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Title: Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors
Author(s): Usset, J.L.; Raghavan, R.; Tyrer, J.P.; McGuire, V.; Sieh, W.; Webb, P.; Chang-Claude, J.; Rudolph, A.; Anton-Culver, H.; Berchuck, A.; Brinton, L.; Cunningham, J.M.; Defazio, A.; Doherty, J.A.; Edwards, R.P.; Gayther, S.A.; Gentry-Maharaj, A.; Goodman, M.T.; Hogdall, E.; Jensen, A.; Johnatty, S.E.; Kiemeney, L.A.L.M. ; Kjaer, S.K.; Larson, M.C.; Lurie, G.; Massuger, L.F. ; Menon, U.; Modugno, F.; Moysich, K.B.; Ness, R.B.; Pike, M.C.; Ramus, S.J.; Rossing, M.A.; Rothstein, J.; Song, H.; Thompson, P.J.; Berg, D.J. Van Den; Vierkant, R.A.; Wang-Gohrke, S.; Wentzensen, N.; Whittemore, A.S.; Wilkens, L.R.; Wu, A.H.; Yang, H.; Pearce, C.L.; Schildkraut, J.M.; Pharoah, P.; Goode, E.L.; Fridley, B.L.
Publication year: 2016
Source: Cancer Epidemiology, Biomarkers & Prevention, vol. 25, iss. 5, (2016), pp. 780-790
ISSN: 1055-9965
DOI: https://doi.org/10.1158/1055-9965.EPI-15-1039
Publication type: Article / Letter to editor

Please use this identifier to cite or link to this item : http://hdl.handle.net/2066/171815   http://hdl.handle.net/2066/171815

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Subject: Radboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences
Radboudumc 17: Women's cancers RIMLS: Radboud Institute for Molecular Life Sciences
Organization: Urology
Health Evidence
Gynaecology
Journal title:
Cancer Epidemiology, Biomarkers & Prevention
Volume: vol. 25
Issue: iss. 5
Page start: p. 780
Page end: p. 790
Abstract:
BACKGROUND: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal 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 SNPs within 80 candidate genes related to hormone biosynthesis and 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 = 4.9 x 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 x 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 x 10(-6)). CONCLUSIONS: We have demonstrated the feasibility of assessing multifactor 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, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. (c)2016 AACR.

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