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Background: Fatty acids impact obesity, estrogens, and inflammation, which are risk factors for ovarian cancer. Few epidemiologic studies have investigated the association of fatty acids with ovarian cancer. Methods: Within the European Prospective Investigation into Cancer and Nutrition (EPIC), 1,486 incident ovarian cancer cases were identified. Cox proportional hazard models with adjustment for ovarian cancer risk factors were used to estimate HRs of ovarian cancer across quintiles of intake of fatty acids. False discovery rate was computed to control for multiple testing. Multivariable conditional logistic regression models were used to estimate ORs of ovarian cancer across tertiles of plasma fatty acids among 633 cases and two matched controls in a nested case–control analysis. Results: A positive association was found between ovarian cancer and intake of industrial trans elaidic acid [HR comparing fifth with first quintileQ5-Q1 ¼ 1.29; 95% confidence interval (CI) ¼ 1.03–1.62; Ptrend ¼ 0.02, q-value ¼ 0.06]. Dietary intakes of n-6 linoleic acid (HRQ5-Q1 ¼ 1.10; 95% CI ¼ 1.01–1.21; Ptrend ¼ 0.03) and n-3 a-linolenic acid (HRQ5-Q1 ¼ 1.18; 95% CI ¼ 1.05–1.34; Ptrend ¼ 0.007) from deep-frying fats were also positively associated with ovarian cancer. Suggestive associations were reported for circulating elaidic (OR comparing third with first tertileT3-T1 ¼ 1.39; 95% CI ¼ 0.99–1.94; Ptrend ¼ 0.06) and a-linolenic acids (ORT3-T1 ¼ 1.30; 95% CI ¼ 0.98–1.72; Ptrend ¼ 0.06). Conclusions: Our results suggest that higher intakes and circulating levels of industrial trans elaidic acid, and higher intakes of linoleic acid and a-linolenic acid from deep-frying fat, may be associated with greater risk of ovarian cancer. Impact: If causal, eliminating industrial trans-fatty acids could offer a straightforward public health action for reducing ovarian cancer risk. © 2020 American Association for Cancer Research.

Animal and experimental data suggest that anti-Müllerian hormone (AMH) serves as a marker of ovarian reserve and inhibits the growth of ovarian tumors. However, few epidemiologic studies have examined the association between AMH and ovarian cancer risk. We conducted a nested case-control study of 302 ovarian cancer cases and 336 matched controls from nine cohorts. Prediagnostic blood samples of premenopausal women were assayed for AMH using a picoAMH enzyme-linked immunosorbent assay. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable-adjusted conditional logistic regression. AMH concentration was not associated with overall ovarian cancer risk. The multivariable-adjusted OR (95% CI), comparing the highest to the lowest quartile of AMH, was 0.99 (0.59-1.67) (Ptrend : 0.91). The association did not differ by age at blood draw or oral contraceptive use (all Pheterogeneity : ≥0.26). There also was no evidence for heterogeneity of risk for tumors defined by histologic developmental pathway, stage, and grade, and by age at diagnosis and time between blood draw and diagnosis (all Pheterogeneity : ≥0.39). In conclusion, this analysis of mostly late premenopausal women from nine cohorts does not support the hypothesized inverse association between prediagnostic circulating levels of AMH and risk of ovarian cancer.

Background: Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. Methods: We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. Result: The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. Conclusions: The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker. © 2019 The Author(s).

Background: Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women. Methods: We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (-35 U/mL) CA125 prediction models and externally validated an abridged model restricting to available predictors among 473 premenopausal women in the European Prospective Investigation into Cancer and Nutrition Study (EPIC). Results: The final linear CA125 prediction model included age, race, tubal ligation, endometriosis, menstrual phase at blood draw, and fibroids, which explained 7% of the total variance of CA125. The correlation between observed and predicted CA125 levels based on the abridged model (including age, race, and menstrual phase at blood draw) had similar correlation coefficients in NEC (r = 0.22) and in EPIC (r= 0.22). The dichotomous CA125 prediction model included age, tubal ligation, endometriosis, prior personal cancer diagnosis, family history of ovarian cancer, number of miscarriages, menstrual phase at blood draw, and smoking status with AUC of 0.83. The abridged dichotomous model (including age, number of miscarriages, menstrual phase at blood draw, and smoking status) showed similar AUCs in NEC (0.73) and in EPIC (0.78). Conclusions: We identified a combination of factors associated with CA125 levels in premenopausal women. Impact: Our model could be valuable in identifying healthy women likely to have elevated CA125 and consequently improve its specificity for ovarian cancer screening. © 2019 American Association for Cancer Research.

