Article | Open | Published:

Integrated genomic characterization of endometrial carcinoma

Nature volume 497, pages 6773 (02 May 2013) | Download Citation

  • An Erratum to this article was published on 12 June 2013


We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and 25% of high-grade endometrioid tumours had extensive copy number alterations, few DNA methylation changes, low oestrogen receptor/progesterone receptor levels, and frequent TP53 mutations. Most endometrioid tumours had few copy number alterations or TP53 mutations, but frequent mutations in PTEN, CTNNB1, PIK3CA, ARID1A and KRAS and novel mutations in the SWI/SNF chromatin remodelling complex gene ARID5B. A subset of endometrioid tumours that we identified had a markedly 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 affect post-surgical adjuvant treatment for women with aggressive tumours.


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 49,500 new cases and 8,200 deaths in 2013 (ref. 1). Most patients present with low-grade, early-stage disease. The majority of patients with more aggressive, high-grade tumours who have disease spread beyond the uterus will progress within 1 year (refs 2, 3). Endometrial cancers have been broadly classified into two groups4. Type I endometrioid tumours are linked to oestrogen excess, obesity, hormone-receptor positivity, and favourable prognosis compared with type II, primarily serous, tumours that are more common in older, non-obese women and have a worse outcome. Early-stage endometrioid cancers are often treated with adjuvant radiotherapy, whereas serous tumours are treated with chemotherapy, similar to advanced-stage cancers of either histological subtype. Therefore, proper subtype classification is crucial for selecting appropriate adjuvant therapy.

Several previous reports suggest that PTEN mutations occur early in the neoplastic process of type I tumours and co-exist frequently with other mutations in the phosphatidylinositol-3-OH kinase (PI(3)K)/AKT pathway5,6. Other commonly mutated genes in type I tumours include FGFR2, ARID1A, CTNNB1, PIK3CA, PIK3R1 and KRAS7,8,9. Microsatellite instability (MSI) is found in approximately one-third of type I tumours, but is infrequent in type II tumours10. TP53, PIK3CA and PPP2R1A mutations are frequent in type II tumours11,12. Most of these studies have been limited to DNA sequencing only with samples of heterogeneous histological subtypes and tumour 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 tumour classification, which may have a direct effect on treatment recommendations for patients, and provides opportunities for genome-guided clinical trials and drug development.


Tumour 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 pathological characteristics of the samples generally reflect a cross-section of individuals with recurrent endometrial cancer2,3 (Supplementary Table 1.1). 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 centres 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 tumours and 2% of serous tumours.

Somatic copy number alterations

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

Figure 1: SCNAs in endometrial carcinomas.
Figure 1

a, Tumours were hierarchically clustered into four groups based on SCNAs. The heat map shows SCNAs in each tumour (horizontal axis) plotted by chromosomal location (vertical axis). Chr., chromosome. b, Kaplan–Meier curves of progression-free survival for each copy-number cluster.

Most of the serous (50 out of 53; 94%) and mixed histology (8 out of 13; 62%) tumours clustered with 36 (12%) of the 289 endometrioid tumours, 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 SCNAs (Supplementary Fig. 2.1; focal SCNAs with false discovery rate (FDR) < 0.15, and Supplementary Data 2.1). Cluster 4 tumours 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 tumours also had frequent TP53 mutations (90%), little MSI (6%), and fewer PTEN mutations (11%) than other endometrioid tumours (84%). Overall, these findings suggest that a subset of endometrial tumours contain distinct patterns of SCNAs and mutations that do not correlate with traditional tumour histology or grade.

As expected, tumours in the ‘serous-like’ cluster (cluster 4) had significantly worse progression-free survival than tumours 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 and FGFR3, and LRP1B deletion, which was recently associated with resistance to liposomal doxorubicin in serous ovarian cancer14.

Exome sequence analysis

We sequenced the exomes of 248 tumour/normal pairs. On the basis of a combination of somatic nucleotide substitutions, MSI and SCNAs, the endometrial tumours were classified into four groups (Fig. 2a, b): (1) an ultramutated group with unusually high mutation rates (232 × 10−6 mutations per Mb) and a unique nucleotide change spectrum; (2) a hypermutated group (18 × 10−6 mutations per Mb) of MSI tumours, most with MLH1 promoter methylation; (3) a group with lower mutation frequency (2.9 × 10−6 mutations per 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 per Mb). The ultramutated group consisted of 17 (7%) tumours 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, c). POLE is a catalytic subunit of DNA polymerase epsilon involved in nuclear DNA replication and repair. We identified hotspot mutations in POLE at Pro286Arg and Val411Leu present in 13 (76%) of the 17 ultramutated samples. Significantly mutated genes (SMGs) identified at low FDRs (Q) in this subset included PTEN (94%, 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 tumours exceeded those found in any other lineage including lung cancer and melanoma15,16,17. Germline susceptibility variants have been reported in POLE (Leu424Val) and POLD1 (Ser478Asn), but were not found in our endometrial normal exome-seq reads18.

