1082. doi: 10.1097/RLU.0b013e318266cde2. [DOI] [PubMed] [Google Scholar]
14. Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA. Highly recurrent TERT promoter mutations in human melanoma. Science. 2013;339:957–959. doi: 10.1126/science.1229259. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Jemal A, Simard E, Dorell C, Noone A, Markowitz L, Kohler B, Eheman C, Saraiya M, Bandi P, Saslow D, et al. Annual Report to the Nation on the Status of Cancer, 1975-2009, featuring the burden and trends in human papillomavirus(HPV)-associated cancers and HPV vaccination coverage levels. J Natl Cancer Inst. 2013;105:175–201. doi: 10.1093/jnci/djs491. [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Kano M, Seki N, Kikkawa N, Fujimura L, Hoshino I, Akutsu Y, Chiyomaru T, Enokida H, Nakagawa M, Matsubara H. miR-145, miR-133a and miR-133b: Tumor-suppressive miRNAs target FSCN1 in esophageal squamous cell carcinoma. Int J Cancer. 2010;127:2804–2814. doi: 10.1002/ijc.25284. [DOI] [PubMed] [Google Scholar]
17. Larman TC, DePalma SR, Hadjipanayis AG, Cancer_Genome_Atlas_Research_Network. Protopopov A, Zhang J, Gabriel SB, Chin L, Seidman CE, Kucherlapati R, et al. Spectrum of somatic mitochondrial mutations in five cancers. Proc Natl Acad Sci U S A. 2012;109:14087–14091. doi: 10.1073/pnas.1211502109. [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Lawrence M, Stojanov P, Mermel C, Robinson J, Garraway L, Golub T, Meyerson M, Gabriel S, Lander E, Getz G. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature. 2014;505:495–501. doi: 10.1038/nature12912. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Linehan W. Genetic basis of kidney cancer: role of genomics for the development of disease-based therapeutics. Genome Res. 2012;22:2089–2100. doi: 10.1101/gr.131110.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Mayr JA, Meierhofer D, Zimmermann F, Feichtinger R, Kogler C, Ratschek M, Schmeller N, Sperl W, Kofler B. Loss of complex I due to mitochondrial DNA mutations in renal oncocytoma. Clin Cancer Res. 2008;14:2270–2275. doi: 10.1158/1078-0432.CCR-07-4131. [DOI] [PubMed] [Google Scholar]
21. Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome biology. 2011;12:R41. doi: 10.1186/gb-2011-12-4-r41. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Nickerson M, Warren M, Toro J, Matrosova V, Glenn G, Turner M, Duray P, Merino M, Choyke P, Pavlovich C, et al. Mutations in a novel gene lead to kidney tumors, lung wall defects, and benign tumors of the hair follicle in patients with the Birt-Hogg-Dube syndrome. Cancer Cell. 2002;2:157–164. doi: 10.1016/s1535-6108(02)00104-6. [DOI] [PubMed] [Google Scholar]
23. Nik-Zainal S, Alexandrov LB, Wedge DC, Van Loo P, Greenman CD, Raine K, Jones D, Hinton J, Marshall J, Stebbings LA, et al. Mutational processes molding the genomes of 21 breast cancers. Cell. 2012;149:979–993. doi: 10.1016/j.cell.2012.04.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Pavlovich C, Walther M, Eyler R, Hewitt S, Zbar B, Linehan W, Merino M. Renal tumors in the Birt-Hogg-Dubé syndrome. Am J Surg Pathol. 2002;26:1542–1552. doi: 10.1097/00000478-200212000-00002. [DOI] [PubMed] [Google Scholar]
25. Perou C, Sørlie T, Eisen M, van de Rijn M, Jeffrey S, Rees C, Pollack J, Ross D, Johnsen H, Akslen L, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. doi: 10.1038/35021093. [DOI] [PubMed] [Google Scholar]
26. Prasad SR, Narra VR, Shah R, Humphrey PA, Jagirdar J, Catena JR, Dalrymple NC, Siegel CL. Segmental disorders of the nephron: histopathological and imaging perspective. The British journal of radiology. 2007;80:593–602. doi: 10.1259/bjr/20129205. [DOI] [PubMed] [Google Scholar]
27. Przybycin CG, Cronin AM, Darvishian F, Gopalan A, Al-Ahmadie HA, Fine SW, Chen YB, Bernstein M, Russo P, Reuter VE, et al. Chromophobe renal cell carcinoma: a clinicopathologic study of 203 tumors in 200 patients with primary resection at a single institution. Am J Surg Pathol. 2011;35:962–970. doi: 10.1097/PAS.0b013e31821a455d. [DOI] [PubMed] [Google Scholar]
