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Background: There is suggestive evidence that inflammation is related to ovarian cancer survival. However, more research is needed to identify inflammation-related factors that are associated with ovarian cancer survival and to determine their combined effects. Methods: This analysis used pooled data on 8,147 women with invasive epithelial ovarian cancer from the Ovarian Cancer Association Consortium. The prediagnosis inflammation-related exposures of interest included alcohol use; aspirin use; other nonsteroidal anti-inflammatory drug use; body mass index; environmental tobacco smoke exposure; history of pelvic inflammatory disease, polycystic ovarian syndrome, and endometriosis; menopausal hormone therapy use; physical inactivity; smoking status; and talc use. Using Cox proportional hazards models, the relationship between each exposure and survival was assessed in 50% of the data. A weighted inflammation-related risk score (IRRS) was developed, and its association with survival was assessed using Cox proportional hazards models in the remaining 50% of the data. Results: There was a statistically significant trend of increasing risk of death per quartile of the IRRS [HR = 1.09; 95% confidence interval (CI), 1.03–1.14]. Women in the upper quartile of the IRRS had a 31% higher death rate compared with the lowest quartile (95% CI, 1.11–1.54). Conclusions: A higher prediagnosis IRRS was associated with an increased mortality risk after an ovarian cancer diagnosis. Further investigation is warranted to evaluate whether postdiagnosis exposures are also associated with survival. Impact: Given that pre- and postdiagnosis exposures are often correlated and many are modifiable, our study results can ultimately motivate the development of behavioral recommendations to enhance survival among patients with ovarian cancer.

Funder: Funding details are provided in the Supplementary Material, Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, ���select and shrink for summary statistics��� (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28���1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08���1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30���1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

PURPOSE: Prior studies of menopausal hormone therapy (MHT) and ovarian cancer survival have been limited by lack of hormone regimen detail and insufficient sample sizes. To address these limitations, a comprehensive analysis of 6419 post-menopausal women with pathologically confirmed ovarian carcinoma was conducted to examine the association between MHT use prior to diagnosis and survival. METHODS: Data from 15 studies in the Ovarian Cancer Association Consortium were included. MHT use was examined by type (estrogen-only (ET) or estrogen+progestin (EPT)), duration, and recency of use relative to diagnosis. Cox proportional hazards models were used to estimate the association between hormone therapy use and survival. Logistic regression and mediation analysis was used to explore the relationship between MHT use and residual disease following debulking surgery. RESULTS: Use of ET or EPT for at least five years prior to diagnosis was associated with better ovarian cancer survival (hazard ratio, 0.80; 95% CI, 0.74 to 0.87). Among women with advanced stage, high-grade serous carcinoma, those who used MHT were less likely to have any macroscopic residual disease at the time of primary debulking surgery (p for trend

PURPOSE To estimate age-specific relative and absolute cancer risks of breast cancer and to estimate risks of ovarian, pancreatic, male breast, prostate, and colorectal cancers associated with germline PALB2 pathogenic variants (PVs) because these risks have not been extensively characterized. METHODS We analyzed data from 524 families with PALB2 PVs from 21 countries. Complex segregation analysis was used to estimate relative risks (RRs; relative to country-specific population incidences) and absolute risks of cancers. The models allowed for residual familial aggregation of breast and ovarian cancer and were adjusted for the family-specific ascertainment schemes. RESULTS We found associations between PALB2 PVs and risk of female breast cancer (RR, 7.18; 95% CI, 5.82 to 8.85; P = 6.5 × 10−76), ovarian cancer (RR, 2.91; 95% CI, 1.40 to 6.04; P = 4.1 × 10−3), pancreatic cancer (RR, 2.37; 95% CI, 1.24 to 4.50; P = 8.7 × 10−3), and male breast cancer (RR, 7.34; 95% CI, 1.28 to 42.18; P = 2.6 × 10−2). There was no evidence for increased risks of prostate or colorectal cancer. The breast cancer RRs declined with age ( P for trend = 2.0 × 10−3). After adjusting for family ascertainment, breast cancer risk estimates on the basis of multiple case families were similar to the estimates from families ascertained through population-based studies ( P for difference = .41). On the basis of the combined data, the estimated risks to age 80 years were 53% (95% CI, 44% to 63%) for female breast cancer, 5% (95% CI, 2% to 10%) for ovarian cancer, 2%-3% (95% CI females, 1% to 4%; 95% CI males, 2% to 5%) for pancreatic cancer, and 1% (95% CI, 0.2% to 5%) for male breast cancer. CONCLUSION These results confirm PALB2 as a major breast cancer susceptibility gene and establish substantial associations between germline PALB2 PVs and ovarian, pancreatic, and male breast cancers. These findings will facilitate incorporation of PALB2 into risk prediction models and optimize the clinical cancer risk management of PALB2 PV carriers.

Genome-wide analysis identifies 32 loci associated with breast cancer susceptibility, accounting for estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype(1-3). To identify novel loci, we performed a genome-wide association study including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 status and tumor grade. We identified 32 novel susceptibility loci (P < 5.0 x 10(-8)), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate < 0.05). Five loci showed associations (P < 0.05) in opposite directions between luminal and non-luminal subtypes. In silico analyses showed that these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 54.2% for luminal A-like disease and 37.6% for triple-negative disease. The odds ratios of polygenic risk scores, which included 330 variants, for the highest 1% of quantiles compared with middle quantiles were 5.63 and 3.02 for luminal A-like and triple-negative disease, respectively. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

Funder: Department of Health; FundRef: http://dx.doi.org/10.13039/501100000276, Funder: Ontario Research Fund, Funder: CHU de Quebec Foundation, Funder: Fondation du cancer du sein du Qu��bec; FundRef: http://dx.doi.org/10.13039/100016328, Funder: The Eve Appeal, Background: Epithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify women at higher risk who could benefit from targeted screening and prevention. Methods: We developed a multifactorial EOC risk model for women of European ancestry incorporating the effects of pathogenic variants (PVs) in BRCA1, BRCA2, RAD51C, RAD51D and BRIP1, a Polygenic Risk Score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively. Results: Based on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the first to 99th percentiles of the EOC risk distribution. The corresponding range for women with an affected first-degree relative is 1.9%���10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well calibrated in quintiles of predicted risk. Conclusion: This multifactorial risk model can facilitate stratification, in particular among women with FH of cancer and/or moderate-risk and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

Contains fulltext : 237689.pdf (Publisher’s version ) (Open Access) Familial, sequencing, and genome-wide association studies (GWASs) and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWASs) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through application of a pleiotropy-guided conditional/conjunction false discovery rate (FDR) approach in the setting of a TWASs. This identified 14 candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were >1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. The 22 genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

Research into factors affecting treatment response or survival in patients with cancer frequently involves cohorts that span the most common range of clinical outcomes, as such patients are most readily available for study. However, attention has turned to highly unusual patients who have exceptionally favourable or atypically poor responses to treatment and/or overall survival, with the expectation that patients at the extremes may provide insights that could ultimately improve the outcome of individuals with more typical disease trajectories. While clinicians can often recount surprising patients whose clinical journey was very unusual, given known clinical characteristics and prognostic indicators, there is a lack of consensus among researchers on how best to define exceptional patients and little has been proposed for the optimal design of studies to identify factors that dictate unusual outcome. In this Opinion article, we review different approaches to identifying exceptional patients with cancer, and possible study designs to investigate extraordinary clinical outcomes. We discuss pitfalls with finding these rare patients, including challenges associated with accrual of patients across different treatment centres and time periods. We describe recent molecular and immunological factors that have been identified as contributing to unusual patient outcome, and make recommendations for future studies on these intriguing patients.

DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10−7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

Author(s): Lee, Alice W; Rosenzweig, Stacey; Wiensch, Ashley; Australian Ovarian Cancer Study Group; Ramus, Susan J; Menon, Usha; Gentry-Maharaj, Aleksandra; Ziogas, Argyrios; Anton-Culver, Hoda; Whittemore, Alice S; Sieh, Weiva; Rothstein, Joseph H; McGuire, Valerie; Wentzensen, Nicolas; Bandera, Elisa V; Qin, Bo; Terry, Kathryn L; Cramer, Daniel W; Titus, Linda; Schildkraut, Joellen M; Berchuck, Andrew; Goode, Ellen L; Kjaer, Susanne K; Jensen, Allan; Jordan, Susan J; Ness, Roberta B; Modugno, Francesmary; Moysich, Kirsten; Thompson, Pamela J; Goodman, Marc T; Carney, Michael E; Chang-Claude, Jenny; Rossing, Mary Anne; Harris, Holly R; Doherty, Jennifer Anne; Risch, Harvey A; Khoja, Lilah; Alimujiang, Aliya; Phung, Minh Tung; Brieger, Katharine; Mukherjee, Bhramar; Pharoah, Paul DP; Wu, Anna H; Pike, Malcolm C; Webb, Penelope M; Pearce, Celeste Leigh | Abstract: BackgroundParity is associated with decreased risk of invasive ovarian cancer; however, the relationship between incomplete pregnancies and invasive ovarian cancer risk is unclear. This relationship was examined using 15 case-control studies from the Ovarian Cancer Association Consortium (OCAC). Histotype-specific associations, which have not been examined previously with large sample sizes, were also evaluated.MethodsA pooled analysis of 10 470 invasive epithelial ovarian cancer cases and 16 942 controls was conducted. Odds ratios (ORs) and 95% confidence intervals (CIs) for the association between incomplete pregnancies and invasive epithelial ovarian cancer were estimated using logistic regression. All models were conditioned on OCAC study, race and ethnicity, age, and education level and adjusted for number of complete pregnancies, oral contraceptive use, and history of breastfeeding. The same approach was used for histotype-specific analyses.ResultsEver having an incomplete pregnancy was associated with a 16% reduction in ovarian cancer risk (OR = 0.84, 95% CI = 0.79 to 0.89). There was a trend of decreasing risk with increasing number of incomplete pregnancies (2-sided Ptrend l .001). An inverse association was observed for all major histotypes; it was strongest for clear cell ovarian cancer.ConclusionsIncomplete pregnancies are associated with a reduced risk of invasive epithelial ovarian cancer. Pregnancy, including incomplete pregnancy, was associated with a greater reduction in risk of clear cell ovarian cancer, but the result was broadly consistent across histotypes. Future work should focus on understanding the mechanisms underlying this reduced risk.

Simple Summary Several genes have been confirmed as risk genes for epithelial ovarian cancer (EOC). There are five main types of EOC, with different molecular changes and clinical characteristics, suggesting they should be considered different diseases. This review summarises the contribution of rare inherited mutations to EOC susceptibility, focussing on the frequency in each EOC type. Susceptibility genes can have a major clinical impact, reducing ovarian cancer incidence by screening of family members to detect women at higher risk than the general population. They can also lead to the development of new targeted treatments. Abstract A family history of ovarian or breast cancer is the strongest risk factor for epithelial ovarian cancer (EOC). Germline deleterious variants in the BRCA1 and BRCA2 genes confer EOC risks by age 80, of 44% and 17% respectively. The mismatch repair genes, particularly MSH2 and MSH6, are also EOC susceptibility genes. Several other DNA repair genes, BRIP1, RAD51C, RAD51D, and PALB2, have been identified as moderate risk EOC genes. EOC has five main histotypes; high-grade serous (HGS), low-grade serous (LGS), clear cell (CCC), endometrioid (END), and mucinous (MUC). This review examines the current understanding of the contribution of rare genetic variants to EOC, focussing on providing frequency data for each histotype. We provide an overview of frequency and risk for pathogenic variants in the known susceptibility genes as well as other proposed genes. We also describe the progress to-date to understand the role of missense variants and the different breast and ovarian cancer risks for each gene. Identification of susceptibility genes have clinical impact by reducing disease-associated mortality through improving risk prediction, with the possibility of prevention strategies, and developing new targeted treatments and these clinical implications are also discussed.

