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Integrated genomic characterization of endometrial carcinoma

Integrated genomic characterization of endometrial carcinoma OPEN ARTICLE doi:10.1038/nature12113 Integrated genomic characterization of endometrial carcinoma The Cancer Genome Atlas Research Network* 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 frequentTP53 mutations. Most endometrioid tumours had few copy numberalterations orTP53 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 all tissue acquisition. The clinical and pathological characteristics of the most common malignancy among women in the United States, with an samples generally reflect a cross-section of individuals with recurrent 2,3 estimated 49,500 new cases and 8,200 deaths in 2013 (ref. 1). Most endometrial cancer (Supplementary Table 1.1). The median follow-up patients present with low-grade, early-stage disease. The majority of of the cohort was 32 months (range, 1–195 months); 21% of the patients patients with more aggressive, high-grade tumours who have disease have recurred, and 11% have died. Comprehensive molecular analyses spread beyond the uterus will progress within 1 year (refs 2, 3). were performed at independent centres using six genomic or proteomic Endometrial cancers have been broadly classified into two groups . platforms (Supplementary Table 1.2). MSI testing performed on all Type I endometrioid tumours are linked to oestrogen excess, obesity, samples using seven repeat loci (Supplementary Table 1.3) found MSI hormone-receptor positivity, and favourable prognosis compared with in 40% of endometrioid tumours and 2% of serous tumours. type II, primarily serous, tumours that are more common in older, Somatic copy number alterations non-obese women and have a worse outcome. Early-stage endome- trioid cancers are often treated with adjuvant radiotherapy, whereas Somatic copy number alterations (SCNAs) were assessed in 363 endo- serous tumours are treated with chemotherapy, similar to advanced- metrial carcinomas. Unsupervised hierarchical clustering grouped the stage cancers of either histological subtype. Therefore, proper subtype tumours into four clusters (Fig. 1a). The first three copy-number clus- classification is crucial for selecting appropriate adjuvant therapy. ters were composed almost exclusively (97%) of endometrioid tumours Several previous reports suggest that PTEN mutations occur early in without significant differences in tumour grades. Cluster 1 tumours the neoplastic process of type I tumours and co-exist frequently with were nearly devoid of broad SCNAs, averaging less than 0.5% genome other mutations in the phosphatidylinositol-3-OH kinase (PI(3)K)/ alteration, with no significant recurrent events. Cluster 1 tumours also 5,6 AKT pathway . Other commonly mutated genes in type I tumours had significantly increased non-synonymous mutation rates com- 7–9 26 26 include FGFR2, ARID1A, CTNNB1, PIK3CA, PIK3R1 and KRAS . pared to all others (median 7.23 10 versus 1.73 10 mutations Microsatellite instability (MSI) is found in approximately one-third per megabase (Mb), P, 0.001). Copy-number clusters 2 and 3 con- of type I tumours, but is infrequent in type II tumours . TP53, PIK3CA sisted mainly of endometrioid tumours, distinguished by more fre- 11,12 and PPP2R1A mutations are frequent in type II tumours . Most of quent 1q amplification in cluster 3 than cluster 2 (100% of cluster 3 these studies have been limited to DNA sequencing only with samples tumours versus 33% of cluster 2 tumours) and worse progression-free of heterogeneous histological subtypes and tumour grades. We present survival (P5 0.003, log-rank versus clusters 1 and 2; Fig. 1b). a comprehensive, multiplatform analysis of 373 endometrial carcino- Most of the serous (50 out of 53; 94%) and mixed histology (8 out of mas including low-grade endometrioid, high-grade endometrioid, and 13; 62%) tumours clustered with 36 (12%) of the 289 endometrioid serous carcinomas. This integrated analysis provides key molecular tumours, including 24% of grade 3 and 5% of grade 1 or 2, into copy- insights into tumour classification, which may have a direct effect on number cluster 4; a single group characterized by a very high degree of treatment recommendations for patients, and provides opportunities SCNAs (Supplementary Fig. 2.1; focal SCNAs with false discovery rate for genome-guided clinical trials and drug development. (FDR), 0.15, and Supplementary Data 2.1). Cluster 4 tumours were characterized by significantly recurrent previously reported focal Results amplifications of the oncogenes MYC (8q24.12), ERBB2 (17q12) Tumour samples and corresponding germline DNA were collected and CCNE1 (19q12) , and by SCNAs previously unreported in endo- from 373 patients, including 307 endometrioid and 66 serous (53) or metrial cancers including those containing FGFR3 (4p16.3) and SOX17 mixed histology (13) cases. Local Institutional Review Boards approved (8q11.23). Cluster 4 tumours also had frequent TP53 mutations (90%), *Lists of participants and their affiliations appear at the end of the paper. 00 MO N T H 2 013 | V O L 000 | N AT URE | 1 ©2013 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE chr. 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%, Q5 0), PIK3R1 (65%, Q5 8.33 10 ), PIK3CA (71%, 25 24 Q5 9.13 10 ), FBXW7 (82%, Q5 1.43 10 ), KRAS (53%, 4 24 23 Q5 9.23 10 ) and POLE (100%, Q5 4.23 10 ). Mutation rates in POLE mutant endometrial and previously reported ultramutated colo- rectal tumours exceeded those found in any other lineage including lung 15–17 cancer and melanoma . Germline susceptibility variants have been reported in POLE (Leu424Val) and POLD1 (Ser478Asn), but were not 10 found in our endometrial normal exome-seq reads . The MSI endometrioid tumours had a mutation frequency approxi- mately tenfold greater than MSS endometrioid tumours, few SCNAs, frameshift deletions in RPL22, frequent non-synonymous KRAS muta- tions, and few mutations in FBXW7, CTNNB1, PPP2R1A and TP53. 19 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 23 4 Cluster 12 3 4 high group contained all of the remaining serous cases and one-quarter b 100 of the grade 3 endometrioid cases. Most of these tumours had TP53 mutations and a high frequency of FBXW7 (22%, Q5 0) and PPP2R1A 80 216 (22%, Q5 1.73 10 ) 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 Cluster 1 might benefit from treatment approaches that parallel those for serous Cluster 2 tumours. Cluster 3 There were 48 genes with differential mutation frequencies across Cluster 4 Log-rank P = 0.0004 the four groups (Fig. 2d and Supplementary Data 3.1). ARID5B,a 0 20 40 60 80 100 120 member of the same AT-rich interaction domain (ARID) family as Survival (months) ARID1A, was more frequently mutated in MSI (23.1%) than in either MSS endometrioid (5.6%) or high SCNA serous tumours (0%), a Figure 1 | SCNAs in endometrial carcinomas. a, Tumours were novel finding for endometrial cancer. Frameshifting RPL22 indels hierarchically clustered into four groups based on SCNAs. The heat map shows near a homopolymer at Lys 15 were almost exclusively found in the SCNAs in each tumour (horizontal axis) plotted by chromosomal location (vertical axis). Chr., chromosome. b, Kaplan–Meier curves of progression-free MSI group (36.9%). The TP53 mutation frequency (.90%) in serous survival for each copy-number cluster. 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 little MSI (6%), and fewer PTEN mutations (11%) than other endome- PTEN, compared to only 1 out of 39 (2.6%) serous tumours with non- trioid tumours (84%). Overall, these findings suggest that a subset of silent TP53 mutations. Although TP53 mutations are not restricted to endometrial tumours contain distinct patterns of SCNAs and muta- serous tumours, the co-existing PTEN mutations in the endometrioid tions that do not correlate with traditional tumour histology or grade. cases suggest a distinct tumorigenic mechanism. As expected, tumours in the ‘serous-like’ cluster (cluster 4) had signifi- Comparisons of 66 SMGs between traditional histological subtypes cantly worse progression-free survival than tumours in the endometrioid cluster groups (P5 0.003, log-rank, Fig. 1b). Potential therapeutically are provided (Supplementary Methods 3), and SMGs across other subcohorts can be found in Supplementary Data 3.2. The spectrum relevant SCNAs included the cluster 2 15q26.2 focal amplification, which contained IGF1R; and cluster 4 amplifications of ERBB2, FGFR1 and of PIK3CA and PTEN mutations in endometrial cancer also differed from other solid tumours (Supplementary Methods 3). Integrated FGFR3,andLRP1B deletion, which was recently associated with resistance to liposomal doxorubicin in serous ovarian cancer . analysis may be useful for identifying histologically misclassified cases. For example, a single serous case was identified without a Exome