Cross-Cancer Genome-Wide Association Study of Endometrial Cancer and Epithelial Ovarian Cancer Identifies Genetic Risk Regions Associated with Risk of Both Cancers

Epithelial ovarian cancer accounts for ∼90% of ovarian tumors and is commonly divided into five major histotypes: high-grade serous, low-grade serous, mucinous, clear cell, and endometrioid (1). Herein, “ovarian cancer” refers to epithelial types of this disease. On both histologic and molecular levels, it is evident that ovarian cancer is a highly heterogeneous disease. Endometrial cancer (cancer of the uterine lining) is a comparatively understudied gynecologic cancer, although it ranks fifth for cancer incidence in women globally (2). Endometrial cancer also has several histotypes, the most common being endometrioid (∼80% of cases) but also includes serous, mucinous, and clear cell.

Comparison of the epidemiology and histopathology of endometrial cancer and ovarian cancer has identified a number of similarities suggesting that shared molecular mechanisms underlie the pathology of these two diseases. Both cancers are hormone related, with epidemiologic studies showing concordant direction of effect in relation to exposure to estrogen and progesterone (reviewed by Cramer; ref. 3). Protective factors for both types of cancer include early menopause (4, 5), late age of menarche (6, 7), longer periods of breastfeeding (8, 9), and longer use of contraceptives that include progesterone (refs. 10, 11; i.e., factors that decrease exposure to unopposed estrogen). Although more strongly associated with endometrial cancer risk, higher body mass index (BMI) has been reported to be associated with increased risk of both cancers (12, 13).

The histotypes of endometrial cancer mirror those of ovarian cancer, albeit with varied frequencies observed across the two cancers. For example, serous histology is found in ∼70% of ovarian tumors, compared with 10% of endometrial tumors, while endometrioid histology is found in ∼10% of ovarian tumors and 80% of endometrial tumors. Clear cell and mucinous histologies are found in a relatively low frequency in both ovarian and endometrial tumors. Common features have been observed in similar histotypes regardless of the organ of origin. Tumors with serous histology from both the endometrium and ovary are characterized by somatic defects in the tumor suppressor gene, TP53 (14, 15). Endometrioid endometrial and endometrioid ovarian tumors have both been found to contain somatic alterations in PTEN, PIK3CA, ARID1A, PPP2R1A, and CTNNB1, although the frequencies of these mutations vary by tissue type (reviewed by McConechy and colleagues; ref. 16). Methylation profiling has found endometrioid endometrial and endometrioid ovarian tumors cluster together (17), and similar gene-expression patterns have been observed for clear cell endometrial and clear cell ovarian tumors (18). Further, there is increasing evidence that clear cell and endometrioid ovarian tumors arise in part from endometriosis (reviewed by King and colleagues; ref. 19). Endometriosis is a chronic disease affecting reproductive-aged women, in which endometrium grows outside of the uterus, suggesting these ovarian cancer subtypes and endometrial cancer develop from similar precursor endometrial epithelial cells.

Some, but not all, germline cancer risk variants are also shared between endometrial cancer and ovarian cancer. Lynch syndrome, characterized by germline pathogenic variants in the mismatch-repair genes (i.e., MLH1, MSH2, and MSH6), is associated with 40% to 60% and 8% to 15% lifetime risks of endometrial cancer and ovarian cancer, respectively (20). Additionally, separate genome-wide association studies (GWAS) of the two cancer types have identified four genetic risk regions common to both cancers (21, 22).

Meta-analyses of GWAS data sets across etiologically related diseases have successfully been used to increase statistical power and identify novel genetic risk regions (23, 24). Hence, in the current study, we have performed a joint meta-analysis of the largest endometrial cancer and ovarian cancer GWAS data sets to identify novel genetic loci associated with risk of both cancers, including risk variation specific to less common ovarian cancer subtypes. To identify candidate target genes at such loci, we have intersected risk variation with chromatin looping data enriched for promoter–enhancer interactions. We have also assessed associations between risk variation and gene expression to provide evidence of candidate target gene regulation and reveal further candidate genes.