Background: Accumulating evidence suggests a relationship between endometrial cancer and ovarian cancer. Independent genome-wide association studies (GWAS) for endometrial cancer and ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. We aimed to identify joint endometrial and ovarian cancer risk loci by performing a meta-analysis of GWAS summary statistics from these two cancers. Methods: Using LDScore regression, we explored the genetic correlation between endometrial cancer and ovarian cancer. To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e., inverse-variance meta-analysis, colocalization, and M-values) and performed analyses stratified by subtype. Candidate target genes were then prioritized using functional genomic data. Results: Genetic correlation analysis revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel subgenome-wide regions at 7p22.2, 7q22.1, 9p12, and 11q13.3 were identified (P < 5 × 10−7). Promoter-associated HiChIP chromatin loops from immortalized endometrium and ovarian cell lines and expression quantitative trait loci data highlighted candidate target genes for further investigation. Conclusions: Using cross-cancer GWAS meta-analysis, we have identified several joint endometrial and ovarian cancer risk loci and candidate target genes for future functional analysis. Impact: Our research highlights the shared genetic relationship between endometrial cancer and ovarian cancer. Further studies in larger sample sets are required to confirm our findings.

BackgroundEpithelial tubo-ovarian cancer (EOC) has high mortality partly due to late diagnosis. Prevention is available but may be associated with adverse effects. A multifactorial risk model based on known genetic and epidemiological risk factors (RFs) for EOC can help identify females at higher risk who could benefit from targeted screening and prevention.MethodsWe developed a multifactorial EOC risk model for females of European ancestry incorporating the effects of pathogenic variants (PVs) inBRCA1, BRCA2, RAD51C, RAD51DandBRIP1, a polygenic risk score (PRS) of arbitrary size, the effects of RFs and explicit family history (FH) using a synthetic model approach. The PRS, PV and RFs were assumed to act multiplicatively.ResultsBased on a currently available PRS for EOC that explains 5% of the EOC polygenic variance, the estimated lifetime risks under the multifactorial model in the general population vary from 0.5% to 4.6% for the 1stto 99thpercentiles of the EOC risk-distribution. The corresponding range for females with an affected first-degree relative is 1.9% to 10.3%. Based on the combined risk distribution, 33% of RAD51D PV carriers are expected to have a lifetime EOC risk of less than 10%. RFs provided the widest distribution, followed by the PRS. In an independent partial model validation, absolute and relative 5-year risks were well-calibrated in quintiles of predicted risk.ConclusionThis multifactorial risk model can facilitate stratification, in particular among females with FH of cancer and/or moderate- and high-risk PVs. The model is available via the CanRisk Tool (www.canrisk.org).

BACKGROUND: Anti-microtubule agents are widely used to treat ovarian cancers, but the efficacy is often compromised by drug resistance. We investigated co-targeting the actin/tropomyosin cytoskeleton and microtubules to increase treatment efficacy in ovarian cancers and potentially overcome resistance. METHODS: The presence of tropomyosin-3.1 (Tpm3.1) was examined in clinical specimens from ovarian cancer patients using immunohistochemistry. Combinatorial effects of an anti-Tpm3.1 compound, ATM-3507, with vinorelbine and paclitaxel were evaluated in ovarian cancer cells via MTS and apoptosis assays. The mechanisms of action were established using live- and fixed-cell imaging and protein analysis. RESULTS: Tpm3.1 is overexpressed in 97% of tumour tissues (558 of 577) representing all histotypes of epithelial ovarian cancer. ATM-3507 displayed synergy with both anti-microtubule agents to reduce cell viability. Only vinorelbine synergised with ATM-3507 in causing apoptosis. ATM-3507 significantly prolonged vinorelbine-induced mitotic arrest with elevated activity of the spindle assembly checkpoint and mitotic cell death; however, ATM-3507 showed minor impact on paclitaxel-induced mitotic defects. Both combinations substantially increased post-mitotic G1 arrest with cyclin D1 and E1 downregulation and an increase of p21(Cip) and p27(Kip). CONCLUSION: Combined targeting of Tpm3.1/actin and microtubules is a promising treatment strategy for ovarian cancer that should be further tested in clinical settings.

Contains fulltext : 229292.pdf (Publisher’s version ) (Open Access) PURPOSE: We assessed the associations between population-based polygenic risk scores (PRS) for breast (BC) or epithelial ovarian cancer (EOC) with cancer risks for BRCA1 and BRCA2 pathogenic variant carriers. METHODS: Retrospective cohort data on 18,935 BRCA1 and 12,339 BRCA2 female pathogenic variant carriers of European ancestry were available. Three versions of a 313 single-nucleotide polymorphism (SNP) BC PRS were evaluated based on whether they predict overall, estrogen receptor (ER)-negative, or ER-positive BC, and two PRS for overall or high-grade serous EOC. Associations were validated in a prospective cohort. RESULTS: The ER-negative PRS showed the strongest association with BC risk for BRCA1 carriers (hazard ratio [HR] per standard deviation = 1.29 [95% CI 1.25-1.33], P = 3×10(-72)). For BRCA2, the strongest association was with overall BC PRS (HR = 1.31 [95% CI 1.27-1.36], P = 7×10(-50)). HR estimates decreased significantly with age and there was evidence for differences in associations by predicted variant effects on protein expression. The HR estimates were smaller than general population estimates. The high-grade serous PRS yielded the strongest associations with EOC risk for BRCA1 (HR = 1.32 [95% CI 1.25-1.40], P = 3×10(-22)) and BRCA2 (HR = 1.44 [95% CI 1.30-1.60], P = 4×10(-12)) carriers. The associations in the prospective cohort were similar. CONCLUSION: Population-based PRS are strongly associated with BC and EOC risks for BRCA1/2 carriers and predict substantial absolute risk differences for women at PRS distribution extremes.

BACKGROUND: While childbearing protects against risk of epithelial ovarian cancer (EOC), few studies have explored the impact on maternal EOC risk of sex of offspring, which may affect the maternal environment during pregnancy. METHODS: We performed a pooled analysis among parous participants from 12 case-controls studies comprising 6,872 EOC patients and 9,101 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable logistic regression for case-control associations and polytomous logistic regression for histotype-specific associations, all adjusted for potential confounders. RESULTS: In general, no associations were found between offspring sex and EOC risk. However, compared to bearing only female offspring, bearing one or more male offspring was associated with increased risk of mucinous EOC (OR=1.45; 95%CI=1.01–2.07), which appeared to be limited to women reporting menarche before age 13 compared to later menarche (OR=1.71 vs 0.99; P-interaction=0.02). Bearing increasing numbers of male offspring was associated with greater risks of mucinous tumors (OR=1.31, 1.84, 2.31, for 1, 2 and 3 or more male offspring, respectively; trend-p = 0.005). Stratifying by hormonally-associated conditions suggested that compared to bearing all female offspring, bearing a male offspring was associated with lower risk of endometrioid cancer among women with a history of adult acne, hirsutism, or polycystic ovary syndrome (OR=0.49, 95%CI=0.28–0.83) but with higher risk among women without any of those conditions (OR=1.64 95%CI=1.14–2.34; P-interaction=0.003). CONCLUSION: Offspring sex influences the childbearing-EOC risk relationship for specific histotypes and conditions. These findings support the differing etiologic origins of EOC histotypes and highlight the importance of EOC histotype-specific epidemiologic studies. These findings also suggest the need to better understand how pregnancy affects EOC risk

We report a meta-analysis of breast, prostate, ovarian, and endometrial cancer genome-wide association data (effective sample size: 237,483 cases/317,006 controls). This identified 465 independent lead variants (P−8) across 192 genomic regions. Four lead variants were >1Mb from previously identified risk loci for the four cancers and an additional 23 lead variant-cancer associations were novel for one of the cancers. Bayesian models supported pleiotropic effects involving at least two cancers at 222/465 lead variants in 118/192 regions. Gene-level association analysis identified 13 shared susceptibility genes (P−6) in 13 regions not previously implicated in any of the four cancers and not uncovered by our variant-level meta-analysis. Several lead variants had opposite effects across cancers, including a cluster of such variants in the TP53 pathway. Fifty-four lead variants were associated with blood cell traits and suggested genetic overlaps with clonal hematopoiesis. Our study highlights the remarkable pervasiveness of pleiotropy across hormone-related cancers, further illuminating their shared genetic and mechanistic origins at variant- and gene-level resolution.

Accumulating evidence suggests a relationship between endometrial cancer and epithelial ovarian cancer. For example, endometrial cancer and epithelial ovarian cancer share epidemiological risk factors and molecular features observed across histotypes are held in common (e.g. serous, endometrioid and clear cell). Independent genome-wide association studies (GWAS) for endometrial cancer and epithelial ovarian cancer have identified 16 and 27 risk regions, respectively, four of which overlap between the two cancers. Using GWAS summary statistics, we explored the shared genetic etiology between endometrial cancer and epithelial ovarian cancer. Genetic correlation analysis using LD Score regression revealed significant genetic correlation between the two cancers (rG = 0.43, P = 2.66 × 10−5). To identify loci associated with the risk of both cancers, we implemented a pipeline of statistical genetic analyses (i.e. inverse-variance meta-analysis, co-localization, and M-values), and performed analyses by stratified by subtype. We found seven loci associated with risk for both cancers (PBonferroni < 2.4 × 10−9). In addition, four novel regions at 7p22.2, 7q22.1, 9p12 and 11q13.3 were identified at a sub-genome wide threshold (P < 5 × 10−7). Integration with promoter-associated HiChIP chromatin loops from immortalized endometrium and epithelial ovarian cell lines, and expression quantitative trait loci (eQTL) data highlighted candidate target genes for further investigation.

Familial, genome-wide association (GWAS), and sequencing studies and genetic correlation analyses have progressively unraveled the shared or pleiotropic germline genetics of breast and ovarian cancer. In this study, we aimed to leverage this shared germline genetics to improve the power of transcriptome-wide association studies (TWAS) to identify candidate breast cancer and ovarian cancer susceptibility genes. We built gene expression prediction models using the PrediXcan method in 681 breast and 295 ovarian tumors from The Cancer Genome Atlas and 211 breast and 99 ovarian normal tissue samples from the Genotype-Tissue Expression project and integrated these with GWAS meta-analysis data from the Breast Cancer Association Consortium (122,977 cases/105,974 controls) and the Ovarian Cancer Association Consortium (22,406 cases/40,941 controls). The integration was achieved through novel application of a pleiotropy-guided conditional/conjunction false discovery rate approach for the first time in the setting of a TWAS. This identified 14 new candidate breast cancer susceptibility genes spanning 11 genomic regions and 8 new candidate ovarian cancer susceptibility genes spanning 5 genomic regions at conjunction FDR < 0.05 that were > 1 Mb away from known breast and/or ovarian cancer susceptibility loci. We also identified 38 candidate breast cancer susceptibility genes and 17 candidate ovarian cancer susceptibility genes at conjunction FDR < 0.05 at known breast and/or ovarian susceptibility loci. Overlaying candidate causal risk variants identified by GWAS fine mapping onto expression prediction models for genes at known loci suggested that the association for 55% of these genes was driven by the underlying GWAS signal.SignificanceThe 22 new genes identified by our cross-cancer analysis represent promising candidates that further elucidate the role of the transcriptome in mediating germline breast and ovarian cancer risk.

Background PTEN loss is a putative driver in histotypes of ovarian cancer (high-grade serous (HGSOC), endometrioid (ENOC), clear cell (CCOC), mucinous (MOC), low-grade serous (LGSOC)). We aimed to characterise PTEN expression as a biomarker in epithelial ovarian cancer in a large population-based study. Methods Tumours from 5400 patients from a multicentre observational, prospective cohort study of the Ovarian Tumour Tissue Analysis Consortium were used to evaluate associations between immunohistochemical PTEN patterns and overall survival time, age, stage, grade, residual tumour, CD8+ tumour-infiltrating lymphocytes (TIL) counts, expression of oestrogen receptor (ER), progesterone receptor (PR) and androgen receptor (AR) by means of Cox proportional hazard models and generalised Cochran–Mantel–Haenszel tests. Results Downregulation of cytoplasmic PTEN expression was most frequent in ENOC (most frequently in younger patients; p value = 0.0001) and CCOC and was associated with longer overall survival in HGSOC (hazard ratio: 0.78, 95% CI: 0.65–0.94, p value = 0.022). PTEN expression was associated with ER, PR and AR expression (p values: 0.0008, 0.062 and 0.0002, respectively) in HGSOC and with lower CD8 counts in CCOC (p value p value = 0.019) and associated with higher CD8 counts (p value = 0.0016). Conclusions PTEN loss is a frequent driver in ovarian carcinoma associating distinctly with expression of hormonal receptors and CD8+ TIL counts in HGSOC and CCOC histotypes.

Background The purpose of this study was to estimate precise age-specific tubo-ovarian carcinoma (TOC) and breast cancer (BC) risks for carriers of pathogenic variants in RAD51C and RAD51D. Methods We analyzed data from 6178 families, 125 with pathogenic variants in RAD51C, and 6690 families, 60 with pathogenic variants in RAD51D. TOC and BC relative and cumulative risks were estimated using complex segregation analysis to model the cancer inheritance patterns in families while adjusting for the mode of ascertainment of each family. All statistical tests were two-sided. Results Pathogenic variants in both RAD51C and RAD51D were associated with TOC (RAD51C: relative risk [RR] = 7.55, 95% confidence interval [CI] = 5.60 to 10.19; P = 5 × 10-40; RAD51D: RR = 7.60, 95% CI = 5.61 to 10.30; P = 5 × 10-39) and BC (RAD51C: RR = 1.99, 95% CI = 1.39 to 2.85; P = 1.55 × 10-4; RAD51D: RR = 1.83, 95% CI = 1.24 to 2.72; P = .002). For both RAD51C and RAD51D, there was a suggestion that the TOC relative risks increased with age until around age 60 years and decreased thereafter. The estimated cumulative risks of developing TOC to age 80 years were 11% (95% CI = 6% to 21%) for RAD51C and 13% (95% CI = 7% to 23%) for RAD51D pathogenic variant carriers. The estimated cumulative risks of developing BC to 80 years were 21% (95% CI = 15% to 29%) for RAD51C and 20% (95% CI = 14% to 28%) for RAD51D pathogenic variant carriers. Both TOC and BC risks for RAD51C and RAD51D pathogenic variant carriers varied by cancer family history and could be as high as 32–36% for TOC, for carriers with two first-degree relatives diagnosed with TOC, or 44–46% for BC, for carriers with two first-degree relatives diagnosed with BC. Conclusions These estimates will facilitate the genetic counseling of RAD51C and RAD51D pathogenic variant carriers and justify the incorporation of RAD51C and RAD51D into cancer risk prediction models.

Objective: To evaluate myeloid differentiation primary response gene 88 (MyD88) and Toll-like receptor 4 (TLR4) expression in relation to clinical features of epithelial ovarian cancer, histologic subtypes, and overall survival. Patients and Methods: We conducted centralized immunohistochemical staining, semi-quantitative scoring, and survival analysis in 5263 patients participating in the Ovarian Tumor Tissue Analysis consortium. Patients were diagnosed between January 1, 1978, and December 31, 2014, including 2865 high-grade serous ovarian carcinomas (HGSOCs), with more than 12,000 person-years of follow-up time. Tissue microarrays were stained for MyD88 and TLR4, and staining intensity was classified using a 2-tiered system for each marker (weak vs strong). Results: Expression of MyD88 and TLR4 was similar in all histotypes except clear cell ovarian cancer, which showed reduced expression compared with other histotypes (P

Anti-microtubule agents are widely used to treat ovarian cancers in the first line and recurrent setting either in combination with platinum or as single agents. However, the majority of patients will experience a recurrence, and most will die with drug resistant disease. We investigated co-targeting the actin cytoskeleton in combination with anti-microtubule agents to increase efficacy of treatment in epithelial ovarian cancers and potentially overcome resistance mechanisms. We examined the presence of actin/tropomyosin 3.1 (Tpm3.1) filaments in a large cohort of clinical specimens from patients with epithelial ovarian cancer of all histotypes using immunohistochemistry. Combinatorial effects of an anti-Tpm3.1 compound (ATM) with both vinorelbine and paclitaxel were evaluated in three ovarian cancer cell lines using cell viability and apoptosis assays. The mechanisms of synergy of both combinations were established using live-cell imaging, fluorescent microscopy, and pathway analysis. We found that Tpm3.1 is abundant and overexpressed in 97% of ovarian cancers examined (558 of 577) representing all histotypes of epithelial ovarian cancer. High levels of Tpm3.1 were also present in all sites sampled and similar at primary diagnosis and at recurrence. ATM displayed both single agent activity as well as synergy with both anti-microtubule drugs to reduce cell viability in all ovarian cancer cell lines tested, including one with platinum resistance. Only vinorelbine, however, synergised with ATM in the induction of apoptosis. Vinorelbine-induced mitotic arrest was significantly prolonged by ATM with elevated activity of the spindle assembly checkpoint, leading to almost one third of total cells dying in mitosis. In contrast, ATM showed minor impact on paclitaxel-induced mitotic defects. Both combinations resulted in a substantial increase in cells arrested in the subsequent G1 phase with a large decrease of both cyclin D1 and E1 as compared to single agents. Upregulation of p21Cip and p27Kip were associated with both combinations. In summary, targeting Tpm3.1-associated actin filaments in combination with anti-microtubule drugs is a promising treatment strategy that should be tested in clinical trials and is potentially applicable to all histotypes of ovarian cancer. Citation Format: Xing Xu, Yao Wang, Nicole Bryce, Katrina Tang, Nicola S. Meagher, Eun Young Kang, Linda E. Kelemen, Martin Köbel, Susan J. Ramus, Michael Friedlander, Caroline E. Ford, Edna C. Hardeman, Peter W. Gunning. Combined targeting of actin/tropomyosin and microtubules underlies a potential treatment strategy of epithelial ovarian cancer with cell-cycle dependent synergy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1045.

Purpose: The high fatality-to-case ratio of ovarian cancer is directly related to platinum resistance. Exportin-1 (XPO1) is a nuclear exporter that mediates nuclear export of multiple tumor suppressors. We investigated possible clinicopathologic correlations of XPO1 expression levels and evaluated the efficacy of XPO1 inhibition as a therapeutic strategy in platinum-sensitive and -resistant ovarian cancer. Experimental Design: XPO1 expression levels were analyzed to define clinicopathologic correlates using both TCGA/GEO datasets and tissue microarrays (TMA). The effect of XPO1 inhibition, using the small-molecule inhibitors KPT-185 and KPT-330 (selinexor) alone or in combination with a platinum agent on cell viability, apoptosis, and the transcriptome was tested in immortalized and patient-derived ovarian cancer cell lines (PDCL) and platinum-resistant mice (PDX). Seven patients with late-stage, recurrent, and heavily pretreated ovarian cancer were treated with an oral XPO1 inhibitor. Results: XPO1 RNA overexpression and protein nuclear localization were correlated with decreased survival and platinum resistance in ovarian cancer. Targeted XPO1 inhibition decreased cell viability and synergistically restored platinum sensitivity in both immortalized ovarian cancer cells and PDCL. The XPO1 inhibitor–mediated apoptosis occurred through both p53-dependent and p53-independent signaling pathways. Selinexor treatment, alone and in combination with platinum, markedly decreased tumor growth and prolonged survival in platinum-resistant PDX and mice. In selinexor-treated patients, tumor growth was halted in 3 of 5 patients, including one with a partial response, and was safely tolerated by all. Conclusions: Taken together, these results provide evidence that XPO1 inhibition represents a new therapeutic strategy for overcoming platinum resistance in women with ovarian cancer. Clin Cancer Res; 23(6); 1552–63. ©2016 AACR.

PurposeThe known EOC susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes.MethodsWe sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases verses controls were further examined in an independent set of 14,146 EOC cases and 28,661 controls from the ovarian cancer association consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics.ResultsThe odds ratios (OR) associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 – 5.68; P = 0.00068), 1.99 for POLK (95% CI 1.15 – 3.43; P = 0.014), and 4.07 for SLX4 (95% CI 1.34-12.4; P = 0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 −1.00, P=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive.ConclusionsWe have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling.

Breast cancer susceptibility variants frequently show heterogeneity in associations by tumor subtype. To identify novel loci, we performed a genome-wide association study (GWAS) including 133,384 breast cancer cases and 113,789 controls, plus 18,908 BRCA1 mutation carriers (9,414 with breast cancer) of European ancestry, using both standard and novel methodologies that account for underlying tumor heterogeneity by estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status and tumor grade. We identified 32 novel susceptibility loci (P-8), 15 of which showed evidence for associations with at least one tumor feature (false discovery rate PIn-silico analyses showed these five loci contained cell-specific enhancers that differed between normal luminal and basal mammary cells. The genetic correlations between five intrinsic-like subtypes ranged from 0.35 to 0.80. The proportion of genome-wide chip heritability explained by all known susceptibility loci was 37.6% for triple-negative and 54.2% for luminal A-like disease. These findings provide an improved understanding of genetic predisposition to breast cancer subtypes and will inform the development of subtype-specific polygenic risk scores.

Primary ovarian mucinous tumors can be difficult to distinguish from metastatic gastrointestinal neoplasms by histology alone. The expected immunoprofile of a suspected metastatic lower gastrointestinal tumor is CK7-/CK20+/CDX2+/PAX8-. This study assesses the addition of a novel marker SATB2, to improve the diagnostic algorithm. A test cohort included 155 ovarian mucinous tumors (105 carcinomas and 50 borderline tumors) and 230 primary lower gastrointestinal neoplasms (123 colorectal adenocarcinomas and 107 appendiceal neoplasms). All cases were assessed for SATB2, PAX8 CK7, CK20, and CDX2 expression on tissue microarrays. Expression was scored in a 3-tier system as absent, focal (1-50% of tumor cells) and diffuse ( >50% of tumor cells) and then categorized into either absent/present or nondiffuse/diffuse. SATB2 and PAX8 expression was further evaluated in ovarian tumors from an international cohort of 2876 patients (expansion cohort, including 159 mucinous carcinomas and 46 borderline mucinous tumors). The highest accuracy of an individual marker in distinguishing lower gastrointestinal from ovarian mucinous tumors was CK7 (91.7%, nondiffuse/diffuse cut-off) followed by SATB2 (88.8%, present/absent cut-off). The most effective combination was CK7 and SATB2 with accuracy of 95.3% using the 3-tier interpretation, absent/focal/diffuse. This combination outperformed the standard clinical set of CK7, CK20 and CDX2 (87.5%). Re-evaluation of outlier cases confirmed ovarian origin for all but one case. The accuracy of SATB2 was confirmed in the expansion cohort (91.5%). SATB2 expression was also detected in 15% of ovarian endometrioid carcinoma but less than 5% of other ovarian histotypes. A simple two marker combination of CK7 and SATB2 can distinguish lower gastrointestinal from ovarian primary mucinous tumors with greater than 95% accuracy. PAX8 and CDX2 have value as second-line markers. The utility of CK20 in this setting is low and this warrants replacement of this marker with SATB2 in clinical practice.