Figure 2: Mutation spectra across endometrial carcinomas.
Figure 2

a, Mutation frequencies (vertical axis, top panel) plotted for each tumour (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. CN, copy number. b, Tumours were stratified into the four groups by (1) nucleotide substitution frequencies and patterns, (2) MSI status, and (3) copy-number cluster. SNV, single nucleotide variant. c, POLE-mutant tumours have significantly better progression-free survival, whereas copy-number high tumours 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, asterisk denotes FDR < 0.05).

The MSI endometrioid tumours had a mutation frequency approximately tenfold greater than MSS endometrioid tumours, few SCNAs, frameshift deletions in RPL22, frequent non-synonymous KRAS mutations, and few mutations in FBXW7, CTNNB1, PPP2R1A and TP53. The MSS, copy-number low, endometrioid tumours 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 tumours 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 tumours had similar SCNAs and mutation spectra as uterine serous carcinomas, suggesting that these patients might benefit from treatment approaches that parallel those for serous tumours.

There were 48 genes with differential mutation frequencies across the four groups (Fig. 2d and Supplementary Data 3.1). 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 tumours (0%), a novel finding for endometrial cancer. Frameshifting RPL22 indels near a homopolymer at Lys 15 were almost exclusively found in the MSI group (36.9%). The TP53 mutation frequency (>90%) in serous tumours differentiated them from the endometrioid subtypes (11.4%). However, many (10 out of 20; 50%) endometrioid tumours with a non-silent TP53 mutation also had non-silent mutations in PTEN, compared to only 1 out of 39 (2.6%) serous tumours with non-silent TP53 mutations. Although TP53 mutations are not restricted to serous tumours, the co-existing PTEN mutations in the endometrioid cases suggest a distinct tumorigenic mechanism.

Comparisons of 66 SMGs between traditional histological subtypes are provided (Supplementary Methods 3), and SMGs across other subcohorts can be found in Supplementary Data 3.2. The spectrum of PIK3CA and PTEN mutations in endometrial cancer also differed from other solid tumours (Supplementary Methods 3). Integrated analysis may be useful for identifying histologically misclassified cases. For example, a single serous case was identified without a TP53 mutation or extensive SCNAs and with a KRAS mutation and high mutation rate. After re-review of the histological section, the case was deemed consistent with a grade 3 endometrioid tumour, demonstrating how molecular analysis could reclassify tumour histology and potentially affect treatment decisions.

Multiplatform subtype classifications

All of the endometrial tumours were examined for messenger RNA expression (n = 333), protein expression (n = 293), microRNA expression (n = 367), and DNA methylation (n = 373) (Supplementary Methods 4–7). 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, copy-number low and copy-number 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, probably due to its promoter methylation. Increased progesterone receptor (PGR) expression was noted in the copy-number low cluster, suggesting responsiveness to hormonal therapy. The copy-number high cluster, which included most of the serous and serous-like endometrioid tumours, exhibited the greatest transcriptional activity exemplified by increased cell cycle deregulation (for example, CCNE1, PIK3CA, MYC and CDKN2A) and TP53 mutation (Supplementary Figs 4.2 and 4.3). This is consistent with reports that increased CDKN2A can distinguish serous from endometrioid carcinomas19. Approximately 85% of cases in the copy-number high cluster shared membership with the ‘mitotic’ mRNA subtype.

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

a, Supervised analysis of 1,500 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 reverse phase protein array (RPPA) expression data was consistent with loss of function for many of the mutated genes (Fig. 3b). TP53 was frequently mutated in the copy-number high group (P = 2.5 × 10−27) and its protein expression was also increased, suggesting that 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 copy-number high group also had decreased levels of phospho-AKT, consistent with downregulation of the AKT pathway. The copy-number low group had raised RAD50 expression, which is associated with DNA repair, explaining some of the differences between the copy-number high and low groups. The POLE group had high expression of ASNS and CCNB1, whereas the MSI tumours 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) described in colon cancers and glioblastomas20,21,22 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 tumours and some endometrioid tumours (Supplementary Fig. 7.1) and contained most of the copy-number high tumours.

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 copy-number high tumours23 (Supplementary Fig. 8.1). 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 four 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 10).

