Germline pathogenic variants in RAD51D and XRCC2 , which encode RAD51 paralogs that form a heterodimer within the BCDX2 complex, confer increased cancer risk and homologous recombination deficiency. However, most RAD51D and XRCC2 variants in ClinVar are classified as variants of uncertain significance (VUS), limiting clinical utility. Here, we applied saturation genome editing (SGE) to measure the effects of 5,412 RAD51D and 3,743 XRCC2 variants on cellular fitness and additionally assessed 2,876 and 2,069 variants in these genes for effects on RNA expression. Fitness scores discriminated pathogenic from benign variants with near-perfect accuracy (AUC=0.994 for RAD51D ; AUC=1.000 for XRCC2 ). Integration of RNA expression data revealed RAD51D , but not XRCC2 , is exceptionally sensitive to splice-altering variation, with 24% of RAD51D loss-of-function missense variants acting through RNA-mediated mechanisms compared to only 5% in XRCC2 . These SGE datasets provide strong, splice-resolved functional evidence to support variant classification across both genes.

BACKGROUND:All breast cancer (BC) patients have a risk of developing contralateral BC (CBC). This will be higher in women carrying a BC susceptibility gene pathogenic variant (PV), but specific risk estimates are lacking. We addressed this in a study-level meta-analysis of two large, recent cohort studies that used Breast Cancer Risk after Diagnostic Gene Sequencing (BRIDGES) project and Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium data. METHODS:We used population risks from the Netherlands to estimate 10-year cumulative CBC risks for BRCA1, BRCA2, PALB2, CHEK2, and ATM PV carriers. We estimated pooled hazard ratios (HRs) in meta-analyses using the generic inverse-variance method and assuming random effects. We stratified by first BC estrogen receptor (ER) status. RESULTS:Among 462 BRCA1, 590 BRCA2, 796 CHEK2, 297 PALB2, and 345 ATM PV carriers, we estimated 10-year cumulative CBC risks of 10.1% (95% confidence interval (CI):7.5-13.6%) for BRCA1, 10.2% (95%CI:7.4-13.9%) for BRCA2, 7.1% (95%CI:3.4-14.3%) for PALB2, 8.0% (95%CI:5.7-11.1%) for CHEK2 (any PV), 8.5% (95%CI:5.9-12.3%) for CHEK2 del.1100 C, and 4.5% (95%CI:2.5-8.0%) for ATM PV carriers. We found elevated HRs in BRCA1 (HR:2.8, 95%CI:2.1-3.6), BRCA2 (HR:2.8, 95%CI:2.1-3.7), and CHEK2 PV carriers (all PVs: HR:2.1, 95%CI:1.6-2.9. del.1100 C PVs only: HR:2.3, 95%CI:1.6-3.3). We found no significantly elevated risks in PALB2 (HR:1.9, 95%CI:0.9-3.8) or ATM (HR:1.2, 95%CI:0.7-2.1) PV carriers. We saw increased risks in BRCA1/2 and CHEK2 PV carriers following ER-positive BC, and in BRCA1/2 and PALB2 PV carriers following ER- negative BC. CONCLUSION:BC survivors carrying BRCA1, BRCA2, and CHEK2 PVs and ER-negative BC survivors carrying PALB2 PVs are at two-fold or higher CBC risks. These estimates may assist clinical decision making for management of the contralateral breast.

Studies have identified genetic and epidemiologic factors associated with mammographic density (MD) phenotypes. However, MD-associated genetic variants only account for a small proportion of the total estimated heritability. Interrogating interactions between genetic and epidemiologic factors could potentially identify additional MD-associated loci, expand our understanding of the genetic basis of MD phenotypes, and clarify how epidemiologic factors modulate relationships between genetic variants and MD. We conducted six separate genome-wide, gene-environment (GxE) interaction analyses, applying 2 degrees of freedom (df) and 1df interaction tests, for each of three MD phenotypes (percent density, dense area (DA), and nondense area (NDA)). The six epidemiologic factors considered were height, ever parous, parity, ever menopausal hormone therapy, ever breastfeeding, and months of breastfeeding. We included European ancestry participants from multiple studies within the Markers of Density consortium and the Breast Cancer Association Consortium (n = 4895-16 218 depending on specific analyses). We identified 11 loci with genome-wide significant (P < 5 × 10-8) interaction tests including two novel common genetic signals interacting with parity (8p21.2) and ever breastfeeding (19p13.2) for NDA. Our results suggest that epidemiologic risk factors might influence relationships between common genetic variants and MD phenotypes at particular genomic loci.