GWAS summary statistics were obtained from the latest meta-analyses performed by the Endometrial Cancer Association Consortium (ECAC; ref. 21) and the Ovarian Cancer Association Consortium (OCAC; ref. 22). Because of the low number of nonendometrioid endometrial cancer available in ECAC, summary statistics were provided for all endometrial cancer risks (including all endometrial cancer cases), and analyses were restricted to endometrioid cases only. OCAC summary statistics were available for all ovarian cancer risks (including all ovarian cancer cases), as well as analyses were restricted to eight different subtypes: endometrioid histology, serous (including borderline, high-grade, and low-grade serous cases), serous high-grade histology, serous low-grade histology, serous borderline histology, serous low-grade and borderline cases combined, clear cell histology, and mucinous histology. Sample sizes for each study and subgroups analyzed are provided in Table 1. Details on genotyping, quality control, and imputation have been previously described (21, 22). Data for approximately 10 million genetic variants (imputation quality score > 0.4 and minor allele frequency > 0.01) were available for both cancers for the present study.

Genetic correlation (i.e., the estimated proportion of variance shared between two traits due to genetic factors) between endometrial cancer and ovarian cancer was assessed using linkage disequilibrium (LD) score regression (25). Genetic correlation was also assessed between each of the ovarian cancer subtypes analyzed by OCAC and all endometrial cancer as well as restricted to endometrioid endometrial cancer. For this analysis, the complete set of GWAS variants were pruned to the HapMap3 variant list (∼1 million variants) to provide variants with high confidence imputation scores. The major histocompatibility complex (MHC) region was removed from this analysis because of its complex LD structure.

To identify joint endometrial and ovarian cancer genetic risk variants, summary statistics from ECAC and OCAC were combined by inverse-variance meta-analysis, adjusting for unknown sample overlap using MTAG (26). Because of the significant heterogeneity in risk estimates observed for genetic variants across ovarian cancer subtypes (22), we additionally performed meta-analysis combining results from ECAC (all endometrial cancer or endometrioid endometrial cancers) with summary statistics from each of the nine ovarian cancer subtypes analyzed by OCAC (listed in Table 1). To minimize false positives, output variants were restricted to those meeting the following criteria: (i) concordant direction of effect on risk of both cancers; (ii) no significant heterogeneity in risk estimates between the two cancers (P_het > 0.05); and (iii) associated with each cancer at nominal significance (P < 0.05). Counts of variants meeting these criteria are provided in Supplementary Table S1. M-values (27) were generated for variants reaching suggestive evidence of association (P < 5 × 10⁻⁷) using METASOFT (28). This analysis assesses whether an effect is observed for the variant in each study contributing to the meta-analysis. Variants with a posterior probability for an effect in each study (M-value > 0.9) were retained for further consideration.

Loci containing variants with suggestive evidence of association (P < 5 × 10⁻⁷) that met all the above criteria in the meta-analysis were further evaluated for colocalization by GWAS-PW (29), using all genetic variants at the query locus. Query loci were defined using LD from the European 1000 Genomes Phase I reference panel (30) and coordinates provided in Supplementary Table S3. GWAS-PW estimates Bayes factors and posterior probabilities of association (PPA) for four models: (i) a locus associates with risk of endometrial cancer only; (ii) a locus associates with risk of ovarian cancer only; (iii) a locus contains a risk signal that associates with risk of both endometrial and ovarian cancers; or (iv) a locus contains two risk signals that associate independently with risk of either endometrial or ovarian cancer. Risk signals located in loci that were classified as meeting model (iii) were considered to be joint endometrial and ovarian cancer signals (PPA > 0.5).

IOSE11 (immortalized ovarian surface epithelial; ref. 31) cells were gifted from Prof S Gayther (Cedars-Sinai Medical Center). Cells were authenticated using STR profiling and confirmed to be negative for Mycoplasma contamination. IOSE11 were grown in 1:1 MCDB105:Medium 199 with 15% FBS and antibiotics (100 IU/mL penicillin and 100 μg/mL streptomycin).

IOSE11 cells (∼80% confluent on 10-cm tissue culture plates) were washed with PBS and fixed at room temperature in 1% formaldehyde in PBS. After 10 minutes, the reaction was quenched by washing with 125 mmol/L glycine in PBS and then adding fresh glycine-PBS. Cells were removed from the dish with a cell scraper and washed with PBS before storing cell pellets at −80°C. HiChIP libraries were generated as previously described (32). Sequencing libraries were generated using HiChIP libraries and the Nextera DNA preparation kit (Illumina). Size selection was performed using Ampure XP beads to capture 300 to 700 bp fragments. Two independent sequencing libraries were pooled to provide 25 μL of library at ≥10 nmol/L for Illumina HiSeq4000 (AGRF) paired-end sequencing with read lengths of 75 bp.