Publisher's version (útgefin grein)., Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10−8), breast and ovarian cancer (rg = 0.24, p = 7 × 10−5), breast and lung cancer (rg = 0.18, p =1.5 × 10−6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10−4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis., The authors in this manuscript were working on behalf of BCAC, CCFR, CIMBA, CORECT, GECCO, OCAC, PRACTICAL, CRUK, BPC3, CAPS, PEGASUS, TRICL-ILCCO, ABCTB, APCB, BCFR, CONSIT TEAM, EMBRACE, GC-HBOC, GEMO, HEBON, kConFab/AOCS Mod SQuaD, and SWE-BRCA. The breast cancer genome-wide association analyses: BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union’s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSR-SIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978), and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112—the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia), and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) is generously supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The ACP study is funded by the Breast Cancer Research Trust, UK. The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, and BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. For BIGGS, ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust in partnership with King’s College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. BOCS is supported by funds from Cancer Research UK (C8620/A8372/A15106) and the Institute of Cancer Research (UK). BOCS acknowledges NHS funding to the Royal Marsden/Institute of Cancer Research NIHR Specialist Cancer Biomedical Research Centre. The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CAMA study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). Sample collection and processing was funded in part by grants from the National Cancer Institute (NCI R01CA120120 and K24CA169004). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l’Alimentation, de l’Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The CNIO-BCS was supported by the Instituto de Salud Carlos III, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070). COLBCCC is supported by the German Cancer Research Center (DKFZ), Heidelberg, Germany. D.T. was in part supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. H.A.C eceives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). FHRISK is funded from NIHR grant PGfAR 0707-10031. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: Rita K. Schmutzler, Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, and 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0, and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GEPARSIXTO study was conducted by the German Breast Group GmbH. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). GLACIER was supported by Breast Cancer Now, CRUK and Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, and by the Rudolf-Bartling Foundation. The HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, and the Sigrid Juselius Foundation. The HERPACC was supported by MEXT Kakenhi (No. 170150181 and 26253041) from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan, by National Cancer Center Research and Development Fund, and “Practical Research for Innovative Cancer Control (15ck0106177h0001)” from Japan Agency for Medical Research and development, AMED, and Cancer Bio Bank Aichi. The HMBCS was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. The HUBCS was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), and by the Russian Foundation for Basic Research and the Federal Agency for Scientific Organizations for support the Bioresource collections and RFBR grants 14-04-97088, 17-29-06014, and 17-44-020498. ICICLE was supported by Breast Cancer Now, CRUK, and Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (A.L.F.) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (E.V.O.) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01-1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, The Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia (NHMRC; 400413, 400281, 199600). G.C.-T. and P.W. are supported by the NHMRC. RB was a Cancer Institute NSW Clinical Research Fellow. The KOHBRA study was partially supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 1020350; 1420190). LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018, 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP) which is supported under subcontract by the California Department of Health. The CSP is also part of the National Cancer Institute’s Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. L.M.B.C. is supported by the ‘Stichting tegen Kanker’. D.L. is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology “Georgi D. Efremov” and supported by the German Academic Exchange Program, DAAD. The MARIE study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects “5 × 1000”). The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839, and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286, and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. MYBRCA is funded by research grants from the Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Malaysia. MYMAMMO is supported by research grants from Yayasan Sime Darby LPGA Tournament and Malaysian Ministry of Higher Education (RP046B-15HTM). The NBCS has been supported by the Research Council of Norway grant 193387/V50 (to A.-L. Børresen-Dale and V.N. Kristensen) and grant 193387/H10 (to A.-L. Børresen-Dale and V.N. Kristensen), South Eastern Norway Health Authority (grant 39346 to A.-L. Børresen-Dale and 27208 to V.N. Kristensen) and the Norwegian Cancer Society (to A.-L. Børresen-Dale and 419616 - 71248 - PR-2006-0282 to V.N. Kristensen). It has received funding from the K.G. Jebsen Centre for Breast Cancer Research (2012-2015). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, and U01 CA179715), and the North Carolina University Cancer Research Fund. The NGOBCS was supported by Grants-in-Aid for the Third Term Comprehensive Ten-Year Strategy for Cancer Control from the Ministry of Health, Labor and Welfare of Japan, and for Scientific Research on Priority Areas, 17015049 and for Scientific Research on Innovative Areas, 221S0001, from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support Foundation, and the special Governmental EVO funds for Oulu University Hospital-based research activities. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956, and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, UMCA182910, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The scientific development and funding of this project were, in part, supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network U19 CA148065. The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. The SCCS is supported by a grant from the National Institutes of Health (R01 CA092447). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. SEBCS was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347). SGBCC is funded by the NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant and the NMRC Clinician Scientist Award. Additional controls were recruited by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC), which was funded by the Biomedical Research Council, grant number: 05/1/21/19/425. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. The TBCS was funded by The National Cancer Institute, Thailand. The TNBCC was supported by a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. The TWBCS is supported by the Taiwan Biobank project of the Institute of Biomedical Sciences, Academia Sinica, Taiwan. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The US3SS study was supported by Massachusetts (K.M.E., R01CA47305), Wisconsin (P.A.N., R01 CA47147) and New Hampshire (L.T.-E., R01CA69664) centers, and Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WAABCS study was supported by grants from the National Cancer Institute of the National Institutes of Health (R01 CA89085 and P50 CA125183 and the D43 TW009112 grant), Susan G. Komen (SAC110026), the Dr. Ralph and Marian Falk Medical Research Trust, and the Avon Foundation for Women. The WHI program is funded by the National Heart, Lung, and Blood Institute, the US National Institutes of Health and the US Department of Health and Human Services (HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C). This work was also funded by NCI U19 CA148065-01. D.G.E. is supported by the all Manchester NIHR Biomedical research center Manchester (IS-BRC-1215-20007). HUNBOCS, Hungarian Breast and Ovarian Cancer Study was supported by Hungarian Research Grant KTIA-OTKA CK-80745, NKFI_OTKA K-112228. C.I. received support from the Nontherapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI P30-CA-51008) and the Jess and Mildred Fisher Center for Hereditary Cancer and Clinical Genomics Research. K.M. is supported by CRUK C18281/A19169. City of Hope Clinical Cancer Community Research Network and the Hereditary Cancer Research Registry, supported in part by Award Number RC4CA153828 (PI: J Weitzel) from the National Cancer Institute and the office of the Directory, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The colorectal cancer genome-wide association analyses: Colorectal Transdisciplinary Study (CORECT): The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the CORECT Consortium, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the CORECT Consortium. We are incredibly grateful for the contributions of Dr. Brian Henderson and Dr. Roger Green over the course of this study and acknowledge them in memoriam. We are also grateful for support from Daniel and Maryann Fong. ColoCare: we thank the many investigators and staff who made this research possible in ColoCare Seattle and ColoCare Heidelberg. ColoCare was initiated and developed at the Fred Hutchinson Cancer Research Center by Drs. Ulrich and Grady. CCFR: the Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and financial support from the U.S. National Cancer Institute, without which this important registry would not exist. Galeon: GALEON wishes to thank the Department of Surgery of University Hospital of Santiago (CHUS), Sara Miranda Ponte, Carmen M Redondo, and the staff of the Department of Pathology and Biobank of CHUS, Instituto de Investigación Sanitaria de Santiago (IDIS), Instituto de Investigación Sanitaria Galicia Sur (IISGS), SERGAS, Vigo, Spain, and Programa Grupos Emergentes, Cancer Genetics Unit, CHUVI Vigo Hospital, Instituto de Salud Carlos III, Spain. MCCS: this study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited participants and continue to work on follow-up. We would also like to express our gratitude to the many thousands of Melbourne residents who took part in the study and provided blood samples. SEARCH: We acknowledge the contributions of Mitul Shah, Val Rhenius, Sue Irvine, Craig Luccarini, Patricia Harrington, Don Conroy, Rebecca Mayes, and Caroline Baynes. The Swedish low-risk colorectal cancer study: we thank Berith Wejderot and the Swedish low-risk colorectal cancer study group. Genetics & Epidemiology of Colorectal Cancer Consortium (GECCO): we thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. ASTERISK: we are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. DACHS: we thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance. HPFS, NHS and PHS: we acknowledge Patrice Soule and Hardeep Ranu of the Dana-Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS, HPFS, and PHS under the supervision of Dr. Immaculata Devivo and Dr. David Hunter, Qin (Carolyn) Guo, and Lixue Zhu who assisted in programming for NHS and HPFS and Haiyan Zhang who assisted in programming for the PHS. We thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-Up Study, for their valuable contributions as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A., W.Y. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. We assume full responsibility for analyses and interpretation of these data. PLCO: we thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the Screening Center investigators and staff or the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, Mr. Tom Riley and staff, Information Management Services Inc., Ms. Barbara O’Brien and staff, Westat Inc. and Drs. Bill Kopp, Wen Shao and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions for making this study possible. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. PMH: we thank the study participants and staff of the Hormones and Colon Cancer study. WHI: we thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short20List.pdf. CORECT: The CORECT Study was supported by the National Cancer Institute, National Institutes of Health (NCI/NIH), U.S. Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350; P01 CA196569; and R01 CA201407) and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678). The ATBC Study was supported by the US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184, U01 CA206110, 2P30CA015704-40 (Gilliland)), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative. Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): funding for GECCO was provided by the National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (grant numbers U01 CA137088, R01 CA059045, and U01 CA164930). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704. The Colon Cancer Family Registry (CFR) Illumina GWAS was supported by funding from the National Cancer Institute, National Institutes of Health (grant numbers U01 CA122839, R01 CA143247). The Colon CFR/CORECT Affymetrix Axiom GWAS and OncoArray GWAS were supported by funding from National Cancer Institute, National Institutes of Health (grant number U19 CA148107 to S.G.). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by the National Cancer Institute, National Institutes of Health (grant number UM1 CA167551) and through cooperative agreements with the following Colon CFR centers: Australasian Colorectal Cancer Family Registry (NCI/NIH grant numbers U01 CA074778 and U01/U24 CA097735), USC Consortium Colorectal Cancer Family Registry (NCI/NIH grant numbers U01/U24 CA074799), Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (NCI/NIH grant number U01/U24 CA074800), Ontario Familial Colorectal Cancer Registry (NCI/NIH grant number U01/U24 CA074783), Seattle Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074794), and University of Hawaii Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074806), Additional support for case ascertainment was provided from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute to Fred Hutchinson Cancer Research Center (Control Nos. N01-CN-67009 and N01-PC-35142, and Contract No. HHSN2612013000121), the Hawai’i Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No. HHSN26120100037C, and the California Department of Public Health (contracts HHSN261201000035C awarded to the University of Southern California, and the following state cancer registries: A.Z., C.O., M.N., N.C., N.H., and by the Victoria Cancer Registry and Ontario Cancer Registry. ESTHER/VERDI was supported by grants from the Baden–Württemberg Ministry of Science, Research and Arts and the German Cancer Aid. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. GALEON: FIS Intrasalud (PI13/01136). The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MSKCC: the work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the National Institutes of Health (grant numbers R01 CA189184, P30 CA076292), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (grant number P30 CA076292). SEARCH: Cancer Research UK (C490/A16561). The Spanish study was supported by Instituto de Salud Carlos III, co-funded by FEDER funds –a way to build Europe– (grants PI14-613 and PI09-1286), Catalan Government DURSI (grant 2014SGR647), and Junta de Castilla y León (grant LE22A10-2). The Swedish Low-risk Colorectal Cancer Study: the study was supported by grants from the Swedish research council; K2015-55 × -22674-01-4, K2008-55 × -20157-03-3, K2006-72 × -20157-01-2 and the Stockholm County Council (ALF project). CIDR genotyping for the Oncoarray was conducted under contract 268201200008I (to K.D.), through grant 101HG007491-01 (to C.I.A.). The Norris Cotton Cancer Center - P30CA023108, The Quantitative Biology Research Institute - P20GM103534, and the Coordinating Center for Screen Detected Lesions - U01CA196386 also supported efforts of C.I.A. This work was also supported by the National Cancer Institute (grant numbers U01 CA1817700, R01 CA144040). ASTERISK: a Hospital Clinical Research Program (PHRC) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC). COLO2&3: National Institutes of Health (grant number R01 CA060987). DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1, and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A, and 01ER1505B). DALS: National Institutes of Health (grant number R01 CA048998 to M.L.S). HPFS is supported by National Institutes of Health (grant numbers P01 CA055075, UM1 CA167552, R01 137178, and P50 CA127003), NHS by the National Institutes of Health (grant numbers UM1 CA186107, R01 CA137178, P01 CA087969, and P50 CA127003), NHSII by the National Institutes of Health (grant numbers R01 050385CA and UM1 CA176726), and PHS by the National Institutes of Health (grant number R01 CA042182). MEC: National Institutes of Health (grant numbers R37 CA054281, P01 CA033619, and R01 CA063464). OFCCR: National Institutes of Health, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (grant number U01 CA074783); see Colon CFR section above. As subset of ARCTIC, OFCCR is supported by a GL2 grant from the Ontario Research Fund, the Canadian Institutes of Health Research, and the Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society Research Institute. T.J.H. and B.W.Z. are recipients of Senior Investigator Awards from the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation. PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Additionally, a subset of control samples was genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS, Colon CGEMS pancreatic cancer scan (PanScan), and the Lung Cancer and Smoking study. The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems.cancer.gov/data/) accession numbers phs000207.v1.p1 and phs000206.v3.p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www.ncbi.nlm.nih.gov/gap) through accession number phs000093.v2.p2. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, 23 and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. PMH: National Institutes of Health (grant number R01 CA076366). VITAL: National Institutes of Health (grant number K05-CA154337). WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. The head and neck cancer genome-wide association analyses: The study was supported by NIH/NCI: P50 CA097190, and P30 CA047904, Canadian Cancer Society Research Institute (no. 020214) and Cancer Care Ontario Research Chair to R.H. The Princess Margaret Hospital Head and Neck Cancer Translational Research Program is funded by the Wharton family, Joe’s Team, Gordon Tozer, Bruce Galloway and the Elia family. Geoffrey Liu was supported by the Posluns Family Fund and the Lusi Wong Family Fund at the Princess Margaret Foundation, and the Alan B. Brown Chair in Molecular Genomics. This publication presents data from Head and Neck 5000 (H&N5000). H&N5000 was a component of independent research funded by the UK National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Human papillomavirus (HPV) in H&N5000 serology was supported by a Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant number: C18281/A19169). National Cancer Institute (R01-CA90731); National Institute of Environmental Health Sciences (P30ES10126). The authors thank all the members of the GENCAPO team/The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). CPS-II recruitment and maintenance is supported with intramural research funding from the American Cancer Society. Genotyping performed at the Center for Inherited Disease Research (CIDR) was funded through the U.S. National Institute of Dental and Craniofacial Research (NIDCR) grant 1 × 01HG007780-0. The University of Pittsburgh head and neck cancer case-control study is supported by National Institutes of Health grants P50 CA097190 and P30 CA047904. The Carolina Head and Neck Cancer Study (CHANCE) was supported by the National Cancer Institute (R01-CA90731). The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). The authors thank all the members of the GENCAPO team. The HN5000 study was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034), the views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Toronto study was funded by the Canadian Cancer Society Research Institute (020214) and the National Cancer Institute (U19-CA148127) and the Cancer Care Ontario Research Chair. The alcohol-related cancers and genetic susceptibility study in Europe (ARCAGE) was funded by the European Commission’s 5th Framework Program (QLK1-2001-00182), the Italian Association for Cancer Research, Compagnia di San Paolo/FIRMS, Region Piemonte, and Padova University (CPDA057222). The Rome Study was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) IG 2011 10491 and IG2013 14220 to S.B., and Fondazione Veronesi to S.B. The IARC Latin American study was funded by the European Commission INCO-DC programme (IC18-CT97-0222), with additional funding from Fondo para la Investigacion Cientifica y Tecnologica (Argentina) and the Fundação de Amparo à Pesquisa do Estado de São Paulo (01/01768-2). We thank Leticia Fernandez, Instituto Nacional de Oncologia y Radiobiologia, La Habana, Cuba and Sergio and Rosalina Koifman, for their efforts with the IARC Latin America study São Paulo center. The IARC Central Europe study was supported by European Commission’s INCO-COPERNICUS Program (IC15- CT98-0332), NIH/National Cancer Institute grant CA92039, and the World Cancer Research Foundation grant WCRF 99A28. The IARC Oral Cancer Multicenter study was funded by grant S06 96 202489 05F02 from Europe against Cancer; grants FIS 97/0024, FIS 97/0662, and BAE 01/5013 from Fondo de Investigaciones Sanitarias, Spain; the UICC Yamagiwa-Yoshida Memorial International Cancer Study; the National Cancer Institute of Canada; Associazione Italiana per la Ricerca sul Cancro; and the Pan-American Health Organization. Coordination of the EPIC study is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The lung cancer genome-wide association analyses: Transdisciplinary Research for Cancer in Lung (TRICL) of the International Lung Cancer Consortium (ILCCO) was supported by (U19-CA148127, CA148127S1, U19CA203654, and Cancer Prevention Research Institute of Texas RR170048). The ILCCO data harmonization is supported by Cancer Care Ontario Research Chair of Population Studies to R. H. and Lunenfeld-Tanenbaum Research Institute, Sinai Health System. The TRICL-ILCCO OncoArray was supported by in-kind genotyping by the Centre for Inherited Disease Research (26820120008i-0-26800068-1). The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN. The work performed in the CARET study was supported by the National Institute of Health/National Cancer Institute: UM1 CA167462 (PI: Goodman), National Institute of Health UO1-CA6367307 (PIs Omen, Goodman); National Institute of Health R01 CA111703 (PI Chen), National Institute of Health 5R01 CA151989-01A1(PI Doherty). The Liverpool Lung project is supported by the Roy Castle Lung Cancer Foundation. The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578, CA074386. The Multi-ethnic Cohort Study was partially supported by NIH Grants CA164973, CA033619, CA63464, and CA148127. The work performed in MSH-PMH study was supported by The Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. NJLCS was funded by the State Key Program of National Natural Science of China (81230067), the National Key Basic Research Program Grant (2011CB503805), the Major Program of the National Natural Science Foundation of China (81390543). The Norway study was supported by Norwegian Cancer Society, Norwegian Research Council. The Shanghai Cohort Study (SCS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The Singapore Chinese Health Study (SCHS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The work in TLC study has been supported in part the James & Esther King Biomedical Research Program (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997), and by a Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (grant number P30-CA76292). The Vanderbilt Lung Cancer Study—BioVU dataset used for the analyses described was obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. Dr. Aldrich was supported by NIH/National Cancer Institute K07CA172294 (PI: Aldrich) and Dr. Bush was supported by NHGRI/NIH U01HG004798 (PI: Crawford). The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The NELCS study: Grant Number P20RR018787 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). The Kentucky Lung Cancer Research Initiative was supported by the Department of Defense [Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program] under award number: 10153006 (W81XWH-11-1-0781). Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense. This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The M.D. Anderson Cancer Center study was supported in part by grants from the NIH (P50 CA070907, R01 CA176568) (to X.W.), Cancer Prevention & Research Institute of Texas (RP130502) (to X.W.), and The University of Texas MD Anderson Cancer Center institutional support for the Center for Translational and Public Health Genomics. The deCODE study of smoking and nicotine dependence was funded in part by a grant from NIDA (R01- DA017932). The study in Lodz center was partially funded by Nofer Institute of Occupational Medicine, under task NIOM 10.13: Predictors of mortality from non-small cell lung cancer—field study. Genetic sharing analysis was funded by NIH grant CA194393. The research undertaken by M.D.T., L.V.W., and M.S.A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.D.T. holds a Medical Research Council Senior Clinical Fellowship (G0902313). The work to assemble the FTND GWAS meta-analysis was supported by the National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA) grant number R01 DA035825 (Principal Investigator [PI]: DBH). The study populations included COGEND (dbGaP phs000092.v1.p1 and phs000404.v1.p1), COPDGene (dbGaP phs000179.v3.p2), deCODE Genetics, EAGLE (dbGaP phs000093.vs.p2), and SAGE. dbGaP phs000092.v1.p1). See Hancock et al. Transl Psychiatry 2015 (PMCID: PMC4930126) for the full listing of funding sources and other acknowledgments. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT)study was funded by the Sheffield Hospitals Charity, Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. The ovarian cancer genome-wide association analysis: The Ovarian Cancer Association Consortium (OCAC) is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium that was funded by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The OCAC OncoArray genotyping project was funded through grants from the U.S. National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.), and R01-CA058598 (M.T.G.); Canadian Institutes of Health Research (MOP-86727 (L.E.K.) and the Ovarian Cancer Research Fund (A.B.). The COGS project was funded through a European Commission’s Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175) and through a grant from the U.S. National Institutes of Health (R01-CA122443 (E.L.G)). Funding for individual studies: AAS: National Institutes of Health (RO1-CA142081); AOV: The Canadian Institutes for Health Research (MOP-86727); AUS: The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia (199600, 400413 and 400281), Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania and Cancer Foundation of Western Australia (Multi-State Applications 191, 211, and 182). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; BAV: ELAN Funds of the University of Erlangen-Nuremberg; BEL: National Kankerplan; BGS: Breast Cancer Now, Institute of Cancer Research; BVU: Vanderbilt CTSA grant from the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) (ULTR000445); CAM: National Institutes of Health Research Cambridge Biomedical Research Centre and Cancer Research UK Cambridge Cancer Centre; CHA: Innovative Research Team in University (PCSIRT) in China (IRT1076); CNI: Instituto de Salud Carlos III (PI12/01319); Ministerio de Economía y Competitividad (SAF2012); COE: Department of Defense (W81XWH-11-2-0131); CON: National Institutes of Health (R01-CA063678, R01-CA074850; and R01-CA080742); DKE: Ovarian Cancer Research Fund; DOV: National Institutes of Health R01-CA112523 and R01-CA87538; EMC: Dutch Cancer Society (EMC 2014-6699); EPC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom); GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center (DKFZ); GRC: This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund; GRR: Roswell Park Cancer Institute Alliance Foundation, P30 CA016056; HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424, and N01-PC-67001); HJO: Intramural funding; Rudolf-Bartling Foundation; HMO: Intramural funding; Rudolf-Bartling Foundation; HOC: Helsinki University Research Fund; HOP: Department of Defense (DAMD17-02-1-0669) and NCI (K07-CA080668, R01-CA95023, P50-CA159981 MO1-RR000056 R01-CA126841); HUO: Intramural funding; Rudolf-Bartling Foundation; JGO: JSPS KAKENHI grant; JPN: Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare; KRA: This study (Ko-EVE) was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 0920010); LAX: American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124; LUN: ERC-2011-AdG 294576-risk factors cancer, Swedish Cancer Society, Swedish Research Council, Beta Kamprad Foundation; MAC: National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; Fraternal Order of Eagles; MAL: Funding for this study was provided by research grant R01- CA61107 from the National Cancer Institute, Bethesda, MD, research grant 94 222 52 from the Danish Cancer Society, Copenhagen, Denmark; and the Mermaid I project; MAS: Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation; MAY: National Institutes of Health (R01-CA122443, P30-CA15083, and P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; MCC: Cancer Council Victoria, National Health and Medical Research Council of Australia (NHMRC) grants number 209057, 251533, 396414, and 504715; MDA: DOD Ovarian Cancer Research Program (W81XWH-07-0449); MEC: NIH (CA54281, CA164973, CA63464); MOF: Moffitt Cancer Center, Merck Pharmaceuticals, the state of Florida, Hillsborough County, and the city of Tampa; NCO: National Institutes of Health (R01-CA76016) and the Department of Defense (DAMD17-02-1-0666); NEC: National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NHS: UM1 CA186107, P01 CA87969, R01 CA49449, R01-CA67262, UM1 CA176726; NJO: National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; If Sara Olson and/or Irene Orlow is a co-author, please add NCI CCSG award (P30-CA008748) to the funding sources; NOR: Helse Vest, The Norwegian Cancer Society, The Research Council of Norway; NTH: Radboud University Medical Centre; OPL: National Health and Medical Research Council (NHMRC) of Australia (APP1025142) and Brisbane Women’s Club; ORE: OHSU Foundation; OVA: This work was supported by Canadian Institutes of Health Research grant (MOP-86727) and by NIH/NCI 1 R01CA160669-01A1; PLC: Intramural Research Program of the National Cancer Institute; POC: Pomeranian Medical University; POL: Intramural Research Program of the National Cancer Institute; PVD: Canadian Cancer Society and Cancer Research Society GRePEC Program; RBH: National Health and Medical Research Council of Australia; RMH: Cancer Research UK, Royal Marsden Hospital; RPC: National Institute of Health (P50-CA159981, R01-CA126841); SEA: Cancer Research UK (C490/A10119 C490/A10124); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; SIS: NIH, National Institute of Environmental Health Sciences, Z01-ES044005 and Z01-ES049033; SMC: The bbSwedish Research Council-SIMPLER infrastructure; the Swedish Cancer Foundation; SON: National Health Research and Development Program, Health Canada, grant 6613-1415-53; SRO: Cancer Research UK (C536/A13086, C536/A6689) and Imperial Experimental Cancer Research Centre (C1312/A15589); STA: NIH grants U01 CA71966 and U01 CA69417; SWE: Swedish Cancer foundation, WeCanCureCancer and VårKampMotCancer foundation; SWH: NIH (NCI) grant R37-CA070867; TBO: National Institutes of Health (R01-CA106414-A2), American Cancer Society (CRTG-00-196-01-CCE), Department of Defense (DAMD17-98-1-8659), Celma Mastery Ovarian Cancer Foundation; TOR: NIH grants R01-CA063678 and R01 CA063682; UCI: NIH R01-CA058860 and the Lon V Smith Foundation grant LVS39420; UHN: Princess Margaret Cancer Centre Foundation-Bridge for the Cure; UKO: The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre; UKR: Cancer Research UK (C490/A6187), UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; USC: P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389V-20170, 2II0200); VAN: BC Cancer Foundation, VGH & UBC Hospital Foundation; VTL: NIH K05-CA154337; WMH: National Health and Medical Research Council of Australia, Enabling Grants ID 310670 & ID 628903. Cancer Institute NSW Grants 12/RIG/1-17 & 15/RIG/1-16; WOC: National Science Centren (N N301 5645 40). The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academia Reserve. The prostate cancer genome-wide association analyses: we pay tribute to Brian Henderson, who was a driving force behind the OncoArray project, for his vision and leadership, and who sadly passed away before seeing its fruition. We also thank the individuals who participated in these studies enabling this work. The ELLIPSE/PRACTICAL (http//:practical.icr.ac.uk) prostate cancer consortium and his collaborating partners were supported by multiple funding mechanisms enabling this current work. ELLIPSE/PRACTICAL Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) (U19 CA148537 for ELucidating Loci Involved in Prostate Cancer SuscEptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I). Additional analytical support was provided by NIH NCI U01 CA188392 (F.R.S.). Funding for the iCOGS infrastructure came from the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, and C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112; the GAME-ON initiative), the Department of Defense (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. This work was supported by the Canadian Institutes of Health Research, European Commission’s Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692, C16913/A6135, C5047/A21332 and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative grant: No. 1 U19 CA148537-01 (the GAME-ON initiative). We also thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, and The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The Prostate Cancer Program of Cancer Council Victoria also acknowledge grant support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, and 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, and Tattersall’s. E.A.O., D.M.K., and E.M.K. acknowledge the Intramural Program of the National Human Genome Research Institute for their support. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233 to D.J.H., U01-CA98710 to S.M.G., U01-CA98216 to E.R., and U01-CA98758 to B.E.H., and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). CAPS GWAS study was supported by the Swedish Cancer Foundation (grant no 09-0677, 11-484, 12-823), the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant no K2010-70 × -20430-04-3, 2014-2269). The Hannover Prostate Cancer Study was supported by the Lower Saxonian Cancer Society. PEGASUS was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. RAPPER was supported by the NIHR Manchester Biomedical Research Center, Cancer Research UK (C147/A25254, C1094/A18504) and the EU’s 7th Framework Programme Grant/Agreement no 60186. Overall: this research has been conducted using the UK Biobank Resource (application number 16549). NHS is supported by UM1 CA186107 (NHS cohort infrastructure grant), P01 CA87969, and R01 CA49449. NHSII is supported by UM1 CA176726 (NHSII cohort infrastructure grant), and R01-CA67262. A.L.K. is supported by R01 MH107649. We would like to thank the participants and staff of the NHS and NHSII for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Menopausal estrogen-alone therapy (ET) is a well-established risk factor for serous and endometrioid ovarian cancer. Genetics also plays a role in ovarian cancer, which is partly attributable to 18 confirmed ovarian cancer susceptibility loci identified by genome-wide association studies. The interplay among these loci, ET use and ovarian cancer risk has yet to be evaluated. We analyzed data from 1,414 serous cases, 337 endometrioid cases and 4,051 controls across 10 case-control studies participating in the Ovarian Cancer Association Consortium (OCAC). Conditional logistic regression was used to determine the association between the confirmed susceptibility variants and risk of serous and endometrioid ovarian cancer among ET users and non-users separately and to test for statistical interaction. A splicing variant in TERT, rs10069690, showed a statistically significant interaction with ET use for risk of serous ovarian cancer (pint = 0.013). ET users carrying the T allele had a 51% increased risk of disease (OR = 1.51, 95% CI 1.19-1.91), which was stronger for long-term ET users of 10+ years (OR = 1.85, 95% CI 1.28-2.66, pint = 0.034). Non-users showed essentially no association (OR = 1.08, 95% CI 0.96-1.21). Two additional genomic regions harboring rs7207826 (C allele) and rs56318008 (T allele) also had significant interactions with ET use for the endometrioid histotype (pint = 0.021 and pint = 0.037, respectively). Hence, three confirmed susceptibility variants were identified whose associations with ovarian cancer risk are modified by ET exposure; follow-up is warranted given that these interactions are not adjusted for multiple comparisons. These findings, if validated, may elucidate the mechanism of action of these loci.

