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. Author manuscript; available in PMC: 2013 Nov 2.
Published in final edited form as: Nature. 2013 May 2;497(7447):67–73. doi: 10.1038/nature12113

Integrated Genomic Characterization of Endometrial Carcinoma

The Cancer Genome Atlas Research Network
PMCID: PMC3704730  NIHMSID: NIHMS458933  PMID: 23636398

Summary

We performed an integrated genomic, transcriptomic, and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumors and ~25% of high-grade endometrioid tumors have extensive copy number alterations, few DNA methylation changes, low ER/PR levels, and frequent TP53 mutations. Most endometrioid tumors have few copy number alterations or TP53 mutations but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A, KRAS and novel mutations in the SWI/SNF gene ARID5B. A subset of endometrioid tumors we identified had a dramatically increased transversion mutation frequency, and newly identified hotspot mutations in POLE. Our results classified endometrial cancers into four categories: POLE ultramutated, microsatellite instability hypermutated, copy number low, and copy number high. Uterine serous carcinomas share genomic features with ovarian serous and basal-like breast carcinomas. We demonstrated that the genomic features of endometrial carcinomas permit a reclassification that may impact post-surgical adjuvant treatment for women with aggressive tumors.

Endometrial cancer arises from the lining of the uterus. It is the fourth most common malignancy among women in the United States, with an estimated 47,000 new cases and 8,000 deaths in 2012.1 Most patients present with low-grade, early-stage disease. The majority of patients with more aggressive, high-grade tumors who have disease spread beyond the uterus will progress within 1 year.2,3 Endometrial cancers have been broadly classified into two groups.4 Type I endometrioid tumors are linked to estrogen excess, obesity, hormone-receptor positivity, and favorable prognosis compared with type II, primarily serous, tumors that are more common in older, non-obese women and have worse outcome. Early-stage endometrioid cancers are often treated with adjuvant radiotherapy, whereas serous tumors are treated with chemotherapy, similar to advanced-stage cancers of either histologic subtype. Therefore, proper subtype classification is critical for selecting appropriate adjuvant therapy.

Several prior reports suggest that PTEN mutations occur early in the neoplastic process of Type I tumors and co-exist frequently with other mutations in the PI3K/AKT pathway.5,6 Other commonly mutated genes in Type I tumors include FGFR2, ARID1A, CTNNB1, PIK3CA, PIK3R1, and KRAS.7–9 Microsatellite instability (MSI) is found in approximately one-third of Type I tumors but is infrequent in Type II tumors.10 TP53, PIK3CA, and PPP2R1A mutations are frequent in Type II tumors.11,12 Most of these studies have been limited to DNA sequencing only with samples of heterogeneous histologic subtypes and tumor grades. We present a comprehensive, multiplatform analysis of 373 endometrial carcinomas including low-grade endometrioid, high-grade endometrioid, and serous carcinomas. This integrated analysis provides key molecular insights into tumor classification, which may have direct impact on treatment recommendations for patients, and provides opportunities for genome-guided clinical trials and drug development.

Results

Tumor samples and corresponding germline DNA were collected from 373 patients, including 307 endometrioid and 66 serous (53) or mixed histology (13) cases. Local institutional review boards approved all tissue acquisition. The clinical and pathologic characteristics of the samples generally reflect a cross-section of individuals with recurrent endometrial cancer (Supplementary Table 1.1).2,3 The median follow-up of the cohort was 32 months (range, 1–195 months); 21% of the patients have recurred, and 11% have died. Comprehensive molecular analyses were performed at independent centers using six genomic or proteomic platforms (Supplementary Table 1.2). MSI testing performed on all samples using seven repeat loci (Supplementary Table 1.3) found MSI in 40% of endometrioid tumors and 2% of serous tumors.

