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The subset of ovarian cancer (OC) diagnosed ≤ 30yo represents a distinct subgroup exhibiting disparities from late-onset OC in many aspects, including indefinite germline cancer predisposition. We performed DNA/RNA-WES with HLA-typing, PRS assessment and survival analysis in 123 early-onset OC-patients compared to histology/stage-matched late-onset and unselected OC-patients, and population-matched controls. Only 6/123(4.9%) early-onset OC-patients carried a germline pathogenic variant (GPV) in high-penetrance OC-predisposition genes. Nevertheless, our comprehensive germline analysis of early-onset OC-patients revealed two divergent trajectories of potential germline susceptibility. Firstly, overrepresentation analysis highlighted a connection to breast cancer (BC) that was supported by the CHEK2 GPV enrichment in early-onset OC(p = 1.2 × 10 -4 ), and the presumably BC-specific PRS 313 , which successfully stratified early-onset OC-patients from controls(p = 0.03). The second avenue pointed towards the impaired immune response, indicated by LY75-CD302 GPV(p = 8.3 × 10 -4 ) and diminished HLA diversity compared with controls(p = 3 × 10 -7 ). Furthermore, we found a significantly higher overall GPV burden in early-onset OC-patients compared to controls(p = 3.8 × 10 -4 ). The genetic predisposition to early-onset OC appears to be a heterogeneous and complex process that goes beyond the traditional Mendelian monogenic understanding of hereditary cancer predisposition, with a significant role of the immune system. We speculate that rather a cumulative overall GPV burden than specific GPV may potentially increase OC risk, concomitantly with reduced HLA diversity., (© 2024. The Author(s).)

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 ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. 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 ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, 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., (© 2021. The Author(s).)