Telomeres cap chromosome ends, protecting them from degradation, double-strand breaks, and end-to-end fusions. Telomeres are maintained by telomerase, a reverse transcriptase encoded by TERT, and an RNA template encoded by TERC. Loci in the TERT and adjoining CLPTM1L region are associated with risk of multiple cancers. We therefore investigated associations between variants in 22 telomere structure and maintenance gene regions and colorectal, breast, prostate, ovarian, and lung cancer risk. We performed subset-based meta-analyses of 204,993 directly-measured and imputed SNPs among 61,851 cancer cases and 74,457 controls of European descent. Independent associations for SNP minor alleles were identified using sequential conditional analysis (with gene-level p value cutoffs ≤3.08 × 10-5 ). Of the thirteen independent SNPs observed to be associated with cancer risk, novel findings were observed for seven loci. Across the DCLRE1B region, rs974494 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Across the TERC region, rs75316749 was positively associated with colorectal, breast, ovarian, and lung cancers. Across the DCLRE1B region, rs974404 and rs12144215 were inversely associated with prostate and lung cancers, and colorectal, breast, and prostate cancers, respectively. Near POT1, rs116895242 was inversely associated with colorectal, ovarian, and lung cancers, and RTEL1 rs34978822 was inversely associated with prostate and lung cancers. The complex association patterns in telomere-related genes across cancer types may provide insight into mechanisms through which telomere dysfunction in different tissues influences cancer risk.