Somatic copy number alterations

Somatic copy number alterations (SCNAs) were assessed in 363 endometrial carcinomas. Unsupervised hierarchical clustering grouped the tumors into four clusters (Fig. 1a). The first three copy number clusters were composed almost exclusively (97%) of endometrioid tumors without significant differences in tumor grades. Cluster 1 tumors were nearly devoid of broad SCNAs, averaging less than 0.5% genome alteration, with no significant recurrent events. Cluster 1 tumors also had significantly elevated non-synonymous mutation rates compared to all others (median 7.2 × 10−6 vs. 1.7 × 10−6 mutations per megabase (Mb), P<0.001). Copy number clusters 2 and 3 consisted mainly of endometrioid tumors, distinguished by more frequent 1q amplification in cluster 3 than cluster 2 (100% of cluster 3 tumors vs. 33% of cluster 2 tumors) and worse progression-free survival (PFS, P=0.003, log-rank vs. clusters 1 and 2; Fig. 1b).

Figure 1. Somatic copy number alterations in endometrial carcinomas.

Figure 1

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a, Tumors were hierarchically clustered into four groups based on SCNAs. The heatmap shows SCNAs in each tumor (vertical axis) plotted by chromosomal location (horizontal axis). Vertical color bars to the right of the heatmap show genomic features. b, Kaplan-Meier curves of PFS for each copy number cluster.

Most of the serous (50 of 53 [94%]) and mixed histology tumors (8 of 13 [62%]) clustered with 36 (12%) of the 289 endometrioid tumors, including 24% of grade 3 and 5% of grade 1 or 2, into copy number cluster 4; a single group characterized by a very high degree of copy-number alterations (Supplementary Fig. 2.1; focal SCNAs with FDR < 0.15, and Supplementary Data File 2.1). Cluster 4 tumors were characterized by significantly recurrent previously reported focal amplifications of the oncogenes MYC (8q24.12), ERBB2 (17q12), and CCNE1 (19q12),13 and by SCNAs previously unreported in endometrial cancers including those containing FGFR3 (4p16.3) and SOX17 (8q11.23). Cluster 4 tumors also had frequent TP53 mutations (90%), little MSI (6%), and fewer PTEN mutations (11%) than other endometrioid tumors (84%). Overall, these findings suggest that a subset of endometrial tumors contain distinct patterns of SCNAs and mutations that do not correlate with traditional tumor histology or grade.

As expected, tumors in the ‘serous-like’ cluster (cluster 4) had significantly worse PFS than tumors in the endometrioid cluster groups (P=0.003, log-rank, Fig. 1b). Potential therapeutically relevant SCNAs included the cluster 2 15q26.2 focal amplification, which contained IGF1R; and cluster 4 amplifications of ERBB2, FGFR1, FGFR3, and LRP1B deletion, which was recently associated with resistance to liposomal doxorubicin in serous ovarian cancer.14

Exome sequence analysis

We sequenced the exomes of 248 tumor/normal pairs. Based on a combination of somatic nucleotide substitutions, MSI, and SCNA, the endometrial tumors were classified into four groups (Fig. 2a and b): 1) an ultra-mutated group with unusually high mutation rates (232×10−6 mutations/Mb) and a unique nucleotide change spectrum; 2) a hypermutated group (18×10−6 mutations/Mb) of MSI tumors, most with MLH1 promoter methylation; 3) a group with lower mutation frequency (2.9×10−6 mutations/Mb) and most of the microsatellite stable (MSS) endometrioid cancers; and 4) a group that consists primarily of serous-like cancers with extensive SCNA (copy-number cluster 4) and a low mutation rate (2.3×10−6 mutations/Mb). The ultra-mutated group consisted of 17 (7%) tumors exemplified by an increased C→A transversion frequency, all with mutations in the exonuclease domain of POLE, and an improved progression-free survival (Fig. 2a and 2c). POLE is a catalytic subunit of DNA polymerase epsilon involved in nuclear DNA replication and repair. We identified hotspot mutations in POLE at P286R and V411L present in 13 (76%) of the 17 ultramutated samples. Significantly mutated genes (SMGs) identified at low False Discovery Rates (Q) in this subset included PTEN (at 94% with Q=0), PIK3R1 (65%, Q=8.3×10−7), PIK3CA (71%, Q=9.1×10−5), FBXW7 (82%, Q=1.4×10−4), KRAS (53%, Q=9.2×10−4), and POLE (100%, Q=4.2×10−3). Mutation rates in POLE mutant endometrial and previously reported ultramutated colorectal tumors exceed that found in any other lineage including lung cancer and melanoma.15–17 Germline susceptibility variants have been reported in POLE (L424V) and POLD1 (S478N), but were not found in our endometrial normal exome-seq reads.18

Figure 2. Mutation spectra across endometrial carcinomas.

