Genetic and Nongenetic Risk Factors for Breast Cancer Risk Estimation

Importance Most breast cancers in Africa are diagnosed at advanced stages. Improved risk prediction tools to optimize screening and earlier diagnosis are urgently needed. Objective To build a comprehensive breast cancer risk estimation model by integrating a polygenic risk score (PRS), pathogenic variants (PVs) in high- or moderate-penetrance genes, and a questionnaire-based risk calculator. Design, Setting, and Participants This multicenter case-control study initially enrolled women in Nigeria in 1998 and expanded to Cameroon and Uganda in 2011; enrollment ended in 2018. Women with breast cancer (hereafter cases) were enrolled through hospital oncology units, whereas women without breast cancer (hereafter controls) were recruited from other outpatient clinics and the community. Participants whose genetic data were used in PRS development were excluded from the development of the comprehensive breast cancer risk estimation model. Analyses were performed from September 2023 to January 2025. Exposures Lifetime absolute risk estimation models that integrated a PRS only (previously developed using data from women of African ancestry and European ancestry), PRS plus PVs in high- or moderate-penetrance genes ( BRCA1 , BRCA2 , PALB2 , ATM , CHEK2 , TP53 , BARD1 , RAD51C , and RAD51D ), epidemiologic risk factors only (ascertained from NBCS questionnaires), and a combined model containing these 3 components. Main Outcomes and Measures Lifetime absolute risk of breast cancer was estimated, accounting for an association between family history and genetic factors. Participants’ lifetime estimated absolute risk was categorized by the following risk thresholds: lower than 3%, 3%, 5%, and 10% or higher. Results A total of 1686 women, of whom 996 were cases (mean [SD] age at enrollment, 49.5 [12.2] years) and 690 were controls (mean [SD] age at enrollment, 41.5 [13.8] years), were included in the main analyses. The age-adjusted area under the receiver operating characteristic curve (AUROC) was 0.579 (95% CI, 0.549-0.610) for the PRS only model and 0.609 (95% CI, 0.579-0.638) for the PRS plus PV model. In the combined model containing both genetic and nongenetic risk factors, age-adjusted AUROC increased to 0.723 (95% CI, 0.698-0.748). Using a threshold of 10% or higher lifetime absolute risk, the combined model classified 12.0% of cases (120) as high risk compared with 3.7% of cases (37) using the epidemiologic factors only model and 5.0% of cases (50) using the PRS plus PV model. Conclusions and Relevance In this case-control study, a breast cancer risk estimation model was developed that combines genetic and nongenetic factors and refines a previous model that includes epidemiologic risk factors. Further development and validation of this model are necessary to advance breast cancer risk assessment in sub-Saharan Africa.