Limited estimates exist on risk factors for epithelial ovarian cancer (EOC) in Asian, Hispanic, and Native Hawaiian/Pacific Islander women. Participants in this study included 1734 Asian (n = 785 case and 949 control participants), 266 Native Hawaiian/Pacific Islander (n = 99 case and 167 control participants), 1149 Hispanic (n = 505 case and 644 control participants), and 24 189 White (n = 9981 case and 14 208 control participants) from 11 studies in the Ovarian Cancer Association Consortium. Logistic regression models estimated odds ratios (ORs) and 95% CIs for risk associations by race and ethnicity. Heterogeneity in EOC risk associations by race and ethnicity (P ≤ .02) was observed for oral contraceptive (OC) use, parity, tubal ligation, and smoking. We observed inverse associations with EOC risk for OC use and parity across all groups; associations were strongest in Native Hawaiian/Pacific Islander and Asian women. The inverse association for tubal ligation with risk was most pronounced for Native Hawaiian/Pacific Islander participants (odds ratio (OR) = 0.25; 95% CI, 0.13-0.48) compared with Asian and White participants (OR = 0.68 [95% CI, 0.51-0.90] and OR = 0.78 [95% CI, 0.73-0.85], respectively). Differences in EOC risk factor associations were observed across racial and ethnic groups, which could be due, in part, to varying prevalence of EOC histotypes. Inclusion of greater diversity in future studies is essential to inform prevention strategies. This article is part of a Special Collection on Gynecological Cancers.

Background:Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). Methods:We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan. Results:Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10-9). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62). Conclusions:This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.

PDF file - 145K, Supplementary Table 1. Studies in the Ovarian Cancer Association Consortium (OCAC) are listed, as well as the abbreviation, location, type, and number of cases and controls for each study. Supplementary Table 2. The number of cases included in the analysis from each OCAC study site is listed by histologic subtype. Supplementary Table 3. Gene symbols and IDs are listed for NF-κB pathway genes that were tagged in this study, as well as chromosome position, number of SNPs tagged, and bin coverage of each gene using Hapmap or 1000 genomes as a reference. Supplementary Table 4. Reasons and numbers of samples excluded from the analysis following the sample quality control are described. Supplementary Table 5. We evaluated rs17561 and rs6785617 for interactions with known epidemiologic risk factors for risk of clear cell and LMP tumors, respectively, and report interaction p-values in the table below.

Supplementary Table 1 from An Admixture Scan in 1,484 African American Women with Breast Cancer

Our objective was to test whether p53 expression status is associated with survival for women diagnosed with the most common ovarian carcinoma histotypes (high-grade serous carcinoma [HGSC], endometrioid carcinoma [EC], and clear cell carcinoma [CCC]) using a large multi-institutional cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium. p53 expression was assessed on 6,678 cases represented on tissue microarrays from 25 participating OTTA study sites using a previously validated immunohistochemical (IHC) assay as a surrogate for the presence and functional effect of TP53 mutations. Three abnormal expression patterns (overexpression, complete absence, and cytoplasmic) and the normal (wild type) pattern were recorded. Survival analyses were performed by histotype. The frequency of abnormal p53 expression was 93.4% (4,630/4,957) in HGSC compared to 11.9% (116/973) in EC and 11.5% (86/748) in CCC. In HGSC, there were no differences in overall survival across the abnormal p53 expression patterns. However, in EC and CCC, abnormal p53 expression was associated with an increased risk of death for women diagnosed with EC in multivariate analysis compared to normal p53 as the reference (hazard ratio [HR] = 2.18, 95% confidence interval [CI] 1.36-3.47, p = 0.0011) and with CCC (HR = 1.57, 95% CI 1.11-2.22, p = 0.012). Abnormal p53 was also associated with shorter overall survival in The International Federation of Gynecology and Obstetrics stage I/II EC and CCC. Our study provides further evidence that functional groups of TP53 mutations assessed by abnormal surrogate p53 IHC patterns are not associated with survival in HGSC. In contrast, we validate that abnormal p53 IHC is a strong independent prognostic marker for EC and demonstrate for the first time an independent prognostic association of abnormal p53 IHC with overall survival in patients with CCC.

Supplementary Table 1. Relationship of square-root transformed percent density to age at menarche and late adolescent BMI; Supplementary Table 2. Relationship of cube-root transformed dense area to age at menarche and late adolescent BMI ;Supplementary Table 3. Effect modification by menopause of the associations of late adolescent BMI with percent density and dense area.