sequence analysis TP53 mutation or extensive SCNAs and with a KRAS mutation and high mutation rate. After re-review of the histological section, the case We sequenced the exomes of 248 tumour/normal pairs. On the basis was deemed consistent with a grade 3 endometrioid tumour, dem- of a combination of somatic nucleotide substitutions, MSI and onstrating how molecular analysis could reclassify tumour histology SCNAs, the endometrial tumours were classified into four groups and potentially affect treatment decisions. (Fig. 2a, b): (1) an ultramutated group with unusually high mutation rates (2323 10 mutations per Mb) and a unique nucleotide change Multiplatform subtype classifications spectrum; (2) a hypermutated group (183 10 mutations per Mb) of MSI tumours, most with MLH1 promoter methylation; (3) a group All of the endometrial tumours were examined for messenger RNA with lower mutation frequency (2.93 10 mutations per Mb) and expression (n5 333), protein expression (n5 293), microRNA expres- most of the microsatellite stable (MSS) endometrioid cancers; and (4) sion (n5 367), and DNA methylation (n5 373) (Supplementary a group that consists primarily of serous-like cancers with extensive Methods 4–7). Unsupervised k-means clustering of mRNA expression SCNA (copy-number cluster 4) and a low mutation rate (2.33 10 from RNA sequencing identified three robust clusters termed ‘mitotic’, mutations per Mb). The ultramutated group consisted of 17 (7%) ‘hormonal’ and ‘immunoreactive’ (Supplementary Fig. 4.1) that were tumours exemplified by an increased CRA transversion frequency, significantly correlated with the four integrated clusters; POLE,MSI, all with mutations in the exonuclease domain of POLE, and an improved copy-number low and copy-number high (P, 0.0001). Supervised progression-free survival (Fig. 2a, c). POLE is a catalytic subunit of DNA analysis identified signature genes of the POLE cluster (n5 17) mostly polymerase epsilon involved in nuclear DNA replication and repair. We involved in cellular metabolism (Fig. 3a). Among the few signature genes 2 | N ATURE | V O L 000 | 0 0 M ONT H 201 3 ©2013 Macmillan Publishers Limited. All rights reserved Progression-free survival (%) CN cluster 4 ARTICLE RESEARCH POLE (ultramutated) MSI (hypermutated) Copy-number low (endometrioid) Copy-number high (serous-like) n = 17 n = 65 n = 90 n = 60 0.5 POLE MSI/MLH1 CN cluster PTEN TP53 Histology Tumour grade Nucleotide substitutions POLE mutations MSI DNA methylation CN cluster Mutations Histology Tumour grade NA MLH1 silent CA CG CT TA TC TG V411L P286R Other MSI high MSI low MS stable 1 2 3 4 Nonsense Missense Frameshift Serous Mixed Endometrioid 3 2 1 100 100 bc d (232) 60 60 (215) POLE (ultramutated) (17) 40 40 Log-rank P = 0.02 (150) MSI (hypermutated) (65) 20 POLE (ultramutated) 20 MSI (hypermutated) Copy-number low (endometrioid) 0 Copy-number high (serous-like) *(%[CA] > 0.2) AND Copy-number low Copy-number high **** * **** **** ** * ** ** * * * * (%[CG] < 0.03) AND 0 20 40 60 80 100 120 (endometrioid) (90) (serous-like) (60) (SNV count > 500) Months Figure 2 | Mutation spectra across endometrial carcinomas. a, Mutation cluster. SNV, single nucleotide variant. c, POLE-mutant tumours have frequencies (vertical axis, top panel) plotted for each tumour (horizontal axis). significantly better progression-free survival, whereas copy-number high Nucleotide substitutions are shown in the middle panel, with a high frequency tumours have the poorest outcome. d, Recurrently mutated genes are different of C-to-A transversions in the samples with POLE exonuclease mutations. CN, between the four subgroups. Shown are the mutation frequencies of all genes copy number. b, Tumours were stratified into the four groups by (1) nucleotide that were significantly mutated in at least one of the four subgroups (MUSiC, substitution frequencies and patterns, (2) MSI status, and (3) copy-number asterisk denotes FDR , 0.05). 20–22 in the MSI cluster was decreased MLH1 mRNA expression, probably (CIMP) described in colon cancers and glioblastomas was associated due to its promoter methylation. Increased progesterone receptor (PGR) with the MSI subtype and attributable to promoter hypermethylation of expression was noted in the copy-number low cluster, suggesting res- MLH1. A serous-like cluster (MC3) with minimal DNA methylation ponsiveness to hormonal therapy. The copy-number high cluster, which changes was composed primarily of serous tumours and some endome- included most of the serous and serous-like endometrioid tumours, trioid tumours (Supplementary Fig. 7.1) and contained most of the copy- exhibited the greatest transcriptional activity exemplified by increased number high tumours. cell cycle deregulation (for example, CCNE1, PIK3CA, MYC and Integrative clustering using the iCluster framework returned two CDKN2A) and TP53 mutation (Supplementary Figs 4.2 and 4.3). major clusters split primarily on serous and endometrioid histology This is consistent with reports that increased CDKN2A can distinguish highlighting TP53 mutations, lack of PTEN mutation and encompas- serous from endometrioid carcinomas . Approximately 85% of cases sing almost exclusively copy-number high tumours (Supplementary in the copy-number high cluster shared membership with the ‘mitotic’ Fig. 8.1). We developed a new clustering algorithm, called Super- mRNA subtype. Cluster, to derive overall subtypes based on sample cluster member- ships across all data types (Supplementary Fig. 9.1). SuperCluster Supervised clustering of the reverse phase protein array (RPPA) expression data was consistent with loss of function for many of the identified four clusters that generally confirmed the contributions of individual platforms to the overall integrated clusters. No major batch mutated genes (Fig. 3b). TP53 was frequently mutated in the copy- number high group (P5 2.53 10 ) and its protein expression was effects were identified for any platform (Supplementary Methods 10). also increased, suggesting that these mutations are associated with Structural aberrations increased expression. By contrast, PTEN (P5 2.83 10 ) and ARID1A (P5 1.23 10 ) had high mutation rates in the remaining To identify somatic chromosomal aberrations, we performed low- groups, but their expression was decreased, suggesting inactivating pass, paired-end, whole-genome sequencing on 106 tumours with mutations in both genes. The copy-number high group also had matched normals. We found recurrent translocations involving genes decreased levels of phospho-AKT, consistent with downregulation of in several pathways including WNT, EGFR–RAS–MAPK, PI(3)K, the AKT pathway. The copy-number low group had raised RAD50 protein kinase A, retinoblastoma and apoptosis. The most frequent expression, which is associated with DNA repair, explaining some of translocations (5 out of 106) involved a member of the BCL family the differences between the copy-number high and low groups. The (BCL2, BCL7A, BCL9 and BCL2L11). Four of these were confirmed by POLE group had high expression of ASNS and CCNB1, whereas the identification of the translocation junction point and two were also MSI tumours had both high phospho-AKT and low PTEN expression. confirmed by high-throughput RNA sequencing (RNA-Seq). In all Unsupervised clustering of DNA methylation data generated from cases the translocations result in in-frame fusions and are predicted Illumina Infinium DNA methylation arrays revealed four unique subtypes to result in activation or increased expression of the BCL family (MC1–4) that support the four integrative clusters. A heavily methylated members (Supplementary Fig. 3.2). Translocations involving mem- subtype (MC1) reminiscent of the CpG island methylator phenotype bers of the BCL family leading to reduced apoptosis have been 00 MO N T H 2 013 | V O L 000 | N AT URE | 3 ©2013 Macmillan Publishers Limited. All rights reserved Spectrum* MSI high PTEN TP53 PIK3CA PIK3R1 ARID1A ARID5B KRAS CTCF CTNNB1 FBXW7 PPP2R1A RPL22 Substitution Mutations per Mb frequency (%) Progression-free survival (%) Mutated samples (% per subtype) Truncating Missense POLE MSI Copy-number low Copy-number high RESEARCH ARTICLE a alterations of all three genes. Overall, 93% of endometrioid tumours had mutations that suggested potential for targeted therapy with TCF25 CTU2 PI(3)K/AKT pathway inhibitors. GCAT Consensus clustering of copy number, mRNA expression and path- MED11 way interaction data for 324 samples yielded five PARADIGM clusters SLC25A35 DNAH9 31 with distinct pathway activation patterns (Fig. 4c and Supplementary PGR Methods 11). PARADIGM cluster 1 had the lowest level of MYC pathway activation and highest level of WNT pathway activation, CCNE1 AURKA consistent with its composition of copy-number low cases having fre- PIK3CA quent CTNNB1 mutations. PARADIGM cluster 3 was composed pre- MYC dominantly 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 