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.

Little research has been conducted to determine multifactor gene–environment interaction at the candidate gene or genome-wide level despite the emerging evidence to show that these types of complex relationships do exist (1–3). In addition to the lack of studies assessing complex interactions in cancer risk, only a limited number of studies have assessed gene–environment (GE) interactions by histologic subtype, as genetic and environmental risk factors have been found to differ by the histology. Recently, consortia have been established to give the large sample size needed to detect SNPs with small effects, providing the ability to study GE interactions. In April 2005, the Ovarian Cancer Association Consortium (OCAC) was formed; the largest international consortium conducting genetic epidemiology studies for epithelial ovarian cancer (EOC; ref.4). This international effort comprises more than 40 different genetic epidemiologic studies, with the focus on assessing single SNP associations with EOC.

To date, OCAC has identified 18 confirmed novel susceptibility loci that are associated with EOC risk (5–12). In addition to finding new risk loci, GWAS also confirm the biologic distinction of the various EOC histologies. For example, risk alleles in 8q24 and 19p13 associate almost exclusively with serous EOC (8, 13), yet those in 2q31 and 17q12 are also associated with other subtypes (8, 14). However, it is hypothesized that the known risk loci are likely to represent only a fraction of the common risk alleles for EOC and that numerous undetected common variant loci still remain to be discovered (15).

In addition to genetic susceptibility loci, there are several confirmed EOC environmental risk factors. Similar to other hormone-related cancers in women, many of these risk factors related to hormone exposure, including obesity (risk; refs.16–19), history of endometriosis (risk; ref.20), estrogen use menopausal hormonal therapy (MHT; risk; ref.21), estrogen plus progesterone MHT (risk; ref.21), oral contraceptive use (protective effect that increases with time of use; ref.22), parity (protective effect increases with number of live births; refs.23, 24), tubal ligation (protective; ref.25), and breast feeding (protective; refs.26, 27). Similar to genetic risk factors, environmental risk factors also differ by histology (28); for example, endometriosis is associated with risk of only clear cell, low-grade serous, and endometrioid EOC (20, 29). The vast majority of epidemiologic studies of EOC risk have focused on marginal effects of genetic and environmental factors. A recent study by OCAC investigators assessed GE interactions across six known genetic risk loci (30). While this study looked at GE by histotype, this study did not investigate a three-way interaction involving obesity.

Obesity is associated with an increase in insulin levels, resulting in an increase in insulin-like growth factor 1 (IGF1) activity (31, 32). Increased levels of adiposity also lead to increased aromatase activity, and thus to an increase in estrogen levels (31, 33–35). After menopause, adipose tissue is the major source of estrogen in women. In breast cancer, evidence suggests that increased estrogen levels might underlie the association between BMI, breast cancer risk and MHT (31). It has been found that in postmenopausal women, the association between breast cancer and BMI is stronger in women who have never received MHT, compared with women who have used MHT (36). Similarly, a recent meta-analysis (2012) found that use of MHT attenuated the effect of BMI on EOC risk (17). A recent OCAC study found that high BMI was associated with increased risk of EOC in 15 case–control studies (16). In addition to finding an association between BMI and EOC risk, they found that this association was more pronounced in borderline serous, invasive endometrioid, and invasive mucinous histotypes. However, they found that MHT did not attenuate the effect of BMI on EOC risk when the analyses were restricted to postmenopausal women. In addition, they also found no association of BMI with risk of ovarian cancer in the most common serous histotype (16). On the basis of these data, we hypothesize that GE effects could differ between obese and non-obese women.

On the basis of the complex relationship between hormone exposure, obesity, growth factors/insulin levels, and genetic factors we hypothesize that GE effects could be histology dependent and differ between obese and non-obese women. This hypothesis is illustrated in Supplementary Fig. S1. In this candidate gene study, we sought to detect both two-way and multifactor obesity–GE interactions for EOC risk. Overall, we assessed 11,441 SNPs located within 80 candidate genes related to hormone biosynthesis and metabolism in addition to those in insulin-like growth factors (IGF). The case–control analyses were run separately for case groups that involve (i) all EOC invasive cases; (ii) high-grade serous (HGS) invasive cases; and (iii) endometrioid (ENDO) invasive cases. Candidate gene analyses specific to the less common histotypes were excluded due to the difficulty of assessing three-way interactions.

Study participants

Supplementary Tables S1 and 2 summarize the characteristics of the 14 OCAC studies used to assess GE interactions (37–49). The 14 studies included in this analysis were part of the Collaborative Oncological Gene Environment Consortium (COGS) study in which approximately 200,000 SNPs were genotyped in breast, ovarian and prostate cancers. Each OCAC study included in the analyses had to contribute at least 50 ovarian cancer cases and 50 controls, with controls further required to be sampled from the same population as the cases. Thus, 6,247 invasive cases and 10,379 controls of European descent were included in this analysis. GE interactions have been explored in these studies previously (28) and are described in further detail therein. Each study provided information on age at diagnosis or enrollment, BMI and other reproductive and lifestyle factors as well as information regarding tumor histology (serous, endometrioid, clear cell, mixed, other), tumor behavior (invasive or borderline), and tumor grade (well differentiated, moderately differentiated, poorly differentiated, undifferentiated). All patients provided informed consent, including for passive and active follow-up, using protocols approved by the appropriate Institutional Review Board. Table 1 describes the clinical features of EOC cases (6,247 all EOC, 3,019 HGS, 961 ENDO) and controls (N = 10,379).

Table 1.

Clinical features in EOC cases and controls included in the GE and BMI-GE analyses