Supplemental Digital Content is available in the text., There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ2 Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials.

Contains fulltext : 170949.pdf (Publisher’s version ) (Closed access) BACKGROUND: Observational studies have reported a positive association between body mass index (BMI) and ovarian cancer risk. However, questions remain as to whether this represents a causal effect, or holds for all histological subtypes. The lack of association observed for serous cancers may, for instance, be due to disease-associated weight loss. Mendelian randomization (MR) uses genetic markers as proxies for risk factors to overcome limitations of observational studies. We used MR to elucidate the relationship between BMI and ovarian cancer, hypothesizing that genetically predicted BMI would be associated with increased risk of non-high grade serous ovarian cancers (non-HGSC) but not HGSC. METHODS: We pooled data from 39 studies (14 047 cases, 23 003 controls) in the Ovarian Cancer Association Consortium. We constructed a weighted genetic risk score (GRS, partial F-statistic = 172), summing alleles at 87 single nucleotide polymorphisms previously associated with BMI, weighting by their published strength of association with BMI. Applying two-stage predictor-substitution MR, we used logistic regression to estimate study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted BMI and risk, and pooled these using random-effects meta-analysis. RESULTS: Higher genetically predicted BMI was associated with increased risk of non-HGSC (pooled OR = 1.29, 95% CI 1.03-1.61 per 5 units BMI) but not HGSC (pooled OR = 1.06, 95% CI 0.88-1.27). Secondary analyses stratified by behaviour/subtype suggested that, consistent with observational data, the association was strongest for low-grade/borderline serous cancers (OR = 1.93, 95% CI 1.33-2.81). CONCLUSIONS: Our data suggest that higher BMI increases risk of non-HGSC, but not the more common and aggressive HGSC subtype, confirming the observational evidence.

A majority of patients with high-grade serous ovarian cancer (HGSC) develop progressive disease following primary treatment, with a five-year survival rate of approximately 40%, and less than 10% survive more than 10 years. Profound genomic instability, treatment with DNA-damaging drugs, and a large tumor burden promote the development of acquired resistance in a large proportion of patients, including multiple convergent resistant events within individuals. Despite the propensity of HGSC to develop drug resistance, a small proportion of patients with advanced disease at diagnosis become long-term survivors. These exceptional patients include those who had suboptimal surgical clearance, and therefore were not cured surgically, and others who have had no evidence of disease recurrence following initial surgery and adjuvant chemotherapy. Our study, which is part of the international collaborative Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG), seeks to identify molecular, immunologic, and epidemiologic factors that influence long-term survival in HGSC. This presentation describes our genomic and immunologic analysis of exceptional survival of this deadly disease. Patient characteristics and clinical histories were evaluated to identify patients diagnosed with advanced stage (Stage IIIC/IV) and histopathologically confirmed HGSC with greater than 10-year overall survival. Whole-genome sequencing (WGS) was performed on primary tumors (median 78x coverage) and germline samples (median 39x coverage) of 55 long-term survivors. Primary tumor samples were also characterized by RNA sequencing, DNA methylation profiling, and immunohistochemistry. Thirty-eight (69%) of long-term surviving patients had residual disease following surgery, suggestive of highly chemosensitive disease. Most patients (41, 75%) were alive at last follow-up and 26 (47%) were progression free. Somatic mutation burden was higher in primary tumors of long-term survivors relative to controls. Genome-wide mutational signatures were predominantly Signature 3 (associated with homologous recombination deficiency), Signature 1 (age related), and Signatures 5, 8, and 16 (unknown etiology). Inactivation of the tumor suppressor RB1 by structural rearrangements or homozygous deletion was frequent in long-term survivors, with 33% of tumors showing associated loss of RB1 protein expression by immunohistochemistry compared to 13% of unselected HGSC controls (n=207; P = 0.001). In an independent HGSC cohort (n=847), RB1 protein loss was associated with prolonged survival (HR: 0.75, P < 0.001) compared to patients with RB1-positive tumors. Furthermore, co-occurrence of germline mutations in BRCA1 or BRCA2 and RB1 loss was associated with a significantly longer overall survival compared to patients with retained RB1 protein expression and no germline BRCA mutation (HR: 0.44, P < 0.001). Multiplexed immunohistochemical analysis with immune cell panels demonstrated distinct associations with long-term survivors. This study delineates the full landscape of genomic alterations in HGSC of long-term survivors. Our findings indicate that specific mutations are associated with enhanced host immune responses and long-term survival. Citation Format: Dale Garsed, Ahwan Pandey, Sian Fereday, Kathryn Alsop, Maartje Wouters, Flurina Saner, Catherine Kennedy, Celeste Pearce, Malcolm Pike, Susan Ramus, Martin Kobel, Anna deFazio, Ellen Goode, Brad Nelson, David Bowtell. Molecular analysis of exceptional response in high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA05.

