Hormone replacement therapy (HRT) is used to treat menopause symptoms in >10% of women in developed countries aged 49-60 and is associated with a decrease in all-cause mortality. The current opinion is likely that the benefits outweigh the risks, and thus HRT is largely considered acceptable for use. However, it is debated as to whether a person’s risk of breast cancer increases while they are taking it. There is limited information about how a person’s genetics interact with HRT use to determine their overall breast cancer risk. Previous studies are based on either single variants that could plausibly interact with HRT to increase breast cancer risk, or are based on preconstructed single nucleotide polymorphism (SNP) arrays.
We have identified a genetic variant that appears to massively increase risk in carriers who use HRT. This variant is not present on SNP arrays, suggesting that there are likely further synergistic gene x HRT associations that affect breast cancer risk that are yet to be discovered. We aim to use the UK Biobank to replicate the association with our lead genetic variant, and to identify further synergistic gene x HRT associations in the human genome. To identify variants that synergise with HRT to increase breast cancer risk, we will apply our analysis pipeline to each individual polymorphism in the UK Biobank. This will allow us to define the network of genomic interactions with HRT and breast cancer risk. We will build a predictor model and test its accuracy, specificity and sensitivity for predicting breast cancer risk in HRT users vs non-users in this cohort. This model could be used as a predictive tool for breast cancer risk for women considering using HRT.
Ultimately, this study will empower potential HRT users with more refined knowledge of their breast cancer risk, and subsequently decrease the incidence of breast cancer.