Despite widespread use of clinico-pathologic and genomic risk scores in early breast cancer (EBC), questions remain as to whether they are predictive, prognostic, or both. We evaluated risk score performance with actual patient outcomes in a trial dataset. Discrimination and calibration of PREDICT 2.1 and PREDICT v3 for overall survival (OS) and RSClin for distant metastasis-free survival (DMFS) were compared with actual patient outcomes in 645 postmenopausal, node-negative, hormone-positive patients with EBC from the TEAM pathology substudy. Estimated chemotherapy benefit (low < 3 ClinicalTrials.gov , NCT00279448, NCT00032136, and NCT00036270; NTR 267; Ethics Commission Trial 27/2001; and UMIN, C000000057

Table S1. Summary statistics of 7 cancer types tested with ARIC plasma proteome models, FDR<0.05, with single- and multi-variant COLOC results.

BACKGROUND:The 70-gene signature (70-GS) has been shown to identify women at low-risk of distant recurrence who can safely forgo adjuvant chemotherapy. Incorporating this GS into the well-validated and widely used PREDICT breast cancer model could improve the model's ability to estimate breast cancer prognosis, and thereby further reduce overtreatment and its long-term impact on patients' quality of life. We incorporated the 70-GS into PREDICT-v2.3 and assessed the new PREDICT-GS model's ability to predict 5-year risk of breast cancer death. METHODS:Data from the MINDACT trial (N = 5920) was used to estimate the 70-GS's prognostic effect (coefficient = 0.70), which was then incorporated into PREDICT-v2.3. Netherlands Cancer Registry (NCR) data (N = 3323) was used to assess PREDICT-GS's discrimination (area under curve (AUC)), calibration and clinical utility. RESULTS:Compared to PREDICT-v2.3 (AUC: 0.71 (95 % CI: 0.63-0.79)), PREDICT-GS (AUC: 0.76 (95 % CI: 0.69-0.83)) had better discrimination. Both models tended to overestimate the 5-year risk of breast cancer death in the NCR cohort, but the absolute overestimation was smaller for PREDICT-GS. Regarding clinical utility, only at the 10 % decision threshold did we find modest improvement: four extra patients per 1000 tests were correctly classified as not needing chemotherapy by PREDICT-GS compared to PREDICT-v2.3. CONCLUSION:Extending PREDICT-v2.3 with 70-GS led to modest improvement in its ability to predict 5-year risk of breast cancer death. Future research should focus on assessing the added value of the 70-GS for longer-term prediction of recurrence and death with the incorporation of quality of life in risk prediction tools.