Figure 2

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a, Mutation frequencies (vertical axis, upper panel) plotted for each tumor (horizontal axis). Nucleotide substitutions are shown in the middle panel with a high frequency of C to A transversions in the samples with POLE exonuclease mutations. b, Tumors were stratified into the four groups by 1) nucleotide substitution frequencies and patterns, 2) MSI status, and 3) copy-number cluster. c, POLE-mutant tumors have significantly better PFS, while CN high tumors have the poorest outcome. d, Recurrently mutated genes are different between the four subgroups. Shown are the mutation frequencies of all genes that were significantly mutated in at least one of the four subgroups (MUSiC, FDR < 0.05, indicated by asterisk).

The MSI endometrioid tumors had a mutation frequency approximately 10-fold greater than MSS endometrioid tumors, few SCNAs, frame-shift deletions in RPL22, frequent non-synonymous KRAS mutations, and few mutations in FBXW7, CTNNB1, PPP2R1A, and TP53. The MSS, copy number low, endometrioid tumors had an unusually high frequency of CTNNB1 mutations (52%); the only gene with a higher mutation frequency than the MSI samples. The copy number high group contained all of the remaining serous cases and one-quarter of the grade 3 endometrioid cases. Most of these tumors had TP53 mutations and a high frequency of FBXW7 (22%, Q=0) and PPP2R1A (22%, Q=1.7×10−16) mutations, previously reported as common in uterine serous but not endometrioid carcinomas. Thus, a subset of high-grade endometrioid tumors had similar SCNAs and mutation spectra as uterine serous carcinomas, suggesting these patients might benefit from treatment approaches that parallel those for serous tumors.

There were 48 genes with differential mutation frequencies across the four groups (Fig. 2d, Supplementary Data File S3.1). ARID5B, a member of the same AT-rich interaction domain (ARID) family as ARID1A, was more frequently mutated in MSI (23.1%), than in either MSS endometrioid (5.6%) or high SCNA serous tumors (0%), a novel finding for endometrial cancer. Frame-shifting RPL22 indels near a homopolymer at Lys15 were almost exclusively found in the MSI group (36.9%). The TP53 mutation frequency (>90%) in serous tumors differentiates them from the endometrioid subtypes (11.4%). However, many (10 of 20; 50%) endometrioid tumors with a non-silent TP53 mutation also had non-silent mutations in PTEN, compared to only 1 of 39 (2.6%) serous tumors with TP53 non-silent mutations. Though TP53 mutations are not restricted to serous tumors, the co-existing PTEN mutations in the endometrioid cases suggest a distinct tumorigenic mechanism.

Comparisons of 66 SMGs between traditional histologic subtypes are provided (Supplementary Methods S3) and SMGs across other subcohorts can be found in Supplementary Data File S3.2. The spectrum of PIK3CA and PTEN mutations in endometrial cancer also differs from other solid tumors (Supplementary Methods S3). Integrated analysis may be useful for identifying histologically misclassified cases. For example, a single serous case was identified without a TP53 mutation or extensive SCNA and with a KRAS mutation and high mutation rate. Upon re-review of the histologic section, the case was deemed consistent with a grade 3 endometrioid tumor demonstrating how molecular analysis could reclassify tumor histology and potentially impact treatment decisions.