Supplementary Figure S1. LocusZoom regional association plots for the seven new cross-cancer loci that were > 1 Mb from known index SNPs. Supplementary Figure S2A-B. Box plots showing eQTL associations between (A) rs9375701 and L3MBTL3 in normal breast and prostate tissues and (B) rs8037137 and RCCD1 in normal breast and ovarian tissues. Supplementary Figure S3. Interactions between BCL2L11 and the 32 Biocarta "Death Pathway" genes. Interactions were identified using the GeneMania server. Circles contain gene names, lines represent interactions, and the color of the line indicates a specific type of interaction as listed in the legend.

Background The role of ovulation in epithelial ovarian cancer (EOC) is supported by the consistent protective effects of parity and oral contraceptive use. Whether these factors protect through anovulation alone remains unclear. We explored the association between lifetime ovulatory years (LOY) and EOC. Methods LOY was calculated using 12 algorithms. Odds ratios (ORs) and 95% confidence intervals (CIs) estimated the association between LOY or LOY components and EOC among 26 204 control participants and 21 267 case patients from 25 studies. To assess whether LOY components act through ovulation suppression alone, we compared beta coefficients obtained from regression models with expected estimates assuming 1 year of ovulation suppression has the same effect regardless of source. Results LOY was associated with increased EOC risk (OR per year increase = 1.014, 95% CI = 1.009 to 1.020 to OR per year increase = 1.044, 95% CI = 1.041 to 1.048). Individual LOY components, except age at menarche, also associated with EOC. The estimated model coefficient for oral contraceptive use and pregnancies were 4.45 times and 12- to 15-fold greater than expected, respectively. LOY was associated with high-grade serous, low-grade serous, endometrioid, and clear cell histotypes (ORs per year increase = 1.054, 1.040, 1.065, and 1.098, respectively) but not mucinous tumors. Estimated coefficients of LOY components were close to expected estimates for high-grade serous but larger than expected for low-grade serous, endometrioid, and clear cell histotypes. Conclusions LOY is positively associated with nonmucinous EOC. Differences between estimated and expected model coefficients for LOY components suggest factors beyond ovulation underlie the associations between LOY components and EOC in general and for non-HGSOC.

Supplementary Tables 1-2 from Genetic Variation in TYMS in the One-Carbon Transfer Pathway Is Associated with Ovarian Carcinoma Types in the Ovarian Cancer Association Consortium

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

Supplemental Table 1: Study Sites Information Supplemental Table 2: Sample Sizes by Study Site Supplemental Table 3: Candidate Gene Summary Supplemental Table 4: Candidate Gene GE Analyses top hits (p-value < 10-3) Supplemental Table 5: Candidate Gene BMI-GE Analyses top hits (p-value < 10-3) Supplemental Table 6: Sample Sizes by Low/High status across environmental factors. Supplemental Table 7: Sample Sizes by young adult BMI (Low/High) and Environmental (Low/High) status across 7 environmental factors.

Background Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. Methods We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40–74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. Results The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18–1.57), 0.85 (0.77–0.93) and 1.44 (1.26–1.66) for LIBRA and 1.44 (1.33–1.55), 0.81 (0.74–0.89) and 1.54 (1.34–1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2–5 years and 5–10 years after the baseline mammogram. Conclusion Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.

Supplementary Tables S1-6, Figures S1-2. Supplementary Table S1: Summary of serous EOC GWAS data sets; Supplementary Table S2: Genes in the six significant co-expression networks; Supplementary Table S3: GSEA results after LD-based clumping of SNPs; Supplementary Table S4: GSEA results for the replication data set (COGS); Supplementary Table S5: GSEA results for all networks with > 10 genes; Supplementary Table S6: Number of intragenic SNPs and genes covered by SNPs in the different analyses; Supplementary Figure S1: Q-Q plots of the minimum P-value among all SNPs in each gene with and without the modified Sidak correction; Supplementary Figure S2: Disease Association Protein-Protein Link Evaluator (DAPPLE) protein-protein interaction network

Potential hormonal mechanism to motivate investigation of multi-factor obesity-hormone related risk factors - SNP interactions.

PDF - 149K, Supplemental Table 1. Participating invasive epithelial ovarian cancer studies. Supplementary Table 2. NF-κB genes studied. Supplemental Table 3. Association between clinical variables and overall survival.