CA125 is the best ovarian cancer early detection marker to date; however, sensitivity is limited and complementary markers are required to improve discrimination between ovarian cancer cases and non-cases. Anti-CA125 autoantibodies are observed in circulation. Our objective was to evaluate whether these antibodies (1) can serve as early detection markers, providing evidence of an immune response to a developing tumor, and (2) modify the discriminatory capacity of CA125 by either masking CA125 levels (resulting in lower discrimination) or acting synergistically to improve discrimination between cases and non-cases. We investigated these objectives using a nested case–control study within the European Prospective Investigation into Cancer and Nutrition cohort (EPIC) including 250 cases diagnosed within 4 years of blood collection and up to four matched controls. Circulating CA125 antigen and antibody levels were quantified using an electrochemiluminescence assay. Adjusted areas under the curve (aAUCs) by 2-year lag-time intervals were calculated using conditional logistic regression calibrated toward the absolute risk estimates from a pre-existing epidemiological risk model as an offset-variable. Anti-CA125 levels alone did not discriminate cases from controls. For cases diagnosed

Immuno-proteomic screening has identified several tumor-associated autoantibodies (AAb) that may have diagnostic capacity for invasive epithelial ovarian cancer, with AAbs to P53 proteins and cancer-testis antigens (CTAGs) as prominent examples. However, the early detection potential of these AAbs has been insufficiently explored in prospective studies. We performed ELISA measurements of AAbs to CTAG1A, CTAG2, P53 and NUDT11 proteins, for 194 patients with ovarian cancer and 705 matched controls from the European EPIC cohort, using serum samples collected up to 36 months prior to diagnosis under usual care. CA125 was measured using electrochemo-luminiscence. Diagnostic discrimination statistics were calculated by strata of lead-time between blood collection and diagnosis. With lead times ≤6 months, ovarian cancer detection sensitivity at 0.98 specificity (SE98) varied from 0.19 [95% CI 0.08–0.40] for CTAG1A, CTAG2 and NUDT1 to 0.23 [0.10–0.44] for P53 (0.33 [0.11–0.68] for high-grade serous tumors). However, at longer lead-times, the ability of these AAb markers to distinguish future ovarian cancer cases from controls declined rapidly; at lead times >1 year, SE98 estimates were close to zero (all invasive cases, range: 0.01–0.11). Compared to CA125 alone, combined logistic regression scores of AAbs and CA125 did not improve detection sensitivity at equal level of specificity. The added value of these selected AAbs as markers for ovarian cancer beyond CA125 for early detection is therefore limited. © 2018 UICC

Background: TheMullerian ducts are the embryological precursors of the female reproductive tract, including the uterus; anti-Mullerian hormone (AMH) has a key role in the regulation of foetal sexual differentiation. Anti-Mullerian hormone inhibits endometrial tumour growth in experimental models by stimulating apoptosis and cell cycle arrest. To date, there are no prospective epidemiologic data on circulating AMH and endometrial cancer risk. Methods: We investigated this association among women premenopausal at blood collection in a multicohort study including participants from eight studies located in the United States, Europe, and China. We identified 329 endometrial cancer cases and 339 matched controls. Anti- Mullerian hormone concentrations in blood were quantified using an enzyme-linked immunosorbent assay. Conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CI) across tertiles and for a doubling of AMH concentrations (ORlog2). Subgroup analyses were performed by ages at blood donation and diagnosis, oral contraceptive use, and tumour characteristics. Results: Anti-Mullerian hormone was not associated with the risk of endometrial cancer overall (ORlog2: 1.07 (0.99-1.17)), or with any of the examined subgroups. Conclusions: Although experimental models implicate AMH in endometrial cancer growth inhibition, our findings do not support a role for circulating AMH in the aetiology of endometrial cancer. © 2017 Cancer Research UK. All Rights Reserved.

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimina-tion. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigat-ed for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selec-tion process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were select-ed into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including eti-ologic markers on independent pathways and genetic markers may further improve discrimination.