CharacteristicsControls: N (%)Cases: N (%)P
Age (years)   <0.0001 
 Mean ± SD 57.5 ± 11.6 58.3 (11.0)  
Age (categorical)   <0.0001 
 <50 years 2,604 (25.1) 1,366 (21.9)  
 50–55 years 1,424 (13.7) 946 (15.1)  
 55–60 years 1,691 (16.3) 1,071 (17.1)  
 60–65 years 1,629 (15.7) 1,015 (16.2)  
 >65 years 3,031 (29.2) 1,849 (29.6)  
Young adult BMI (kg/m2  <0.0001 
 Underweight/normal (<25) 7,607 (91.8) 4,427 (89.7)  
 Overweight/obese (>25) 679 (8.2) 508 (10.3)  
Parity   <0.0001 
 0 full births 1,415 (14.7) 1,453 (25.1)  
 >0 full births 8,234 (85.3) 4,328 (74.9)  
Breast feed   <0.0001 
 No 2,312 (30.3) 1,641 (39.9)  
 Yes 5,320 (69.7) 2,467 (60.1)  
Oral contraceptive use   <0.0001 
 (≤2 years) 4,895 (47.4) 3,487 (57.1)  
 (>2 years) 5,428 (52.6) 2,616 (42.9)  
Estrogen use   0.44 
 No 3,986 (78.9) 2,250 (78.1)  
 Yes 1,068 (21.1) 631 (21.9)  
EPP MHT use   <0.0001 
 No 3,420 (67.7) 2,105 (73.3)  
 Yes 1,631 (32.3) 765 (26.7)  
Endometriosis   <0.0001 
 No 8,738 (93.9) 4,802 (90.0)  
 Yes 568 (6.1) 533 (10.0)  
Tubal ligation   <0.0001 
 No 6,924 (77.8) 4,692 (83.5)  
 Yes 1,976 (22.2) 926 (16.5)  
Tumor grade 
 Well-differentiated  739 (12.1)  
 Moderately differentiated  1,358 (22.2)  
 Poorly differentiated  2,911 (47.6)  
 Undifferentiated  459 (7.5)  
 Other  647 (10.6)  
Histotypes 
 Serous  3,589 (57.4)  
 Mucinous  403 (6.5)  
 Endometrioid  961 (15.4)  
 Clear cell  468 (7.5)  
 Others  827 (13.2)  
CharacteristicsControls: N (%)Cases: N (%)P
Age (years)   <0.0001 
 Mean ± SD 57.5 ± 11.6 58.3 (11.0)  
Age (categorical)   <0.0001 
 <50 years 2,604 (25.1) 1,366 (21.9)  
 50–55 years 1,424 (13.7) 946 (15.1)  
 55–60 years 1,691 (16.3) 1,071 (17.1)  
 60–65 years 1,629 (15.7) 1,015 (16.2)  
 >65 years 3,031 (29.2) 1,849 (29.6)  
Young adult BMI (kg/m2  <0.0001 
 Underweight/normal (<25) 7,607 (91.8) 4,427 (89.7)  
 Overweight/obese (>25) 679 (8.2) 508 (10.3)  
Parity   <0.0001 
 0 full births 1,415 (14.7) 1,453 (25.1)  
 >0 full births 8,234 (85.3) 4,328 (74.9)  
Breast feed   <0.0001 
 No 2,312 (30.3) 1,641 (39.9)  
 Yes 5,320 (69.7) 2,467 (60.1)  
Oral contraceptive use   <0.0001 
 (≤2 years) 4,895 (47.4) 3,487 (57.1)  
 (>2 years) 5,428 (52.6) 2,616 (42.9)  
Estrogen use   0.44 
 No 3,986 (78.9) 2,250 (78.1)  
 Yes 1,068 (21.1) 631 (21.9)  
EPP MHT use   <0.0001 
 No 3,420 (67.7) 2,105 (73.3)  
 Yes 1,631 (32.3) 765 (26.7)  
Endometriosis   <0.0001 
 No 8,738 (93.9) 4,802 (90.0)  
 Yes 568 (6.1) 533 (10.0)  
Tubal ligation   <0.0001 
 No 6,924 (77.8) 4,692 (83.5)  
 Yes 1,976 (22.2) 926 (16.5)  
Tumor grade 
 Well-differentiated  739 (12.1)  
 Moderately differentiated  1,358 (22.2)  
 Poorly differentiated  2,911 (47.6)  
 Undifferentiated  459 (7.5)  
 Other  647 (10.6)  
Histotypes 
 Serous  3,589 (57.4)  
 Mucinous  403 (6.5)  
 Endometrioid  961 (15.4)  
 Clear cell  468 (7.5)  
 Others  827 (13.2)  

NOTE: Sample sizes vary as not all studies collected data on each lifestyle and reproductive factor.

Environmental and genetic risk factors

Young adult BMI.

To quantify obesity, we used BMI calculated in early adulthood (18–29 years of age) as opposed to BMI at diagnosis as early adulthood BMI would better approximate subjects obesity levels integrated over a lifetime (18, 50), and thus exposure to estrogen derived from adipose tissue. Measurement of weight in early adulthood was conducted in 9 of the 14 studies used for the GE analyses (16); and, therefore, the three-way BMI–GE interaction analyses were limited to these 9 studies. Five studies reported weight at age 18 (DOV, HAW, HOP, POL, UCI), two studies reported weight “in your 20s” (MAL, USC), and two studies reported weight at age 20 (AUS, GER). The calculated BMIs were classified according World Health Organization (WHO) standards: (<18.5 “underweight”; 18.5–24.9 “normal weight”; 25–29.9 “overweight”; 30-34.9 “class I obesity”; 35-39.9 “class II obesity”; and ≥40 “class III obesity”; ref.51). From these WHO standards, the subjects BMI were further categorized into two groups for GE analyses: (i) underweight or normal weight individuals with BMI less than 25 and (ii) overweight or obese individuals BMI greater than 25.

Hormone-related environmental factors.

The GE analyses included seven hormone-related environmental factors: oral contraceptive use, parity, breast feeding, tubal ligation status, endometriosis, estrogen MHT, and estrogen plus progesterone MHT. To facilitate testing for multifactor interactions, each environmental factor was dichotomized to ensure reasonable sample sizes in the various groups. Oral contraceptive use (years) was divided into (<1 year; ≥1 year), parity (0 full births; ≥ 1 full birth), breast feeding was separated into (ever/never), estrogen MHT and estrogen plus progesterone MHT were categorized as (never/ever), while endometriosis and tubal ligation were included in terms of yes/no status.

Genetic markers.

We searched the literature to determine a set of candidate genes related to steroid biosynthesis, estrogen signaling, and IGFs, as we hypothesize that genetic variants within these candidate genes modify EOC risk and that these effects are modified by hormone-related risk factors and obesity (52–54), and identified a list of 80 candidate genes (Supplementary Table S3). Using the National Center for Biotechnology Information (NCBI) website, SNPs were selected within 20 Kb of the first or last exon, as this was expected to sufficiently cover the promoter regions of most genes, as well as SNPs in LD with variation in the gene region (55). Because of power limitations for testing multifactor GE interactions, SNPs were excluded from the analysis if the minor allele frequency (MAF) was less than 10%. This approach extracted 11,441 candidate gene SNPs. The candidate gene SNPs were imputed using the 1000 Genomes project (56), from an original set of >200,000 genotyped SNPs from the COGS custom Illumina SNP array (57, 58). Details on the number of imputed SNPs for each candidate gene are included in (Supplementary Table S3).

Statistical analysis

The study population was restricted to individuals of European descent based on LAMP analyses (59) with complete covariate information, and only invasive EOC cases were considered. For analyses involving the MHTs, either estrogen use or estrogen plus progesterone (EPP) use, the cases and controls were further restricted to postmenopausal women. For both the GE and BMI-GE (or GEE) analyses, the presence or absence of the environmental factors were coded as either 0 or 1. Separate analyses were conducted for case groups that included: (i) all invasive EOC cases, (ii) HGS cases, and (iii) ENDO cases. Analyses were adjusted for age of diagnosis (enrollment), study site, and the first 5 principal component scores from a principal component analysis to adjust for population substructure. With the goal to determine GE effects and not general genetic association, assessment of significance was restricted to the higher level interaction effects (as opposed to “omnibus” tests for both genetic main and interaction effects; ref. 60).

The following logistic regression model was used to assess GE interaction for each SNP. For i = 1,…,n let

formula

where Di represents that disease status (case =1, control = 0) for subject i, Gij represents the number of minor alleles observed for subject i for SNP j, Eik represents the absence or presence of environmental factor k for subject i, and Zi represents a vector of covariates for subject i to account for potential confounding, and each |${\beta _{GE}}$| represents a corresponding interaction regression coefficient. For each SNP j and environmental factor k, we tested the null hypothesis of no GE interaction versus an alternative hypothesis that a GE interaction is present |$({\rm{i}}{\rm{.e}}{\rm{., null\ hypothesis\!:}\,} {\beta _{GE}} = 0\ \ {\rm{vs}}{\rm{.\ alternative\ hypothesis\!:}}\ {\beta _{GE}} \ne0)$|⁠. The hypothesis was tested with the likelihood ratio test statistic |$D = - 2\ln \left({\frac{\rm{likelihood\ for\ reduced}({{\rm{null}})}){\rm{model}}}{\rm{likelihood\ for\ full}({{\rm{alternative}})}{\rm{model}}}\right) \sim{\chi_1^2}.$|

Similarly, to test whether GE interactions could be modified by BMI, we considered the following logistic regression model. For i = 1,…n let

formula

where E1i represents the BMI status (low/high) at young adulthood of subject i, E2i represents the presence of absence of the second environmental factor for subject i, and Zi represents a vector of covariates for subject i that account for potential confounding, and each |$\beta| represents a corresponding regression coefficient. To test whether GE interactions differ between non-obese and obese individuals, we test the null hypothesis of no GEE interaction versus an alternative hypothesis of GEE interaction is present (i.e., null hypothesis |${\beta _{GEE}} = 0{\rm{\ versus}}\ {\rm{alternative\ hypothesis\!}}: {\beta _{GEE}} \ne \,0$|⁠). This hypothesis was tested using a likelihood ratio test statistic |$D = - 2\ln \left({\frac{\rm{likelihood\ for\ reduced}({{\rm{null}})}){\rm{model}}}{\rm{likelihood\ for\ full}({{\rm{alternative}})}{\rm{model}}}\right) \sim{\chi_1^2}.$|

GE interaction

In total, the GE analyses were run across 11,441 candidate gene SNPs, and included 91,528 GE combinations [11,441 SNPs × (7 Environmental Factors + BMI)], and these analyses were run across 3 separate case groups (All, HGS, ENDO). However, the imputed SNPs were in high linkage disequilibrium (LD), and the analyses across case groups were also highly correlated. The SimpleM method was used to estimate the effective number of independent SNPs tested within each gene (ref.61; Supplementary Table S3); and, in total, the analyses were estimated to involve independent 2,336 SNPs. Using the estimated effective number of independent tests, the Bonferroni corrections for the number of total candidate gene SNPs was 0.05/2,336 = 2.1 × 10−5, while adjusting for the total number of independent GE combinations gives 0.05/(2,336 × 8) = 2.7 × 10−6, respectively. Several SNP–environment interactions were significant using the former threshold, however using the latter strict threshold, no significant GE was detected. SNPs with GE interaction P < 10−4 are presented in Table 2.

Table 2.

Association with P < 10−4 for GE and BMI-GE analyses

AnalysisHistologyEnvironmentGeneSNPMAFN casesN controlsInteractionP
GE All EPP MHT IGF1R rs41497346 0.28 2,870 5,051 −0.57 4.92 × 10−6 
GE All Endometriosis ESR1 rs12661437 0.34 5,335 9,306 0.53 1.47 × 10−5 
GE ENDO Estrogen MHT HSD17B2 rs2955162 0.23 405 5,054 −1.12 3.44 × 10−5 
GE HGS Endometriosis CYP11A1 rs9944175 0.24 2,578 9,306 −0.87 4.13 × 10−5 
GE ENDO Estrogen MHT AKR1C3 rs61856140 0.10 405 5,054 −1.47 5.30 × 10−5 
GE ENDO Endometriosis CYP11B2 rs28526467 0.43 815 9,306 −1.09 6.83 × 10−5 
GE ENDO OC Use PRL rs72836169 0.10 945 10,323 −0.82 7.09 × 10−5 
BMI-GE ENDO Parity INSR rs8102954 0.37 778 8,284 −2.56 8.35 × 10−6 
BMI-GE All Parity IGFBP2 rs869564 0.11 4,934 8,284 −2.34 1.43 × 10−5 
BMI-GE HGS OC Use CYP11B1 rs113759408 0.17 2,296 8,250 1.49 2.18 × 10−5 
AnalysisHistologyEnvironmentGeneSNPMAFN casesN controlsInteractionP
GE All EPP MHT IGF1R rs41497346 0.28 2,870 5,051 −0.57 4.92 × 10−6 
GE All Endometriosis ESR1 rs12661437 0.34 5,335 9,306 0.53 1.47 × 10−5 
GE ENDO Estrogen MHT HSD17B2 rs2955162 0.23 405 5,054 −1.12 3.44 × 10−5 
GE HGS Endometriosis CYP11A1 rs9944175 0.24 2,578 9,306 −0.87 4.13 × 10−5 
GE ENDO Estrogen MHT AKR1C3 rs61856140 0.10 405 5,054 −1.47 5.30 × 10−5 
GE ENDO Endometriosis CYP11B2 rs28526467 0.43 815 9,306 −1.09 6.83 × 10−5 
GE ENDO OC Use PRL rs72836169 0.10 945 10,323 −0.82 7.09 × 10−5 
BMI-GE ENDO Parity INSR rs8102954 0.37 778 8,284 −2.56 8.35 × 10−6 
BMI-GE All Parity IGFBP2 rs869564 0.11 4,934 8,284 −2.34 1.43 × 10−5 
BMI-GE HGS OC Use CYP11B1 rs113759408 0.17 2,296 8,250 1.49 2.18 × 10−5 

NOTE: Results highlighted in green or red in Figs. 1 and 3. More detailed summaries of these top hits are shown in Supplementary Tables S4 and S5.

Figure 1 provides an image map that highlights interaction tests of environmental factors and candidate genes with at least one SNP P value less than the predefined significance thresholds: P = 10−3, P = 10−4, and P = 10−5. Within this plot, the candidate genes are grouped alphabetically according to their involvement in the production of hormones hypothesized to influence EOC risk (ref.62; Androgen, Estrogen, Progesterone, Gonadotropins, Insulin-related). A full list of SNPs with minimum P values (P < 10−3) in candidate genes for the GE interaction analyses are presented in Supplementary Table S4.

Figure 1.

Image map of top P values for GE interactions results for 80 candidate gene SNPs and 7 hormone-related environmental factors as well as BMI.

Figure 1.

Image map of top P values for GE interactions results for 80 candidate gene SNPs and 7 hormone-related environmental factors as well as BMI.