Structural aberrations

To identify somatic chromosomal aberrations, we performed low-pass, paired-end, whole-genome sequencing on 106 tumours with matched normals. We found recurrent translocations involving genes in several pathways including WNT, EGFR–RAS–MAPK, PI(3)K, protein kinase A, retinoblastoma and apoptosis. The most frequent translocations (5 out of 106) involved a member of the BCL family (BCL2, BCL7A, BCL9 and BCL2L11). Four of these were confirmed by identification of the translocation junction point and two were also confirmed by high-throughput RNA sequencing (RNA-Seq). In all cases the translocations result in in-frame fusions and are 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 tumour 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 copy-number 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 signalling in endometrioid endometrial cancer. Activating KRAS mutations have been shown to increase the stability of β-catenin via glycogen synthase kinase 3β (GSK-3β), leading to an alternative mechanism of β-catenin activation other than adenomatous polyposis coli degradation26. SOX17, which mediates proteasomal degradation of β-catenin27,28, is mutated exclusively in the copy-number low group (8%) at recurrent positions (Ala96Gly and Ser403Ile) not previously described. Other genes with mutually exclusive alteration patterns in this module were FBXW7, FGFR2 and ERBB2 (ref. 29). ERBB2 was focally amplified with protein overexpression in 25% of the serous or serous-like tumours, suggesting a potential role for human epidermal growth factor receptor 2 (HER2)-targeted inhibitors. A small clinical trial of trastuzumab found no activity in endometrial carcinoma, but accrued few HER2 fluorescence in situ hybridization (FISH)-amplified serous carcinomas30.

Figure 4: Pathway alterations in endometrial carcinomas.
Figure 4

a, The RTK/RAS/β-catenin pathway is altered through several 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 PI(3)K pathway has mutually exclusive PIK3CA and PIK3R1 alterations that frequently co-occur with PTEN alterations in the MSI and copy-number low subgroups. c, Heat map display of top 1,000 varying pathway features within PARADIGM consensus clusters. Samples were arranged in order of their consensus cluster membership. The genomic subtype 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 tumour types, they co-occurred with PTEN mutations in the MSI and copy-number low subgroups as previously reported5,9 (Fig. 4b). The copy-number high subgroup showed mutual exclusivity between alterations of all three genes. Overall, 93% of endometrioid tumours had mutations that suggested potential for targeted therapy with PI(3)K/AKT pathway inhibitors.

Consensus clustering of copy number, mRNA expression and pathway interaction data for 324 samples yielded five PARADIGM clusters with distinct pathway activation patterns31 (Fig. 4c and Supplementary Methods 11). PARADIGM cluster 1 had the lowest level of MYC pathway activation and highest level of WNT pathway activation, consistent with its composition of copy-number low cases having frequent CTNNB1 mutations. PARADIGM cluster 3 was composed predominantly of the copy-number high cases, with relatively high MYC/MAX signalling but low oestrogen receptor/FOXA1 signalling 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 signalling 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 carcinoma32. Focal SCNA patterns were similar between these three tumour subtypes and unsupervised clustering identified relatedness (Fig. 5a and Supplementary Fig. 12.1). Supervised analysis of transcriptome data sets showed high correlation between tumour subtypes (Supplementary Fig. 12.2). The MC3 DNA methylation subtype with minimal DNA methylation changes was also similar to basal-like breast and HGSOCs (Supplementary Fig. 12.3). A high frequency of TP53 mutations is shared across these tumour subtypes (uterine serous, 91%; HGSOC, 96%; basal-like breast, 84%)33,34, as is the very low frequency of PTEN mutations (uterine serous, 2%; HGSOC, 1%; basal-like breast, 1%). Differences included a higher frequency of FBXW7, PPP2R1A and PIK3CA mutations in uterine serous compared to basal-like breast and HGSOCs (Fig. 5b). We showed that uterine serous carcinomas share many molecular features with both HGSOCs and basal-like breast carcinomas, despite more frequent mutations, suggesting new opportunities for overlapping treatment paradigms.

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

a, SCNAs for each tumour type. b, Frequency of genomic alterations present in at least 10% of one tumour type.


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 new groups of tumours based on integrated genomic data, including a novel POLE subtype in 10% of endometrioid tumours. Ultrahigh 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 tumours have few SCNAs, most serous and serous-like tumours exhibit extensive SCNAs, and the extent of SCNA roughly correlates with progression-free survival.

Endometrial cancer has more frequent mutations in the PI(3)K/AKT pathway than any other tumour type studied by The Cancer Genome Atlas (TCGA) so far. 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 signalling; yet endometrial carcinomas have novel exclusivity of KRAS and CTNNB1 mutations and a distinct mechanism of pathway activation. Uterine serous carcinomas share many similar characteristics with basal-like breast and HGSOCs; three tumour types with high-frequency non-silent TP53 mutations and extensive SCNA. However, the high frequency of PIK3CA, FBXW7, PPP2R1A and ARID1A mutations in uterine serous carcinomas 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 carcinomas11, but similar to another study12. Uterine serous carcinomas have ERBB2 amplification in 27% of tumours and PIK3CA mutations in 42%, which provide translational opportunities for targeted therapeutics.

