Optimisation of polygenic risk scores in BRCA1/2 pathogenic variant heterozygotes in epithelial ovarian cancer
AffiliationDivision of Evolution, Infection and Genomics, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, UK;
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AbstractPurpose: A third of familial epithelial ovarian cancer (EOC) is explained by BRCA1/2 pathogenic variants (PVs). 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 optimisation using logistic regression to combine models with clinical and hormonal data. Results: Unadjusted AUC values were 0.526-0.551 with 2.2-2.3-fold increase in OR between lowest and highest quintiles (BRCA1-heterozygotes) and 0.574-0.585 AUC values with a 6.3-7.7-fold increase (BRCA2-heterozygotes). The optimised model (parity, age at menarche, menopause and first full-term pregnancy) estimated AUC values 0.872-0.876 and 21-23-fold increase in OR (BRCA1-heterozygotes) and AUC values of 0.857-0.867 and 40-41-fold increase (BRCA2-heterozygotes). Conclusions: 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.
CitationFlaum N, Bowes J, Smith MJ, Crosbie EJ, Edmondson R, Lophatananon A, et al. Optimisation of polygenic risk scores in BRCA1/2 pathogenic variant heterozygotes in epithelial ovarian cancer. Genetics in medicine : official journal of the American College of Medical Genetics. 2023 May 19:100898. PubMed PMID: 37212253. Epub 2023/05/22. eng.
JournalGenetics in Medicine