10511 Background: Pathogenic germline variants (PGV) in cancer-predisposition genes influence the development of many cancer types but our understanding of cancer risks in PGV carriers remains underexplored. This study aims to further characterize the spectrum of cancers associated with PGVs and factors contributing to the development of multiple primary cancers among PGV carriers. Methods: A case-control analysis of 61,453 cancer cases and 366,709 controls in the UK Biobank (UKBB) was performed to test for the associations between risks of 43 solid tumor types and PGVs in 237 cancer predisposition genes. We evaluated each association according to the ClinGen Gene-Disease Validity framework and categorized those with moderate or less evidence as novel. An independent validation cohort of 103,321 cases 340,786 controls from All of Us, Mass General Brigham Biobank, TCGA, Memorial Sloan Kettering IMPACT, and a case-control study of ovarian cancer was used to replicate novel associations. Results: We identified 51 novel associations between solid tumor development and PGVs in the UKBB. Out of these, 32 were also significantly associated (p<0.05) in our validation cohorts (Table 1). Among PGV carriers in the UKBB, 16% had one primary malignancy and 2% had two or more. Across most PGV carriers, we observed higher risks of multiple primary cancers compared to single primary cancers. Using cox proportional hazards models, we found that PGV carriers with a personal history of cancer showed a higher hazard ratio of second cancer compared to healthy controls, particularly among those diagnosed with the first cancer earlier in life. The association between PGVs and second cancer remained significant in case-only analysis limited to cancer survivors and adjusted for primary tumor type suggesting this was not explained by shared risk factors. Conclusions: These findings expand our understanding of spectrum of cancer risks associated with predisposition genes and highlight that PGV carriers are at high risk of developing multiple primary cancers. In addition to family history, personal history of cancer should be considered for tailored cancer screening in genetically predisposed individuals. Novel associations between PGV genes and selected cancers. Shown are the odds ratio estimates from the meta-analysis of replication cohorts. Cancer Genes Odds ratio (95% CI) Breast BAP1 4.68 (2.13-10.25) BRIP1 1.63 (1.18-2.26) LZTR1 2.01 (1.56-2.6) Colorectal ATM 1.43 (1.07-1.93) BARD1 2.33 (1.28-4.26) BRCA1 1.69 (1.2-2.37) BRCA2 1.69 (1.27-2.25) FLCN 2.6 (1.17-5.74) Melanoma BLM 1.68 (1.05-2.71) BRCA1 2.15 (1.29-3.58) Lung BRCA2 3.16 (2.36-4.23) NBN 1.94 (1.07-3.53) Endometrial BRCA1 8.05 (4.83-13.4) BRCA2 2.32 (1.19-4.54) MSH3 2.25 (1.07-4.72) Urinary ATM 1.71 (1.21-2.44) Renal MITF p.E318K 1.85 (1.16-2.97) WRN 2.71 (1.39-5.29) Head and neck CDKN2A 6.22 (3.31-11.7) FANCM 2.2 (1.38-3.52) Ovary DDX41 4.56 (1.56-13.36) PALB2 3.33 (1.98-5.61)

BOADICEA is a widely used algorithm for predicting breast and ovarian cancer risks, using a combination of genetic and lifestyle, hormonal and reproductive risk factors. However, it has largely been developed using data from White/European individuals, limiting its applicability to other ethnicities. Here, we updated BOADICEA to provide ethnicity-specific risk estimates. We utilised data from multiple sources to derive estimates for the distributions and effect sizes of risk factors in major UK ethnic groups (White, Black, South Asian, East Asian, and Mixed), along with ethnicity-specific population cancer incidences. We also developed a method for deriving adjusted polygenic scores for individuals of mixed genetic ancestry. The predicted average absolute risks were smaller in all non-White ethnic groups than in Whites, and the risk distributions were narrower. The proportion of women classified as at moderate or high risk of breast or ovarian cancer, according to national guidelines, was considerably smaller in non-Whites. The updated BOADICEA, available in the CanRisk tool ( www.canrisk.org ), is based on more appropriate estimates for non-White women in the UK. Further validation of the model in prospective studies is required. Considering these findings, risk classification guidelines for non-White women may need to be revised.

Rare, germline loss-of-function variants in a handful of DNA repair genes are associated with epithelial ovarian cancer. The aim of this study was to evaluate the role of rare, coding, loss-of-function variants across the genome in epithelial ovarian cancer. We carried out a gene-by-gene burden test with various histotypes using data from 2573 non-mucinous cases and 13,923 controls. Twelve genes were associated at a False Discovery Rate of less than 0.1 of which seven were the known ovarian cancer susceptibility genes BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, MSH6 and PALB2. The other five genes were OR2T35, HELB, MYO1A and GABRP which were associated with non-high-grade serous ovarian cancer and MIGA1 which was associated with high-grade serous ovarian cancer. Further support for the association of HELB association comes from the observation that loss-of-function variants in HELB are associated with age at natural menopause and Mendelian randomisation analysis shows an association between genetically predicted age at natural menopause and endometrioid ovarian cancer, but not high-grade serous ovarian cancer.