HiChIP reads (fastq files) were aligned to the human reference genome (hg19) using HiC-Pro v2.9.0 (33) and default settings used to filter for valid interactions as previously (32). IOSE11 HiChIP reads and valid interactions can be downloaded from GEO (accession GSE155328; https://www.ncbi.nlm.nih.gov/geo/). All valid interactions from Hi-Pro were processed by the hichipper pipeline v0.7.0 (34) as previously described (32). Chromatin interactions were filtered using a minimum distance of 5 kb and a maximum of 2 Mb. The final set of chromatin loops used were interactions supported by a minimum of two unique paired-end tags and with a Mango (35) q-value < 5%. Promoter-associated chromatin loops were defined as HiChIP loops with anchors within ±3 kb of a transcription start site. Promoter-associated chromatin looping data were also available from our previous analysis of a normal immortalized endometrial cell line (E6E7hTERT; ref. 32).

Using 100:1 log likelihood ratios, “credible variants” (CV) were identified at each of the joint endometrial and ovarian cancer risk regions. To identify genes that could be distally regulated by a CV, intersections of CVs with promoter-associated chromatin loops were performed using bedtools v2.28.0. Identification of genes whose expression is associated with a CV was performed by lookup of publicly available eQTL databases, including precomputed eQTL results from 336 endometrial and 318 ovarian tumors from The Cancer Genome Atlas (https://albertlab.shinyapps.io/tcga_eqtl; ref. 36) and from 101 noncancerous uterus samples and 122 ovarian tissue samples from GTEx (data release v7; http://gtexportal.org; ref. 37). Additionally, due to the substantially increased power the sample size provided over solid tissue analyses, we accessed eQTL results from 31,684 whole-blood samples (http://eqtlgen.org; ref. 38). Genes were considered potential targets if their expression associated with CVs that had P values within two orders of magnitude of the best eQTL variant in any of these eQTL data sets.

Significant genetic correlation was observed between all endometrial cancer and all ovarian cancer (r_G = 0.43, P = 2.66 × 10⁻⁵; Table 2). When broken down by ovarian cancer subtype, we observed significant correlation between endometrial cancer and the following subgroups: endometrioid (r_G = 0.53, P = 7.0 × 10⁻³), serous (r_G = 0.42, P = 1.0 × 10⁻⁴), and high-grade serous ovarian cancers (r_G = 0.44, P = 1.0 × 10⁻⁴). These correlations remained significant, although attenuated, when using endometrioid endometrial cancers only (Table 2).

Seven genetic loci displaying evidence of a joint association with risk of both endometrial cancer (all or endometrioid histology) and ovarian cancer (all or one of the subtypes; i.e., PPA > 0.5 for GWAS-PW model iii), passed Bonferroni correction for multiple testing (5 × 10⁻⁸/17 tests = 2.9 × 10⁻⁹; Table 3). Three of these loci belong to regions that have previously been reported as being associated with risk of both cancers (8q24, 17q12, and 17q21.32), although the 17q21.32 region had not been reported to be associated with the specific subtypes of ovarian cancer found in this meta-analysis (Table 3). One of the seven loci (2p16.1) has been previously reported as being associated with risk of endometrial cancer, but not with ovarian cancer risk. The three remaining loci (5p15.33, 9q34.2, and 10p12.31) have been previously reported as associated with risk of all ovarian cancer and serous ovarian cancer but not with endometrial cancer risk below GWAS significance levels; however, associations between endometrial cancer and variants in the 5p15.33 (TERT) region have been reported in a candidate-region study (39). Additionally, we identified four novel loci with sub-GWAS significance levels (P < 5 × 10⁻⁷) that had not been previously reported as being associated with risk of either cancer at genome-wide levels of significance (7p22.2, 7q22.1, 9p12, and 11q13.3; Fig. 1).