impli- cated as loss-of-function mutations, suggesting different classes of p53 α-catenin E-cadherin mutations may have distinct signalling consequences. PARADIGM PDK1 pS241 AKT pT308 cluster 5 was enriched for hormone receptor expression. AKT pS473 ERα pS118 SYK Comparison to ovarian and breast cancers RAD50 STAT3 pY705 BAX The clinical and pathologic features of uterine serous carcinoma and PTEN high-grade serous ovarian carcinoma (HGSOC) are quite similar. ARID1A P53 HGSOC shares many similar molecular features with basal-like breast CHK2 pT68 Cyclin E1 32 carcinoma . Focal SCNA patterns were similar between these three PI(3)K p110α Cyclin B1 tumour subtypes and unsupervised clustering identified relatedness CDK1 ASNS (Fig. 5a and Supplementary Fig. 12.1). Supervised analysis of trans- Subgroup RNA/protein expression criptome data sets showed high correlation between tumour subtypes POLE (ultramutated) MSI (hypermutated) (Supplementary Fig. 12.2). The MC3 DNA methylation subtype with Copy-number low Copy-number high High Low minimal DNA methylation changes was also similar to basal-like breast and HGSOCs (Supplementary Fig. 12.3). A high frequency of Figure 3 | Gene expression across integrated subtypes in endometrial carcinomas. a, Supervised analysis of ,1,500 genes significantly associated TP53 mutations is shared across these tumour subtypes (uterine ser- 33,34 with integrated subtypes. b, Heat map of protein expression clusters, supervised ous, 91%; HGSOC, 96%; basal-like breast, 84%) , as is the very low by integrated subtypes. Samples are in columns; genes or proteins are in rows. 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 com- described in other tumour types and our results suggest that similar mechanisms may be operative here. pared to basal-like breast and HGSOCs (Fig. 5b). We showed that uterine serous carcinomas share many molecular features with both Pathway alterations HGSOCs and basal-like breast carcinomas, despite more frequent mutations, suggesting new opportunities for overlapping treatment Multiple platform data were integrated to identify recurrently altered paradigms. pathways in the four endometrial cancer integrated subgroups. Because of the high background mutation rate and small sample size, Discussion we excluded the POLE subgroup from this analysis. Considering all This integrated genomic and proteomic analysis of 373 endometrial recurrently mutated, homozygously deleted, and amplified genes, we used MEMo to identify gene networks with mutually exclusive cancers provides insights into disease biology and diagnostic classifica- alteration patterns in each subgroup. The most significant module tion that could have immediate therapeutic application. Our analysis was found in the copy-number low group and contained CTNNB1, identified four new groups of tumours based on integrated genomic KRAS and SOX17 (Fig. 4a). The very strong mutual exclusivity data, including a novel POLE subtype in ,10% of endometrioid between mutations in these three genes suggests that alternative tumours. Ultrahigh somatic mutation frequency, MSS, and common, mechanisms activate WNT signalling in endometrioid endometrial newly identified hotspot mutations in the exonuclease domain of cancer. Activating KRAS mutations have been shown to increase the POLE characterize this subtype. SCNAs add a layer of resolution, stability of b-catenin via glycogen synthase kinase 3b (GSK-3b), lead- revealing that most endometrioid tumours have few SCNAs, most ing to an alternative mechanism of b-catenin activation other than serous and serous-like tumours exhibit extensive SCNAs, and the adenomatous polyposis coli degradation . SOX17, which mediates extent of SCNA roughly correlates with progression-free survival. 27,28 proteasomal degradation of b-catenin , is mutated exclusively in Endometrial cancer has more frequent mutations in the PI(3)K/ the copy-number low group (8%) at recurrent positions (Ala96Gly AKT pathway than any other tumour type studied by The Cancer and Ser403Ile) not previously described. Other genes with mutually Genome Atlas (TCGA) so far. Endometrioid endometrial carcinomas exclusive alteration patterns in this module were FBXW7, FGFR2 and share many characteristics with colorectal carcinoma including a high ERBB2 (ref. 29). ERBB2 was focally amplified with protein overex- frequency of MSI (40% and 11%, respectively), POLE mutations (7% pression in 25% of the serous or serous-like tumours, suggesting a and 3%, respectively) leading to ultrahigh mutation rates, and fre- potential role for human epidermal growth factor receptor 2 (HER2)- quent activation of WNT/CTNNB1 signalling; yet endometrial carci- targeted inhibitors. A small clinical trial of trastuzumab found no nomas have novel exclusivity of KRAS and CTNNB1 mutations and a activity in endometrial carcinoma, but accrued few HER2 fluor- distinct mechanism of pathway activation. Uterine serous carcinomas escence in situ hybridization (FISH)-amplified serous carcinomas . share many similar characteristics with basal-like breast and HGSOCs; PIK3CA and PIK3R1 mutations were frequent and showed a strong three tumour types with high-frequency non-silent TP53 mutations tendency for mutual exclusivity in all subgroups, but unlike other and extensive SCNA. However, the high frequency ofPIK3CA, FBXW7, tumour types, they co-occurred with PTEN mutations in the MSI PPP2R1A and ARID1A mutations in uterine serous carcinomas are not 5,9 and copy-number low subgroups as previously reported (Fig. 4b). found in basal-like breast and HGSOCs. The frequency of mutations in The copy-number high subgroup showed mutual exclusivity between PIK3CA, FBXW7 and PPP2R1A was ,30% higher than in a recently 4 | NA TU RE | V OL 000 | 00 M ONTH 2013 ©2013 Macmillan Publishers Limited. All rights reserved mRNA ARTICLE RESEARCH a c PARADIGM clusters RTK/RAS/β-catenin Somatic Amplification 70% of samples altered mutation FGFR2 ERBB2 MSI (hypermut.) (71%) CN low (endometrioid) (82%) CN high (serous) (50%) MYC repressed, 16% 11% 5% 9% 1% 25% WNT, NRG1/ERBB FGFR2 signalling ERBB2 HIF1/metabolic KRAS CTNNB1 36% 15% 3% SOX17 FBXW7 KRAS 0% 8% 0% 12% 5% 22% GSK3B SOX17 FBXW7 p53, ATM/ATR, CTNNB1 DNA repair 19% 53% 3% SOX17 mutations S403I FOXA1/ER Missense A96G Proliferation Inactivating Activating Gene High mobility group domain C-terminal transactivation MSI CN low CN high binds TCF/LEF domain MYC activated Homozygous Somatic PI(3)K pathway 84% of samples altered mutation deletion PTEN 88% 77% 15% MSI (hypermut.) (95%) CN low (endometrioid) (92%) CN high (serous) (60%) PIK3CA PARADIGM 55% 12 3 4 5 2 53% 47% χ test PTEN PIK3R1 cluster 40% 34% 13% Subgroup P < 0.0001 PIK3R1 POLE MSI CN low CN high Proliferation, cell Missing (ultramut.) (hypermut.) (endometrioid) (serous-like) PIK3CA survival, translation Figure 4 | Pathway alterations in endometrial carcinomas. a, The RTK/ frequently co-occur with PTEN alterations in the MSI and copy-number low RAS/b-catenin pathway is altered through several mechanisms that exhibit subgroups. c, Heat map display of top 1,000 varying pathway features within mutually exclusive patterns. Alteration frequencies are expressed as a PARADIGM consensus clusters. Samples were arranged in order of their percentage of all cases. The right panel shows patterns of occurrence. b,The consensus cluster membership. The genomic subtype for each sample is PI(3)K pathway has mutually exclusive PIK3CA and PIK3R1 alterations that displayed below the consensus clusters. reported study of 76 uterine serous carcinomas , but similar to another Early stage type I endometrioid tumours are often treated with study . Uterine serous carcinomas have ERBB2 amplification in 27% of adjuvant radiotherapy, whereas similarly staged type II serous tumours tumours and PIK3CA mutations in 42%, which provide translational are treated with chemotherapy. High-grade serous and endometrioid endometrial carcinomas are difficult to subtype correctly, and intra- opportunities for targeted therapeutics. 7,34–36 observer concordance among speciality pathologists is low . Our molecular characterization data demonstrate that ,25% of tumours classified as high-grade endometrioid by pathologists have a molecular Serous-like Serous Basal-like phenotype similar to uterine serous carcinomas, including frequent endometrial ovarian breast chr. TP53 mutations and extensive SCNA. The compelling similarities between this subset of endometrioid tumours and uterine serous car- cinomas suggest that genomic-based classification may lead to 3 improved management of these patients. Clinicians should carefully 4 consider treating copy-number-altered endometrioid patients with 5 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 10 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- 20 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 events . The exomes of 248 Loss Gain tumours were sequenced to a read-depth of at least320. We performed low-pass 100 whole-genome sequencing on 107 tumours to a mean depth of 36. Consensus Genomic alterations Tumour subtype clustering was used to analyse mRNA, miRNA, RPPA and methylation data with Somatic mutation Serous-like endometrial 38–40 80 Amplification Serous ovarian methods previously described . Integrated cross-platform analyses were per- Homozygous deletion Basal-like breast 25,31 formed using MEMo, iCluster and PARADIGM . mRNA upregulation Epigenetic silencing Received 10 December 2012; accepted 21 March 2013. 