Multiplatform subtype classifications

All of the endometrial tumors were examined for mRNA expression (n=333), protein expression (n=293), miRNA expression (n=367), and DNA methylation (n=373) (Supplementary Methods S4–S7). Unsupervised k-means clustering of mRNA expression from RNA sequencing identified three robust clusters termed ‘mitotic’, ‘hormonal’, and ‘immunoreactive’ (Supplementary Fig. 4.1) that were significantly correlated with the four integrated clusters; POLE, MSI, CN low and CN high (P<0.0001). Supervised analysis identified signature genes of the POLE cluster (n = 17) mostly involved in cellular metabolism (Fig. 3a). Among the few signature genes in the MSI cluster was decreased MLH1 mRNA expression likely due to its promoter methylation. Elevated progesterone receptor (PGR) expression was noted in the CN low cluster, suggesting responsiveness to hormonal therapy. The CN high cluster, which included most serous and serous-like endometrioid tumors, exhibited the greatest transcriptional activity exemplified by increased cell cycle deregulation (e.g. CCNE1, PIK3CA, MYC, and CDKN2A) and TP53 mutation (Supplementary Figs. 4.2 and 4.3). This is consistent with reports that elevated CDKN2A can distinguish serous from endometrioid carcinomas.19 Approximately 85% of cases in the CN high cluster shared membership with the ‘mitotic’ mRNA subtype.

Figure 3. Gene expression across integrated subtypes in endometrial carcinomas.

Figure 3

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a, Supervised analysis of ~1500 genes significantly associated with integrated subtypes b, Heat map of protein expression clusters, supervised by integrated subtypes. Samples are in columns; genes or proteins are in rows.

Supervised clustering of the RPPA expression data was consistent with loss of function for many of the mutated genes (Fig. 3b). TP53 was frequently mutated in the CN high group (P=2.5×10−27) and its protein expression was also elevated, suggesting these mutations are associated with increased expression. By contrast, PTEN (P=2.8×10−19) and ARID1A (P=1.2×10−6) had high mutation rates in the remaining groups, but their expression was decreased, suggesting inactivating mutations in both genes. The CN high group also had decreased levels of phospho-AKT, consistent with down regulation of the AKT pathway. The CN low group had elevated RAD50 expression, which is associated with DNA repair, explaining some of the differences between the CN high and CN low groups. The POLE group had high expression of ASNS and CCNB1, whereas the MSI tumors had both high phospho-AKT and low PTEN expression.

Unsupervised clustering of DNA methylation data generated from Illumina Infinium DNA methylation arrays revealed four unique subtypes (MC1-4) that support the four integrative clusters. A heavily methylated subtype (MC1) reminiscent of the CpG island methylator phenotype (CIMP) phenotype described in colon cancers and glioblastomas,20–22 was associated with the MSI subtype and attributable to promoter hypermethylation of MLH1. A serous-like cluster (MC3) with minimal DNA methylation changes was composed primarily of serous tumors and some endometrioid tumors (Supplementary Fig. 7.1) and contained most of the CN high tumors.

Integrative clustering using the iCluster framework returned two major clusters split primarily on serous and endometrioid histology highlighting TP53 mutations, lack of PTEN mutation and encompassing almost exclusively CN high tumors (Supplementary Fig. 8.1).23 We developed a new clustering algorithm, called SuperCluster, to derive overall subtypes based on sample cluster memberships across all data types (Supplementary Fig. 9.1). SuperCluster identified 4 clusters that generally confirmed the contributions of individual platforms to the overall integrated clusters. No major batch effects were identified for any platform (Supplementary Methods S10).

Structural Aberrations

To identify somatic chromosomal aberrations, we performed low-pass, paired-end, whole-genome sequencing on 106 tumors with matched normals. We found recurrent translocations involving genes in several pathways including WNT, EGFR-RAS-MAPK, PI3K, Protein Kinase A, RB and apoptosis. The most frequent translocations (5/106) involved a member of the BCL family (BCL2, BCL7, BCL9 and BCL2L11). Four of these were confirmed by identification of the translocation junction point and two were also confirmed by RNA-Seq. In all cases the translocations result in in-frame fusions and predicted to result in activation or increased expression of the BCL family members (Supplementary Fig. 3.2). Translocations involving members of the BCL family leading to reduced apoptosis have been described in other tumor types24 and our results suggest that similar mechanisms may be operative here.