We identified a total of 22 candidate target genes at the 11 identified joint endometrial and ovarian cancer risk loci using a number of approaches (Table 4; Supplementary Table S2). Log likelihood ratios identified a median of 20 CVs per locus (range, 1–73; Supplementary Table S3). Using H3K27Ac-associated chromatin looping data from normal immortalized ovarian surface epithelial cells and the same data previously generated from a normal immortalized endometrium cell line (32), we intersected CVs coincident with putative enhancers (marked by H3K27Ac) belonging to promoter-associated loops. We found looping between such enhancers and the promoters of 14 genes (at five of the 11 loci) to be common to both immortalized endometrium and ovarian surface epithelial cell lines (e.g., Fig. 1). Four of the 14 candidate target genes identified by chromatin looping also had a CV located in the promoter, indicating potential to regulate expression (Table 4). An additional five genes were identified as candidate targets with CVs located in the corresponding promoters (Table 4). Interrogation of five relevant public eQTL databases revealed CVs to be associated with the expression of four genes (ABO, BCL11A, HOXB2, and SNX11), highlighting them as candidate targets. One of these, SNX11, had also been identified through the chromatin looping analyses and a CV was located in its promoter. Notably, we observed that increased expression of ABO associated with risk allele of CVs at the 9q34.2 locus in all five eQTL data sets: blood, noncancerous uterine and ovarian tissues, and endometrial and ovarian tumors.

In this study, we have performed the first cross-cancer GWAS analysis of endometrial cancer and ovarian cancer. Genetic correlation analyses found significant correlation between the two cancers, particularly between all endometrial cancer (and its endometrioid subtype) and the serous (high- and low-grade combined) or endometrioid ovarian cancer subtypes. Our pipeline of genetic analyses, stratifying by subtype, allowed us to identify seven joint endometrial cancer and ovarian cancer genetic risk loci. Three of these loci were located in regions that had been previously associated with both cancers, one was located in a known endometrial cancer risk region and the remaining three were located in known ovarian cancer risk regions. Four novel genetic risk loci for these two cancers did not reach the statistical threshold for significance but were highlighted as of potential interest, requiring further study to confirm their status.

Joint endometrial and ovarian cancer risk loci are located in the 8q24.21 and 5p15.33 regions, previously described as “cancer GWAS nexus regions” (40) because genetic variation at these regions has been associated with many different types of cancer. 8q24.21 has been previously identified as a genetic risk region for both endometrial cancer and ovarian cancer (21, 22). CVs in a putative enhancer at the 8q24.21 joint endometrial and ovarian cancer risk locus showed evidence of chromatin looping to the promoter of the pan-cancer MYC oncogene in the endometrial and ovarian cell lines. A previous study of the 5p15.33 multicancer risk region, containing the TERT gene, identified two independent signals for ovarian cancer risk: one (lead variant rs7705526) associated with serous borderline ovarian cancer risk and the other (lead variant rs10069690) associated with serous invasive ovarian cancer risk (41). Although not previously associated with risk of endometrial cancer at genome-wide significance, a candidate fine-mapping study of 5p15.33 did highlight three independent endometrial cancer risk signals at this locus at study-wide significance (39), one of which was shared with the serous borderline ovarian cancer risk signal. The present analysis identified this signal as a joint endometrial and ovarian cancer risk signal, with CVs in the TERT promoter highlighting this gene as a likely target. Moreover, TERT has been heavily implicated in cancer development (reviewed in Yuan and colleagues; ref. 42) and has oncogenic interactions with MYC (reviewed in Pestana and colleagues; ref. 43).

Our results suggest, at a subgenome-wide significance level, a potential joint endometrial and ovarian cancer risk signal at another cancer GWAS nexus region, 11q13.3. Originally identified as a prostate cancer risk locus, 11q13.3 also contains risk signals for melanoma, breast cancer, and renal cancer (https://www.ebi.ac.uk/gwas/). Although the results from the present study require validation, the identification of a shared endometrial and ovarian cancer risk signal at 11q13.3 provides further evidence that this region is important for cancer development. At this locus, chromatin looping data showed that CVs in a putative enhancer looped to the promoters of MYEOV and CCND1. CCND1 (encoding cyclin D1) is of particular interest as it is frequently amplified in human cancers and has been identified as a pan-cancer driver gene (44). Cyclin D1 is considered an oncogene due to its central role in cell-cycle regulation, and ability to promote cell proliferation (45). CCND1 has been found to be significantly mutated in gynecologic (including endometrial and ovarian cancers) and breast cancers (46). The results of our analyses provide additional support that CCND1 is important in the development of endometrial cancer and ovarian cancer.