Early stage type I endometrioid tumours are often treated with adjuvant radiotherapy, whereas similarly staged type II serous tumours are treated with chemotherapy. High-grade serous and endometrioid endometrial carcinomas are difficult to subtype correctly, and intra-observer concordance among speciality pathologists is low7,34,35,36. Our molecular characterization data demonstrate that 25% of tumours classified as high-grade endometrioid by pathologists have a molecular phenotype similar to uterine serous carcinomas, including frequent TP53 mutations and extensive SCNA. The compelling similarities between this subset of endometrioid tumours 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. Furthermore, the marked molecular differences between endometrioid and serous-like tumours suggest that these tumours warrant separate clinical trials to develop the independent treatment paradigms that have improved outcomes in other tumour 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 SCNAs in 363 samples and GISTIC analysis to identify recurrent events37. The exomes of 248 tumours were sequenced to a read-depth of at least ×20. We performed low-pass whole-genome sequencing on 107 tumours to a mean depth of ×6. Consensus clustering was used to analyse mRNA, miRNA, RPPA and methylation data with methods previously described38,39,40. Integrated cross-platform analyses were performed using MEMo, iCluster and PARADIGM25,31.


  1. 1.

    , & Cancer statistics, 2013. CA Cancer J. Clin. 63, 11–30 (2013)

  2. 2.

    et al. Phase III trial of doxorubicin plus cisplatin with or without paclitaxel plus filgrastim in advanced endometrial carcinoma: a Gynecologic Oncology Group Study. J. Clin. Oncol. 22, 2159–2166 (2004)

  3. 3.

    et al. Whole abdominal radiotherapy in the adjuvant treatment of patients with stage III and IV endometrial cancer: a gynecologic oncology group study. Gynecol. Oncol. 97, 755–763 (2005)

  4. 4.

    & A dualistic model for endometrial carcinogenesis based on immunohistochemical and molecular genetic analyses. Verh. Dtsch. Ges. Pathol. 81, 228–232 (1997)

  5. 5.

    et al. High frequency of PIK3R1 and PIK3R2 mutations in endometrial cancer elucidates a novel mechanism for regulation of PTEN protein stability. Cancer Discov. 1, 170–185 (2011)

  6. 6.

    et al. PTEN mutations and microsatellite instability in complex atypical hyperplasia, a precursor lesion to uterine endometrioid carcinoma. Cancer Res. 58, 3254–3258 (1998)

  7. 7.

    et al. Use of mutation profiles to refine the classification of endometrial carcinomas. J. Pathol. 228, 20–30 (2012)

  8. 8.

    et al. FGFR2 point mutations in 466 endometrioid endometrial tumors: relationship with MSI, KRAS, PIK3CA, CTNNB1 mutations and clinicopathological features. PLoS ONE 7, e30801 (2012)

  9. 9.

    et al. PIK3R1 (p85α) is somatically mutated at high frequency in primary endometrial cancer. Cancer Res. 71, 4061–4067 (2011)

  10. 10.

    et al. Microsatellite instability and epigenetic inactivation of MLH1 and outcome of patients with endometrial carcinomas of the endometrioid type. J. Clin. Oncol. 25, 2042–2048 (2007)

  11. 11.

    et al. Identification of molecular pathway aberrations in uterine serous carcinoma by genome-wide analyses. J. Natl. Cancer Inst. 104, 1503–1513 (2012)

  12. 12.

    et al. Exome sequencing of serous endometrial tumors identifies recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes. Nature Genet. 44, 1310–1315 (2012)

  13. 13.

    et al. Integrated genomic profiling of endometrial carcinoma associates aggressive tumors with indicators of PI3 kinase activation. Proc. Natl Acad. Sci. USA 106, 4834–4839 (2009)

  14. 14.

    et al. LRP1B deletion in high-grade serous ovarian cancers is associated with acquired chemotherapy resistance to liposomal doxorubicin. Cancer Res. 72, 4060–4073 (2012)

  15. 15.