28. Rafnar T, Sulem P, Stacey S, Geller F, Gudmundsson J, Sigurdsson A, Jakobsdottir M, Helgadottir H, Thorlacius S, Aben K, et al. Sequence variants at the TERT-CLPTM1L locus associate with many cancer types. Nature genetics. 2009;41:221–227. doi: 10.1038/ng.296. [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Reid J, Carroll A, Veeraraghavan N, Dahdouli M, Sundquist A, English A, Bainbridge M, White S, Salerno W, Buhay C, et al. Launching genomics into the cloud: deployment of Mercury, a next generation sequence analysis pipeline. BMC Bioinformatics. 2014;15:30. doi: 10.1186/1471-2105-15-30. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Roberts SA, Lawrence MS, Klimczak LJ, Grimm SA, Fargo D, Stojanov P, Kiezun A, Kryukov GV, Carter SL, Saksena G, et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nature genetics. 2013;45:970–976. doi: 10.1038/ng.2702. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Schmidt LS, Warren MB, Nickerson ML, Weirich G, Matrosova V, Toro JR, Turner ML, Duray P, Merino M, Hewitt S, et al. Birt-Hogg-Dube syndrome, a genodermatosis associated with spontaneous pneumothorax and kidney neoplasia, maps to chromosome 17p11.2. Am J Hum Genet. 2001;69:876–882. doi: 10.1086/323744. [DOI] [PMC free article] [PubMed] [Google Scholar]
32. Shen H, Laird PW. Interplay between the cancer genome and epigenome. Cell. 2013;153:38–55. doi: 10.1016/j.cell.2013.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
33. Shuch B, Ricketts CJ, Vocke CD, Komiya T, Middelton LA, Kauffman EC, Merino MJ, Metwalli AR, Dennis P, Linehan WM. Germline PTEN Mutation Cowden Syndrome: An Under-Appreciated Form of Hereditary Kidney Cancer. The Journal of urology. 2013 doi: 10.1016/j.juro.2013.06.012. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Simonnet H, Demont J, Pfeiffer K, Guenaneche L, Bouvier R, Brandt U, Schagger H, Godinot C. Mitochondrial complex I is deficient in renal oncocytomas. Carcinogenesis. 2003;24:1461–1466. doi: 10.1093/carcin/bgg109. [DOI] [PubMed] [Google Scholar]
35. Speicher MR, Schoell B, du Manoir S, Schrock E, Ried T, Cremer T, Storkel S, Kovacs A, Kovacs G. Specific loss of chromosomes 1, 2, 6, 10, 13, 17, and 21 in chromophobe renal cell carcinomas revealed by comparative genomic hybridization. Am J Pathol. 1994;145:356–364. [PMC free article] [PubMed] [Google Scholar]
36. Storkel S, Eble JN, Adlakha K, Amin M, Blute ML, Bostwick DG, Darson M, Delahunt B, Iczkowski K. Classification of renal cell carcinoma: Workgroup No. 1. Union Internationale Contre le Cancer (UICC) and the American Joint Committee on Cancer (AJCC) Cancer. 1997;80:987–989. doi: 10.1002/(sici)1097-0142(19970901)80:5<987::aid-cncr24>3.0.co;2-r. [DOI] [PubMed] [Google Scholar]
37. The_Cancer_Genome_Atlas_Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70. doi: 10.1038/nature11412. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. The_Cancer_Genome_Atlas_Research_Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499:43–49. doi: 10.1038/nature12222. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Tickoo SK, Lee MW, Eble JN, Amin M, Christopherson T, Zarbo RJ, Amin MB. Ultrastructural observations on mitochondria and microvesicles in renal oncocytoma, chromophobe renal cell carcinoma, and eosinophilic variant of conventional (clear cell) renal cell carcinoma. Am J Surg Pathol. 2000;24:1247–1256. doi: 10.1097/00000478-200009000-00008. [DOI] [PubMed] [Google Scholar]
40. Varela I, Tarpey P, Raine K, Huang D, Ong C, Stephens P, Davies H, Jones D, Lin M, Teague J, et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature. 2011;469:539–542. doi: 10.1038/nature09639. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Ward P, Thompson C. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell. 2012;21:297–308. doi: 10.1016/j.ccr.2012.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Weir B, Woo M, Getz G, Perner S, Ding L, Beroukhim R, Lin W, Province M, Kraja A, Johnson L, et al. Characterizing the cancer genome in lung adenocarcinoma. Nature. 2007;450:893–898. doi: 10.1038/nature06358. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Y C, Adams A, Robbins A, Lin Q, Khavari P. Use of human tissue to assess the oncogenic activity of melanoma-associated mutations. Nat Genet. 2005;37:745–749. doi: 10.1038/ng1586. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Yang L, Luquette L, Gehlenborg N, Xi R, Haseley P, Hsieh C, Zhang C, Ren X, Protopopov A, Chin L, et al. Diverse mechanisms of somatic structural variations in human cancer genomes. Cell. 2013;153:919–929. doi: 10.1016/j.cell.2013.04.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Zhao Y, Wang S, Popova E, Grigoryev S, Zhu J. Rearrangement of upstream sequences of the hTERT gene during cellular immortalization. Genes Chromosomes Cancer. 2009;48:963–974. doi: 10.1002/gcc.20698. [DOI] [PMC free article] [PubMed] [Google Scholar]

Supplementary Materials