Objectives To investigate the association between bilateral salpingo-oophorectomy (BSO) and long-term health outcomes in women with a personal history of breast cancer.Methods and analysis We used data on women diagnosed with invasive breast cancer between 1995 and 2019 from the National Cancer Registration Dataset (NCRD) in England. The data were linked to the Hospital Episode Statistics-Admitted Patient Care dataset to identify BSO delivery. Long-term health outcomes were selected from both datasets. Multivariable Cox regression was used to examine the associations, with BSO modelled as a time-dependent covariate. The associations were investigated separately by age at BSO.Results We identified 568 883 women, 23 401 of whom had BSO after the breast cancer diagnosis. There was an increased risk of total cardiovascular diseases with an HR of 1.10 (95% CI 1.04 to 1.16) in women who had BSO<55 years and 1.07 (95% CI 1.01 to 1.13) for women who had BSO≥55 years. There was an increased risk of ischaemic heart diseases, but there was no association with cerebrovascular diseases. BSO at any age was associated with an increased risk of depression (HR 1.20, 95% CI 1.12 to 1.28) and increased risk of second non-breast cancer in older women (HR 1.21, 95%CI 1.08 to 1.35). BSO in older women was associated with reduced risk of all-cause mortality (HR 0.92, 95% CI 0.87 to 096), but not in women who had BSO<55 years.Conclusion In women with a personal history of breast cancer, BSO before and after the age of 55 years is associated with an increased risk of long-term outcomes. BSO after 55 years is associated with reduced all-cause mortality. Family history or genetic predisposition may confound these associations.

Table S3. PWAS meta-FDR<0.05 proteins with ovarian cancer associations.

Supplementary Figure S1: Workflow illustrating the methods.

Etiologic heterogeneity in breast carcinogenesis needs to be well characterized for targeted prevention. Associations between menopausal hormonal therapy (MHT) and oral contraceptive (OC) use and breast cancer intrinsic-like subtypes are not well understood. To examine whether exogenous hormone use is differentially associated with breast cancer subtypes and to evaluate heterogeneity by intrinsic-like subtypes. This study pooled data from 31 nested and population-based case-control studies involved in the Breast Cancer Association Consortium. The study population included individuals with breast cancer and control participants from 13 case-control studies nested in prospective cohorts (recruited between 1982 and 2011) and 18 population-based case-control studies (recruited between 1990 and 2013). Data analysis was performed in June 2024. MHT use (estrogen-progestin therapy [EPT] or estrogen-only therapy [ET]) in postmenopausal women and OC use in premenopausal women (never, past use, or current use). Breast cancer intrinsic-like subtypes (luminal A–like, luminal B–like, luminal B–ERBB2 [formerly HER2 or HER2/neu]-like, ERBB2 enriched–like, or triple-negative) were determined by immunohistochemistry of tumor sections. Polytomous logistic regression was performed to estimate the association between exogenous hormones and risk of breast cancer by intrinsic-like subtypes. Analyses by subtypes were stratified by body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]; healthy weight, 18.5-<25; overweight, 25-<30; or obesity, ≥30). This study included 42 269 individuals with breast cancer (11 901 [28.2%] premenopausal and 30 368 [71.8%] postmenopausal; 23 353 [55.2%] had a known intrinsic-like subtype) and 71 072 control participants. The mean (SD) age of all participants was 57.9 (10.9) years. In postmenopausal women, associations between current MHT use (EPT or ET) and breast cancer differed by subtype. Current EPT users with healthy weight were more likely to be diagnosed with luminal A–like (odds ratio [OR], 2.51 [95% CI, 2.26-2.80]) or luminal B–ERBB2-like (OR, 1.95 [95% CI, 1.61-2.37]) subtypes. These associations were attenuated but remained for individuals with overweight (OR, 1.40 [95% CI, 1.02-1.92]) or obesity (OR, 1.68 [95% CI, 1.01-2.78]). EPT use increased the odds of being diagnosed with luminal B–like tumors solely in women with healthy weight (OR, 1.47 [95% CI, 1.17-1.86]). Current ET use was positively associated with luminal A–like disease in women with healthy weight only (OR, 1.16 [95% CI, 1.01-1.32]), showing inverse associations with higher BMI (obesity: OR, 0.65 [95% CI, 0.50-0.85]). In premenopausal women, recent OC use was associated with luminal B–ERBB2-like (OR, 1.50 [95% CI, 1.09-2.08]), ERBB2 enriched–like (OR, 2.33 [95% CI, 1.55-3.51]), and triple-negative (OR, 1.75 [95% CI, 1.33-2.29]; P < .04 for heterogeneity) tumors. In this study, clear differences were observed in associations between current EPT use and luminal-like breast cancer subtypes and other subtypes. EPT users with healthy weight were more likely to be diagnosed with luminal-like breast cancer compared with nonusers. Subtype heterogeneity was less apparent in associations of OC and ET use. Future studies on contemporary formulations, patterns of use, and routes of administration of exogenous hormone usage are warranted.