Our analysis identified the 17q12 region as a joint endometrial and ovarian cancer risk region, associating with clear cell ovarian cancer. The 17q12 region, containing HNF1B, has been previously associated with risk of endometrial cancer and ovarian cancer (47–50). Significant heterogeneity in risk estimates has been observed across ovarian cancer histotypes at this locus. The minor allele of the lead ovarian cancer risk variant previously identified at this region associated with increased serous (high- and low-grade combined) ovarian cancer risk but decreased clear cell ovarian cancer risk (49, 50). Further genotyping had resolved this region into two risk signals for ovarian cancer risk: one in intron 1 of HNF1B for clear cell ovarian cancer risk (rs11651775; the same signal for endometrial cancer risk) and another in intron 3 for serous ovarian cancer risk (rs7405776; ref. 50). Our results confirm that joint endometrial and ovarian cancer risk variants at 17q12 map to the same signal as that for that previously reported for endometrial cancer and the clear cell ovarian subtype. HNF1B is a likely target of endometrial and ovarian cancer risk variation, with CVs located in its promoter region. We have previously demonstrated that these variants affect activity of the HNF1B promoter (47), which may lead to increased secretion of insulin, a risk factor for endometrial cancer (51).

The 17q21.32 region is a known shared endometrial (21) and ovarian cancer (22) risk region. The joint endometrial and ovarian cancer signal found in the present study (lead SNP rs882380) is the same as that previously identified for endometrial cancer, but is independent of the signal previously found for all invasive and high-grade serous ovarian cancer risk (lead SNP rs7207826, r² = 0.06 with rs882380). The joint endometrial and ovarian cancer signal associates specifically with risk of clear cell, endometrioid, serous low-grade, serous low-grade and borderline combined, and serous borderline ovarian cancer subtypes. Clear cell, endometrioid, and serous low-grade ovarian cancers are often referred to as endometriosis-associated ovarian cancers due to the increased risk of these ovarian cancer subtypes with endometriosis (52). Epidemiologic and molecular data provide strong evidence that clear cell and endometrioid ovarian cancer arise in part from endometriosis (reviewed by King and colleagues; ref. 19). The joint endometrial and ovarian cancer signal identified in the present study at 17q21.32 was also found in a joint GWAS analysis of endometrial cancer and endometriosis (53), and subsequently found to be associated with endometriosis risk independently (54). Five candidate target genes were identified at this locus, all of which we had previously found to be candidate targets of the original endometrial cancer signal through chromatin looping studies (32).

Another potential joint endometrial and ovarian cancer signal, 9p12, associated with risk of serous low-grade ovarian cancer, has also been previously identified as a joint endometrial cancer and endometriosis risk locus (53). These findings at 17q21.32 and 9p12 add to the body of evidence for the relationship between endometriosis and specific ovarian cancer subtypes (19, 52), and provide further support for shared genetic etiology between endometriosis and endometrial cancer (53). CVs at the 9p12 joint risk locus were located intronic to PTPRD, but no candidate target genes were identified. PTPRD is involved in the STAT3 pathway, which has been implicated as a potential target for both endometrial cancer (55) and ovarian cancer (56).

The 2p16.1 region is a known endometrial cancer risk locus and was found to be associated with the risk of clear cell ovarian cancer only. Interestingly, we previously found evidence that this locus may have a stronger association with risk of nonendometrioid endometrial cancer, with the strongest effect observed for clear cell endometrial cancer subtype (128 cases and 26,638 controls; rs148261157; OR, 2.36; 95% CI, 1.07-5.19; ref. 21). BCL11A was identified as a candidate target gene through eQTL analysis of endometrial tumors. We had previously found that BCL11A was a candidate target gene at the endometrial cancer risk locus through chromatin looping studies in endometrial cancer cells (32). The eQTL finding suggested that reduced expression of BCL11A may increase endometrial/clear cell ovarian cancer risk. Indeed, some studies have shown that BCL11A acts as a proto-oncogene (57, 58); however, others suggest that overexpression of BCL11A results in anticancer effects (59). Notably, BCL11A has been found to be mutated in clear cell ovarian cancer (60, 61), providing further evidence that BCL11A may underlie the risk association with endometrial cancer and clear cell ovarian cancer at this locus.

