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Cancer Epidemiol Biomarkers Prev. 2016 May;25(5):780-90. doi: 10.1158/1055-9965.EPI-15-1039. Epub 2016 Mar 14.

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors.

Author information

1
Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas.
2
Department of Oncology, University of Cambridge Strangeways Research Laboratory, Cambridge, United Kingdom.
3
Department of Health Research and Policy - Epidemiology, Stanford University School of Medicine, Stanford, California.
4
Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
5
Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
6
Department of Epidemiology, University of California Irvine, Irvine, California.
7
Department of Obstetrics and Gynecology, Duke University Medical Center, Durham, North Carolina.
8
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland.
9
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
10
Discipline of Obstetrics, Gynecology, and Neonatology, University of Sydney, Westmead Institute for Cancer Research, Westmead Millennium Institute, Westmead, New South Wales, Australia.
11
Department of Epidemiology, Geisel School of Medicine, Hanover, New Hampshire.
12
Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania.
13
Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California.
14
Women's Cancer, Institute for Women's Health, University College London, London, United Kingdom.
15
Department of Virus, Lifestyle, and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Department of Pathology, Herlev Hospital, University of Copenhagen, Copenhagen, Denmark.
16
Department of Virus, Lifestyle, and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark.
17
Division of Genetics and Public Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
18
Department of Health Evidence, Radboud University Medical Centre, Nijmegen, the Netherlands.
19
Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
20
Department of Health Science Research, Mayo Clinic, Rochester, Minnesota.
21
Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii.
22
Department of Obstetrics & Gynecology, Radboud University Medical Center, Nijmegen, the Netherlands.
23
Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania.
24
Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York.
25
School of Public Health, The University of Texas, Houston, Texas.
26
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York.
27
Department of Preventive Medicine, University of Southern California, Los Angeles, California.
28
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington. Department of Epidemiology, University of Washington, Seattle, Washington.
29
Department of Obstetrics and Gynecology, University of Ulm, Ulm, Germany.
30
Department of Preventive Medicine, University of Southern California, Los Angeles, California. Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.
31
Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.
32
Department of Oncology, University of Cambridge Strangeways Research Laboratory, Cambridge, United Kingdom. Department of Public Health and Primary Care, University of Cambridge Strangeways Research Laboratory, Cambridge, United Kingdom.
33
Department of Biostatistics, University of Kansas Medical Center, Kansas City, Kansas. bfridley@kumc.edu.

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 × 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 factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR.

PMID:
26976855
PMCID:
PMC4873330
DOI:
10.1158/1055-9965.EPI-15-1039
[Indexed for MEDLINE]
Free PMC Article

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