Breast cancer, the most common cancer among women globally, remains a significant public health challenge. Prognosis, influenced by factors such as tumor characteristics, patient demographics, and treatment choices, is a major concern for patients. Tools like the PREDICT Breast v3 model (https://breast.v3.predict.cam/) help weigh treatment benefits against short- and long-term risks, enabling more personalised prognostic predictions. However, its performance disparities among non-Hispanic Black and non-Hispanic Asian patients in the US highlight the need for more equitable and inclusive tools. Therefore, this study aims to develop a breast cancer prognostic model that improves predictive accuracy across diverse populations and reduces disparities in prognostic outcomes. Here, we analysed data from 273, 918 breast cancer patients in the Surveillance, Epidemiology, and End Results (SEER) database, each with clinical records and socioeconomic status (SES) information. The data were split 7:3 for model training and validation, with 10-fold cross-validation applied for parameter tuning. We first evaluated both conventional methods (PREDICT v3) and advanced machine learning models, including survival XGBoost, survival random forest, DeepHit, and DeepSurv, using calibration and discrimination as evaluation metrics. After comparing the models, we selected the DeepHit neural network as the best-performing approach due to its lowest calibration error and highest discrimination. We then examined whether incorporating SES improved performance by comparing SES-adjusted and unadjusted DeepHit models. Our results showed that the SES-adjusted DeepHit model achieved strong calibration (within 1.3% of observed deaths) and reduced racial disparities compared to PREDICT v3, which exhibited calibration errors of 30% for non-Hispanic Asians and 20% for non-Hispanic Blacks. These findings emphasise the importance of incorporating SES factors to enhance model accuracy and equity, underscoring the critical need for fairness in prognostic prediction. This work advances the application of machine learning in cancer prognosis and provides a foundation for addressing health disparities in clinical prediction tools. Yi-Wen Hsiao, Gordon C. Wishart, Paul D.P. Pharoah, Pei-Chen Peng. PREDICT Breast v4: Advancing equity in breast cancer prognosis for diverse populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4913.

Mucinous ovarian carcinoma (MOC) is a rare histotype of epithelial ovarian cancer. Its origins are obscure: while many mucinous tumours in the ovary are metastases from the gastrointestinal tract, MOC can occur as an ovarian primary; however, the cell of origin is not well established. In this review we summarise the pathological, epidemiological, and molecular evidence for the cellular origins of MOC. We propose a model for the origins of the various tumours of the ovary with mucinous differentiation. We distinguish Müllerian from gastrointestinal-type mucinous differentiation. A small proportion of the latter arise from teratoma and a distinct terminology has been proposed. Other gastrointestinal mucinous tumours are associated with Brenner tumours and arise from their associated benign lesions, Walthard nests. The remaining mucinous tumours develop either through mucinous metaplasia in established Müllerian tumours or with even greater plasticity through gastrointestinal metaplasia of epithelial or mesothelial ovarian inclusions. This model remains to be validated and mechanistically understood and we discuss future research directions. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

BACKGROUND:Most breast biopsies are diagnosed as benign breast disease, with 1.5- to 4-fold increased breast cancer risk. Apart from pathologic diagnoses of atypical hyperplasia, few factors aid in breast cancer risk assessment of these patients. We assessed whether a 313-single nucleotide variation (formerly single-nucleotide polymorphism) polygenic risk score stratifies risk of benign breast disease patients. METHODS:We pooled data from 5 Breast Cancer Association Consortium case-control studies (mean age = 59.9 years), including 6706 participants with breast cancer and 8488 participants without breast cancer. Using logistic regression, we estimated breast cancer risk associations by self-reported benign breast disease history and strata of polygenic risk score, with median polygenic risk score category among women without benign breast disease as the referent. We assessed interactions and mediation of benign breast disease and polygenic risk score with breast cancer risk. RESULTS:Benign breast disease history was associated with increased breast cancer risk (odds ratio [OR] = 1.48, 95% confidence interval [CI] = 1.37 to 1.60; P < .001). Polygenic risk score increased breast cancer risk, irrespective of benign breast disease history (Pinteraction = .48), with minimal evidence of mediation of either factor by the other. Women with benign breast disease and polygenic risk score in the highest tertile had more than twofold increased odds of breast cancer (OR = 2.73, 95% CI = 2.41 to 3.09), and those with benign breast disease and polygenic risk score in the lowest tertile experienced reduced breast cancer risk (OR = 0.79, 95% CI = 0.70 to 0.91) compared with the referent group. Women with benign breast disease and polygenic risk score in the highest decile had a 3.7-fold increase (95% CI = 3.00 to 4.61) compared with those with median polygenic risk score without benign breast disease. CONCLUSION:Breast cancer risks are elevated among women with benign breast disease and increase progressively with polygenic risk score, suggesting that optimal combinations of these factors may improve risk stratification.