1. Siegel, R., Naishadham, D. & Jemal, A. Cancer statistics, 2013. CA Cancer J. Clin. 63, 11–30 (2013). 2. Fleming, G. F. 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. Sutton, G. 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). 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Department of 24 26 24 24 24 10 Sumer , Barry S. Taylor , Ethan Cerami , Nils Weinhold , Nikolaus Schultz , Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. Center for 27 28 Ronglai Shen ; University of California, Santa Cruz/Buck Institute Stephen Benz , Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA. 28 28 28 28 28 11 Ted Goldstein , David Haussler ,Sam Ng , Christopher Szeto , Joshua Stuart , Institute for Applied Cancer Science, Department of Genomic Medicine, University of 29 29 12 Christopher C. Benz , Christina Yau ; The University of Texas MD Anderson Cancer Texas MD Anderson Cancer Center, Houston, Texas 77054, USA. Informatics Program, 30,31 30,31,32 33 33 13 Center Wei Zhang , Matti Annala , Bradley M. Broom , Tod D. Casasent , Boston Children’s Hospital, Boston, Massachusetts 02115, USA. Eshelman School of 33 33 30,31 34 33 Zhenlin Ju , Han Liang , Guoyan Liu , Yiling Lu , Anna K. Unruh ,Chris Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, 33 33 33 30,31 14 Wakefield , John N. Weinstein , Nianxiang Zhang , Yuexin Liu , Russell USA. Institute for Pharmacogenetics and Individualized Therapy, University of North 31 33 34 Broaddus , Rehan Akbani , Gordon B. Mills Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Department of Internal Medicine, Division of Medical Biospecimen core resource: Nationwide Children’s Hospital Christopher Adams , 35 35 35 35 35 Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, Thomas Barr , Aaron D. Black , Jay Bowen ,John Deardurff , Jessica Frick ,Julie 35,36 35 35 35 USA. Department of Biology, University of North Carolina at Chapel Hill, North Carolina M. Gastier-Foster , Thomas Grossman , Hollie A. Harper , Melissa Hart-Kothari , 35 35 35 35 27599,USA. Departmentof Genetics, University of North Carolina at Chapel Hill, Chapel Carmen Helsel , Aaron Hobensack , Harkness Kuck , Kelley Kneile ,Kristen M. 35 35 35 35 Hill, North Carolina 27599, USA. Department of Pathology and Laboratory Medicine, Leraas ,TaraM. Lichtenberg , Cynthia McAllister ,Robert E. Pyatt ,Nilsa C. 35,36 35 35 35 35 University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Ramirez ,Teresa R.Tabler ,Nathan Vanhoose , Peter White ,Lisa Wise ,Erik Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, Zmuda North Carolina 27599, USA. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, 37 37 Tissue source sites: Asterand Nandita Barnabas , Charlenia Berry-Green ,Victoria USA. University of Southern California Epigenome Center, University of Southern 37 38 37 37 37 Blanc ,Lori Boice , Michael Button , Adam Farkas , Alex Green ,Jean California, Los Angeles, California 90089, USA. Institute for Systems Biology, Seattle, 37 37 MacKenzie ,DanaNicholson ; British Columbia Cancer Agency Steve E. Washington 98109, USA. Computational Biology Center, Memorial Sloan-Kettering 39,40 39,40 41 Kalloger , C. Blake Gilks ; Cedars-Sinai Medical Center Beth Y. Karlan , Jenny Cancer Center, New York, New York 10065, USA. Human Oncology and Pathogenesis 41 41 42 42 Lester ,Sandra Orsulic ; Christiana Care Mark Borowsky ,Mark Cadungog , Program, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 42 42 42 42 Christine Czerwinski , Lori Huelsenbeck-Dill , Mary Iacocca , Nicholas Petrelli , Helen Diller Family Comprehensive Cancer Center, University of California, San 42 42 43 Brenda Rabeno , Gary Witkin ; Cureline Elena Nemirovich-Danchenko ,Olga Francisco, San Francisco, California 94158, USA. Department of Epidemiology and 43 43 44 Potapova ,DaniilRotin ; Duke University Andrew Berchuck ; Gynecologic Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 45 46 47 Oncology Group Michael Birrer , Phillip DiSaia , Laura Monovich ; International Department of Biomolecular Engineering and Center for Biomolecular Science and 48 48 48 Genomics Consortium Erin Curley , Johanna Gardner , David Mallery , Robert Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. 48 49 49 50 Penny ; Mayo Clinic Sean C. Dowdy , Boris Winterhoff , Linda Dao ,Bobbie 29 30 Buck Institute for Age Research, Novato, California 94945, USA. Cancer Genomics 49 49 49 Gostout , Alexandra Meuter ,Attila Teoman ; Memorial Sloan-Kettering Cancer Core Laboratory, University of Texas MD Anderson Cancer Center, Houston, Texas 77054, 51 51 51 52 Center Fanny Dao , Narciso Olvera , Faina Bogomolniy , Karuna Garg , Robert A. USA. Department of Pathology, University of Texas MD Anderson Cancer Center, 52 51 Soslow , Douglas A. Levine ; N. N. Blokhin Russian Cancer Research Center Mikhail Houston, Texas 77030, USA. Tampere University of Technology Korkeakoulunkatu 10, 53 54 54 Abramov ; Ontario Tumour Bank John M. S. Bartlett , Sugy Kodeeswaran ,Jeremy FI-33720 Tampere, Finland. Department of Bioinformatics and Computational Biology, 55 56 Parfitt ; St Petersburg Academic University Fedor Moiseenko ; University Health The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA. 57 58,59 Network Blaise A. Clarke ; University of Hawaii Marc T. Goodman ,Michael E. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, 58 58 38 Carney , Rayna K. Matsuno ; University of North Carolina Jennifer Fisher ,Mei Houston, Texas 77030, USA. The Research Institute at Nationwide Children’s Hospital, 38 15 38 38 Huang , W. 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Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA. 15,18 62 63 64 65 Hoadley , Ari B. Kahn , Daphne W. Bell , Pamela M. Pollock ,Chen Wang , Helen F Graham Cancer Center at Christiana Care, Newark, Delaware 19713, USA. 66 66 41 44 43 44 David A.Wheeler , Eve Shinbrot ,Beth Y. Karlan , Andrew Berchuck , Sean C. Cureline, Inc., South San Francisco, California 94080, USA. Duke University Medical 49 49 58,59 3 Dowdy , Boris Winterhoff , Marc T. Goodman , A. Gordon Robertson ,Rameen Center, Duke Cancer Institute, Durham, North Carolina 27710, USA. Harvard Medical 1,8 1,4,5 1,6,7 22 1 Beroukhim , Itai Pashtan , Helga B. Salvesen , Peter W. Laird , Michael Noble , School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, 28 2 2 39,40 52 Joshua Stuart ,LiDing , Cyriac Kandoth , C. Blake Gilks , Robert A. Soslow ,Paul 46 USA. University of California Medical Center, Irvine, Orange California 92868, USA. 36,61 61 31 30,31 J. 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Ontario Tumour Bank, Project team: National Cancer Institute Kenna R. Mills Shaw , Margi Sheth ,Tanja 67 68 69 67 67 Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada. Ontario Davidsen , Greg Eley ; Martin L. Ferguson ,John A. Demchok , Liming Yang ; 70 Tumour Bank, London Health Sciences Centre, London, Ontario N6A 5A5, Canada. St National Human Genome Research Institute Mark S. Guyer ,Bradley A. 70 70 Petersburg Academic University, St Petersburg 199034, Russia. Department of Ozenberger , Heidi J. Sofia Pathology, University Health Network, Toronto, Ontario M5G 2C4, Canada. University of Hawaii, Honolulu, Hawaii 96813, USA. Cedars-Sinai Medical Center, Los Angeles, 2 24 1 Writing committee: Cyriac Kandoth , Nikolaus Schultz , Andrew D. Cherniack , California 90024, USA. University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. 33 30,31 22 3 1,4,5 61 62 Rehan Akbani , Yuexin Liu , Hui Shen , A. 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All rights reserved CORRECTIONS & AMENDMENTS ERRATUM doi:10.1038/nature12325 Erratum: Integrated genomic characterization of endometrial carcinoma The Cancer Genome Atlas Research Network Nature 497, 67–73 (2013); doi:10.1038/nature12113 In the ‘Results’ section of this Article, the range in the sentence ‘‘The median follow-up of the cohort was 32 months (range, 1–19 months); 21% of the patients have recurred, and 11% have died.’’ should have been 1–195 months. This error has been corrected in the HTML and PDF versions of the paper. 2 4 2 | N ATURE | V O L 500 | 8 A UG US T 2 013 ©2013 Macmillan Publishers Limited. All rights reserved http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nature Springer Journals

Integrated genomic characterization of endometrial carcinoma

Levine, Douglas A.
Nature , Volume 497 (7447) – May 1, 2013
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Springer Journals
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Copyright © 2013 by The Author(s)
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Science, Humanities and Social Sciences, multidisciplinary; Science, Humanities and Social Sciences, multidisciplinary; Science, multidisciplinary
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0028-0836
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10.1038/nature12113
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Abstract

OPEN ARTICLE doi:10.1038/nature12113 Integrated genomic characterization of endometrial carcinoma The Cancer Genome Atlas Research Network* 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 frequentTP53 mutations. Most endometrioid tumours had few copy numberalterations orTP53 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 all tissue acquisition. The clinical and pathological characteristics of the most common malignancy among women in the United States, with an samples generally reflect a cross-section of individuals with recurrent 2,3 estimated 49,500 new cases and 8,200 deaths in 2013 (ref. 1). Most endometrial cancer (Supplementary Table 1.1). The median follow-up patients present with low-grade, early-stage disease. The majority of of the cohort was 32 months (range, 1–195 months); 21% of the patients patients with more aggressive, high-grade tumours who have disease have recurred, and 11% have died. Comprehensive molecular analyses spread beyond the uterus will progress within 1 year (refs 2, 3). were performed at independent centres using six genomic or proteomic Endometrial cancers have been broadly classified into two groups . platforms (Supplementary Table 1.2). MSI testing performed on all Type I endometrioid tumours are linked to oestrogen excess, obesity, samples using seven repeat loci (Supplementary Table 1.3) found MSI hormone-receptor positivity, and favourable prognosis compared with in 40% of endometrioid tumours and 2% of serous tumours. type II, primarily serous, tumours that are more common in older, Somatic copy number alterations non-obese women and have a worse outcome. Early-stage endome- trioid cancers are often treated with adjuvant radiotherapy, whereas Somatic copy number alterations (SCNAs) were assessed in 363 endo- serous tumours are treated with chemotherapy, similar to advanced- metrial carcinomas. Unsupervised hierarchical clustering grouped the stage cancers of either histological subtype. Therefore, proper subtype tumours into four clusters (Fig. 1a). The first three copy-number clus- classification is crucial for selecting appropriate adjuvant therapy. ters were composed almost exclusively (97%) of endometrioid tumours Several previous reports suggest that PTEN mutations occur early in without significant differences in tumour grades. Cluster 1 tumours the neoplastic process of type I tumours and co-exist frequently with were nearly devoid of broad SCNAs, averaging less than 0.5% genome other mutations in the phosphatidylinositol-3-OH kinase (PI(3)K)/ alteration, with no significant recurrent events. Cluster 1 tumours also 5,6 AKT pathway . Other commonly mutated genes in type I tumours had significantly increased non-synonymous mutation rates com- 7–9 26 26 include FGFR2, ARID1A, CTNNB1, PIK3CA, PIK3R1 and KRAS . pared to all others (median 7.23 10 versus 1.73 10 mutations Microsatellite instability (MSI) is found in approximately one-third per megabase (Mb), P, 0.001). Copy-number clusters 2 and 3 con- of type I tumours, but is infrequent in type II tumours . TP53, PIK3CA sisted mainly of endometrioid tumours, distinguished by more fre- 11,12 and PPP2R1A mutations are frequent in type II tumours . Most of quent 1q amplification in cluster 3 than cluster 2 (100% of cluster 3 these studies have been limited to DNA sequencing only with samples tumours versus 33% of cluster 2 tumours) and worse progression-free of heterogeneous histological subtypes and tumour grades. We present survival (P5 0.003, log-rank versus clusters 1 and 2; Fig. 1b). a comprehensive, multiplatform analysis of 373 endometrial carcino- Most of the serous (50 out of 53; 94%) and mixed histology (8 out of mas including low-grade endometrioid, high-grade endometrioid, and 13; 62%) tumours clustered with 36 (12%) of the 289 endometrioid serous carcinomas. This integrated analysis provides key molecular tumours, including 24% of grade 3 and 5% of grade 1 or 2, into copy- insights into tumour classification, which may have a direct effect on number cluster 4; a single group characterized by a very high degree of treatment recommendations for patients, and provides opportunities SCNAs (Supplementary Fig. 2.1; focal SCNAs with false discovery rate for genome-guided clinical trials and drug development. (FDR), 0.15, and Supplementary Data 2.1). Cluster 4 tumours were characterized by significantly recurrent previously reported focal Results amplifications of the oncogenes MYC (8q24.12), ERBB2 (17q12) Tumour samples and corresponding germline DNA were collected and CCNE1 (19q12) , and by SCNAs previously unreported in endo- from 373 patients, including 307 endometrioid and 66 serous (53) or metrial cancers including those containing FGFR3 (4p16.3) and SOX17 mixed histology (13) cases. Local Institutional Review Boards approved (8q11.23). Cluster 4 tumours also had frequent TP53 mutations (90%), *Lists of participants and their affiliations appear at the end of the paper. 00 MO N T H 2 013 | V O L 000 | N AT URE | 1 ©2013 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE chr. 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%, Q5 0), PIK3R1 (65%, Q5 8.33 10 ), PIK3CA (71%, 25 24 Q5 9.13 10 ), FBXW7 (82%, Q5 1.43 10 ), KRAS (53%, 4 24 23 Q5 9.23 10 ) and POLE (100%, Q5 4.23 10 ). Mutation rates in POLE mutant endometrial and previously reported ultramutated colo- rectal tumours exceeded those found in any other lineage including lung 15–17 cancer and melanoma . Germline susceptibility variants have been reported in POLE (Leu424Val) and POLD1 (Ser478Asn), but were not 10 found in our endometrial normal exome-seq reads . The MSI endometrioid tumours had a mutation frequency approxi- mately tenfold greater than MSS endometrioid tumours, few SCNAs, frameshift deletions in RPL22, frequent non-synonymous KRAS muta- tions, and few mutations in FBXW7, CTNNB1, PPP2R1A and TP53. 19 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 23 4 Cluster 12 3 4 high group contained all of the remaining serous cases and one-quarter b 100 of the grade 3 endometrioid cases. Most of these tumours had TP53 mutations and a high frequency of FBXW7 (22%, Q5 0) and PPP2R1A 80 216 (22%, Q5 1.73 10 ) 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 Cluster 1 might benefit from treatment approaches that parallel those for serous Cluster 2 tumours. Cluster 3 There were 48 genes with differential mutation frequencies across Cluster 4 Log-rank P = 0.0004 the four groups (Fig. 2d and Supplementary Data 3.1). ARID5B,a 0 20 40 60 80 100 120 member of the same AT-rich interaction domain (ARID) family as Survival (months) ARID1A, was more frequently mutated in MSI (23.1%) than in either MSS endometrioid (5.6%) or high SCNA serous tumours (0%), a Figure 1 | SCNAs in endometrial carcinomas. a, Tumours were novel finding for endometrial cancer. Frameshifting RPL22 indels hierarchically clustered into four groups based on SCNAs. The heat map shows near a homopolymer at Lys 15 were almost exclusively found in the SCNAs in each tumour (horizontal axis) plotted by chromosomal location (vertical axis). Chr., chromosome. b, Kaplan–Meier curves of progression-free MSI group (36.9%). The TP53 mutation frequency (.90%) in serous survival for each copy-number cluster. 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 little MSI (6%), and fewer PTEN mutations (11%) than other endome- PTEN, compared to only 1 out of 39 (2.6%) serous tumours with non- trioid tumours (84%). Overall, these findings suggest that a subset of silent TP53 mutations. Although TP53 mutations are not restricted to endometrial tumours contain distinct patterns of SCNAs and muta- serous tumours, the co-existing PTEN mutations in the endometrioid tions that do not correlate with traditional tumour histology or grade. cases suggest a distinct tumorigenic mechanism. As expected, tumours in the ‘serous-like’ cluster (cluster 4) had signifi- Comparisons of 66 SMGs between traditional histological subtypes cantly worse progression-free survival than tumours in the endometrioid cluster groups (P5 0.003, log-rank, Fig. 1b). Potential therapeutically are provided (Supplementary Methods 3), and SMGs across other subcohorts can be found in Supplementary Data 3.2. The spectrum relevant SCNAs included the cluster 2 15q26.2 focal amplification, which contained IGF1R; and cluster 4 amplifications of ERBB2, FGFR1 and of PIK3CA and PTEN mutations in endometrial cancer also differed from other solid tumours (Supplementary Methods 3). Integrated FGFR3,andLRP1B deletion, which was recently associated with resistance to liposomal doxorubicin in serous ovarian cancer . analysis may be useful for identifying histologically misclassified cases. For example, a single serous case was identified without a Exome sequence analysis TP53 mutation or extensive SCNAs and with a KRAS mutation and high mutation rate. After re-review of the histological section, the case We sequenced the exomes of 248 tumour/normal pairs. On the basis was deemed consistent with a grade 3 endometrioid tumour, dem- of a combination of somatic nucleotide substitutions, MSI and onstrating how molecular analysis could reclassify tumour histology SCNAs, the endometrial tumours were classified into four