Pathway alterations

Multiple platform data were integrated to identify recurrently altered pathways in the four endometrial cancer integrated subgroups. Because of the high background mutation rate and small sample size, we excluded the POLE subgroup from this analysis. Considering all recurrently mutated, homozygously deleted, and amplified genes, we used MEMo25 to identify gene networks with mutually exclusive alteration patterns in each subgroup. The most significant module was found in the CN low group and contained CTNNB1, KRAS, and SOX17 (Fig. 4a). The very strong mutual exclusivity between mutations in these three genes suggests that alternative mechanisms activate WNT signaling in endometrioid endometrial cancer. Activating KRAS mutations have been shown to increase the stability of beta-catenin via GSK3beta leading to an alternative mechanism of beta-catenin activation to APC degradation.26 SOX17, which mediates proteasomal degradation of beta-catenin,27,28 is mutated exclusively in the CN low group (8%) at recurrent positions (A96G and S403I) not previously described. Other genes with mutually exclusive alteration patterns in this module were FBXW7, FGFR2, and ERBB2.29 ERBB2 was focally amplified with protein overexpression in 25% of the serous or serous-like tumors, suggesting a potential role for HER2 targeted inhibitors. A small clinical trial of trastuzumab found no activity in endometrial carcinoma, but accrued few HER2 FISH-amplified serous carcinomas.30

Figure 4. Pathway alterations in endometrial carcinomas.

Figure 4

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a, The RTK/RAS/beta-catenin pathway is altered through multiple mechanisms that exhibit mutually exclusive patterns. Alteration frequencies are expressed as a percentage of all cases. The right panel shows patterns of occurrence. b, The PI3K pathway has mutually exclusive PIK3CA and PIK3R1 alterations that frequently co-occur with PTEN alterations in the MSI and CN low subgroups. c, Heatmap display of top 1000 varying pathway features within PARADIGM consensus clusters. Samples were arranged in order of their consensus cluster membership. The mutation spectrum for each sample is displayed below the consensus clusters.

PIK3CA and PIK3R1 mutations were frequent and showed a strong tendency for mutual exclusivity in all subgroups, but unlike other tumor types, they co-occurred with PTEN mutations in the MSI and CN low subgroups as previously reported (Fig. 4b).5,9 The CN high subgroup showed mutual exclusivity between alterations of all three genes. Overall, 93% of endometrioid tumors had mutations that suggested potential for targeted therapy with PI3K/AKT pathway inhibitors.

Consensus clustering of copy number, mRNA expression, and pathway interaction data for 324 samples yielded 5 PARADIGM clusters with distinct pathway activation patterns (Fig. 4c, Supplementary Methods, S11).31 Paradigm cluster 1 had the lowest level of MYC pathway activation and highest level of WNT pathway activation, consistent with its composition of CN low cases having frequent CTNNB1 mutations. PARADIGM cluster 3 was composed predominantly of the CN high cases with relatively high MYC/Max but low ER/FOXA1 signaling and p53 activity. Only TP53 truncation and not missense mutations were implicated as loss-of-function mutations, suggesting different classes of p53 mutations may have distinct signaling consequences. Paradigm cluster 5 was enriched for hormone receptor expression.

Comparison to ovarian and breast cancers

The clinical and pathologic features of uterine serous carcinoma and high-grade serous ovarian carcinoma (HGSOC) are quite similar. HGSOC shares many similar molecular features with basal-like breast carcinoma.32 Focal SCNA patterns were similar between these three tumor subtypes and unsupervised clustering identified relatedness (Fig. 5a, Supplementary Fig. 12.1). Supervised analysis of transcriptome datasets showed high correlation between tumor subtypes (Supplementary Fig. 12.2). The MC3 DNA methylation subtype with minimal DNA methylation changes also was similar to basal-like breast and HGSOCs (Supplementary Fig. 12.3). High frequency of TP53 mutations is shared across these tumor subtypes (uterine serous, 91%; ovarian serous, 96%; basal-like breast, 84%),33,34 as is the very low frequency of PTEN mutations (uterine serous carcinoma, 2%; HGSOC, 1%; basal-like breast carcinoma, 1%). Differences include a higher frequency of FBXW7, PPP2R1A, and PIK3CA mutations in uterine serous compared to basal-like breast and HGSOCs (Fig. 5b). We show that uterine serous carcinomas share many molecular features with both HGSOC and basal-like breast carcinomas, despite more frequent mutations, suggesting new opportunities for overlapping treatment paradigms.

