Purpose

Mutations in TP53 induce autoantibody immune responses in a subset of cancer patients, which have been proposed as biomarkers for early detection. Here, we investigate the association of p53-specific autoantibodies with multiple tumor subtypes and determine the association with p53 mutation status and epitope specificity.

Experimental design

IgG p53 autoantibodies (p53-AAb), were quantified in 412 serum samples using a programmable ELISA assay from patients with serous ovarian, pancreatic adenocarcinoma, and breast cancer. To determine if patients generated mutation-specific autoantibodies we designed a panel of the most relevant 51 p53 point mutant proteins, to be displayed on custom programmable protein microarrays. To determine the epitope specificity we displayed 12 overlapping tiling fragments and 38 N- and C-terminal deletions spanning the length of the wild-type p53 protein.

Results

We detected p53-AAb with sensitivities of 58.8% (ovarian), 22% (pancreatic), 32% (triple negative breast cancer), and 10.2% (HER2+ breast cancer) at 94% specificity. Sera with p53-AAb contained broadly reactive autoantibodies to 51 displayed p53 mutant proteins, demonstrating a polyclonal response to common epitopes. All p53-AAb displayed broad polyclonal immune response to both continuous and discontinuous epitopes at the N- and C-terminus as well as the DNA-binding domain.

Conclusion and clinical relevance

In this comprehensive analysis, mutations in tumor p53 induce strong, polyclonal autoantibodies with broadly reactive epitope specificity.

Objective

To evaluate the associations between 10 well-established ovarian cancer risk factors and risk of ovarian cancer among women with vs. without endometriosis.

Design

Pooled analysis of 9 case-control studies in the Ovarian Cancer Association Consortium.

Setting

Population-based.

Patient(s)

We included 8,500 women with ovarian cancer, 13,592 control women.

Intervention(s)

Ten well-established ovarian cancer risk factors.

Main outcome measure(s)

Risk of ovarian cancer for women with and without endometriosis.

Result(s)

Most risk factor-ovarian cancer associations were similar when comparing women with and without endometriosis, and no interactions were statistically significant. However, body mass index (BMI) 25-<30 kg/m² was associated with increased ovarian cancer risk among women with endometriosis (odds ratio [OR] = 1.27, 95% confidence interval [CI] 1.00-1.60), but not associated with the risk among women without endometriosis (OR = 0.97; 95% CI, 0.91-1.05) when compared with BMI 18.5-<25 kg/m²; an increased risk was observed for a BMI ≥30 kg/m², although there was little difference comparing women with endometriosis (OR = 1.21; 95% CI, 0.94-1.57) to women without (OR = 1.13; 95% CI, 1.04-1.22) (P-interaction = .51). Genital talcum powder use and long-term menopausal estrogen-only therapy use showed increased ovarian cancer risk, but risk appeared greater for those with endometriosis vs. those without (genital talcum powder: OR = 1.38; 95% CI, 1.04-1.84 vs. OR = 1.12; 95% CI, 1.01-1.25, respectively; ≥10 years of estrogen-only therapy: OR = 1.88; 95% CI, 1.09-3.24 vs. OR = 1.42; 95% CI, 1.14-1.76, respectively); neither of these interactions were statistically significant (P-interaction = .65 and P-interaction = .96, respectively).

Conclusion(s)

The associations between ovarian cancer and most risk factors were similar among women with and without endometriosis. However, there was some suggestion of differences by endometriosis status for BMI, menopausal hormone therapy use, and genital talcum powder use, highlighting the complexity of ovarian cancer etiology.

Purpose

Cancer antigen 125 (CA125) is a glycoprotein expressed by epithelial cells of several normal tissue types and overexpressed by several epithelial cancers. Serum CA125 levels are mostly used as an aid in the diagnosis of ovarian cancer patients, to monitor response to treatment and detect cancer recurrence. Besides tumor characteristics, CA125 levels are also influenced by several epidemiologic factors, such as age, parity, and oral contraceptive use. Identifying factors that influence CA125 levels in ovarian cancer patients could aid in the interpretation of CA125 values for individuals.

Methods

We evaluated predictors of pretreatment CA125 in 13 studies participating in the Ovarian Cancer Association Consortium. This analysis included a total of 5,091 women with invasive epithelial ovarian cancer with pretreatment CA125 measurements. We used probit scores to account for variability in CA125 between studies and linear regression to estimate the association between epidemiologic factors and tumor characteristics and pretreatment CA125 levels.

Results

In age-adjusted models, older age, history of pregnancy, history of tubal ligation, family history of breast cancer, and family history of ovarian cancer were associated with higher CA125 levels while endometriosis was associated with lower CA125 levels. After adjusting for tumor-related characteristics (stage, histology, grade), body mass index (BMI) higher than 30 kg/m² was associated with 10% (95% CI 2, 19%) higher CA125 levels, while race (non-white vs. white) was associated with 15% (95% CI 4, 27%) higher CA125 levels.

Conclusion

Our results suggest that high BMI and race may influence CA125 levels independent of tumor characteristics. Validation is needed in studies that use a single assay for CA125 measurement and have a diverse study population.