    The Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012)

  16. 16.

    et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012)

  17. 17.

    et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010)

  18. 18.

    et al. Germline mutations affecting the proofreading domains of POLE and POLD1 predispose to colorectal adenomas and carcinomas. Nature Genet. 45, 136–144 (2013)

  19. 19.

    et al. Endometrial carcinomas: a review emphasizing overlapping and distinctive morphological and immunohistochemical features. Adv. Anat. Pathol. 18, 415–437 (2011)

  20. 20.

    et al. CpG island methylator phenotype in colorectal cancer. Proc. Natl Acad. Sci. USA 96, 8681–8686 (1999)

  21. 21.

    et al. Genome-scale analysis of aberrant DNA methylation in colorectal cancer. Genome Res. 22, 271–282 (2012)

  22. 22.

    et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010)

  23. 23.

    , & Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906–2912 (2009)

  24. 24.

    , , , & Bcl-2 is an inner mitochondrial membrane protein that blocks programmed cell death. Nature 348, 334–336 (1990)

  25. 25.

    , , & Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398–406 (2012)

  26. 26.

    , , , & Oncogenic K-ras stimulates Wnt signaling in colon cancer through inhibition of GSK-3β. Gastroenterology 128, 1907–1918 (2005)

  27. 27.

    et al. Regulation of Wnt signaling by Sox proteins: XSox17 α/β and XSox3 physically interact with β-catenin. Mol. Cell 4, 487–498 (1999)

  28. 28.

    et al. Sox17 and Sox4 differentially regulate β-catenin/T-cell factor activity and proliferation of colon carcinoma cells. Mol. Cell. Biol. 27, 7802–7815 (2007)

  29. 29.

    et al. Frequent activating FGFR2 mutations in endometrial carcinomas parallel germline mutations associated with craniosynostosis and skeletal dysplasia syndromes. Oncogene 26, 7158–7162 (2007)

  30. 30.

    et al. Phase II trial of trastuzumab in women with advanced or recurrent, HER2-positive endometrial carcinoma: a Gynecologic Oncology Group study. Gynecol. Oncol. 116, 15–20 (2010)

  31. 31.

    et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010)

  32. 32.

    The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012)

  33. 33.

    The Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011)

  34. 34.

    & Endometrial carcinoma: controversies in histopathological assessment of grade and tumour cell type. J. Clin. Pathol. 63, 410–415 (2010)

  35. 35.

    et al. Utility of p16 expression for distinction of uterine serous carcinomas from endometrial endometrioid and endocervical adenocarcinomas: immunohistochemical analysis of 201 cases. Am. J. Surg. Pathol. 33, 1504–1514 (2009)

  36. 36.

    , & Poor inter-observer reproducibility in the diagnosis of high-grade endometrial carcinoma. Am. J. Surg. Pathol. 91, 248A (2012)

  37. 37.

    et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011)

  38. 38.

    & A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010)

  39. 39.

    et al. Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics 9, 365 (2008)

  40. 40.

    , , & Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 4164–4169 (2004)

Download references


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 US 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, U54HG003067-11, U54HG003079-10 and U54HG003273-10.

Author information


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

    • Gad Getz
    • , Stacey B. Gabriel
    • , Kristian Cibulskis
    • , Eric Lander
    • , Andrey Sivachenko
    • , Carrie Sougnez
    • , Mike Lawrence
    • , Andrew D. Cherniack
    • , Itai Pashtan
    • , Gordon Saksena
    • , Robert C. Onofrio
    • , Steven E. Schumacher
    • , Barbara Tabak
    • , Scott L. Carter
    • , Bryan Hernandez
    • , Jeff Gentry
    • , Helga B. Salvesen
    • , Kristin Ardlie
    • , Wendy Winckler
    • , Rameen Beroukhim
    • , Matthew Meyerson
    • , Lynda Chin
    • , Juok Cho
    • , Daniel DiCara
    • , Scott Frazer
    • , David Heiman
    • , Rui Jing
    • , Pei Lin
    • , Will Mallard
    • , Doug Voet
    • , Hailei Zhang
    • , Lihua Zou
    •  & Michael Noble
  2. The Genome Institute, Washington University, St Louis, Missouri 63108, USA.

    • Cyriac Kandoth
    • , David Dooling
    • , Robert Fulton
    • , Lucinda Fulton
    • , Joelle Kalicki-Veizer
    • , Michael D. McLellan
    • , Michelle O’Laughlin
    • , Heather Schmidt
    • , Richard K. Wilson
    • , Kai Ye
    • , Li Ding
    •  & Elaine R. Mardis
  3. Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada.

    • Adrian Ally
    • , Miruna Balasundaram
    • , Inanc Birol
    • , Yaron S. N. Butterfield
    • , Rebecca Carlsen
    • , Candace Carter
    • , Andy Chu
    • , Eric Chuah
    • , Hye-Jung E. Chun
    • , Noreen Dhalla
    • , Ranabir Guin
    • , Carrie Hirst
    • , Robert A. Holt
    • , Steven J. M. Jones
    • , Darlene Lee
    • , Haiyan I. Li
    • , Marco A. Marra
    • , Michael Mayo
    • , Richard A. Moore
    • , Andrew J. Mungall
    • , Patrick Plettner
    • , Jacqueline E. Schein
    • , Payal Sipahimalani
    • , Angela Tam
    • , Richard J. Varhol
    •  & A. Gordon Robertson
  4. Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and Women’s Hospital, Boston, Massachusetts 02115, USA.