Figure 5. Genomic relationships between endometrial serous, ovarian serous, and basal-like breast carcinomas.

Figure 5

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a, Somatic copy number alterations for each tumor type. b, Frequency of genomic alterations present in at least 10% of one tumor type.

Discussion

This integrated genomic and proteomic analysis of 373 endometrial cancers provides insights into disease biology and diagnostic classification that could have immediate therapeutic application. Our analysis identified four novel groups of tumors based on integrated genomic data, including a novel POLE subtype in ~10% of endometrioid tumors. Ultra-high somatic mutation frequency, MSS, and common, newly identified hotspot mutations in the exonuclease domain of POLE characterize this subtype. SCNAs add a layer of resolution, revealing that most endometrioid tumors have few SCNAs, most serous and serous-like tumors exhibit extensive SCNAs, and the extent of SCNA roughly correlates with PFS.

Endometrial cancer has more frequent mutations in the PI3K/AKT pathway than any other tumor type studied by TCGA to-date. Endometrioid endometrial carcinomas share many characteristics with colorectal carcinoma including a high frequency of MSI (40% and 11%, respectively), POLE mutations (7% and 3%, respectively) leading to ultrahigh mutation rates, and frequent activation of WNT/CTNNB1 signaling; yet endometrial carcinomas have novel exclusivity of KRAS and CTNNB1 mutation and a distinct mechanism of pathway activation. Uterine serous carcinomas share many similar characteristics with basal-like breast and HGSOCs; three tumor types with a high frequency non-silent TP53 mutations and extensive SCNA. However, the high frequency of PIK3CA, FBXW7, PPP2R1A, and ARID1A mutations in uterine serous carcinoma are not found in basal-like breast and HGSOCs. The frequency of mutations in PIK3CA, FBXW7, and PPP2R1A was ~30% higher than in a recently reported study of 76 uterine serous carcinomas,11 but similar to another study.12 Uterine serous carcinomas have ERBB2 amplification in 27% of tumors and PIK3CA mutations in 42%, which provide translational opportunities for targeted therapeutics.

Early stage Type I endometrioid tumors are often treated with adjuvant radiotherapy, while similarly staged Type II serous tumors are treated with chemotherapy. High-grade serous and endometrioid endometrial carcinomas are difficult to correctly subtype, and intra-observer concordance among specialty pathologists is low.7,34–36 Our molecular characterization data demonstrate that ~25% of tumors classified as high-grade endometrioid by pathologists have a molecular phenotype similar to uterine serous carcinoma, including frequent TP53 mutations and extensive SCNA. The compelling similarities between this subset of endometrioid tumors and uterine serous carcinomas suggest that genomic-based classification may lead to improved management of these patients. Clinicians should carefully consider treating copy number altered endometrioid patients with chemotherapy rather than adjuvant radiotherapy and formally test such hypotheses in prospective clinical trials. Further, the marked molecular differences between endometrioid and serous-like tumors suggest that these tumors warrant separate clinical trials to develop the independent treatment paradigms that have improved outcomes in other tumor types, such as breast cancer.

Methods Summary

Biospecimens were obtained from 373 patients after institutional review board-approved consents. DNA and RNA were co-isolated using a modified AllPrep kit (Qiagen). We used Affymetrix SNP 6.0 microarrays to detect SCNA in 363 samples and GISTIC analysis to identify recurrent events.37 The exomes of 248 tumors were sequenced to a read-depth of at least 20×. We performed low-pass whole-genome sequencing on 107 tumors to a mean depth of 6×. Consensus clustering was used to analyze mRNA, miRNA, RPPA, and methylation data with methods previously described.38–40 Integrated cross-platform analyses were performed using MEMo, iCluster, and PARADIGM. 25,31

Supplementary Material

1
2

Acknowledgments

We wish to thank all patients and families who contributed to this study. We thank M. Sheth and L. Lund for administrative coordination of TCGA activities, G. Monemvasitis for editing the manuscript, and C. Gunter for critical reading of the manuscript. This work was supported by the following grants from the USA National Institutes of Health: 5U24CA143799-04, 5U24CA143835-04, 5U24CA143840-04, 5U24CA143843-04, 5U24CA143845-04, 5U24CA143848-04, 5U24CA143858-04, 5U24CA143866-04, 5U24CA143867-04, 5U24CA143882-04, 5U24CA143883-04, 5U24CA144025-04, U54HG003067, U54HG003079, and U54HG003273.

