Genetic Variants in Metabolic Signaling Pathways and Their Interaction with Lifestyle Factors on Breast Cancer Risk: A Random Survival Forest Analysis

Cancer Prev Res (Phila). 2018 Jan;11(1):44-51. doi: 10.1158/1940-6207.CAPR-17-0143. Epub 2017 Oct 26.

Abstract

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 (SNP) in genes related to IGF-I/insulin phenotypes and signaling pathways and lifestyle factors in relation to postmenopausal breast cancer, using data from 6,567 postmenopausal women in the Women's Health Initiative Harmonized and Imputed Genome-Wide Association Studies. We used a machine-learning method, two-stage random survival forest analysis. We identified three genetic variants (AKT1 rs2494740, AKT1 rs2494744, and AKT1 rs2498789) and two lifestyle factors [body mass index (BMI) and dietary alcohol intake] as the top five most influential predictors for breast cancer risk. The combination of the three SNPs, BMI, and alcohol consumption (≥1 g/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. Cancer Prev Res; 11(1); 44-51. ©2017 AACR.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Biomarkers, Tumor / genetics*
  • Breast Neoplasms / genetics*
  • Breast Neoplasms / mortality*
  • Breast Neoplasms / pathology
  • Data Interpretation, Statistical
  • Female
  • Follow-Up Studies
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Humans
  • Life Style*
  • Middle Aged
  • Models, Statistical*
  • Polymorphism, Single Nucleotide*
  • Postmenopause
  • Prognosis
  • Risk Factors
  • Signal Transduction*
  • Survival Rate

Substances

  • Biomarkers, Tumor