Purpose: A third of familial epithelial ovarian cancer (EOC) is explained by BRCA1/2 pathogenic variants. Polygenic risk scores (PRSs) for BRCA1/2 heterozygotes associated with EOC have been created, but impact of combination with clinical and hormonal risk factors is unclear.
Methods: We genotyped 300 cases and 355 controls and constructed modified PRSs based on those validated by Barnes et al. Model discrimination and EOC risk was assessed by area under the curve (AUC) values and difference between lowest and highest quintile odds ratios (ORs). We investigated model optimization using logistic regression to combine models with clinical and hormonal data.
Results: Unadjusted AUC values ranged from 0.526 to 0.551 with 2.2- to 2.3-fold increase in OR between lowest and highest quintiles (BRCA1 heterozygotes) and 0.574 to 0.585 AUC values with a 6.3- to 7.7-fold increase (BRCA2 heterozygotes). The optimized model (parity, age at menarche, menopause, and first full-term pregnancy) estimated AUC values of 0.872 to 0.876 and 21- to 23-fold increase in OR (BRCA1 heterozygotes) and AUC values of 0.857 to 0.867 and 40- to 41-fold increase (BRCA2 heterozygotes).
Conclusion: The combination of PRS with age, family history, and hormonal factors significantly improved the EOC risk discrimination ability. However, the contribution of the PRS was small. Larger prospective studies are needed to assess if combined-PRS models could provide information to inform risk-reducing decisions.
Keywords: BRCA; Epithelial ovarian cancer; Polygenic risk scores; Risk models; Risk prediction.
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.