Dr. First Name Last Name Institution #1 ADD ZIPCODE Institution #2
Dr. Christopher Adams The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. Thomas Barr The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. Aaron D. Black The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. Jay Bowen The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. John Deardurff The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Jessica Frick The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Dr. Julie M. Gastier-Foster The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 The Ohio State University, Columbus, OH 43210
Mr. Thomas Grossman The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Hollie A. Harper The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Melissa Hart-Kothari The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Carmen Helsel The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. Aaron Hobensack The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Harkness Keck The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Kelley Kneile The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Kristen M. Leraas The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Tara M. Lichtenberg The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Cynthia McAllister The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Dr. Robert E. Pyatt The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Dr. Nilsa C. Ramirez The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205 The Ohio State University, Columbus, OH 43210
Ms. Teresa R. Tabler The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Mr. Nathan Vanhoose The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Dr. Peter White The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Lisa Wise The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Dr. Erik Zmuda The Research Institute at Nationwide Children’s Hospital, Columbus, OH 43205
Ms. Brenda Ayala SRA International, Fairfax, VA, USA
Ms. Anna L. Chu SRA International, Fairfax, VA, USA
Dr. Mark A. Jensen SRA International, Fairfax, VA, USA
Dr. Prachi Kothiyal SRA International, Fairfax, VA, USA
Dr. Todd D. Pihl SRA International, Fairfax, VA, USA
Dr. Joan Pontius SRA International, Fairfax, VA, USA
Dr. David A. Pot SRA International, Fairfax, VA, USA
Dr. Eric E. Snyder SRA International, Fairfax, VA, USA
Mr. Deepak Srinivasan SRA International, Fairfax, VA, USA
Dr. Ari B. Kahn SRA International, Fairfax, VA, USA
Dr. Daphne W. Bell Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA
Dr Pamela Pollock Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, 4059, Australia
Dr. Chen Wang Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905
Dr. David A. Wheeler HumanGenomeSequencing Center, Baylor College ofMedicine,Houston, Texas 77030,
Dr. Eve Shinbrot HumanGenomeSequencing Center, Baylor College ofMedicine,Houston, Texas 77030,
Dr. Beth Y. Karlan Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
Dr. Andrew Berchuck Duke University Medical Center, Duke Cancer Institute, Durham, NC 27710
Dr. Sean C. Dowdy Department of OB Gyn, Division of Gynecologic Oncology, Mayo Clinic, Rochester, MN 55905
Dr. Boris Winterhoff Department of OB Gyn, Division of Gynecologic Oncology, Mayo Clinic, Rochester, MN 55905
Dr. Inanc Birol Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Yaron S.N. Butterfield Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Rebecca Carlsen Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Candace Carter Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Andy Chu Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Eric Chuah Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Hye-Jung E. Chun Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Noreen Dhalla Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Ranabir Guin Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Carrie Hirst Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Dr. Robert A. Holt Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Prof. Steven J.M. Jones Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Darlene Lee Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Haiyan I. Li Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Prof. Marco A. Marra Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Michael Mayo Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Dr. Richard A. Moore Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Dr. Andrew J. Mungall Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Patrick Plettner Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Jacqueline E. Schein Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Payal Sipahimalani Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Ms. Angela Tam Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Mr. Richard J. Varhol Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Dr. A. Gordon Robertson Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada
Dr. Angela Hadjipanayis Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
Dr. Semin Lee Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115
Mr. Harshad S. Mahadeshwar Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054
Dr. Peter Park Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115 Informatics Program, Boston Children’s Hospital, Boston, MA 02115
Dr. Alexei Protopopov Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054
Ms. Xiaojia Ren Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
Mr. Sahil Seth Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054
Dr. Xingzhi Song Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054
Dr. Jiabin Tang Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX 77054
Dr. Ruibin Xi Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115
Dr. Lixing Yang Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115
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Dr mark borowsky Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
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christine czerwinski Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
lori huelsenbeck-Dill Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
Dr mary iacocca Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
Dr nicholas petrelli Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
brenda rabeno Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
Dr gary witkin Helen F Graham Cancer Center at Christiana Care, Newark, DE,19713
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Dr. Karuna Garg Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065
Mr. Narciso Olvera Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065
Dr. Robert A. Soslow Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, NY 10065
Dr. Douglas A. Levine Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY 10065
Dr. Mikhail Abramov N.N. Blokhin Russian Cancer Research Center RAMS, Moscow, Russia
Dr. John M.S. Bartlett Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada
Ms. Sugy Kodeeswaran Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada
Dr. Jeremy Parfitt Ontario Tumour Bank, London Health Sciences Centre, London, Ontario N6A 5A5, Canada
Dr. Fedor Moiseenko St. Petersburg Academic University, St. Petersburg, Russia
Dr. Blaise A. Clarke Department of Pathology, University Health Network, Toronto, Ontario M5G 2C4 Canada
Dr. Michael E. Carney University of Hawaii Cancer Center, Honolulu, HI 96813
Dr. Rayna K. Matsuno University of Hawaii, Honolulu HI 96813
Ms. Jennifer Fisher University of North Carolina, Chapel Hill, NC 27599
Ms. Mei Huang University of North Carolina, Chapel Hill, NC 27599
Dr. W. Kimryn Rathmell Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599
Dr. Leigh Thorne University of North Carolina, Chapel Hill, NC 27599
Dr. Linda Van Le University of North Carolina, Chapel Hill, NC 27599
Dr. Rajiv Dhir University of Pittsburgh, Pittsburgh PA 15213
Dr. Robert Edwards University of Pittsburgh, Pittsburgh PA 15213
Dr. Esther Elishaev University of Pittsburgh, Pittsburgh PA 15213
Dr. Kristin Zorn University of Pittsburgh, Pittsburgh PA 15213
Dr Paul J. Goodfellow Washington University School of Medicine, St Louis, MO.
Dr David Mutch Washington University School of Medicine, St Louis, MO.
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Footnotes