Supplementary Tables 1-4 from Tagging Single Nucleotide Polymorphisms in Cell Cycle Control Genes and Susceptibility to Invasive Epithelial Ovarian Cancer

Supplementary Tables S1-S6, Supplementary References. Supplementary Table S1. Characteristics of included studies; S2. Complete-case analysis of ten years estimated hazard ratios (HR) for all cause death in patients with ovarian cancer. S3. Complete-case analysis of ten years estimated subhazard ratios (SHR) for ovarian cancer death in patients with ovarian cancer. S4. Ten years estimated subhazard ratios (SHR) for ovarian cancer death in patients with ovarian cancer (with multiple imputation for missing grade and stage). S5. Complete-case analysis of ten years estimated hazard ratios (HR) for all cause death in patients with high grade serous ovarian cancer. S6. Ten years estimated hazard ratios (HR) for all cause death in patients with high grade serous ovarian cancer (with multiple imputation for missing stage).

BackgroundThe distribution of ovarian tumour characteristics differs between germline BRCA1 and BRCA2 pathogenic variant carriers and non-carriers. In this study, we assessed the utility of ovarian tumour characteristics as predictors of BRCA1 and BRCA2 variant pathogenicity, for application using the American College of Medical Genetics and the Association for Molecular Pathology (ACMG/AMP) variant classification system.MethodsData for 10,373 ovarian cancer cases, including carriers and non-carriers of BRCA1 or BRCA2 pathogenic variants, were collected from unpublished international cohorts and consortia and published studies. Likelihood ratios (LR) were calculated for the association of ovarian cancer histology and other characteristics, with BRCA1 and BRCA2 variant pathogenicity. Estimates were aligned to ACMG/AMP code strengths (supporting, moderate, strong).ResultsNo histological subtype provided informative ACMG/AMP evidence in favour of BRCA1 and BRCA2 variant pathogenicity. Evidence against variant pathogenicity was estimated for the mucinous and clear cell histologies (supporting) and borderline cases (moderate). Refined associations are provided according to tumour grade, invasion and age at diagnosis.ConclusionsWe provide detailed estimates for predicting BRCA1 and BRCA2 variant pathogenicity based on ovarian tumour characteristics. This evidence can be combined with other variant information under the ACMG/AMP classification system, to improve classification and carrier clinical management.

Supplementary Table 3 from Single Nucleotide Polymorphisms in the TP53 Region and Susceptibility to Invasive Epithelial Ovarian Cancer

Supplementary Figures 1-5, Tables 1-8. Supplementary Figure 1: Overview of the analytic approach. Supplementary Figure 2: QQ plots of SNPs with MAF ≥0.02 and imputation r2 ≥0.9 associated with Overall Survival in A. Supplementary Figure 3: ‘All OCAC’ histology-adjusted analysis. Supplementary Figure 4: Forest plots of promising SNPs. Supplementary Figure 5: KM-Plotter graphs of significant associations with outcome. Supplementary Table 1: All studies (OCAC & TCGA) eligible for analyses according to first-line chemotherapy. Supplementary Table 2: Description of individual OCAC studies included in the secondary 'all OCAC' analysis. Supplementary Table 3a: Overall Survival estimates for selected SNPs (MAF ≥0.02) comparing iCOGS imputed (r2 ≥0.3) and iPLEX genotyped samples. Supplementary Table 3b: Progression-free Survival estimates for selected SNPs (MAF ≥0.02) comparing iCOGS imputed (r2 ≥0.3) and iPLEX genotyped samples. Supplementary Table 4: SNPs with imputation r2 ≥0.9, EAF ≥0.02 and p ≤ 1E-05 for at least one of four outcomes analyzed in cases selected according to first-line chemotherapy. Supplementary Table 5: Meta-analysis of largest possible sample (TCGA, non-overlapping iCOGS and iplex genotyped) for selected promising SNPs analyzed in cases selected according to first-line chemotherapy. Supplementary Table 6: Significant association between protein-coding genes within 1Mb of promising SNPs and ovarian cancer outcomes. Supplementary Table 7: SNPs with imputation r-sq≥0.9 and p ≤ 1E-05 for Overall Survival in histology-adj 'all OCAC' analysis. Supplementary Table 8: iCOGS estimates for SNPs previously identified to be associated with response to chemotherapy as reported in the aNHGRI GWAS catalog.