Mammographic density, pathogenic breast cancer susceptibility gene variants and breast cancer risk.

ImportanceMammographic density (MD) and pathogenic variants (PVs) in breast cancer susceptibility genes are major determinants of breast cancer risk, but their association and joint effects on breast cancer risk are unclear. ObjectiveTo investigate the association between the presence or absence of PVs in breast cancer susceptibility genes and MD measures, and their joint effects on breast cancer risk in an observational study; and to evaluate causality using Mendelian randomisation (MR) analyses. DesignCase-control analyses using data from the Breast Cancer Association Consortium (1991-2016). Sequencing and genotyping took place between 2009 and 2021. SettingMulticenter ParticipantsA total of 6,809 cases and 18,189 controls were included, from 15 studies, comprising women aged 19 to 92 years with mammograms taken at least one year before diagnosis. ExposureMD measures, including dense area (DA), non-dense area (NDA), percentage density (PD) and absolute difference in PD between left and right breasts (ADPD), and PVs in ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C and RAD51D. Main outcomes and measuresBreast cancer risk overall, by oestrogen receptor expression-defined subtypes, and among BRCA1 and BRCA2 PV carriers. ResultsNo association was found between the overall burden of PVs and any MD measure. There was some evidence for a negative interaction between the burden of PVs in the eight genes and PD (OR=0.79,95%CIint=0.62,1.00, PLRT=0.047). This appears to be largely driven by a positive interaction with NDA. MR analyses indicated attenuated effects for BRCA1 (for PD, OR per standard deviation =1.02(95%CI:0.78,1.34) but not BRCA2 PV carriers (1.54,95%CI=1.08,2.24)). Conclusions and RelevanceThere was no evidence of association between PVs in breast cancer susceptibility genes and MD measures, but some suggestion that the association between MD and breast cancer risk may be weaker in PV carriers. Replication of these findings in further large datasets is required.