groups and potentially affect treatment decisions. (Fig. 2a, b): (1) an ultramutated group with unusually high mutation rates (2323 10 mutations per Mb) and a unique nucleotide change Multiplatform subtype classifications spectrum; (2) a hypermutated group (183 10 mutations per Mb) of MSI tumours, most with MLH1 promoter methylation; (3) a group All of the endometrial tumours were examined for messenger RNA with lower mutation frequency (2.93 10 mutations per Mb) and expression (n5 333), protein expression (n5 293), microRNA expres- most of the microsatellite stable (MSS) endometrioid cancers; and (4) sion (n5 367), and DNA methylation (n5 373) (Supplementary a group that consists primarily of serous-like cancers with extensive Methods 4–7). Unsupervised k-means clustering of mRNA expression SCNA (copy-number cluster 4) and a low mutation rate (2.33 10 from RNA sequencing identified three robust clusters termed ‘mitotic’, mutations per Mb). The ultramutated group consisted of 17 (7%) ‘hormonal’ and ‘immunoreactive’ (Supplementary Fig. 4.1) that were tumours exemplified by an increased CRA transversion frequency, significantly correlated with the four integrated clusters; POLE,MSI, all with mutations in the exonuclease domain of POLE, and an improved copy-number low and copy-number high (P, 0.0001). Supervised progression-free survival (Fig. 2a, c). POLE is a catalytic subunit of DNA analysis identified signature genes of the POLE cluster (n5 17) mostly polymerase epsilon involved in nuclear DNA replication and repair. We involved in cellular metabolism (Fig. 3a). Among the few signature genes 2 | N ATURE | V O L 000 | 0 0 M ONT H 201 3 ©2013 Macmillan Publishers Limited. All rights reserved Progression-free survival (%) CN cluster 4 ARTICLE RESEARCH POLE (ultramutated) MSI (hypermutated) Copy-number low (endometrioid) Copy-number high (serous-like) n = 17 n = 65 n = 90 n = 60 0.5 POLE MSI/MLH1 CN cluster PTEN TP53 Histology Tumour grade Nucleotide substitutions POLE mutations MSI DNA methylation CN cluster Mutations Histology Tumour grade NA MLH1 silent CA CG CT TA TC TG V411L P286R Other MSI high MSI low MS stable 1 2 3 4 Nonsense Missense Frameshift Serous Mixed Endometrioid 3 2 1 100 100 bc d (232) 60 60 (215) POLE (ultramutated) (17) 40 40 Log-rank P = 0.02 (150) MSI (hypermutated) (65) 20 POLE (ultramutated) 20 MSI (hypermutated) Copy-number low (endometrioid) 0 Copy-number high (serous-like) *(%[CA] > 0.2) AND Copy-number low Copy-number high **** * **** **** ** * ** ** * * * * (%[CG] < 0.03) AND 0 20 40 60 80 100 120 (endometrioid) (90) (serous-like) (60) (SNV count > 500) Months Figure 2 | Mutation spectra across endometrial carcinomas. a, Mutation cluster. SNV, single nucleotide variant. c, POLE-mutant tumours have frequencies (vertical axis, top panel) plotted for each tumour (horizontal axis). significantly better progression-free survival, whereas copy-number high Nucleotide substitutions are shown in the middle panel, with a high frequency tumours have the poorest outcome. d, Recurrently mutated genes are different of C-to-A transversions in the samples with POLE exonuclease mutations. CN, between the four subgroups. Shown are the mutation frequencies of all genes copy number. b, Tumours were stratified into the four groups by (1) nucleotide that were significantly mutated in at least one of the four subgroups (MUSiC, substitution frequencies and patterns, (2) MSI status, and (3) copy-number asterisk denotes FDR , 0.05). 20–22 in the MSI cluster was decreased MLH1 mRNA expression, probably (CIMP) described in colon cancers and glioblastomas was associated due to its promoter methylation. Increased progesterone receptor (PGR) with the MSI subtype and attributable to promoter hypermethylation of expression was noted in the copy-number low cluster, suggesting res- MLH1. A serous-like cluster (MC3) with minimal DNA methylation ponsiveness to hormonal therapy. The copy-number high cluster, which changes was composed primarily of serous tumours and some endome- included most of the serous and serous-like endometrioid tumours, trioid tumours (Supplementary Fig. 7.1) and contained most of the copy- exhibited the greatest transcriptional activity exemplified by increased number high tumours. cell cycle deregulation (for example, CCNE1, PIK3CA, MYC and Integrative clustering using the iCluster framework returned two CDKN2A) and TP53 mutation (Supplementary Figs 4.2 and 4.3). major clusters split primarily on serous and endometrioid histology This is consistent with reports that increased CDKN2A can distinguish highlighting TP53 mutations, lack of PTEN mutation and encompas- serous from endometrioid carcinomas . Approximately 85% of cases sing almost exclusively copy-number high tumours (Supplementary in the copy-number high cluster shared membership with the ‘mitotic’ Fig. 8.1). We developed a new clustering algorithm, called Super- mRNA subtype. Cluster, to derive overall subtypes based on sample cluster member- ships across all data types (Supplementary Fig. 9.1). SuperCluster Supervised clustering of the reverse phase protein array (RPPA) expression data was consistent with loss of function for many of the identified four clusters that generally confirmed the contributions of individual platforms to the overall integrated clusters. No major batch mutated genes (Fig. 3b). TP53 was frequently mutated in the copy- number high group (P5 2.53 10 ) and its protein expression was effects were identified for any platform (Supplementary Methods 10). also increased, suggesting that these mutations are associated with Structural aberrations increased expression. By contrast, PTEN (P5 2.83 10 ) and ARID1A (P5 1.23 10 ) had high mutation rates in the remaining To identify somatic chromosomal aberrations, we performed low- groups, but their expression was decreased, suggesting inactivating pass, paired-end, whole-genome sequencing on 106 tumours with mutations in both genes. The copy-number high group also had matched normals. We found recurrent translocations involving genes decreased levels of phospho-AKT, consistent with downregulation of in several pathways including WNT, EGFR–RAS–MAPK, PI(3)K, the AKT pathway. The copy-number low group had raised RAD50 protein kinase A, retinoblastoma and apoptosis. The most frequent expression, which is associated with DNA repair, explaining some of translocations (5 out of 106) involved a member of the BCL family the differences between the copy-number high and low groups. The (BCL2, BCL7A, BCL9 and BCL2L11). Four of these were confirmed by POLE group had high expression of ASNS and CCNB1, whereas the identification of the translocation junction point and two were also MSI tumours had both high phospho-AKT and low PTEN expression. confirmed by high-throughput RNA sequencing (RNA-Seq). In all Unsupervised clustering of DNA methylation data generated from cases the translocations result in in-frame fusions and are predicted Illumina Infinium DNA methylation arrays revealed four unique subtypes to result in activation or increased expression of the BCL family (MC1–4) that support the four integrative clusters. A heavily methylated members (Supplementary Fig. 3.2). Translocations involving mem- subtype (MC1) reminiscent of the CpG island methylator phenotype bers of the BCL family leading to reduced apoptosis have been 00 MO N T H 2 013 | V O L 000 | N AT URE | 3 ©2013 Macmillan Publishers Limited. All rights reserved Spectrum* MSI high PTEN TP53 PIK3CA PIK3R1 ARID1A ARID5B KRAS CTCF CTNNB1 FBXW7 PPP2R1A RPL22 Substitution Mutations per Mb frequency (%) Progression-free survival (%) Mutated samples (% per subtype) Truncating Missense POLE MSI Copy-number low Copy-number high RESEARCH ARTICLE a alterations of all three genes. Overall, 93% of endometrioid tumours had mutations that suggested potential for targeted therapy with TCF25 CTU2 PI(3)K/AKT pathway inhibitors. GCAT Consensus clustering of copy number, mRNA expression and path- MED11 way interaction data for 324 samples yielded five PARADIGM clusters SLC25A35 DNAH9 31 with distinct pathway activation patterns (Fig. 4c and Supplementary PGR Methods 11). PARADIGM cluster 1 had the lowest level of MYC pathway activation and highest level of WNT pathway activation, CCNE1 AURKA consistent with its composition of copy-number low cases having fre- PIK3CA quent CTNNB1 mutations. PARADIGM cluster 3 was composed pre- MYC dominantly 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 impli- cated as loss-of-function mutations, suggesting different classes of p53 α-catenin E-cadherin mutations may have distinct signalling consequences. PARADIGM PDK1 pS241 AKT pT308 cluster 5 was enriched for hormone receptor expression. AKT pS473 ERα pS118 SYK Comparison to ovarian and breast cancers RAD50 STAT3 pY705 BAX The clinical and pathologic features of uterine serous carcinoma and PTEN high-grade serous ovarian carcinoma (HGSOC) are quite similar. ARID1A P53 HGSOC shares many similar molecular features with basal-like breast CHK2 pT68 Cyclin E1 32 carcinoma . Focal SCNA patterns were similar between these three PI(3)K p110α Cyclin B1 tumour subtypes and unsupervised clustering identified relatedness CDK1 ASNS (Fig. 5a and Supplementary Fig. 12.1). Supervised analysis of trans- Subgroup RNA/protein expression criptome data sets showed high correlation between tumour subtypes POLE (ultramutated) MSI (hypermutated) (Supplementary Fig. 12.2). The MC3 DNA methylation subtype with Copy-number low Copy-number high High Low minimal DNA methylation changes was also similar to basal-like breast and HGSOCs (Supplementary Fig. 12.3). A high frequency of Figure 3 | Gene expression across integrated subtypes in endometrial carcinomas. a, Supervised analysis of ,1,500 genes significantly associated TP53 mutations is shared across these tumour subtypes (uterine ser- 33,34 with integrated subtypes. b, Heat map of protein expression clusters, supervised ous, 91%; HGSOC, 96%; basal-like breast, 84%) , as is the very low by integrated subtypes. Samples are in columns; genes or proteins are in rows. 