    • Itai Pashtan
  5. Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.

    • Itai Pashtan
  6. Department of Obstetrics and Gynecology, Haukeland University Hospital, 5021 Bergen, Norway.

    • Helga B. Salvesen
  7. Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway.

    • Helga B. Salvesen
  8. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.

    • Rameen Beroukhim
    •  & Matthew Meyerson
  9. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.

    • Angela Hadjipanayis
    • , Xiaojia Ren
    •  & Raju Kucherlapati
  10. Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA.

    • Semin Lee
    • , Peter Park
    • , Ruibin Xi
    •  & Lixing Yang
  11. Institute for Applied Cancer Science, Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas 77054, USA.

    • Harshad S. Mahadeshwar
    • , Alexei Protopopov
    • , Sahil Seth
    • , Xingzhi Song
    • , Jiabin Tang
    • , Dong Zeng
    • , Lynda Chin
    •  & Jianhua Zhang
  12. Informatics Program, Boston Children’s Hospital, Boston, Massachusetts 02115, USA.

    • Peter Park
  13. Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • J. Todd Auman
  14. Institute for Pharmacogenetics and Individualized Therapy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • J. Todd Auman
  15. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • Saianand Balu
    • , Tom Bodenheimer
    • , Elizabeth Buda
    • , D. Neil Hayes
    • , Alan P. Hoyle
    • , Stuart R. Jefferys
    • , Shaowu Meng
    • , Lisle E. Mose
    • , Joel S. Parker
    • , Charles M. Perou
    • , Yan Shi
    • , Janae V. Simons
    • , Mathew G. Soloway
    • , Donghui Tan
    • , Michael D. Topal
    • , Scot Waring
    • , Junyuan Wu
    • , Katherine A. Hoadley
    •  & W. Kimryn Rathmell
  16. Department of Internal Medicine, Division of Medical Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • D. Neil Hayes
  17. Department of Biology, University of North Carolina at Chapel Hill, North Carolina 27599, USA.

    • Corbin D. Jones
  18. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • Piotr A. Mieczkowski
    • , Charles M. Perou
    •  & Katherine A. Hoadley
  19. Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • Charles M. Perou
    •  & Michael D. Topal
  20. Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.

    • Jeff Roach
  21. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, USA.

    • Stephen B. Baylin
  22. University of Southern California Epigenome Center, University of Southern California, Los Angeles, California 90089, USA.

    • Moiz S. Bootwalla
    • , Phillip H. Lai
    • , Timothy J. Triche Jr
    • , David J. Van Den Berg
    • , Daniel J. Weisenberger
    • , Peter W. Laird
    •  & Hui Shen
  23. Institute for Systems Biology, Seattle, Washington 98109, USA.

    • Sheila M. Reynolds
    •  & Ilya Shmulevich
  24. Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

    • B. Arman Aksoy
    • , Yevgeniy Antipin
    • , Giovanni Ciriello
    • , Gideon Dresdner
    • , Jianjiong Gao
    • , Benjamin Gross
    • , Anders Jacobsen
    • , Boris Reva
    • , Chris Sander
    • , Rileen Sinha
    • , S. Onur Sumer
    • , Ethan Cerami
    • , Nils Weinhold
    •  & Nikolaus Schultz
  25. Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

    • Marc Ladanyi
  26. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, California 94158, USA.

    • Barry S. Taylor
  27. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

    • Ronglai Shen
  28. Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA.

    • Stephen Benz
    • , Ted Goldstein
    • , David Haussler
    • , Sam Ng
    • , Christopher Szeto
    •  & Joshua Stuart
  29. Buck Institute for Age Research, Novato, California 94945, USA.

    • Christopher C. Benz
    •  & Christina Yau
  30. Cancer Genomics Core Laboratory, University of Texas MD Anderson Cancer Center, Houston, Texas 77054, USA.

    • Wei Zhang
    • , Matti Annala
    • , Guoyan Liu
    •  & Yuexin Liu
  31. Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.

    • Wei Zhang
    • , Matti Annala
    • , Guoyan Liu
    • , Yuexin Liu
    •  & Russell Broaddus
  32. Tampere University of Technology Korkeakoulunkatu 10, FI-33720 Tampere, Finland.

    • Matti Annala
  33. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.

