TY - JOUR
T1 - Assessment of multifactor gene?environment interactions and ovarian cancer risk
T2 - Candidate genes, obesity, and hormone-related risk factors
AU - On behalf of Ovarian Cancer Association Consortium and the Australian Cancer Study
AU - Usset, Joseph L.
AU - Raghavan, Rama
AU - Tyrer, Jonathan P.
AU - McGuire, Valerie
AU - Sieh, Weiva
AU - Webb, Penelope
AU - Chang-Claude, Jenny
AU - Rudolph, Anja
AU - Anton-Culver, Hoda
AU - Berchuck, Andrew
AU - Brinton, Louise
AU - Cunningham, Julie M.
AU - DeFazio, Anna
AU - Doherty, Jennifer A.
AU - Edwards, Robert P.
AU - Gayther, Simon A.
AU - Gentry-Maharaj, Aleksandra
AU - Goodman, Marc T.
AU - Høgdall, Estrid
AU - Jensen, Allan
AU - Johnatty, Sharon E.
AU - Kiemeney, Lambertus A.
AU - Kjaer, Susanne K.
AU - Larson, Melissa C.
AU - Lurie, Galina
AU - Massuger, Leon
AU - Menon, Usha
AU - Modugno, Francesmary
AU - Moysich, Kirsten B.
AU - Ness, Roberta B.
AU - Pike, Malcolm C.
AU - Ramus, Susan J.
AU - Rossing, Mary Anne
AU - Rothstein, Joseph
AU - Song, Honglin
AU - Thompson, Pamela J.
AU - Van Den Berg, David J.
AU - Vierkant, Robert A.
AU - Wang-Gohrke, Shan
AU - Wentzensen, Nicolas
AU - Whittemore, Alice S.
AU - Wilkens, Lynne R.
AU - Wu, Anna H.
AU - Yang, Hannah
AU - Pearce, Celeste Leigh
AU - Schildkraut, Joellen M.
AU - Pharoah, Paul
AU - Goode, Ellen L.
AU - Fridley, Brooke L.
N1 - Funding Information:
The Ovarian Cancer Association Consortium is supported by a grant from the Ovarian Cancer Research Fund (PPD/RPCI.07). The COGS project was funded through a European Commission's Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175). This work was supported in part by the NIH [P30 CA168524 (to B.L. Fridley), R01 CA112523 (to M.A. Rossing), R01 CA87538 (to M.A. Rossing), R01 CA58598 (to M.T. Goodman), R01 CA61107 (to A. Jensen, S.K. Kjaer), N01 CN55424 (to M.T. Goodman), N01 PC 67001(to M.T. Goodman), R01 CA122443 (to E.L. Goode), P30 CA15083 (to E.L. Goode), P50 CA136393 (to E.L. Goode), R01 CA76016 (to J.M. Schildkraut), U01 CA71966 (to A.S. Whittemore), R01 CA16056 (to K.B. Moysich), K07 CA143047 (to W. Sieh), U01 CA69417 (to W. Sieh), R01 CA058860 (to H. Anton-Culver), P30 CA14089 (to C.L. Pearce and S.J. Ramus), R01 CA61132 (to M.C. Pike), N01 PC67010 (to C.L. Pearce), R03 CA113148 (to C.L. Pearce), R03 CA115195 (to C.L. Pearce), R13 CA110770 (to F. Modugno)]; Danish Cancer Society (94 22252); Mermaid 1; U.S. Army Medical Research and Materiel Command (DAMD17-01-1- 0729), National Health & Medical Research Council of Australia (199600 and 400281); Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania; Cancer Foundation of Western Australia; German Federal Ministry of Education and Research, Program of Clinical Biomedical Research (01GB9401); German Cancer Research Center; US Army Medical Research and Material Command (DAMD17-02-1-0669, DAMD17-02-1-0666); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; Radboud University Nijmegen Medical Centre; Intramural Research Program of the National Cancer Institute (to N. Wentzensen); Cancer Research UK [C490/A10119 and C490/A10124 (to P. Pharoah and H. Song)]; Lon V Smith Foundation (LVS-39420); Eve Appeal; OAK Foundation; and California Cancer Research Program (00-01389V-20170, 2II0200).
Publisher Copyright:
© 2016 American Association for Cancer Research.
PY - 2016/5
Y1 - 2016/5
N2 - 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 nonobese 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 hormonerelated 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 × 10-6) and ESR1 (rs12661437, endometriosis, histology=all, P=1.5×10-5). The most notable obesity?gene?hormone risk factor interaction was within INSR (rs113759408, parity, histology=endometrioid, P= 8.8 × 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 factorsmay vary EOC susceptibility.
AB - 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 nonobese 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 hormonerelated 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 × 10-6) and ESR1 (rs12661437, endometriosis, histology=all, P=1.5×10-5). The most notable obesity?gene?hormone risk factor interaction was within INSR (rs113759408, parity, histology=endometrioid, P= 8.8 × 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 factorsmay vary EOC susceptibility.
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U2 - 10.1158/1055-9965.EPI-15-1039
DO - 10.1158/1055-9965.EPI-15-1039
M3 - Article
C2 - 26976855
AN - SCOPUS:84965130575
SN - 1055-9965
VL - 25
SP - 780
EP - 790
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 5
ER -