The 9q34.2 region is a known ovarian cancer risk locus that is highly pleiotropic, having been previously associated with gastric and pancreatic cancers, in addition to a wide range of traits including blood cell counts, the tumor marker CEA (carcinoembryonic antigen), bone mineral density, and levels of angiogenic proteins (https://www.ebi.ac.uk/gwas/). eQTL data from normal, tumor endometrial, and ovarian tissue, as well as blood, provide evidence that ABO is a regulatory target of CVs at this locus. ABO encodes an enzyme that determines human ABO blood group antigens. It is not immediately apparent how ABO may mediate cancer risk but its encoded glycosyltransferase can affect cell recognition and adhesion, and activation of T and natural killer cells (reviewed by Arend; ref. 62).

The 10p12.31 region is another known ovarian cancer risk locus that is also pleiotropic, having been previously associated with breast cancer as well as with traits related to obesity such as BMI, body fat percentage, and physical activity (https://www.ebi.ac.uk/gwas/). MLLT10 was identified as a candidate target gene at this locus, through chromatin looping analysis and localization of a CV to its promoter, and is a partner gene for chromosomal rearrangements that result in leukemia (63). Another biologically relevant candidate target gene at this locus is MIR1915, whose expression is upregulated by p53 in response to DNA damage, leading to increased apoptosis (64).

Two of the subgenome-wide significant endometrial/ovarian cancer risk regions (7q22.1 and 7p22.2) may relate to circulating hormone levels or regulation. At 7q22.1, GWAS have previously revealed associations with androgen and progesterone levels (65). The sole candidate target gene at this locus, CYP3A43, encodes a cytochrome P450 enzyme that may be involved in androgen metabolism (66) and is upregulated in ovarian tumors (67). At 7p22.2, the candidate target gene GPER1, identified through chromatin looping, encodes an estrogen receptor that induces endometrial and ovarian cancer cell proliferation in response to estrogen (reviewed in Prossnitz and Barton; ref. 68). Further, it appears that androgen can also bind to GPER1 to stimulate cancer cell growth (69).

Despite these findings, the present study does have some limitations. The low numbers of nonendometrioid endometrial cancers meant we could not explore the relationship of these endometrioid histotypes with ovarian cancer. Another limitation was the use of cell lines to model chromatin looping that occurs in tissue, with chromatin looping potentially affected by the immortalization and 2D-culturing processes of cell lines, or mutations gained through passaging. As only one endometrial and one ovarian cell line were used, these experiments should be repeated in additional endometrial and ovarian cell lines, representing tumor subtypes. One of the four regions previously identified to be associated with both cancers, located at 1p34, was not identified in the present analysis. This locus was originally found in a combined analysis of the OCAC with a cohort of BRCA1/2 carriers with ovarian cancer (70), which was not included in the present study, perhaps explaining why it was not identified as a joint endometrial and ovarian cancer locus. Future analysis of this region, in the context of BRCA1/2 carrier status will be required to explore how this region affects endometrial cancer and ovarian cancer risk.

In summary, using endometrial and ovarian cancer GWAS summary statistics, we have identified seven joint risk loci for these cancers, with an additional four novel potential risk regions at a sub-GWAS significance level. Further studies are required to validate these findings in larger sample sets. Notably, we also found significant genetic correlation between the two cancers, supported by the observed epidemiologic and histopathologic similarities. These findings support the need for larger GWAS of endometrial and ovarian cancer, in particular focusing on their minor subtypes to further explore shared genetic etiology. Integration of CVs with chromatin looping and eQTL data has identified several plausible candidate target genes, including those at potentially novel risk loci. Although the role of these genes in endometrial and ovarian cancer development should be explored in future studies, the current findings provide insights into the shared biology of endometrial and ovarian cancer.