    • Bradley M. Broom
    • , Tod D. Casasent
    • , Zhenlin Ju
    • , Han Liang
    • , Anna K. Unruh
    • , Chris Wakefield
    • , John N. Weinstein
    • , Nianxiang Zhang
    •  & Rehan Akbani
  34. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA.

    • Yiling Lu
    •  & Gordon B. Mills
  35. The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio 43205, USA.

    • Christopher Adams
    • , Thomas Barr
    • , Aaron D. Black
    • , Jay Bowen
    • , John Deardurff
    • , Jessica Frick
    • , Julie M. Gastier-Foster
    • , Thomas Grossman
    • , Hollie A. Harper
    • , Melissa Hart-Kothari
    • , Carmen Helsel
    • , Aaron Hobensack
    • , Harkness Kuck
    • , Kelley Kneile
    • , Kristen M. Leraas
    • , Tara M. Lichtenberg
    • , Cynthia McAllister
    • , Robert E. Pyatt
    • , Nilsa C. Ramirez
    • , Teresa R. Tabler
    • , Nathan Vanhoose
    • , Peter White
    • , Lisa Wise
    •  & Erik Zmuda
  36. The Ohio State University, Columbus, Ohio 43210, USA.

    • Julie M. Gastier-Foster
    • , Nilsa C. Ramirez
    •  & Paul J. Goodfellow
  37. Asterand, Detroit, Michigan 48202, USA.

    • Nandita Barnabas
    • , Charlenia Berry-Green
    • , Victoria Blanc
    • , Michael Button
    • , Adam Farkas
    • , Alex Green
    • , Jean MacKenzie
    •  & Dana Nicholson
  38. University of North Carolina, Chapel Hill, North Carolina 27599, USA.

    • Lori Boice
    • , Jennifer Fisher
    • , Mei Huang
    • , Leigh Thorne
    •  & Linda Van Le
  39. OvCaRe British Columbia, British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada.

    • Steve E. Kalloger
    •  & C. Blake Gilks
  40. Department of Pathology & Laboratory Medicine, The University of British Columbia, Vancouver, British Columbia V6T 2B5, Canada.

    • Steve E. Kalloger
    •  & C. Blake Gilks
  41. Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA.

    • Beth Y. Karlan
    • , Jenny Lester
    •  & Sandra Orsulic
  42. Helen F Graham Cancer Center at Christiana Care, Newark, Delaware 19713, USA.

    • Mark Borowsky
    • , Mark Cadungog
    • , Christine Czerwinski
    • , Lori Huelsenbeck-Dill
    • , Mary Iacocca
    • , Nicholas Petrelli
    • , Brenda Rabeno
    •  & Gary Witkin
  43. Cureline, Inc., South San Francisco, California 94080, USA.

    • Elena Nemirovich-Danchenko
    • , Olga Potapova
    •  & Daniil Rotin
  44. Duke University Medical Center, Duke Cancer Institute, Durham, North Carolina 27710, USA.

    • Andrew Berchuck
  45. Harvard Medical School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, USA.

    • Michael Birrer
  46. University of California Medical Center, Irvine, Orange California 92868, USA.

    • Phillip DiSaia
  47. GOG Tissue Bank, The Research Institute at Nationwide Children’s Hospital, Columbus, Ohio 43205, USA.

    • Laura Monovich
  48. International Genomics Consortium, Phoenix, Arizona 85004, USA.

    • Erin Curley
    • , Johanna Gardner
    • , David Mallery
    •  & Robert Penny
  49. Department of OB Gyn, Division of Gynecologic Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA.

    • Sean C. Dowdy
    • , Boris Winterhoff
    • , Bobbie Gostout
    • , Alexandra Meuter
    •  & Attila Teoman
  50. Department of Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA.

    • Linda Dao
  51. Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

    • Douglas A. Levine
    • , Fanny Dao
    • , Narciso Olvera
    •  & Faina Bogomolniy
  52. Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.

    • Karuna Garg
    •  & Robert A. Soslow
  53. N. N. Blokhin Russian Cancer Research Center RAMS, Moscow 115478, Russia.

    • Mikhail Abramov
  54. Ontario Tumour Bank, Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada.

    • John M. S. Bartlett
    •  & Sugy Kodeeswaran
  55. Ontario Tumour Bank, London Health Sciences Centre, London, Ontario N6A 5A5, Canada.

    • Jeremy Parfitt
  56. St Petersburg Academic University, St Petersburg 199034, Russia.

    • Fedor Moiseenko
  57. Department of Pathology, University Health Network, Toronto, Ontario M5G 2C4, Canada.

    • Blaise A. Clarke
  58. University of Hawaii, Honolulu, Hawaii 96813, USA.