Purpose: Ovarian cancer is a heterogeneous disease that can be divided into multiple subtypes with variable etiology, pathogenesis, and prognosis. We analyzed DNA methylation profiling data to identify biologic subgroups of ovarian cancer and study their relationship with histologic subtypes, copy number variation, RNA expression data, and outcomes. Experimental Design: A total of 162 paraffin-embedded ovarian epithelial tumor tissues, including the five major epithelial ovarian tumor subtypes (high- and low-grade serous, endometrioid, mucinous, and clear cell) and tumors of low malignant potential were selected from two different sources: The Polish Ovarian Cancer study, and the Surveillance, Epidemiology, and End Results Residual Tissue Repository (SEER RTR). Analyses were restricted to Caucasian women. Methylation profiling was conducted using the Illumina 450K methylation array. For 45 tumors array copy number data were available. NanoString gene expression data for 39 genes were available for 61 high-grade serous carcinomas (HGSC). Results: Consensus nonnegative matrix factorization clustering of the 1,000 most variable CpG sites showed four major clusters among all epithelial ovarian cancers. We observed statistically significant differences in survival (log-rank test, P = 9.1 × 10−7) and genomic instability across these clusters. Within HGSC, clustering showed three subgroups with survival differences (log-rank test, P = 0.002). Comparing models with and without methylation subgroups in addition to previously identified gene expression subtypes suggested that the methylation subgroups added significant survival information (P = 0.007). Conclusions: DNA methylation profiling of ovarian cancer identified novel molecular subgroups that had significant survival difference and provided insights into the molecular underpinnings of ovarian cancer. See related commentary by Ishak et al., p. 5729

Thymidylate synthase (TYMS) is a crucial enzyme for DNA synthesis. TYMS expression is regulated by its antisense mRNA, ENOSF1. Disrupted regulation may promote uncontrolled DNA synthesis and tumor growth. We sought to replicate our previously reported association between rs495139 in the TYMS-ENOSF1 3′ gene region and increased risk of mucinous ovarian carcinoma (MOC) in an independent sample. Genotypes from 24,351 controls to 15,000 women with invasive OC, including 665 MOC, were available. We estimated per-allele odds ratios (OR) and 95% confidence intervals (CI) using unconditional logistic regression, and meta-analysis when combining these data with our previous report. The association between rs495139 and MOC was not significant in the independent sample (OR = 1.09, 95% CI = 0.97–1.22, p = 0.15, N = 665 cases). Meta-analysis suggested a weak association (OR = 1.13, 95% CI = 1.03–1.24, p = 0.01, N = 1019 cases). No significant association with risk of other OC histologic types was observed (p = 0.05 for tumor heterogeneity). In expression quantitative trait locus (eQTL) analysis, the rs495139 allele was positively associated with ENOSF1 mRNA expression in normal tissues of the gastrointestinal system, particularly esophageal mucosa (r = 0.51, p = 1.7 × 10−28), and nonsignificantly in five MOC tumors. The association results, along with inconclusive tumor eQTL findings, suggest that a true effect of rs495139 might be small.

Background:\ud \ud Observational studies suggest greater height is associated with increased ovarian cancer risk, but cannot exclude bias and/or confounding as explanations for this. Mendelian randomisation (MR) can provide evidence which may be less prone to bias.\ud Methods:\ud \ud We pooled data from 39 Ovarian Cancer Association Consortium studies (16,395 cases; 23,003 controls). We applied two-stage predictor-substitution MR, using a weighted genetic risk score combining 609 single-nucleotide polymorphisms. Study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted height and risk were pooled using random-effects meta-analysis.\ud Results:\ud \ud Greater genetically predicted height was associated with increased ovarian cancer risk overall (pooled-OR (pOR) = 1.06; 95% CI: 1.01–1.11 per 5 cm increase in height), and separately for invasive (pOR = 1.06; 95% CI: 1.01–1.11) and borderline (pOR = 1.15; 95% CI: 1.02–1.29) tumours.\ud Conclusions:\ud \ud Women with a genetic propensity to being taller have increased risk of ovarian cancer. This suggests genes influencing height are involved in pathways promoting ovarian carcinogenesis.

Importance Cytotoxic CD8+tumor-infiltrating lymphocytes (TILs) participate in immune control of epithelial ovarian cancer; however, little is known about prognostic patterns of CD8+TILs by histotype and in relation to other clinical factors. Objective To define the prognostic role of CD8+TILs in epithelial ovarian cancer. Design, Setting, and Participants This was a multicenter observational, prospective survival cohort study of the Ovarian Tumor Tissue Analysis Consortium. More than 5500 patients, including 3196 with high-grade serous ovarian carcinomas (HGSOCs), were followed prospectively for over 24 650 person-years. Exposures Following immunohistochemical analysis, CD8+TILs were identified within the epithelial components of tumor islets. Patients were grouped based on the estimated number of CD8+TILs per high-powered field: negative (none), low (1-2), moderate (3-19), and high (≥20). CD8+TILs in a subset of patients were also assessed in a quantitative, uncategorized manner, and the functional form of associations with survival was assessed using penalized B-splines. Main Outcomes and Measures Overall survival time. Results The final sample included 5577 women; mean age at diagnosis was 58.4 years (median, 58.2 years). Among the 5 major invasive histotypes, HGSOCs showed the most infiltration. CD8+TILs in HGSOCs were significantly associated with longer overall survival; median survival was 2.8 years for patients with no CD8+TILs and 3.0 years, 3.8 years, and 5.1 years for patients with low, moderate, or high levels of CD8+TILs, respectively (Pvalue for trend = 4.2 × 10−16). A survival benefit was also observed among women with endometrioid and mucinous carcinomas, but not for those with the other histotypes. Among HGSOCs, CD8+TILs were favorable regardless of extent of residual disease following cytoreduction, known standard treatment, and germlineBRCA1pathogenic mutation, but were not prognostic forBRCA2mutation carriers. Evaluation of uncategorized CD8+TIL counts showed a near-log-linear functional form. Conclusions and Relevance This study demonstrates the histotype-specific nature of immune infiltration and provides definitive evidence for a dose-response relationship between CD8+TILs and HGSOC survival. That the extent of infiltration is prognostic, not merely its presence or absence, suggests that understanding factors that drive infiltration will be the key to unraveling outcome heterogeneity in this cancer.

Epithelial-mesenchymal transition (EMT) is a process whereby epithelial cells assume mesenchymal characteristics to facilitate cancer metastasis. However, EMT also contributes to the initiation and development of primary tumors. Prior studies that explored the hypothesis that EMT gene variants contribute to epithelial ovarian carcinoma (EOC) risk have been based on small sample sizes and none have sought replication in an independent population. We screened 15,816 single-nucleotide polymorphisms (SNPs) in 296 genes in a discovery phase using data from a genome-wide association study of EOC among women of European ancestry (1,947 cases and 2,009 controls) and identified 793 variants in 278 EMT-related genes that were nominally (P < 0.05) associated with invasive EOC. These SNPs were then genotyped in a larger study of 14,525 invasive-cancer patients and 23,447 controls. A P-value

Genome-wide association studies have identified several risk associations for ovarian carcinomas (OC) but not for mucinous ovarian carcinomas (MOC). Genotypes from OC cases and controls were imputed into the 1000 Genomes Project reference panel. Analysis of 1,644 MOC cases and 21,693 controls identified three novel risk associations: rs752590 at 2q13 (P = 3.3 × 10−8), rs711830 at 2q31.1 (P = 7.5 × 10−12) and rs688187 at 19q13.2 (P = 6.8 × 10−13). Expression Quantitative Trait Locus (eQTL) analysis in ovarian and colorectal tumors (which are histologically similar to MOC) identified significant eQTL associations for HOXD9 at 2q31.1 in ovarian (P = 4.95 × 10−4, FDR = 0.003) and colorectal (P = 0.01, FDR = 0.09) tumors, and for PAX8 at 2q13 in colorectal tumors (P = 0.03, FDR = 0.09). Chromosome conformation capture analysis identified interactions between the HOXD9 promoter and risk SNPs at 2q31.1. Overexpressing HOXD9 in MOC cells augmented the neoplastic phenotype. These findings provide the first evidence for MOC susceptibility variants and insights into the underlying biology of the disease.

Background: The cost-effectiveness of population-based panel testing for high- and moderate-penetrance ovarian cancer (OC)/breast cancer (BC) gene mutations is unknown. We evaluate the cost-effectiveness of population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 mutation testing compared with clinical criteria/family history (FH) testing in unselected general population women. Methods: A decision-analytic model comparing lifetime costs and effects of criteria/FH-based BRCA1/BRCA2 testing is compared with BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing in those fulfilling clinical criteria/strong FH of cancer (≥10% BRCA1/BRCA2 probability) and all women age 30 years or older. Analyses are presented for UK and US populations. Identified carriers undergo risk-reducing salpingo-oophorectomy. BRCA1/BRCA2/PALB2 carriers can opt for magnetic resonance imaging/mammography, chemoprevention, or risk-reducing mastectomy. One-way and probabilistic sensitivity analysis (PSA) enabled model uncertainty evaluation. Outcomes include OC, BC, and additional heart disease deaths. Quality-adjusted life-years (QALYs), OC incidence, BC incidence, and incremental cost-effectiveness ratio (ICER) were calculated. The time horizon is lifetime and perspective is payer. Results: Compared with clinical criteria/FH-based BRCA1/BRCA2 testing, clinical criteria/FH-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is cost-effective (ICER = £7629.65/QALY or $49 282.19/QALY; 0.04 days' life-expectancy gained). Population-based testing for BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 mutations is the most cost-effective strategy compared with current policy: ICER = £21 599.96/QALY or $54 769.78/QALY (9.34 or 7.57 days' life-expectancy gained). At £30 000/QALY and $100 000/QALY willingness-to-pay thresholds, population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 panel testing is the preferred strategy in 83.7% and 92.7% of PSA simulations; criteria/FH-based panel testing is preferred in 16.2% and 5.8% of simulations, respectively. Population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing can prevent 1.86%/1.91% of BC and 3.2%/4.88% of OC in UK/US women: 657/655 OC cases and 2420/2386 BC cases prevented per million. Conclusions: Population-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is more cost-effective than any clinical criteria/FH-based strategy. Clinical criteria/FH-based BRCA1/BRCA2/RAD51C/RAD51D/BRIP1/PALB2 testing is more cost-effective than BRCA1/BRCA2 testing alone.

The OCAC OncoArray genotyping project: et al., To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC., The OCAC OncoArray genotyping project was funded through grants from the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.) and R01-CA058598 (M.T.G.)); Canadian Institutes of Health Research (MOP-86727 (L.E.K.)); and the Ovarian Cancer Research Fund (A.B.). Funding for the CIMBA OncoArray genotyping was provided by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministère de l’Économie, de la Science et de l’Innovation du Québec through Génome Québec, the Quebec Breast Cancer Foundation for the PERSPECTIVE project, the US National Institutes of Health (CA1X01HG007491-01 (C.I.A.)), the Odense University Hospital Research Foundation (M.T.), the National R&D Program for Cancer Control, Ministry of Health and Welfare, Republic of Korea (1420190 (S.K.P.)), the Italian Association for Cancer Research (IG16933 (L.O.)) and German Cancer Aid (110837 (R.K.S.).

Background: Comorbidities can affect survival of ovarian cancer patients by influencing treatment efficacy. However, little evidence exists on the association between individual concurrent comorbidities and prognosis in ovarian cancer patients. Methods: Among patients diagnosed with invasive ovarian carcinoma who participated in 23 studies included in the Ovarian Cancer Association Consortium, we explored associations between histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, and neurological diseases and overall and progression-free survival. Using Cox proportional hazards regression models adjusted for age at diagnosis, stage of disease, histology, and study site, we estimated pooled HRs and 95% confidence intervals to assess associations between each comorbidity and ovarian cancer outcomes. Results: None of the comorbidities were associated with ovarian cancer outcome in the overall sample nor in strata defined by histologic subtype, weight status, age at diagnosis, or stage of disease (local/regional vs. advanced). Conclusions: Histories of endometriosis; asthma; depression; osteoporosis; and autoimmune, gallbladder, kidney, liver, or neurologic diseases were not associated with ovarian cancer overall or progression-free survival. Impact: These previously diagnosed chronic diseases do not appear to affect ovarian cancer prognosis. Cancer Epidemiol Biomarkers Prev; 26(9); 1470–3. ©2017 AACR.