Close modal

The most statistically significant GE interaction was IGF1R [rs41497346, estrogen plus progesterone (EPP) MHT, histology = all, OR = 0.56, P = 4.9 × 10−6; Fig. 2A and B]. The marginal OR estimate of rs41497346 was .96 (P = 0.12). However, within non-EPP MHT users the presence of a minor allele increased risk for EOC (OR = 1.29); while within EPP MHT users rs41497346 provided a protective effect (OR = 0.72). The rs41497346–EPP MHT interaction estimates were qualitatively similar across each histology included in our candidate gene analyses: HGS (OR = 0.55, P = 1.7 × 10−4), and ENDO (OR = 0.77, P = 0.38). The next most significant GE interaction result included ESR1 (rs12661437, endometriosis, histology = all, OR = 1.71, P = 1.5 × 10−5; Fig. 2C and D), where the minor allele decreased EOC risk in patients with no endometriosis and increased risk in patients with endometriosis. The marginal OR estimate of rs12661437 was 0.95 (P = 0.17). However, within women with no endometriosis history, the presence of a rs12661437 minor allele decreased risk for EOC (OR = 0.92); while within women with a history of endometriosis, the rs12661437 minor allele provided increased risk (OR = 1.59). Subtype-specific analyses for rs12661437 also found qualitatively similar effect sizes across all histologies (Supplementary Table S4). Rs12661437 lies in an intron near the 5′ end of ESR1.

Figure 2.

Locus zoom plots and estimated GE interaction effects of top results for IGF1R-Combination use (A and B), ESR1-Endometriosis (C and D), and CYP11A1-Endometriosis (E and F). The vertical black lines represent 95% CIs for estimated ORs.

Figure 2.

Locus zoom plots and estimated GE interaction effects of top results for IGF1R-Combination use (A and B), ESR1-Endometriosis (C and D), and CYP11A1-Endometriosis (E and F). The vertical black lines represent 95% CIs for estimated ORs.

Close modal

When restricting the cases to HGS, the most notable interaction was for CYP11A1 (rs9944175, endometriosis, histology = HGS, OR = 0.42, P = 4.1 × 10−5; Fig. 2E and F). The marginal OR estimate for HGS EOC risk of rs9944175 was 1.06 (P = 0.26). However, for women with no history of endometriosis, the estimated effect of one rs9944175 minor allele increased HGS EOC risk (OR = 1.1) but decreased HGS EOC risk in women with a history of endometriosis (OR = 0.47). This SNP showed no statistically significant interaction for the ENDO histology (OR = 0.69, P = 0.18). rs9944175 lies within 20 Kb of the 3′ end of CYP11A1.

Multifactor or BMI–GE interactions

For each gene, SNPs with notable BMI–GE interaction results (P < 10−3) and their estimated interaction effects are presented (Supplementary Table S5). Figure 3 provides an image map that highlights three-way interaction tests of obesity, lifestyle, and reproductive factors, and candidate genes with at least on SNP P value less than: P = 10−3, P = 10−4, and P = 10−5. This image map groups the candidate genes alphabetically and according to their involvement in the production of hormones hypothesized to influence EOC risk (ref.62; Androgen, Estrogen, Progesterone, Gonadotropins, Insulin-related). No statistically significant SNPs were detected after Bonferroni correction for the effective number of candidate gene SNPs (P < 2.1 × 10−5). A stricter threshold that adjusts for effective number of candidate gene SNPs by 7 environmental factors in the BMI-GE analyses was P < 3.1 × 10−6.

Figure 3.

Image map of smallest P values for multifactor BMI–GE interactions results for candidate gene SNPs and 7 non-obesity–related environmental factors.

Figure 3.

Image map of smallest P values for multifactor BMI–GE interactions results for candidate gene SNPs and 7 non-obesity–related environmental factors.

Close modal

The most statistically significant SNP for the BMI-GE analyses lies in INSR (rs8102954, parity, histology = ENDO, BMI-GE OR = 0.074, P = 8.83 × 10−6; Figs. 4A and B). Within the low-BMI women group, the estimated SNP–Parity interaction of one rs8102954 minor allele for the ENDO cases was negligible (OR GElow BMI = 1.4, P = 0.15); while within high-BMI women the estimated GE effect is (OR GEhigh BMI = 0.10, P = 0.0021). The BMI–GE interaction effect was not significant for analyses with case groups that included all histology and high-grade serous cases. rs8102954 lies in a exonic region near the 3′ end on INSR.

For case–controls analyses including all histologies, the most notable BMI–GE interaction was IGFBP2 (rs869564, parity, histology = All, BMI–GE OR = 0.096, P = 1.43 × 10−5; Fig. 4C and D). For low-BMI women, the estimated SNP–parity interaction effect of one rs869564 minor allele was negligible (OR GElow BMI, P = 0.48); however, within high-BMI women, the estimated GE interaction effect was (OR GEhigh BMI = 0.11, P = 4.14 × 10−5). The three-way BMI–GE interaction effect was significant for the HGS cases (BMI–GE OR = 0.077, P = 1.23 × 10−3), but not the analyses involving the ENDO cases (BMI–GE OR = P = 0.18). rs869564 resides in an exonic region on the 3′ end of IGFBP2.

For HGS cases, the most statistically significant SNP for the BMI–GE analyses lies in CYP11B1 (rs113759408, oral contraceptive use, histology = HGS, BMI–GE OR = 0.072, P = 2.2 × 10−5; Figs. 4E and F). Within the low-BMI women group, the estimated SNP–OC use interaction effect of one rs113759408 minor allele for HGS cases was negligible (OR GElow BMI= −0.90, P = 0.41), while within high-BMI women, the estimated GE effect is large (OR GEhigh BMI = 4.52, P = 0.0028). The BMI–GE interaction effect was not statistically significant for the ENDO histology (BMI-GE OR= 2.11, P = 0.24). rs113759408 lies in an intronic region in the middle of CYP11B1.

In this article, we investigated both GE and multifactor obesity–GE interactions in epithelial ovarian cancer (EOC) risk. We used 14 case–control studies within the Collaborative Oncological Gene Environment Consortium (COGS) and Ovarian Cancer Association Consortium (OCAC) that provided more than 6,000 cases and 10,000 controls. Our main hypothesis was that some EOC risk due to SNPs could be explained by interactions with environmental factors. Similar to breast and endometrial cancers, many EOC risk factors relate to hormone exposure and increased levels of estrogen has been associated with obesity in postmenopausal women. Therefore, we hypothesized that GE interactions dealing with hormone-related risk factors could differ between obese and non-obese women. None of the tests of GE interaction and multi-factor obesity–GE interaction were significant at genome-wide level (P = 5 × 10−8).

The most statistically significant GE interaction result was IGF1R (rs41497346, estrogen plus progesterone MHT, histology = All, OR = 0.56, P = 4.9 × 10−6). Rs41497346 lies in an intronic region near the 3′ end of IGF1R, and is in the same LD block as several SNPs hypothesized to have marginal risk in breast cancer (63). High expression levels of IGF1R were reported by Tang and colleagues (64) in tumor tissue samples from 25 of 36 patients with EOC. Estrogen use is associated with increased IGF1R expression, while progesterone was associated with decreased IGF1R expression in breast cancer cells (65). Variation within the gene ESR1 was also found to be involved in an interaction involving endometriosis in analyses of all histologies (rs12661437, intronic SNP near 5′ end of gene, P = 1.5 × 10−5), where the minor allele decreased EOC risk in patients with no endometriosis and increased risk in patients with endometriosis. Subtype-specific analyses for rs12661437 also found qualitatively similar effect sizes across all histologies. Variation near ESR1 (rs2295190) has been reported to be associated with EOC risk (66); however, the SNPs are in low LD (r2 = 0.001).

For the BMI–GE interaction analyses, the most statistically significant results were INSR (rs8102954, parity, histology = ENDO, BMI-GE OR = 0.074, P = 8.83 × 10−6; Fig. 4A and B) and IGFBP2 (rs869564, parity, histology = All, BMI–GE OR = 0.096, P = 1.43 × 10−5; Figs. 4C and D). No genetic polymorphisms within INSR and IGFBP2 have been associated previously with ovarian cancer risk. Nevertheless, considerable research exists on the role of insulin receptors and cancer as studies have shown that insulin receptors may be involved in the regulation of ovarian cancer cell growth (67) and that increased levels of insulin have been associated with breast and endometrium cancers for which these tumorigenic properties can be modified by insulin receptors (31). Similarly, the role of IGFs have been extensive studied for their role in carcinogenesis (68). Specifically, IGFBP2 has been linked ovarian cancer by promoting cancer cell invasion (69), while common variants in IGF1, IGFBP1 and IGFBP3, have been associated with ovarian (70) and endometrial cancers (71). IGFBP2 has also been linked to other hormone-related cancers (72–74).

For the high-grade serous cases, the most statistically significant SNP for the BMI-GE analyses lies in CYP11B1 (rs113759408, oral contraceptive use, histology = HGS, BMI-GE estimate = 1.49, P = 2.2 × 10−5; Figs. 4E and F). Polymorphism rs113759408 lies in an intronic region in the middle of CYP11B1 (between exons 3 and 4), the gene that encodes for steroid 11β-hydroxylase. Mutations in this gene cause congenital adrenal hyperplasia (OMIM #202010). No research has been published showing a link between EOC risk and variants within this gene. However, genetic variation in CYP11B1 has been reported to be associated with breast cancer risk from a prediction model involving SNP rs4541 in exon 7 of CYP11B1 (75) and the association with serum hormone levels in breast cancer patients (76).

Figure 4.

Locus zoom plots and estimated BMI–GE interaction effects of top results for INSR-Parity-BMI (Histology ENDO; A and B), IGFBP2-Parity-BMI (Histology all; C and D), and CYP11B1-OC Use-BMI (Histology HGS; E and F). The vertical black lines represent 95% CIs for estimated ORs.

Figure 4.

Locus zoom plots and estimated BMI–GE interaction effects of top results for INSR-Parity-BMI (Histology ENDO; A and B), IGFBP2-Parity-BMI (Histology all; C and D), and CYP11B1-OC Use-BMI (Histology HGS; E and F). The vertical black lines represent 95% CIs for estimated ORs.

Close modal

We chose to restrict our analyses to SNPs located within 80 candidate gene and 8 established ovarian cancer reproductive or lifestyle factors. An earlier study investigated two-way interactions between 6 established SNP risk loci and 5 established environmental risk factors (30). Similar to our study results, their two-way interaction analyses were not strong enough to rule out the role of chance. While these initial findings suggest that GE interactions play a modest role in EOC risk, genome-wide studies are necessary to fully examine the potential interplay between SNPs and environmental factors.

For the obesity-GE analyses, a strength of this study was the use of young adult BMI (low, high) as opposed to BMI at diagnosis, as young adult BMI may serve as an indicator of obesity integrated over a life-time and adipose-based estrogen exposure (18, 50). While a biologic rationale exists for higher order interactions, very little literature has focused on multi-factor interactions, perhaps due to the challenge of necessary power to detect these higher order interactions. Therefore, a limitation of the multi-factor GE interaction analyses were modest sample sizes: especially for less well-documented environmental factors and histology-specific analyses (Supplementary Table S7).

In conclusion, we have demonstrated the feasibility of assessing multi-factor interactions in large genetic epidemiology studies. Future work is needed to develop powerful statistical methods able to detect these complex interactions, as they may provide additional information regarding the genetic etiology of ovarian and other hormone-related cancers. Follow-up studies are necessary to assess the robustness of our notable findings in ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. To further follow-up our investigation of multi-factor GE interactions, we will explore other potential modifiers of GE risk, such as BRCA mutation status, and assess BMI-GE in other hormone-related cancers, such as breast, prostate, and endometrial.

M.T. Goodman is a consultant/advisory board member for Johnson and Johnson. No potential conflicts of interest were disclosed by the other authors.

Conception and design: H. Anton-Culver, A. Berchuck, M.T. Goodman, S.K. Kjaer, B.L. Fridley

Development of methodology: J.L. Usset, B.L. Fridley

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): V. McGuire, P. Webb, J. Chang-Claude, A. Rudolph, H. Anton-Culver, A. Berchuck, L.A. Brinton, J.M. Cunningham, A. DeFazio, J.A. Doherty, R.P. Edwards, A. Gentry-Maharaj, M.T. Goodman, A. Jensen, L.A. Kiemeney, S.K. Kjaer, G. Lurie, L. Massuger, U. Menon, F. Modugno, K.B. Moysich, R.B. Ness, M.C. Pike, S.J. Ramus, M.A. Rossing, H. Song, D.J. van den Berg, S. Wang-Gohrke, N. Wentzensen, A.S. Whittemore, L.R. Wilkens, A.H. Wu, J.M. Schildkraut, P. Pharaoh, E.L. Goode

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.L. Usset, J.P. Tyrer, W. Sieh, M.C. Larson, K.B. Moysich, H. Yang, P. Pharaoh, B.L. Fridley

Writing, review, and/or revision of the manuscript: J.L. Usset, J.P. Tyrer, V. McGuire, W. Sieh, P. Webb, J. Chang-Claude, A. Rudolph, H. Anton-Culver, A. Berchuck, L.A. Brinton, J.M. Cunningham, A. DeFazio, J.A. Doherty, R.P. Edwards, S.A. Gayther, A. Gentry-Maharaj, M.T. Goodman, E. Høgdall, A. Jensen, S.E. Johnatty, L.A. Kiemeney, S.K. Kjaer, M.C. Larson, G. Lurie, U. Menon, F. Modugno, K.B. Moysich, R.B. Ness, S.J. Ramus, M.A. Rossing, H. Song, R.A. Vierkant, S. Wang-Gohrke, N. Wentzensen, A.S. Whittemore, A.H. Wu, H. Yang, C.L. Pearce, P. Pharaoh, B.L. Fridley

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. Raghavan, A. Rudolph, A. Berchuck, M.T. Goodman, E. Høgdall, G. Lurie, F. Modugno, S.J. Ramus, J. Rothstein, P.J. Thompson, S. Wang-Gohrke

Study supervision: H. Anton-Culver, L.A. Kiemeney, P.J. Thompson, E.L. Goode, B.L. Fridley

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).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Le Marchand
L
,
Hankin
JH
,
Wilkens
LR
,
Pierce
LM
,
Franke
A
,
Kolonel
LN
, et al
Combined effects of well-done red meat, smoking, and rapid N-acetyltransferase 2 and CYP1A2 phenotypes in increasing colorectal cancer risk
.
Cancer Epidemiol Biomarkers Prev
2001
;
10
:
1259
66
.
2.
Shaw
GM
,
Iovannisci
DM
,
Yang
W
,
Finnell
RH
,
Carmichael
SL
,
Cheng
S
, et al
Endothelial nitric oxide synthase (NOS3) genetic variants, maternal smoking, vitamin use, and risk of human orofacial clefts
.
Am J Epidemiol
2005
;
162
:
1207
14
.
3.
Wei
S
,
Wang
LE
,
McHugh
MK
,
Han
Y
,
Xiong
M
,
Amos
CI
, et al
Genome-wide gene-environment interaction analysis for asbestos exposure in lung cancer susceptibility
.
Carcinogenesis
2012
;
33
:
1531
7
.
4.
Bolton
KL
,
Ganda
C
,
Berchuck
A
,
Pharaoh
PD
,
Gayther
SA
. 
Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the Ovarian Cancer Association Consortium (OCAC)
.
J Intern Med
2012
;
271
:
366
78
.
5.
Kuchenbaecker
KB
,
Ramus
SJ
,
Tyrer
J
,
Lee
A
,
Shen
HC
,
Beesley
J
et al 
Identification of six new susceptibility loci for invasive epithelial ovarian cancer
.
Nat Genet
2015
;
47
:
164
71
.
6.
Bojesen
SE
,
Pooley
KA
,
Johnatty
SE
,
Beesley
J
,
Michailidou
K
,
Tyrer
JP
, et al
Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer
.
Nat Genet
2013
;
45
:
371
84
.
7.
Pharoah
PD
,
Tsai
YY
,
Ramus
SJ
,
Phelan
CM
,
Goode
EL
,
Lawrenson
K
, et al
GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer
.
Nat Genet
2013
;
45
:
362
70
,
70e1
2
.
8.
Goode
EL
,
Chenevix-Trench
G
,
Song
H
,
Ramus
SJ
,
Notaridou
M
,
Lawrenson
K
, et al
A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24
.
Nat Genet
2010
;
42
:
874
9
.
9.
Song
H
,
Ramus
SJ
,
Tyrer
J
,
Bolton
KL
,
Gentry-Maharaj
A
,
Wozniak
E
, et al
A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2
.
Nat Genet
2009
;
41
:
996
1000
.
10.
Shen
H
,
Fridley
BL
,
Song
H
,
Lawrenson
K
,
Cunningham
JM
,
Ramus
SJ
, et al
Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer
.
Nat Commun
2013
;
4
:
1628
.
11.
Permuth-Wey
J
,
Lawrenson
K
,
Shen
HC
,
Velkova
A
,
Tyrer
JP
,
Chen
Z
, et al
Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21
.
Nat Commun
2013
;
4
:
1627
.
12.
Bolton
KL
,
Ganda
C
,
Berchuck
A
,
Pharaoh
PD
,
Gayther
SA
. 
Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the Ovarian Cancer Association Consortium (OCAC)
.
J Intern Med
2012
;
271
:
366
78
.
13.
Bolton
KL
,
Tyrer
J
,
Song
H
,
Ramus
SJ
,
Notaridou
M
,
Jones
C
, et al
Common variants at 19p13 are associated with susceptibility to ovarian cancer
.
Nat Genet
2010
;
42
:
880
4
.
14.
Shen
H
,
Fridley
BL
,
Song
H
,
Lawrenson
K
,
Cunningham
JM
,
Ramus
SJ
, et al
Epigenetic analysis leads to identification of HNF1B as a subtype-specific susceptibility gene for ovarian cancer
.
Nat Commun
2013
;
4
:
1628
.
15.
Bolton
KL
,
Ganda
C
,
Berchuck
A
,
Pharaoh
PD
,
Gayther
SA
. 
Role of common genetic variants in ovarian cancer susceptibility and outcome: progress to date from the ovarian cancer association consortium (OCAC)
.
J Intern Med
2012
;
271
:
366
78
.
16.
Olsen
CM
,
Nagle
CM
,
Whiteman
DC
,
Ness
R
,
Pearce
CL
,
Pike
MC
, et al
Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium
.
Endocr Relat Cancer
2013
;
20
:
251
62
.
17.
Collaborative Group on Epidemiological Studies Of Ovarian Cancer.
Ovarian cancer and body size: individual participant meta-analysis including 25,157 women with ovarian cancer from 47 epidemiological studies
.
PLoS Med
2012
;
9
:
e1001200
.
18.
Leitzmann
MF
,
Koebnick
C
,
Danforth
KN
,
Brinton
LA
,
Moore
SC
,
Hollenbeck
AR
, et al
Body mass index and risk of ovarian cancer
.
Cancer
2009
;
115
:
812
22
.
19.
Feigelson
HS
,
Jonas
CR
,
Teras
LR
,
Thun
MJ
,
Calle
EE
. 
Weight gain, body mass index, hormone replacement therapy, and postmenopausal breast cancer in a large prospective study
.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
220
4
.
20.
Pearce
CL
,
Templeman
C
,
Rossing
MA
,
Lee
A
,
Near
AM
,
Webb
PM
, et al
Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case–control studies
.
Lancet Oncol
2012
;
13
:
385
94
.
21.
Collaborative Group on Epidemiological Studies Of Ovarian Cancer
Beral
V
,
Gaitskell
K
,
Hermon
C
,
Moser
K
,
Reeves
G
, et al
Menopausal hormone use and ovarian cancer risk: individual participant meta-analysis of 52 epidemiological studies
.
Lancet
2015
;
13
:
385
94
.
22.
Collaborative Group on Epidemiological Studies Of Ovarian Cancer.
Ovarian cancer and oral contraceptives: collaborative reanalysis of data from 45 epidemiological studies including 23 257 women with ovarian cancer and 87 303 controls
.
Lancet
2008
;
371
:
303
14
.
23.
Whittmore
AS
,
Harris
R
,
Itnyre
J
. 
Characteristics relating to ovarian cancer risk: collaborative analysis of 12 US case-control studies II. Invasive epithelial ovarian cancers in White women.
Am J Epidemiol
1992
;
136
:
1184
203
.
24.
Adami
H
,
Lambe
M
,
Persson
I
,
Ekbom
A
,
Hsieh
C
,
Trichopoulos
D
, et al
Parity, age at first childbirth, and risk of ovarian cancer
.
Lancet
1994
;
344
:
1250
4
.
25.
Sieh
W
,
Salvador
S
,
McGuire
V
,
Weber
RP
,
Terry
KL
,
Rossing
MA
, et al
Tubal ligation and risk of ovarian cancer subtypes: a pooled analysis of case-control studies
.
Int J Epidemiol
2013
;
42
:
579
89
.
26.
Li
DP
,
Du
C
,
Zhang
ZM
,
Li
GX
,
Yu
ZF
,
Wang
X
, et al
Breastfeeding and ovarian cancer risk: a systematic review and meta-analysis of 40 epidemiological studies
.
Asian Pac J Cancer Prev
2014
;
15
:
4829
37
.
27.
Luan
NN
,
Wu
QJ
,
Gong
TT
,
Vogtmann
E
,
Wang
YL
,
Lin
B
. 
Breastfeeding and ovarian cancer risk: a meta-analysis of epidemiologic studies
.
Am J Clin Nutr
2013
;
98
:
1020
31
.
28.
Pearce
CL
,
Rossing
MA
,
Lee
AW
,
Ness
RB
,
Webb
PM
,
Chenevix-Trench
G
, et al
Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
880
90
.
29.
Pearce
CL
,
Templeman
C
,
Rossing
MA
,
Lee
A
,
Near
AM
,
Webb
PM
, et al
Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case?control studies
.
Lancet Oncol
2012
;
13
:
385
94
.
30.
Pearce
CL
,
Rossing
MA
,
Lee
AW
,
Ness
RB
,
Webb
PM
for Australian Cancer S
et al
Combined and interactive effects of environmental and GWAS-identified risk factors in ovarian cancer
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
880
90
.
31.
Calle
EE
,
Kaaks
R
. 
Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms
.
Nat Rev Cancer
2004
;
4
:
579
91
.
32.
Pugeat
M
,
Crave
JC
,
Elmidani
M
,
Nicolas
MH
,
Garoscio-Cholet
M
,
Lejeune
H
, et al
Pathophysiology of sex hormone binding globulin (SHBG): relation to insulin
.
J Steroid Biochem Mol Biol
1991
;
40
:
841
9
.
33.
Tchernof
A
,
Despres
J
. 
Sex steroid hormones, sex hormone-binding globulin, and obesity in men and women
.
Horm Metab Res
1999
;
32
:
526
36
.
34.
Key
TJ
,
Allen
NE
,
Verkasalo
PK
,
Banks
E
. 
Energy balance and cancer: the role of sex hormones
.
Proc Nutr Soc
2001
;
60
:
81
9
.
35.
Key
TJ
,
Appleby
PN
,
Reeves
GK
,
Roddam
A
,
Dorgan
JF
,
Longcope
C
, et al
Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women
.
J Natl Cancer Inst
2003
;
95
:
1218
26
.
36.
Armer
JM
,
Radina
ME
,
Porock
D
,
Culbertson
SD
. 
Predicting breast cancer-related lymphedema using self-reported symptoms
.
Nurs Res
2003
;
52
:
370
9
.
37.
Merritt
MA
,
Green
AC
,
Nagle
CM
,
Webb
PM
. 
Talcum powder, chronic pelvic inflammation and NSAIDs in relation to risk of epithelial ovarian cancer
.
Int J Cancer
2008
;
122
:
170
6
.
38.
Ness
RB
,
Dodge
RC
,
Edwards
RP
,
Baker
JA
,
Moysich
KB
. 
Contraception methods, beyond oral contraceptives and tubal ligation, and risk of ovarian cancer
.
Ann Epidemiol
2011
;
21
:
188
96
.
39.
Royar
J
,
Becher
H
,
Chang‐Claude
J
. 
Low‐dose oral contraceptives: Protective effect on ovarian cancer risk
.
Int J Cancer
2001
;
95
:
370
4
.
40.
Lurie
G
,
Wilkens
LR
,
Thompson
PJ
,
McDuffie
KE
,
Carney
ME
,
Terada
KY
, et al
Combined oral contraceptive use and epithelial ovarian cancer risk: time-related effects
.
Epidemiology
2008
;
19
:
237
43
.
41.
Glud
E
,
Kjaer
SK
,
Thomsen
BL
,
Høgdall
C
,
Christensen
L
,
Høgdall
E
, et al
Hormone therapy and the impact of estrogen intake on the risk of ovarian cancer
.
Arch Intern Med
2004
;
164
:
2253
9
.
42.
Kelemen
LE
,
Sellers
TA
,
Schildkraut
JM
,
Cunningham
JM
,
Vierkant
RA
,
Pankratz
VS
, et al
Genetic variation in the one-carbon transfer pathway and ovarian cancer risk
.
Cancer Res
2008
;
68
:
2498
506
.
43.
Garcia-Closas
M
,
Brinton
LA
,
Lissowska
J
,
Richesson
D
,
Sherman
ME
,
Szeszenia-Dabrowska
N
, et al
Ovarian cancer risk and common variation in the sex hormone-binding globulin gene: a population-based case-control study
.
BMC Cancer
2007
;
7
:
60
.
44.
Ziogas
A
,
Gildea
M
,
Cohen
P
,
Bringman
D
,
Taylor
TH
,
Seminara
D
, et al
Cancer risk estimates for family members of a population-based family registry for breast and ovarian cancer
.
Cancer Epidemiol Biomarkers Prev
2000
;
9
:
103
11
.
45.
Balogun
N
,
Gentry-Maharaj
A
,
Wozniak
EL
,
Lim
A
,
Ryan
A
,
Ramus
SJ
, et al
Recruitment of newly diagnosed ovarian cancer patients proved challenging in a multicentre biobanking study
.
J Clin Epidemiol
2011
;
64
:
525
30
.
46.
Pike
MC
,
Pearce
CL
,
Peters
R
,
Cozen
W
,
Wan
P
,
Wu
AH
. 
Hormonal factors and the risk of invasive ovarian cancer: a population-based case-control study
.
Fertil Steril
2004
;
82
:
186
95
.
47.
Hoyo
C
,
Berchuck
A
,
Halabi
S
,
Bentley
RC
,
Moorman
P
,
Calingaert
B
, et al
Anthropometric measurements and epithelial ovarian cancer risk in African–American and White women
.
Cancer Causes Control
2005
;
16
:
955
63
.
48.
Rossing
MA
,
Cushing-Haugen
KL
,
Wicklund
KG
,
Doherty
JA
,
Weiss
NS
. 
Menopausal hormone therapy and risk of epithelial ovarian cancer
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2548
56
.
49.
McGuire
V
,
Felberg
A
,
Mills
M
,
Ostrow
K
,
DiCioccio
R
,
John
E
, et al
Relation of contraceptive and reproductive history to ovarian cancer risk in carriers and noncarriers of BRCA1 gene mutations
.
Am J Epidemiol
2004
;
160
:
613
8
.
50.
Engeland
A
,
Tretli
S
,
Bjørge
T
. 
Height, body mass index, and ovarian cancer: a follow-up of 1.1 million Norwegian women
.
J Natl Cancer Inst
2003
;
95
:
1244
8
.
51.
World Health Organisation
. 
Physical status: the use and interpretation of anthropometry
.
Report of a World Health Organisation Expert Committee.
1995
.
52.
Calle
EE
,
Thun
MJ
. 
Obesity and cancer
.
Oncogene
2004
;
23
:
6365
78
.
53.
Canzian
F
,
Cox
DG
,
Setiawan
VW
,
Stram
DO
,
Ziegler
RG
,
Dossus
L
, et al
Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium
.
Hum Mol Genet
2010
;
19
:
3873
84
.
54.
Olson
SH
,
Bandera
EV
,
Orlow
I
. 
Variants in estrogen biosynthesis genes, sex steroid hormone levels, and endometrial cancer: a HuGE review
.
Am J Epidemiol
2007
;
165
:
235
45
.
55.
Smith
AV
,
Thomas
DJ
,
Munro
HM
,
Abecasis
GR
. 
Sequence features in regions of weak and strong linkage disequilibrium
.
Genome Res
2005
;
15
:
1519
34
.
56.
1000 Genomes Project Consortium
Abecasis
GR
,
Auton
A
,
Brooks
LD
,
DePristo
MA
,
Durbin
RM
, et al
An integrated map of genetic variation from 1,092 human genomes
.
Nature
2012
;
491
:
56
65
.
57.
Pharoah
PD
,
Tsai
Y-Y
,
Ramus
SJ
,
Phelan
CM
,
Goode
EL
,
Lawrenson
K
, et al
GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer
.
Nat Genet
2013
;
45
:
362
70
.
58.
Couch
FJ
,
Wang
X
,
McGuffog
L
,
Lee
A
,
Olswold
C
,
Kuchenbaecker
KB
, et al
Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk
.
PLoS Genet
2013
;
9
:
e1003212
.
59.
Sankararaman
S
,
Sridhar
S
,
Kimmel
G
,
Halperin
E
. 
Estimating local ancestry in admixed populations
.
Am J Hum Genet
2008
;
82
:
290
303
.
60.
Kraft
P
,
Yen
YC
,
Stram
DO
,
Morrison
J
,
Gauderman
WJ
. 
Exploiting gene-environment interaction to detect genetic associations
.
Hum Hered
2007
;
63
:
111
9
.
61.
Gao
X
,
Starmer
J
,
Martin
ER
. 
A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms
.
Genet Epidemiol
2008
;
32
:
361
.
62.
Risch
HA
. 
Hormonal etiology of epithelial ovarian cancer, with a hypothesis concerning the role of androgens and progesterone
.
J Natl Cancer Inst
1998
;
90
:
1774
86
.
63.
Christopoulos
PF
,
Msaouel
P
,
Koutsilieris
M
. 
The role of the insulin-like growth factor-1 system in breast cancer
.
Mol Cancer
2015
;
14
:
43
.
64.
Tang
J
,
Li
J
,
Zeng
G
,
Tang
Y
,
Tian
W
,
He
J
, et al
Antisense oligonucleotide suppression of human IGF-1R inhibits the growth and survival of in vitro cultured epithelial ovarian cancer cells
.
J Ovarian Res
2013
;
6
:
71
.
65.
Clarke
RB
,
Howell
A
,
Anderson
E
. 
Type I insulin-like growth factor receptor gene expression in normal human breast tissue treated with oestrogen and progesterone
.
Br J Cancer
1997
;
75
:
251
.
66.
Doherty
JA
,
Rossing
MA
,
Cushing-Haugen
KL
,
Chen
C
,
Van Den Berg
DJ
,
Wu
AH
, et al
ESR1/SYNE1 polymorphism and invasive epithelial ovarian cancer risk: an Ovarian Cancer Association Consortium study
.
Cancer Epidemiol Biomarkers Prev
2010
;
19
:
245
50
.
67.
Kalli
KR
,
Falowo
OI
,
Bale
LK
,
Zschunke
MA
,
Roche
PC
,
Conover
CA
. 
Functional insulin receptors on human epithelial ovarian carcinoma cells: implications for IGF-II mitogenic signaling
.
Endocrinology
2002
;
143
:
3259
67
.
68.
Gallagher
EJ
,
LeRoith
D
. 
The proliferating role of insulin and insulin-like growth factors in cancer
.
Trends Endocrinol Metab
2010
;
21
:
610
8
.
69.
Lee
E-J
,
Mircean
C
,
Shmulevich
I
,
Wang
H
,
Liu
J
,
Niemistö
A
, et al
Insulin-like growth factor binding protein 2 promotes ovarian cancer cell invasion
.
Mol Cancer
2005
;
4
:
7
.
70.
Terry
KL
,
Tworoger
SS
,
Gates
MA
,
Cramer
DW
,
Hankinson
SE
. 
Common genetic variation in IGF1, IGFBP1, and IGFBP3 and ovarian cancer risk
.
Carcinogenesis
. 
2009
;
30
:
2042
6
.
71.
McGrath
M
,
Lee
IM
,
Buring
J
,
De Vivo
I
. 
Common genetic variation within IGFI, IGFII, IGFBP-1, and IGFBP-3 and endometrial cancer risk
.
Gynecol Oncol
2011
;
120
:
174
8
.
72.
Garner
CP
,
Ding
YC
,
John
EM
,
Ingles
SA
,
Olopade
OI
,
Huo
D
, et al
Genetic variation in IGFBP2 and IGFBP5 is associated with breast cancer in populations of African descent
.
Hum Genet
2008
;
123
:
247
55
.
73.
Neuhausen
SL
,
Brummel
S
,
Ding
YC
,
Singer
CF
,
Pfeiler
G
,
Lynch
HT
, et al
Genetic variation in insulin-like growth factor signaling genes and breast cancer risk among BRCA1 and BRCA2 carriers
.
Breast Cancer Res
2009
;
11
:
R76
.
74.
Cust
AE
,
Allen
NE
,
Rinaldi
S
,
Dossus
L
,
Friedenreich
C
,
Olsen
A
, et al
Serum levels of C-peptide, IGFBP-1 and IGFBP-2 and endometrial cancer risk; Results from the European prospective investigation into cancer and nutrition
.
Int J Cancer
2007
;
120
:
2656
64
.
75.
Listgarten
J
,
Damaraju
S
,
Poulin
B
,
Cook
L
,
Dufour
J
,
Driga
A
, et al
Predictive models for breast cancer susceptibility from multiple single nucleotide polymorphisms
.
Clin Cancer Res
2004
;
10
:
2725
37
.
76.
Dudenkov
TM
,
Ingle
JN
,
Buzdar
A
,
Robson
ME
,
Kubo
M
,
Batzler
A
, et al
Genes associated with serum estrone, estrone conjugates, and androstenedione concentrations in postmenopausal women with estrogen receptor-positive breast cancer
.
J Clin Oncol
32
:
5s
, 
2014
(suppl; abstr 593).