    • Marc T. Goodman
    • , Michael E. Carney
    •  & Rayna K. Matsuno
  59. Cedars-Sinai Medical Center, Los Angeles, California 90024, USA.

    • Marc T. Goodman
  60. University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA.

    • Rajiv Dhir
    • , Robert Edwards
    • , Esther Elishaev
    •  & Kristin Zorn
  61. Washington University School of Medicine, St Louis, Missouri 63110, USA.

    • Paul J. Goodfellow
    •  & David Mutch
  62. SRA International, Fairfax, Virgina 22033, USA.

    • Ari B. Kahn
    • , Brenda Ayala
    • , Anna L. Chu
    • , Mark A. Jensen
    • , Prachi Kothiyal
    • , Todd D. Pihl
    • , Joan Pontius
    • , David A. Pot
    • , Eric E. Snyder
    •  & Deepak Srinivasan
  63. Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

    • Daphne W. Bell
  64. Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane 4059, Australia.

    • Pamela M. Pollock
  65. Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA.

    • Chen Wang
  66. Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA.

    • David A.Wheeler
    •  & Eve Shinbrot
  67. The Cancer Genome Atlas Program Office, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

    • Kenna R. Mills Shaw
    • , Margi Sheth
    • , Tanja Davidsen
    • , John A. Demchok
    •  & Liming Yang
  68. Scimentis, LLC, Atlanta, Georgia 30666, USA.

    • Greg Eley Martin L. Ferguson
  69. MLF Consulting, Arlington, Maryland 02474, USA.

    • Greg Eley Martin L. Ferguson
  70. National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.

    • Mark S. Guyer
    • , Bradley A. Ozenberger
    •  & Heidi J. Sofia


  1. The Cancer Genome Atlas Research Network

    (Participants are arranged by area of contribution and then by institution.)

    Genome sequencing centres: Broad Institute

    Washington University in St Louis

    Genome characterization centres: British Columbia Cancer Agency

    Broad Institute

    Harvard Medical School/Brigham & Women’s Hospital/MD Anderson Cancer Center

    University of North Carolina

    University of Southern California & Johns Hopkins

    Genome data analysis centres: Broad Institute

    Institute for Systems Biology

    Memorial Sloan-Kettering Cancer Center

    University of California, Santa Cruz/Buck Institute

    The University of Texas MD Anderson Cancer Center

    Biospecimen core resource: Nationwide Children’s Hospital

    Tissue source sites: Asterand

    British Columbia Cancer Agency

    Cedars-Sinai Medical Center

    Christiana Care


    Duke University

    Gynecologic Oncology Group

    International Genomics Consortium

    Mayo Clinic

    Memorial Sloan-Kettering Cancer Center

    N. N. Blokhin Russian Cancer Research Center

    Ontario Tumour Bank

    St Petersburg Academic University

    University Health Network

    University of Hawaii

    University of North Carolina

    University of Pittsburgh

    The University of Texas MD Anderson Cancer Center

    Washington University School of Medicine

    Disease analysis working group

    Data coordination centre

    Project team: National Cancer Institute

    National Human Genome Research Institute

    Writing committee


  1. Search for Douglas A. Levine in:


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 centres, cancer genome characterization centres and genome data analysis centres. All data were released through the data coordinating centre. 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. Gao (data coordinator); C.K. and L. Ding (DNA sequence analysis); W.Z. and Y.L. (mRNA sequence analysis); H.S. and P.W.L. (DNA methylation analysis); A.D.C. and I.P. (copy number analysis); S.L. and A. Hadjipanayis (translocations); N.S., N.W. G.C., C.C.B. and C.Y. (pathway analysis); Andy C. and A.G.R. (miRNA sequence analysis); R. Broaddus, P.J.G., G.B.M. and R.A.S. (pathology and clinical expertise); G.B.M., H.L. and 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).

Competing interests

The author declares no competing financial interests.

Corresponding author

Correspondence to Douglas A. Levine.

The primary and processed data used to generate the analyses presented here are deposited at the Data Coordinating Center (; all of the primary sequence files are deposited in CGHub ( Sample lists, data matrices and supporting data can be found at: ( The data can be explored via the cBio Cancer Genomics Portal ( Reprints and permissions information is available at The authors declare no competing financial interests. Readers are welcome to comment on the online version of the paper. Correspondence and requests for materials should be addressed to D.A.L. (

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Methods 1-12, which includes Supplementary Figures and Tables and additional references (see pages 1 and 2 for details).

Zip files

  1. 1.

    Supplementary Data

    This zipped file contains Supplementary Data files 1.1, 2.1, 3.1, 3.2 and 5.1 (see Supplementary Information document for details).

About this article

Publication history





Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.