Assessment of multifactor gene-environment interactions and ovarian cancer risk: candidate genes, obesity, and hormone-related risk factors.

Publication Type:

Journal Article


Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology (2016)


BACKGROUND: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure, and elevated estrogen levels are associated with obesity in post-menopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women.

METHODS: We considered interactions between 11,441 single nucleotide polymorphisms (SNPs) within 80 candidate genes related to hormone biosynthesis & metabolism and insulin-like growth factors with six hormone-related factors: oral contraceptive use; parity; endometriosis; tubal ligation; hormone replacement therapy; and estrogen use; and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype specific analyses were also completed.

RESULTS: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, p = 4.9x10-6) and ESR1 (rs12661437, endometriosis, histology = all, p = 1.5x10-5). The most notable obesity - gene - hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, p = 8.8x10-6).

CONCLUSIONS: We have demonstrated the feasibility of assessing multi-factor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions.

IMPACT: Assessment of multifactor interaction is feasible, and, here, suggest that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility.