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

Joseph L Usset; Rama Raghavan; Jonathan P Tyrer; Valerie McGuire; Weiva Sieh; Penelope Webb; Jenny Chang-Claude; Anja Rudolph; Hoda Anton-Culver; Andrew Berchuck; Louise Brinton; Julie M Cunningham; Anna DeFazio; Jennifer A Doherty; Robert P Edwards; Simon A Gayther; Aleksandra Gentry-Maharaj; Marc T Goodman; Estrid Høgdall; Allan Jensen; Sharon E Johnatty; Lambertus A Kiemeney; Susanne K Kjaer; Melissa C Larson; Galina Lurie; Leon Massuger; Usha Menon; Francesmary Modugno; Kirsten B Moysich; Roberta B Ness; Malcolm C Pike; Susan J Ramus; Mary Anne Rossing; Joseph Rothstein; Honglin Song; Pamela J Thompson; David J van den Berg; Robert A Vierkant; Shan Wang-Gohrke; Nicolas Wentzensen; Alice S Whittemore; Lynne R Wilkens; Anna H Wu; Hannah Yang; Celeste Leigh Pearce; Joellen M Schildkraut; Paul Pharoah; Ellen L Goode; Brooke L Fridley; Ovarian Cancer Association Consortium and the Australian Cancer Study

doi:10.1158/1055-9965.epi-15-1039

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

Joseph L Usset, Rama Raghavan, Jonathan P Tyrer, Valerie McGuire, Weiva Sieh, Penelope Webb, Jenny Chang-Claude, Anja Rudolph, Hoda Anton-Culver, Andrew Berchuck, Louise Brinton, Julie M Cunningham, Anna DeFazio, Jennifer A Doherty, Robert P Edwards, Simon A Gayther, Aleksandra Gentry-Maharaj, Marc T Goodman, Estrid Høgdall, Allan JensenSharon E Johnatty, Lambertus A Kiemeney, Susanne K Kjaer, Melissa C Larson, Galina Lurie, Leon Massuger, Usha Menon, Francesmary Modugno, Kirsten B Moysich, Roberta B Ness, Malcolm C Pike, Susan J Ramus, Mary Anne Rossing, Joseph Rothstein, Honglin Song, Pamela J Thompson, David J van den Berg, Robert A Vierkant, Shan Wang-Gohrke, Nicolas Wentzensen, Alice S Whittemore, Lynne R Wilkens, Anna H Wu, Hannah Yang, Celeste Leigh Pearce, Joellen M Schildkraut, Paul Pharoah, Ellen L Goode, Brooke L Fridley, Ovarian Cancer Association Consortium and the Australian Cancer Study

Department of Clinical Medicine

9 Citations (Scopus)

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 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.

Original language	English
Journal	Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Volume	25
Issue number	5
Pages (from-to)	780-90
Number of pages	11
ISSN	1055-9965
DOIs	https://doi.org/10.1158/1055-9965.epi-15-1039
Publication status	Published - May 2016

Keywords

Journal Article

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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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., Høgdall, E., ... Ovarian Cancer Association Consortium and the Australian Cancer Study (2016). Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 25(5), 780-90. https://doi.org/10.1158/1055-9965.epi-15-1039

Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk : Candidate Genes, Obesity, and Hormone-Related Risk Factors. / Usset, Joseph L; Raghavan, Rama; Tyrer, Jonathan P et al.

In: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, Vol. 25, No. 5, 05.2016, p. 780-90.

Research output: Contribution to journal › Journal article › Research › peer-review

Usset, JL, Raghavan, R, Tyrer, JP, McGuire, V, Sieh, W, Webb, P, Chang-Claude, J, Rudolph, A, Anton-Culver, H, Berchuck, A, Brinton, L, Cunningham, JM, DeFazio, A, Doherty, JA, Edwards, RP, Gayther, SA, Gentry-Maharaj, A, Goodman, MT, Høgdall, E, Jensen, A, Johnatty, SE, Kiemeney, LA, Kjaer, SK, Larson, MC, Lurie, G, Massuger, L, Menon, U, Modugno, F, Moysich, KB, Ness, RB, Pike, MC, Ramus, SJ, Rossing, MA, Rothstein, J, Song, H, Thompson, PJ, van den Berg, DJ, Vierkant, RA, Wang-Gohrke, S, Wentzensen, N, Whittemore, AS, Wilkens, LR, Wu, AH, Yang, H, Pearce, CL, Schildkraut, JM, Pharoah, P, Goode, EL, Fridley, BL & Ovarian Cancer Association Consortium and the Australian Cancer Study 2016, 'Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors', Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, vol. 25, no. 5, pp. 780-90. https://doi.org/10.1158/1055-9965.epi-15-1039

Usset JL, Raghavan R, Tyrer JP, McGuire V, Sieh W, Webb P et al. Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2016 May;25(5):780-90. doi: 10.1158/1055-9965.epi-15-1039

Usset, Joseph L ; Raghavan, Rama ; Tyrer, Jonathan P et al. / Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk : Candidate Genes, Obesity, and Hormone-Related Risk Factors. In: Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2016 ; Vol. 25, No. 5. pp. 780-90.

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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 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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T1 - Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk

T2 - Candidate Genes, Obesity, and Hormone-Related Risk Factors

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

AU - Ovarian Cancer Association Consortium and the Australian Cancer Study

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.

KW - Journal Article

U2 - 10.1158/1055-9965.epi-15-1039

DO - 10.1158/1055-9965.epi-15-1039

M3 - Journal article

C2 - 26976855

SN - 1055-9965

VL - 25

SP - 780

EP - 790

JO - Cancer Epidemiology, Biomarkers & Prevention

JF - Cancer Epidemiology, Biomarkers & Prevention

IS - 5

ER -

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

Abstract

Keywords

UN SDGs

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