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 com- described in other tumour types and our results suggest that similar mechanisms may be operative here. pared to basal-like breast and HGSOCs (Fig. 5b). We showed that uterine serous carcinomas share many molecular features with both Pathway alterations HGSOCs and basal-like breast carcinomas, despite more frequent mutations, suggesting new opportunities for overlapping treatment Multiple platform data were integrated to identify recurrently altered paradigms. pathways in the four endometrial cancer integrated subgroups. Because of the high background mutation rate and small sample size, Discussion we excluded the POLE subgroup from this analysis. Considering all This integrated genomic and proteomic analysis of 373 endometrial recurrently mutated, homozygously deleted, and amplified genes, we used MEMo to identify gene networks with mutually exclusive cancers provides insights into disease biology and diagnostic classifica- alteration patterns in each subgroup. The most significant module tion that could have immediate therapeutic application. Our analysis was found in the copy-number low group and contained CTNNB1, identified four new groups of tumours based on integrated genomic KRAS and SOX17 (Fig. 4a). The very strong mutual exclusivity data, including a novel POLE subtype in ,10% of endometrioid between mutations in these three genes suggests that alternative tumours. Ultrahigh somatic mutation frequency, MSS, and common, mechanisms activate WNT signalling in endometrioid endometrial newly identified hotspot mutations in the exonuclease domain of cancer. Activating KRAS mutations have been shown to increase the POLE characterize this subtype. SCNAs add a layer of resolution, stability of b-catenin via glycogen synthase kinase 3b (GSK-3b), lead- revealing that most endometrioid tumours have few SCNAs, most ing to an alternative mechanism of b-catenin activation other than serous and serous-like tumours exhibit extensive SCNAs, and the adenomatous polyposis coli degradation . SOX17, which mediates extent of SCNA roughly correlates with progression-free survival. 27,28 proteasomal degradation of b-catenin , is mutated exclusively in Endometrial cancer has more frequent mutations in the PI(3)K/ the copy-number low group (8%) at recurrent positions (Ala96Gly AKT pathway than any other tumour type studied by The Cancer and Ser403Ile) not previously described. Other genes with mutually Genome Atlas (TCGA) so far. Endometrioid endometrial carcinomas exclusive alteration patterns in this module were FBXW7, FGFR2 and share many characteristics with colorectal carcinoma including a high ERBB2 (ref. 29). ERBB2 was focally amplified with protein overex- frequency of MSI (40% and 11%, respectively), POLE mutations (7% pression in 25% of the serous or serous-like tumours, suggesting a and 3%, respectively) leading to ultrahigh mutation rates, and fre- potential role for human epidermal growth factor receptor 2 (HER2)- quent activation of WNT/CTNNB1 signalling; yet endometrial carci- targeted inhibitors. A small clinical trial of trastuzumab found no nomas have novel exclusivity of KRAS and CTNNB1 mutations and a activity in endometrial carcinoma, but accrued few HER2 fluor- distinct mechanism of pathway activation. Uterine serous carcinomas escence in situ hybridization (FISH)-amplified serous carcinomas . share many similar characteristics with basal-like breast and HGSOCs; PIK3CA and PIK3R1 mutations were frequent and showed a strong three tumour types with high-frequency non-silent TP53 mutations tendency for mutual exclusivity in all subgroups, but unlike other and extensive SCNA. However, the high frequency ofPIK3CA, FBXW7, tumour types, they co-occurred with PTEN mutations in the MSI PPP2R1A and ARID1A mutations in uterine serous carcinomas are not 5,9 and copy-number low subgroups as previously reported (Fig. 4b). found in basal-like breast and HGSOCs. The frequency of mutations in The copy-number high subgroup showed mutual exclusivity between PIK3CA, FBXW7 and PPP2R1A was ,30% higher than in a recently 4 | NA TU RE | V OL 000 | 00 M ONTH 2013 ©2013 Macmillan Publishers Limited. All rights reserved mRNA ARTICLE RESEARCH a c PARADIGM clusters RTK/RAS/β-catenin Somatic Amplification 70% of samples altered mutation FGFR2 ERBB2 MSI (hypermut.) (71%) CN low (endometrioid) (82%) CN high (serous) (50%) MYC repressed, 16% 11% 5% 9% 1% 25% WNT, NRG1/ERBB FGFR2 signalling ERBB2 HIF1/metabolic KRAS CTNNB1 36% 15% 3% SOX17 FBXW7 KRAS 0% 8% 0% 12% 5% 22% GSK3B SOX17 FBXW7 p53, ATM/ATR, CTNNB1 DNA repair 19% 53% 3% SOX17 mutations S403I FOXA1/ER Missense A96G Proliferation Inactivating Activating Gene High mobility group domain C-terminal transactivation MSI CN low CN high binds TCF/LEF domain MYC activated Homozygous Somatic PI(3)K pathway 84% of samples altered mutation deletion PTEN 88% 77% 15% MSI (hypermut.) (95%) CN low (endometrioid) (92%) CN high (serous) (60%) PIK3CA PARADIGM 55% 12 3 4 5 2 53% 47% χ test PTEN PIK3R1 cluster 40% 34% 13% Subgroup P < 0.0001 PIK3R1 POLE MSI CN low CN high Proliferation, cell Missing (ultramut.) (hypermut.) (endometrioid) (serous-like) PIK3CA survival, translation Figure 4 | Pathway alterations in endometrial carcinomas. a, The RTK/ frequently co-occur with PTEN alterations in the MSI and copy-number low RAS/b-catenin pathway is altered through several mechanisms that exhibit subgroups. c, Heat map display of top 1,000 varying pathway features within mutually exclusive patterns. Alteration frequencies are expressed as a PARADIGM consensus clusters. Samples were arranged in order of their percentage of all cases. The right panel shows patterns of occurrence. b,The consensus cluster membership. The genomic subtype for each sample is PI(3)K pathway has mutually exclusive PIK3CA and PIK3R1 alterations that displayed below the consensus clusters. reported study of 76 uterine serous carcinomas , but similar to another Early stage type I endometrioid tumours are often treated with study . Uterine serous carcinomas have ERBB2 amplification in 27% of adjuvant radiotherapy, whereas similarly staged type II serous tumours tumours and PIK3CA mutations in 42%, which provide translational are treated with chemotherapy. High-grade serous and endometrioid endometrial carcinomas are difficult to subtype correctly, and intra- opportunities for targeted therapeutics. 7,34–36 observer concordance among speciality pathologists is low . Our molecular characterization data demonstrate that ,25% of tumours classified as high-grade endometrioid by pathologists have a molecular Serous-like Serous Basal-like phenotype similar to uterine serous carcinomas, including frequent endometrial ovarian breast chr. TP53 mutations and extensive SCNA. The compelling similarities between this subset of endometrioid tumours and uterine serous car- cinomas suggest that genomic-based classification may lead to 3 improved management of these patients. Clinicians should carefully 4 consider treating copy-number-altered endometrioid patients with 5 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 10 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- 20 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 events . The exomes of 248 Loss Gain tumours were sequenced to a read-depth of at least320. We performed low-pass 100 whole-genome sequencing on 107 tumours to a mean depth of 36. Consensus Genomic alterations Tumour subtype clustering was used to analyse mRNA, miRNA, RPPA and methylation data with Somatic mutation Serous-like endometrial 38–40 80 Amplification Serous ovarian methods previously described . Integrated cross-platform analyses were per- Homozygous deletion Basal-like breast 25,31 formed using MEMo, iCluster and PARADIGM . mRNA upregulation Epigenetic silencing Received 10 December 2012; accepted 21 March 2013. 1. Siegel, R., Naishadham, D. & Jemal, A. Cancer statistics, 2013. CA Cancer J. Clin. 63, 11–30 (2013). 2. Fleming, G. F. 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. Sutton, G. 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). 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Cancer Discov. 1, 170–185 (2011). beta distributions. BMC Bioinformatics 9, 365 (2008). 6. Levine, R. L. et al. PTEN mutations and microsatellite instability in complex atypical 40. Brunet, J. P., Tamayo, P., Golub, T. R. & Mesirov, J. P. Metagenes and molecular hyperplasia, a precursor lesion to uterine endometrioid carcinoma. Cancer Res. pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 58, 3254–3258 (1998). 4164–4169 (2004). 7. McConechy, M. K. et al. Use of mutation profiles to refine the classification of Supplementary Information is available in the online version of the paper. endometrial carcinomas. J. Pathol. 228, 20–30 (2012). 8. Byron, S. A. et al. FGFR2 point mutations in 466 endometrioid endometrial tumors: Acknowledgements We wish to thank all patients and families who contributed to this relationship with MSI, KRAS, PIK3CA, CTNNB1 mutations and clinicopathological study. We thankM. Sheth and L. Lund for administrative coordination of TCGA activities, features. PLoS ONE 7, e30801 (2012). G. Monemvasitis for editing the manuscript, and C. Gunter for critical reading of the 9. Urick, M. E. et al. PIK3R1 (p85a) is somatically mutated at high frequency in manuscript. This work was supported by the following grants from the US National primary endometrial cancer. Cancer Res. 71, 4061–4067 (2011). Institutes of Health: 5U24CA143799-04, 5U24CA143835-04, 5U24CA143840-04, 10. Zighelboim, I. et al. Microsatellite instability and epigenetic inactivation of MLH1 5U24CA143843-04, 5U24CA143845-04, 5U24CA143848-04, 5U24CA143858-04, and outcome of patients with endometrial carcinomas of the endometrioid type. 5U24CA143866-04, 5U24CA143867-04, 5U24CA143882-04, 5U24CA143883-04, J. Clin. Oncol. 