D.M. Glubb reports grants from National Health and Medical Research Council and QIMR Berghofer Medical Research Institute during the conduct of the study. D.J. Thompson reports other from Genomics plc (since the completion of the work in this paper, the author left the University of Cambridge and is now employed by Genomics plc) outside the submitted work. M.Q. Bernardini reports personal fees from AstraZeneca outside the submitted work. A. deFazio reports grants from National Health and Medical Research Council of Australia, Cancer Council New South Wales, Cancer Council Victoria, Cancer Council of South Australia, Cancer Council of Queensland, Cancer Council Tasmania, Cancer Foundation of Western Australia, and Cancer Institute New South Wales during the conduct of the study, as well as grants from AstraZeneca outside the submitted work. A. du Bois reports personal fees from AstraZeneca, GSK/Tesaro, Roche, Genmab/Seattle Genetics, Zodiac, and BIOCAD outside the submitted work. P.A. Fasching reports grants and personal fees from Novartis, as well as personal fees from Pfizer, Daiichi-Sankyo, AstraZeneca, Eisai, Merck Sharp & Dohme, Cepheid, Lilly, Pierre Fabre, and Seattle Genetics outside the submitted work. G. Fountzilas reports personal fees from Pfizer (advisory board), Sanofi (advisory board), Roche (advisory board), and AstraZeneca (honoraria) outside the submitted work and Genprex, Daiichi-Sankyo, and ARIAD stock ownership. G.G. Giles reports grants from National Health and Medical Research Council (paid to author's institution Cancer Council Victoria) during the conduct of the study. R. Glasspool reports grants from Sanofi-Aventis (some of the data used in this work were derived from clinical samples that were originally collected in an investigator initiated clinical trial supported by Sanofi-Aventis) during the conduct of the study. P. Harter reports grants and personal fees from AstraZeneca, Roche, and Tesaro/GSK; personal fees from Sotio, Immunogen, Stryker, Clovis, Zai Lab, Merck Sharp & Dohme, and Lilly; and grants from Genmab, European Union, DKH, and DFG outside the submitted work. M.E. Jones reports grants from Breast Cancer Now (research finding charity: program grant) during the conduct of the study. B.Y. Karlan reports grants from Ovarian Cancer Research Fund outside the submitted work. R. Klapdor reports nonfinancial support from Intuitive Surgical outside the submitted work. P. Kraft reports grants from NIH during the conduct of the study. M.C. Larson reports grants from Mayo Clinic during the conduct of the study and grants from Mayo Clinic outside the submitted work. L. Le Marchand reports grants from NCI (to author's institution) during the conduct of the study. D. Liang reports grants from Texas Southern University during the conduct of the study. V. McGuire reports grants from Stanford University during the conduct of the study. I.A. McNeish reports personal fees from Clovis Oncology, AstraZeneca, Tesaro, Roche, and Scancell and grants from AstraZeneca outside the submitted work. U. Menon reports other from Abcodia (shares in Abcodia given to her by UCL) outside the submitted work. F. Modugno reports grants from NCI and Department of Defense during the conduct of the study. H. Nevanlinna reports personal fees from AstraZeneca (honorarium) outside the submitted work. K. Odunsi reports funding from AstraZeneca and Tesaro Pharma. S. Orsulic reports grants from NIH/NCI, Department of Defense, and Ovarian Cancer Research Alliance outside the submitted work and has a patent for US010253368 issued and a patent for EU2908913 issued. T.-W. Park-Simon reports personal fees and other from Roche (advisory role, expert testimony, clinical trials, travel), AstraZeneca (advisory role, expert testimony, clinical trials, travel), Daichii (advisory role, expert testimony, clinical trials, travel), Tesaro (advisory role, expert testimony, clinical trials, travel), Novartis (advisory role, expert testimony, clinical trials, travel), Pfizer (advisory role, expert testimony, clinical trials, travel), Merck Sharp & Dohme (advisory role, expert testimony), and Lilly (advisory role, expert testimony, clinical trials, travel) outside the submitted work. C.L. Pearce reports grants from NIH and Department of Defense during the conduct of the study. J.H. Rothstein reports grants from NIH during the conduct of the study. A.J. Swerdlow reports grants from Breast Cancer Now, during the conduct of the study. L.C.V. Thomsen reports personal fees from Bayer and AstraZeneca, as well as other from AstraZeneca (financial support to a researcher-initiated trial) outside the submitted work. L. Titus reports grants from NIH/NCI during the conduct of the study. I. Vergote reports grants, personal fees, and other from Amgen (fees for consulting paid to author's university, corporate-sponsored research and accommodation, travel expenses) and Roche NV (fees for consulting paid to author's university, corporate-sponsored research and accommodation, travel expenses); personal fees and other from AstraZeneca NV/UK/Belux (fees for consulting paid to author's university, accommodation, travel expenses), Genmab A/S,B.V,US (fees for consulting paid to author's university and contracted research; KU Leuven), Merck Sharp & Dohme Belgium (fees for consulting paid to author's university and accommodation, travel expenses), Oncoinvent AS (fees for consulting paid to author's university and contracted research; KU Leuven), and Tesaro Inc./Bio GmbH (fees for consulting paid to author's university and accommodation, travel expenses); and personal fees (consulting paid to author's university) from Clovis Oncology Inc., Carrick Therapeutics, Debiopharm International SA, F. Hoffman-La Roche Ltd., GSK Pharmaceuticals NV, Immunogen Inc., Millennium Pharmaceuticals, Octimet Oncology NV, Pharmamar-Doctaforum Servicios SL, Sotio a.s., Deciphera Pharmaceuticals, and Verastem Oncology, outside the submitted work. P.M. Webb reports grants from National Health and Medical Research Council of Australia and Brisbane Women's Club during the conduct of the study, as well as grants from Astra Zeneca (to support an unrelated study of ovarian cancer) outside the submitted work. X. Wu reports grants from The University of Texas MD Anderson Cancer Center during the conduct of the study. D. Yannoukakos reports grants from AstraZeneca outside the submitted work. T.A. O'Mara reports grants from National Health and Medical Council of Australia, Cancer Australia, Cure Cancer Australia, and CanToo Foundation during the conduct of the study. No disclosures were reported by the other authors.