Background: PREDICT Breast version 3 (v3) is the latest updated prognostication tool, developed using data from approximately 35,000 women diagnosed with breast cancer between 2000 and 2018 in the United Kingdom. Although an earlier version of PREDICT was tested in the United States, the performance of the latest version remains unknown. This study aims to validate PREDICT Breast v3 using newly released SEER outcome data for patients with breast cancer in the United States and to address potential health disparities. Methods: A total of 615,865 female patients diagnosed with primary breast cancer between 2000 and 2018 and followed for at least 10 years were selected from the SEER database. Predicted and observed 10- and 15-year breast cancer–specific survival outcomes were compared for the overall cohort, stratified by estrogen receptor (ER) status and predefined subgroups. Discriminatory accuracy was evaluated using the area under the receiver operating characteristic curve (AUC). Results: PREDICT Breast v3 demonstrated good calibration and discrimination for long-term breast cancer–specific survival. It provided accurate mortality estimates (within ±10% absolute error) across the US population for 10-year (−10% in ER-positive and 2% in ER-negative breast cancer) and 15-year (4% in ER-positive and 3% in ER-negative breast cancer) all-cause mortality, for both ER statuses. The model also showed good performance for 10- and 15-year all-cause mortality across the US population, with AUC values of 0.769 and 0.794 for ER-positive breast cancer as well as AUC of 0.738 and 0.746 for ER-negative breast cancer, respectively, indicating good discriminatory ability. However, recalibration is needed for specific groups, including non-Hispanic Asian and non-Hispanic Black patients with ER-negative disease. Conclusions: PREDICT v3 accurately predicts 10- and 15-year breast cancer–specific survival in contemporary US patients with breast cancer. Future efforts should focus addressing disparities observed in predictive tools to promote equitable care.

Common genetic variation throughout the genome and rare coding variants identified to date explain about half of the inherited genetic component of epithelial ovarian cancer risk. It is likely that rare variation in the noncoding genome will explain some of the unexplained heritability, but identifying such variants is challenging. The primary problem is a lack of statistical power to identify individual risk variants by association, as power is a function of sample size, effect size, and allele frequency. Power can be increased by using burden tests, which test for the association of carriers of any variant in a specified genomic region. This has the effect of increasing the putative effect allele frequency. PAX8 is a transcription factor that plays a critical role in tumor progression, migration, and invasion. Furthermore, regulatory elements proximal to target genes of PAX8 are enriched for common ovarian cancer risk variants. We hypothesized that rare variation in PAX8 binding sites is also associated with ovarian cancer risk but unlikely to be associated with risk of breast, colorectal, or endometrial cancer. We have used publicly available, whole-genome sequencing data from the UK 100,000 Genomes Project to evaluate the burden of rare variation in PAX8 binding sites across the genome. Data were available for 522 ovarian cancers, 2984 breast cancers, 2696 colorectal cancers, 836 endometrial cancers, and 2253 noncancer controls. Active binding sites were defined using data from multiple PAX8 and H3K27 chromatin immunoprecipitation sequencing experiments. We found no association between the burden of rare variation in PAX8 binding sites (defined in several ways) and risk of ovarian, breast, or endometrial cancer. An apparent association with colorectal cancer was likely to be a technical artifact as a similar association was also detected for rare variation in random regions of the genome. Despite the null result, this study provides a proof-of-principle for using burden testing to identify rare, noncoding germline genetic variation associated with disease. Larger sample sizes available from large-scale sequencing projects, together with improved understanding of the function of the noncoding genome, will increase the potential of similar studies in the future.