Generally, risk stratification models for cancer use effect estimates from risk/protective factor analyses that have not assessed potential interactions between these exposures. We have developed a 4-criterion framework for assessing interactions that includes statistical, qualitative, biological, and practical approaches. We present the application of this framework in an ovarian cancer setting because this is an important step in developing more accurate risk stratification models. Using data from 9 case-control studies in the Ovarian Cancer Association Consortium, we conducted a comprehensive analysis of interactions among 15 unequivocal risk and protective factors for ovarian cancer (including 14 non-genetic factors and a 36-variant polygenic score) with age and menopausal status. Pairwise interactions between the risk/protective factors were also assessed. We found that menopausal status modifies the association among endometriosis, first-degree family history of ovarian cancer, breastfeeding, and depot-medroxyprogesterone acetate use and disease risk, highlighting the importance of understanding multiplicative interactions when developing risk prediction models.

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.

Background

Regular aspirin use is associated with reduced risk of several malignancies. Epidemiologic studies analyzing aspirin, nonaspirin nonsteroidal anti-inflammatory drug (NSAID), and acetaminophen use and ovarian cancer risk have been inconclusive.

Methods

We analyzed pooled data from 12 population-based case-control studies of ovarian cancer, including 7776 case patients and 11843 control subjects accrued between 1992 and 2007. Odds ratios (ORs) for associations of medication use with invasive epithelial ovarian cancer were estimated in individual studies using logistic regression and combined using random effects meta-analysis. Associations between frequency, dose, and duration of analgesic use and risk of ovarian cancer were also assessed. All statistical tests were two-sided.

Results

Aspirin use was associated with a reduced risk of ovarian cancer (OR = 0.91; 95% confidence interval [CI] = 0.84 to 0.99). Results were similar but not statistically significant for nonaspirin NSAIDs, and there was no association with acetaminophen. In seven studies with frequency data, the reduced risk was strongest among daily aspirin users (OR = 0.80; 95% CI = 0.67 to 0.96). In three studies with dose information, the reduced risk was strongest among users of low dose (<100 mg) aspirin (OR = 0.66; 95% CI = 0.53 to 0.83), whereas for nonaspirin NSAIDs, the reduced risk was strongest for high dose (≥500 mg) usage (OR = 0.76; 95% CI = 0.64 to 0.91).

Conclusions

Aspirin use was associated with a reduced risk of ovarian cancer, especially among daily users of low-dose aspirin. These findings suggest that the same aspirin regimen proven to protect against cardiovascular events and several cancers could reduce the risk of ovarian cancer 20% to 34% depending on frequency and dose of use.

Background

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with an estimated prevalence of 4-21% in reproductive aged women. Recently, the Ovarian Cancer Association Consortium (OCAC) reported a decreased risk of invasive ovarian cancer among women with self-reported PCOS. However, given the limitations of self-reported PCOS, the validity of these observed associations remains uncertain. Therefore, we sought to use Mendelian randomization with genetic markers as a proxy for PCOS, to examine the association between PCOS and ovarian cancer.

Methods

Utilizing 14 single nucleotide polymorphisms (SNPs) previously associated with PCOS we assessed the association between genetically predicted PCOS and ovarian cancer risk, overall and by histotype, using summary statistics from a previously conducted genome-wide association study (GWAS) of ovarian cancer among European ancestry women within the OCAC (22 406 with invasive disease, 3103 with borderline disease and 40 941 controls).

Results

An inverse association was observed between genetically predicted PCOS and invasive ovarian cancer risk: odds ratio (OR)=0.92 [95% confidence interval (CI)=0.85-0.99; P = 0.03]. When results were examined by histotype, the strongest inverse association was observed between genetically predicted PCOS and endometrioid tumors (OR = 0.77; 95% CI = 0.65-0.92; P = 0.003). Adjustment for individual-level body mass index, oral contraceptive use and parity did not materially change the associations.

Conclusion

Our study provides evidence for a relationship between PCOS and reduced ovarian cancer risk, overall and among specific histotypes of invasive ovarian cancer. These results lend support to our previous observational study results. Future studies are needed to understand mechanisms underlying this association.

Background: Polycystic ovary syndrome (PCOS), and one of its distinguishing characteristics, oligomenorrhea, have both been associated with ovarian cancer risk in some but not all studies. However, these associations have been rarely examined by ovarian cancer histotypes, which may explain the lack of clear associations reported in previous studies.Methods: We analyzed data from 14 case-control studies including 16,594 women with invasive ovarian cancer (n = 13,719) or borderline ovarian disease (n = 2,875) and 17,718 controls. Adjusted study-specific ORs were calculated using logistic regression and combined using random-effects meta-analysis. Pooled histotype-specific ORs were calculated using polytomous logistic regression.Results: Women reporting menstrual cycle length >35 days had decreased risk of invasive ovarian cancer compared with women reporting cycle length ≤35 days [OR = 0.70; 95% confidence interval (CI) = 0.58-0.84]. Decreased risk of invasive ovarian cancer was also observed among women who reported irregular menstrual cycles compared with women with regular cycles (OR = 0.83; 95% CI = 0.76-0.89). No significant association was observed between self-reported PCOS and invasive ovarian cancer risk (OR = 0.87; 95% CI = 0.65-1.15). There was a decreased risk of all individual invasive histotypes for women with menstrual cycle length >35 days, but no association with serous borderline tumors (P_{heterogeneity} = 0.006). Similarly, we observed decreased risks of most invasive histotypes among women with irregular cycles, but an increased risk of borderline serous and mucinous tumors (P_{heterogeneity} < 0.0001).Conclusions: Our results suggest that menstrual cycle characteristics influence ovarian cancer risk differentially based on histotype.Impact: These results highlight the importance of examining ovarian cancer risk factors associations by histologic subtype. Cancer Epidemiol Biomarkers Prev; 27(2); 174-82. ©2017 AACR.