D.M. Glubb: Conceptualization, formal analysis, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing. D.J. Thompson: Conceptualization, resources, data curation, methodology, writing–original draft, writing–review and editing. K.K.H. Aben: Resources, writing–review and editing. A. Alsulimani: Resources, writing–review and editing. F. Amant: Resources, writing–review and editing. D. Annibali: Resources, writing–review and editing. J. Attia: Resources, writing–review and editing. A. Barricarte: Resources, writing–review and editing. M.W. Beckmann: Resources, writing–review and editing. A. Berchuck: Resources, writing–review and editing. M. Bermisheva: Resources, writing–review and editing. M.Q. Bernardini: Resources, writing–review and editing. K. Bischof: Resources, writing–review and editing. L. Bjorge: Resources, writing–review and editing. C. Bodelon: Resources, writing–review and editing. A.H. Brand: Resources, writing–review and editing. J.D. Brenton: Resources, writing–review and editing. L.A. Brinton: Resources, writing–review and editing. F. Bruinsma: Resources, writing–review and editing. D.D. Buchanan: Resources, writing–review and editing. S. Burghaus: Resources, writing–review and editing. R. Butzow: Resources, writing–review and editing. H. Cai: Resources, writing–review and editing. M.E. Carney: Resources, writing–review and editing. S.J. Chanock: Resources, writing–review and editing. C. Chen: Resources, writing–review and editing. X.Q. Chen: Resources, writing–review and editing. Z. Chen: Resources, writing–review and editing. L.S. Cook: Resources, writing–review and editing. J.M. Cunningham: Resources, writing–review and editing. I. De Vivo: Resources, writing–review and editing. A. deFazio: Resources, writing–review and editing. J.A. Doherty: Resources, writing–review and editing. T. Dörk: Resources, writing–review and editing. 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We thank ECAC and OCAC for provision of summary statistics to perform this study. We thank Siddhartha Kar for his helpful discussions and advice for designing the genetic analysis approaches. Full acknowledgments and funding for ECAC and OCAC can be found in the Supplementary Note. T.A. O'Mara is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1111246) and Investigator Fellowship (APP1173170). A.B. Spurdle is supported by an NHMRC Senior Research Fellowship (APP1061779) and Investigator Fellowship (APP1177524). This work was supported by a Cancer Australia PdCCRS Project Grant, funded by Cure Cancer Australia and the CanToo Foundation (#1138084), NHMRC project grants (APP1158083 and APP1109286), QIMR Berghofer Medical Research Institute Near Miss Funding, and a special purpose donation gratefully received from Sarah Stork.

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