The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper.

Reprints and permissions information is available at www.nature.com/reprints. This paper is distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike license, and the online version of the paper is freely available to all readers.

Author Contributions

The TCGA research network contributed collectively to this study. Biospecimens were provided by the tissue source sites and processed by the biospecimen core resource. Data generation and analyses were performed by the genome sequencing centers, cancer genome characterization centers and genome data analysis centers. All data were released through the data coordinating center. The National Cancer Institute and National Human Genome Research Institute project teams coordinated project activities. We also acknowledge the following TCGA investigators who made substantial contributions to the project: N.S. (manuscript coordinator); J.G. (data coordinator); C.K. and L.D. (DNA sequence analysis); W.Z. and Y.L. (mRNA sequence analysis); H.S. and P.W.L. (DNA methylation analysis); A.D.C., I.P. (copy number analysis); S.L. and A.H. (translocations); N.S., N.W. G.C., C.B, and C.Y. (pathway analysis); A.C. and A.G.R. (miRNA sequence analysis); R.B., P.J.G., G.B.M. and R.A.S. (pathology and clinical expertise); G.B.M. H.L. R.A. (reverse phase protein arrays); P.J.G. and R.B. (disease experts); G.B.M., and R.K. (manuscript editing); D.A.L. and E.R.M. (project chairs).

Supplementary Information is linked to the online version of the paper at www.nature.com/nature.

The Cancer Genome Atlas Research Network (Participants are arranged by area of contribution and then by institution.) See author list in excel spreadsheet.

The primary and processed data used to generate the analyses presented here are deposited at the Data Coordinating Center (https://tcga-data.nci.nih.gov/tcga/tcgaDownload.jsp); all of the primary sequence files are deposited in CGHub (https://cghub.ucsc.edu/). Sample lists, data matrices and supporting data can be found at: https://tcga-data.nci.nih.gov/docs/publications/ucec_2013/). The data can be explored via the cBio Cancer Genomics Portal (http://cbioportal.org).

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Associated Data

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Supplementary Materials

1
NIHMS458933-supplement-1.pdf (14.8MB, pdf)
2
NIHMS458933-supplement-2.zip (698.3KB, zip)

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