Contains fulltext : 174714.pdf (Publisher’s version ) (Closed access) Cigarette smoking is associated with an increased risk of developing mucinous ovarian tumors but whether it is associated with ovarian cancer survival overall or for the different histotypes is unestablished. Furthermore, it is unknown whether the association between cigarette smoking and survival differs according to strata of ovarian cancer stage at diagnosis. In a large pooled analysis, we evaluated the association between various measures of cigarette smoking and survival among women with epithelial ovarian cancer. We obtained data from 19 case-control studies in the Ovarian Cancer Association Consortium (OCAC), including 9,114 women diagnosed with ovarian cancer. Cox regression models were used to estimate adjusted study-specific hazard ratios (HRs), which were combined into pooled hazard ratios (pHR) with corresponding 95% confidence intervals (CIs) under random effects models. Overall, 5,149 (57%) women died during a median follow-up period of 7.0 years. Among women diagnosed with ovarian cancer, both current (pHR = 1.17, 95% CI: 1.08-1.28) and former smokers (pHR = 1.10, 95% CI: 1.02-1.18) had worse survival compared with never smoking women. In histotype-stratified analyses, associations were observed for mucinous (current smoking: pHR = 1.91, 95% CI: 1.01-3.65) and serous histotypes (current smoking: pHR = 1.11, 95% CI: 1.00-1.23; former smoking: pHR = 1.12, 95% CI: 1.04-1.20). Further, our results suggested that current smoking has a greater impact on survival among women with localized than disseminated disease. The identification of cigarette smoking as a modifiable factor associated with survival has potential clinical importance as a focus area to improve ovarian cancer prognosis.

Background: Folate receptor 1 (FOLR1) is expressed in the majority of ovarian carcinomas (OvCa), making it an attractive target for therapy. However, clinical trials testing anti-FOLR1 therapies in OvCa show mixed results and require better understanding of the prognostic relevance of FOLR1 expression. We conducted a large study evaluating FOLR1 expression with survival in different histological types of OvCa. Methods: Tissue microarrays composed of tumour samples from 2801 patients in the Ovarian Tumour Tissue Analysis (OTTA) consortium were assessed for FOLR1 expression by centralised immunohistochemistry. We estimated associations for overall (OS) and progression-free (PFS) survival using adjusted Cox regression models. High-grade serous ovarian carcinomas (HGSC) from The Cancer Genome Atlas (TCGA) were evaluated independently for association between FOLR1 mRNA upregulation and survival. Results: FOLR1 expression ranged from 76% in HGSC to 11% in mucinous carcinomas in OTTA. For HGSC, the association between FOLR1 expression and OS changed significantly during the years following diagnosis in OTTA (Pinteraction=0.01, N=1422) and TCGA (Pinteraction=0.01, N=485). In OTTA, particularly for FIGO stage I/II tumours, patients with FOLR1-positive HGSC showed increased OS during the first 2 years only (hazard ratio=0.44, 95% confidence interval=0.20–0.96) and patients with FOLR1-positive clear cell carcinomas (CCC) showed decreased PFS independent of follow-up time (HR=1.89, 95% CI=1.10–3.25, N=259). In TCGA, FOLR1 mRNA upregulation in HGSC was also associated with increased OS during the first 2 years following diagnosis irrespective of tumour stage (HR: 0.48, 95% CI: 0.25–0.94). Conclusions: FOLR1-positive HGSC tumours were associated with an increased OS in the first 2 years following diagnosis. Patients with FOLR1-negative, poor prognosis HGSC would be unlikely to benefit from anti-FOLR1 therapies. In contrast, a decreased PFS interval was observed for FOLR1-positive CCC. The clinical efficacy of FOLR1-targeted interventions should therefore be evaluated according to histology, stage and time following diagnosis.

The presence of regulatory T cells (Treg) in solid tumors is known to play a role in patient survival in ovarian cancer and other malignancies. We assessed inherited genetic variations via 749 tag single-nucleotide polymorphisms (SNP) in 25 Treg-associated genes (CD28, CTLA4, FOXP3, IDO1, IL10, IL10RA, IL15, 1L17RA, IL23A, IL23R, IL2RA, IL6, IL6R, IL8, LGALS1, LGALS9, MAP3K8, STAT5A, STAT5B, TGFB1, TGFB2, TGFB3, TGFBR1, TGRBR2, and TGFBR3) in relation to ovarian cancer survival. We analyzed genotype and overall survival in 10,084 women with invasive epithelial ovarian cancer, including 5,248 high-grade serous, 1,452 endometrioid, 795 clear cell, and 661 mucinous carcinoma cases of European descent across 28 studies from the Ovarian Cancer Association Consortium (OCAC). The strongest associations were found for endometrioid carcinoma and IL2RA SNPs rs11256497 [HR, 1.42; 95% confidence interval (CI), 1.22–1.64; P = 5.7 × 10−6], rs791587 (HR, 1.36; 95% CI, 1.17–1.57; P = 6.2 × 10−5), rs2476491 (HR, = 1.40; 95% CI, 1.19–1.64; P = 5.6 × 10−5), and rs10795763 (HR, 1.35; 95% CI, 1.17–1.57; P = 7.9 × 10−5), and for clear cell carcinoma and CTLA4 SNP rs231775 (HR, 0.67; 95% CI, 0.54–0.82; P = 9.3 × 10−5) after adjustment for age, study site, population stratification, stage, grade, and oral contraceptive use. The rs231775 allele associated with improved survival in our study also results in an amino acid change in CTLA4 and previously has been reported to be associated with autoimmune conditions. Thus, we found evidence that SNPs in genes related to Tregs seem to play a role in ovarian cancer survival, particularly in patients with clear cell and endometrioid epithelial ovarian cancer. Cancer Immunol Res; 2(4); 332–40. ©2014 AACR.

Purpose: The majority of patients with high-grade serous ovarian cancer (HGSC) develop progressive disease following primary treatment, with a five-year survival rate of ~30%. However, a subset of patients have an extraordinary response to treatment and ~15% survive more than ten years (long-term survivors). The Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) aims to uncover factors that influence long-term survival of HGSC patients. Here, we investigated the genomic and immunologic determinants of exceptional survival of this deadly disease. Experimental Design: Patient characteristics and clinical histories were evaluated to identify patients diagnosed with advanced stage (Stage IIIC/IV) and histopathologically confirmed HGSC with greater than 10-year overall survival. Whole-genome sequencing (WGS) was performed on primary tumors (median 78x coverage) and germline samples (median 39x coverage) of 55 long-term survivors. Primary tumor samples were also characterised by RNA sequencing, DNA methylation profiling and immunohistochemistry. Results: A total 38 (69%) of long-term surviving patients had residual disease following surgery, suggestive of highly chemo-sensitive disease. Most patients (41, 75%) were alive at last follow-up and 26 (47%) were progression-free. Somatic mutation burden was higher in primary tumors of long-term survivors relative to controls (316 unselected HGSC patients in The Cancer Genome Atlas). Genome-wide mutational signatures were predominantly Signature 3 (associated with homologous recombination deficiency), Signature 1 (age related) and Signatures 5, 8 and 16 (unknown etiology). Inactivation of the tumor suppressor RB1 by structural rearrangements or homozygous deletion was frequent in long-term survivors, with 33% of tumors showing associated loss of RB1 protein expression by immunohistochemistry compared to 13% of unselected HGSC controls (n=207; P = 0.001). Staining of adjacent tumor tissue revealed that RB1 loss was associated with increased numbers of PD-1+ tumor-infiltrating lymphocytes (P = 0.015) and MHC class I on tumor cells (P = 0.002). In an independent HGSC cohort (n=847), RB1 protein loss was associated with prolonged survival (HR: 0.75, P < 0.001) compared to patients with RB1 positive tumors. Furthermore, co-occurrence of germline mutations in BRCA1 or BRCA2 and RB1 loss was associated with a significantly longer overall survival compared to patients with retained RB1 protein expression and no germline BRCA mutation (HR: 0.44, P < 0.001). Conclusions: This study delineates the full landscape of genomic alterations in HGSC of long-term survivors. Our findings indicate that specific mutations might be associated with enhanced host immune responses and long-term survival. Citation Format: Dale W. Garsed, Ahwan Pandey, Sian Fereday, Kathryn Alsop, Maartje C. Wouters, Flurina Saner, Jessica A. Beach, Katy Milne, Catherine J. Kennedy, Joy Hendley, Nadia Traficante, Celeste L. Pearce, Malcolm C. Pike, Multidisciplinary Ovarian Cancer Outcomes Group, Susan J. Ramus, Martin Köbel, Brad H. Nelson, Ellen L. Goode, Anna deFazio, David D. Bowtell. The genomic landscape of high-grade serous ovarian cancer in long-term survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2722.

Background: Ovarian carcinoma is composed of five major histologic types, which associate with outcome and predict therapeutic response. Our aim was to evaluate histologic type assessments across the centers participating in the Ovarian Tumor Tissue Analysis (OTTA) consortium using an immunohistochemical (IHC) prediction model. Methods: Tissue microarrays (TMA) and clinical data were available for 524 pathologically confirmed ovarian carcinomas. Centralized IHC was conducted for ARID1A, CDKN2A, DKK1, HNF1B, MDM2, PGR, TP53, TFF3, VIM, and WT1, and three histologic type assessments were compared: the original pathologic type, an IHC-based calculated type (termed TB_COSPv2), and a WT1-assisted TMA core review. Results: The concordance between TB_COSPv2 type and original type was 73%. Applying WT1-assisted core review, the remaining 27% discordant cases subdivided into unclassifiable (6%), TB_COSPv2 error (6%), and original type error (15%). The largest discordant subgroup was classified as endometrioid carcinoma by original type and as high-grade serous carcinoma (HGSC) by TB_COSPv2. When TB_COSPv2 classification was used, the difference in overall survival of endometrioid carcinoma compared with HGSC became significant [RR 0.60; 95% confidence interval (CI), 0.37–0.93; P = 0.021], consistent with previous reports. In addition, 71 cases with unclear original type could be histologically classified by TB_COSPv2. Conclusions: Research cohorts, particularly those across different centers within consortia, show significant variability in original histologic type diagnosis. Our IHC-based reclassification produced more homogeneous types with respect to outcome than original type. Impact: Biomarker-based classification of ovarian carcinomas is feasible, improves comparability of results across research studies, and can reclassify cases which lack reliable original pathology. Cancer Epidemiol Biomarkers Prev; 22(10); 1677–86. ©2013 AACR.

Few biomarkers of ovarian cancer prognosis have been established, partly because subtype-specific associations might be obscured in studies combining all histopathological subtypes. We examined whether tumour expression of the progesterone receptor (PR) and oestrogen receptor (ER) was associated with subtype-specific survival.12 studies participating in the Ovarian Tumor Tissue Analysis consortium contributed tissue microarray sections and clinical data to our study. Participants included in our analysis had been diagnosed with invasive serous, mucinous, endometrioid, or clear-cell carcinomas of the ovary. For a patient to be eligible, tissue microarrays, clinical follow-up data, age at diagnosis, and tumour grade and stage had to be available. Clinical data were obtained from medical records, cancer registries, death certificates, pathology reports, and review of histological slides. PR and ER statuses were assessed by central immunohistochemistry analysis done by masked pathologists. PR and ER staining was defined as negative (1% tumour cell nuclei), weak (1 to50%), or strong (≥50%). Associations with disease-specific survival were assessed.2933 women with invasive epithelial ovarian cancer were included: 1742 with high-grade serous carcinoma, 110 with low-grade serous carcinoma, 207 with mucinous carcinoma, 484 with endometrioid carcinoma, and 390 with clear-cell carcinoma. PR expression was associated with improved disease-specific survival in endometrioid carcinoma (log-rank p0·0001) and high-grade serous carcinoma (log-rank p=0·0006), and ER expression was associated with improved disease-specific survival in endometrioid carcinoma (log-rank p0·0001). We recorded no significant associations for mucinous, clear-cell, or low-grade serous carcinoma. Positive hormone-receptor expression (weak or strong staining for PR or ER, or both) was associated with significantly improved disease-specific survival in endometrioid carcinoma compared with negative hormone-receptor expression, independent of study site, age, stage, and grade (hazard ratio 0·33, 95% CI 0·21-0·51; p0·0001). Strong PR expression was independently associated with improved disease-specific survival in high-grade serous carcinoma (0·71, 0·55-0·91; p=0·0080), but weak PR expression was not (1·02, 0·89-1·18; p=0·74).PR and ER are prognostic biomarkers for endometrioid and high-grade serous ovarian cancers. Clinical trials, stratified by subtype and biomarker status, are needed to establish whether hormone-receptor status predicts response to endocrine treatment, and whether it could guide personalised treatment for ovarian cancer.Carraresi Foundation and others.