25, 2042–2048 (2007). 5U24CA144025-04, U54HG003067-11, U54HG003079-10 and U54HG003273-10. 11. Kuhn, E. et al. 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Department of 24 26 24 24 24 10 Sumer , Barry S. Taylor , Ethan Cerami , Nils Weinhold , Nikolaus Schultz , Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA. Center for 27 28 Ronglai Shen ; University of California, Santa Cruz/Buck Institute Stephen Benz , Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA. 28 28 28 28 28 11 Ted Goldstein , David Haussler ,Sam Ng , Christopher Szeto , Joshua Stuart , Institute for Applied Cancer Science, Department of Genomic Medicine, University of 29 29 12 Christopher C. Benz , Christina Yau ; The University of Texas MD Anderson Cancer Texas MD Anderson Cancer Center, Houston, Texas 77054, USA. Informatics Program, 30,31 30,31,32 33 33 13 Center Wei Zhang , Matti Annala , Bradley M. Broom , Tod D. Casasent , Boston Children’s Hospital, Boston, Massachusetts 02115, USA. Eshelman School of 33 33 30,31 34 33 Zhenlin Ju , Han Liang , Guoyan Liu , Yiling Lu , Anna K. Unruh ,Chris Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, 33 33 33 30,31 14 Wakefield , John N. Weinstein , Nianxiang Zhang , Yuexin Liu , Russell USA. Institute for Pharmacogenetics and Individualized Therapy, University of North 31 33 34 Broaddus , Rehan Akbani , Gordon B. Mills Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Department of Internal Medicine, Division of Medical Biospecimen core resource: Nationwide Children’s Hospital Christopher Adams , 35 35 35 35 35 Oncology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, Thomas Barr , Aaron D. Black , Jay Bowen ,John Deardurff , Jessica Frick ,Julie 35,36 35 35 35 USA. Department of Biology, University of North Carolina at Chapel Hill, North Carolina M. Gastier-Foster , Thomas Grossman , Hollie A. Harper , Melissa Hart-Kothari , 35 35 35 35 27599,USA. Departmentof Genetics, University of North Carolina at Chapel Hill, Chapel Carmen Helsel , Aaron Hobensack , Harkness Kuck , Kelley Kneile ,Kristen M. 35 35 35 35 Hill, North Carolina 27599, USA. Department of Pathology and Laboratory Medicine, Leraas ,TaraM. Lichtenberg , Cynthia McAllister ,Robert E. Pyatt ,Nilsa C. 35,36 35 35 35 35 University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. Ramirez ,Teresa R.Tabler ,Nathan Vanhoose , Peter White ,Lisa Wise ,Erik Research Computing Center, University of North Carolina at Chapel Hill, Chapel Hill, Zmuda North Carolina 27599, USA. Cancer Biology Division, The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, Baltimore, Maryland 21231, 37 37 Tissue source sites: Asterand Nandita Barnabas , Charlenia Berry-Green ,Victoria USA. University of Southern California Epigenome Center, University of Southern 37 38 37 37 37 Blanc ,Lori Boice , Michael Button , Adam Farkas , Alex Green ,Jean California, Los Angeles, California 90089, USA. Institute for Systems Biology, Seattle, 37 37 MacKenzie ,DanaNicholson ; British Columbia Cancer Agency Steve E. Washington 98109, USA. Computational Biology Center, Memorial Sloan-Kettering 39,40 39,40 41 Kalloger , C. Blake Gilks ; Cedars-Sinai Medical Center Beth Y. Karlan , Jenny Cancer Center, New York, New York 10065, USA. 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Department of Epidemiology and 43 43 44 Potapova ,DaniilRotin ; Duke University Andrew Berchuck ; Gynecologic Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. 45 46 47 Oncology Group Michael Birrer , Phillip DiSaia , Laura Monovich ; International Department of Biomolecular Engineering and Center for Biomolecular Science and 48 48 48 Genomics Consortium Erin Curley , Johanna Gardner , David Mallery , Robert Engineering, University of California Santa Cruz, Santa Cruz, California 95064, USA. 48 49 49 50 Penny ; Mayo Clinic Sean C. Dowdy , Boris Winterhoff , Linda Dao ,Bobbie 29 30 Buck Institute for Age Research, Novato, California 94945, USA. Cancer Genomics 49 49 49 Gostout , Alexandra Meuter ,Attila Teoman ; Memorial Sloan-Kettering Cancer Core Laboratory, University of Texas MD Anderson Cancer Center, Houston, Texas 77054, 51 51 51 52 Center Fanny Dao , Narciso Olvera , Faina Bogomolniy , Karuna Garg , Robert A. USA. 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Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 90048, USA. 15,18 62 63 64 65 Hoadley , Ari B. Kahn , Daphne W. Bell , Pamela M. Pollock ,Chen Wang , Helen F Graham Cancer Center at Christiana Care, Newark, Delaware 19713, USA. 66 66 41 44 43 44 David A.Wheeler , Eve Shinbrot ,Beth Y. Karlan , Andrew Berchuck , Sean C. Cureline, Inc., South San Francisco, California 94080, USA. Duke University Medical 49 49 58,59 3 Dowdy , Boris Winterhoff , Marc T. Goodman , A. Gordon Robertson ,Rameen Center, Duke Cancer Institute, Durham, North Carolina 27710, USA. Harvard Medical 1,8 1,4,5 1,6,7 22 1 Beroukhim , Itai Pashtan , Helga B. Salvesen , Peter W. Laird , Michael Noble , School, Massachusetts General Hospital Cancer Center, Boston, Massachusetts 02114, 28 2 2 39,40 52 Joshua Stuart ,LiDing , Cyriac Kandoth , C. Blake Gilks , Robert A. Soslow ,Paul 46 USA. University of California Medical Center, Irvine, Orange California 92868, USA. 36,61 61 31 30,31 J. Goodfellow , David Mutch , Russell Broaddus , Wei Zhang ,Gordon B. 47 GOG Tissue Bank, The Research Institute at Nationwide Children’s Hospital, Columbus, 34 9 2 51 Mills , Raju Kucherlapati , Elaine R. Mardis , Douglas A. Levine 48 Ohio 43205, USA. International Genomics Consortium, Phoenix, Arizona 85004, USA. Department of OB Gyn, Division of Gynecologic Oncology, Mayo Clinic, Rochester, 62 62 62 50 Data coordination centre: Brenda Ayala , Anna L. Chu ,MarkA.Jensen ,Prachi Minnesota 55905, USA. Department of Pathology, Mayo Clinic, Rochester, Minnesota 62 62 62 62 62 51 Kothiyal , Todd D. Pihl ,JoanPontius , David A. Pot , Eric E. Snyder ,Deepak 55905, USA. Gynecology Service, Department of Surgery, Memorial Sloan-Kettering 62 62 52 Srinivasan , Ari B. Kahn Cancer Center, New York, New York 10065, USA. Department of Pathology, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA. N. N. Blokhin Russian 67 67 Cancer Research Center RAMS, Moscow 115478, Russia. Ontario Tumour Bank, Project team: National Cancer Institute Kenna R. Mills Shaw , Margi Sheth ,Tanja 67 68 69 67 67 Ontario Institute for Cancer Research, Toronto, Ontario M5G 0A3, Canada. Ontario Davidsen , Greg Eley ; Martin L. Ferguson ,John A. Demchok , Liming Yang ; 70 Tumour Bank, London Health Sciences Centre, London, Ontario N6A 5A5, Canada. St National Human Genome Research Institute Mark S. Guyer ,Bradley A. 70 70 Petersburg Academic University, St Petersburg 199034, Russia. Department of Ozenberger , Heidi J. Sofia Pathology, University Health Network, Toronto, Ontario M5G 2C4, Canada. University of Hawaii, Honolulu, Hawaii 96813, USA. Cedars-Sinai Medical Center, Los Angeles, 2 24 1 Writing committee: Cyriac Kandoth , Nikolaus Schultz , Andrew D. Cherniack , California 90024, USA. University of Pittsburgh, Pittsburgh, Pennsylvania 15213, USA. 33 30,31 22 3 1,4,5 61 62 Rehan Akbani , Yuexin Liu , Hui Shen , A. Gordon Robertson , Itai Pashtan , Washington University School of Medicine, St Louis, Missouri 63110, USA. SRA 27 29 29 22 2 Ronglai Shen , Christopher C. Benz , Christina Yau , Peter W. Laird ,Li Ding , Wei International, Fairfax, Virgina 22033, USA. Cancer Genetics Branch, National Human 30,31 34 9 2 Zhang , Gordon B. Mills ,Raju Kucherlapati , Elaine R. Mardis & Douglas A. Genome Research Institute, National Institutes of Health, Bethesda, Maryland 20892, Levine USA. Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane 4059, Australia. Department of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA. Human Genome The Eli and Edythe L. Broad Institute of Massachusetts Institute of Technology and 2 67 Harvard University Cambridge, Massachusetts 02142, USA. The Genome Institute, Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA. The Washington University, St Louis, Missouri 63108, USA. Canada’s Michael Smith Genome Cancer Genome Atlas Program Office, National Cancer Institute, National Institutes of Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z, Canada. Health, Bethesda, Maryland 20892, USA. Scimentis, LLC, Atlanta, Georgia 30666, USA. 4 69 70 Department of Radiation Oncology, Dana-Farber Cancer Institute and Brigham and MLF Consulting, Arlington, Maryland 02474, USA. National Human Genome Women’s Hospital, Boston, Massachusetts 02115, USA. Dana-Farber Cancer Institute, Research Institute, National Institutes of Health, Bethesda, Maryland 20892, USA. 00 MONTH 2 01 3 | V O L 0 00 | N A TU R E | 7 ©2013 Macmillan Publishers Limited. All rights reserved CORRECTIONS & AMENDMENTS ERRATUM doi:10.1038/nature12325 Erratum: Integrated genomic characterization of endometrial carcinoma The Cancer Genome Atlas Research Network Nature 497, 67–73 (2013); doi:10.1038/nature12113 In the ‘Results’ section of this Article, the range in the sentence ‘‘The median follow-up of the cohort was 32 months (range, 1–19 months); 21% of the patients have recurred, and 11% have died.’’ should have been 1–195 months. This error has been corrected in the HTML and PDF versions of the paper. 2 4 2 | N ATURE | V O L 500 | 8 A UG US T 2 013 ©2013 Macmillan Publishers Limited. All rights reserved

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Published: May 1, 2013

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