Abstract
Purpose: Obesity–insulin connections have been considered potential risk factors for postmenopausal breast cancer, and the association between insulin resistance (IR) genotypes and phenotypes can be modified by obesity-lifestyle factors, affecting breast cancer risk. We explored the role of IR in those pathways at the genome-wide level. With identified IR genetic factors and selected lifestyles, we further generated risk profiles for breast cancer risk.
Methods: Using large-scale cohort data from postmenopausal women in the Women’s Health Initiative Database for Genotypes and Phenotypes Study, our previous genome-wide association gene–behavior interaction study identified 58 loci for associations with IR phenotypes (homeostatic model assessment–IR, hyperglycemia, and hyperinsulinemia). We evaluated those single-nucleotide polymorphisms (SNPs) and additional 31 lifestyles in relation to breast cancer risk by conducting a two-stage multimodal random survival forest analysis.
Results: We identified the most predictive genetic and lifestyle variables in overall and subgroup analyses (stratified by body mass index [BMI], exercise, and dietary fat intake). Two SNPs (LINC00460 rs17254590 and MKLN1 rs117911989), endogenous and exogenous factors related to lifetime cumulative exposure to estrogen, BMI, and dietary alcohol intake were the most common influential factors across the analyses. Individual SNPs did not have significant associations with breast cancer, but the combined SNPs and lifestyles synergistically increased the risk of breast cancer in a dose-dependent manner. In subgroup analysis, we further stratified women by the two strongest variables (durations of oral contraceptive [OC] and opposed estrogen use). Shorter-duration OC and ever-opposed estrogen users with both risks of genotypes and lifestyles had greater risk (2.5 times and 6.3 times, respectively) than their counterparts without risks in both. This indicates the combined effect of SNPs and lifestyles and the joint effect with OC use and opposed estrogen on breast cancer risk.
Conclusions: Our findings may contribute to more accurate prediction of breast cancer and suggest intervention strategies for those women with genetic risk to reduce their breast cancer risk.
Citation Format: Su Yon Jung, Herbert Yu, Matteo Pellegrini, Jeanette Papp, Eric Sobel, Zuo-Feng Zhang. Genome-wide gene-environmental interaction and random survival forest analyses: Insulin resistance and breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1596.
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