A polygenic score-based approach to identify gene-drug interactions stratifying breast cancer risk

Abstract

An individuals genetics can dramatically influence breast cancer (BC) risk. While clinical measures for prevention do exist, non-invasive personalized measures for reducing BC risk are limited. Commonly-used medications are a promising set of modifiable factors, however no previous study has explored whether a range of widely-taken approved drugs modulate BC genetics. In this study, we describe a quantitative framework for exploring the interaction between the genetic susceptibility of BC and medication usage among UK Biobank women. We computed BC polygenic scores (PGS) that summarize BC genetic risk, and find that the PGS explains nearly three-times greater variation in disease risk within corticosteroid users compared to non-users. We map 35 genes significantly interacting with corticosteroid use (FDR < 0.1), highlighting the transcription factor NRF2 as a common regulator of gene-corticosteroid interactions in BC. Finally, we discover a novel regulatory variant strongly stratifying BC risk according to corticosteroid use. Within risk allele carriers, 18.2% of women taking corticosteroids developed BC, compared to 5.1% of the non-users (with a HR = 3.41 per-allele within corticosteroid users). Overall, this work highlights the clinical relevance of gene-drug interactions in disease risk, and provides a roadmap for repurposing biobanks in drug repositioning and precision medicine.