Genetic variants in metabolic signaling pathways and their interaction with lifestyle factors on breast cancer risk: A random survival forest analysis

Genetic variants in the insulin-like growth factor-I (IGF-I)/insulin resistance axis may interact with lifestyle factors, influencing postmenopausal breast cancer risk, but these interrelated pathways are not fully understood. In this study, we examined 54 single-nucleotide polymorphisms (SNPs) in genes related to IGF-I/insulin phenotypes and signaling pathways and lifestyle factors in relation to post-menopausal breast cancer, using data from 6,567 postmenopausal women in the Women's Health Initiative Harmonized and Imputed Genome-Wide Association Studies. We employed a machine learning method, two-stage random survival forest analysis. We identified 3 genetic variants (AKT1 rs2494740, AKT1 rs2494744, and AKT1 rs2498789) and 2 lifestyle factors (body mass index [BMI] and dietary alcohol intake) as the top 5 most influential predictors for breast cancer risk. The combination of the 3 SNPs, BMI, and alcohol consumption (≥ 1 gm/day) significantly increased the risk of breast cancer in a gene and lifestyle dose-dependent manner. Our findings provide insight into gene-lifestyle interactions and will enable researchers to focus on individuals with risk genotypes to promote intervention strategies. These data also suggest potential genetic targets in future intervention/clinical trials for cancer prevention in order to reduce the risk for breast cancer in postmenopausal women.

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