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BACKGROUND: Associations of body shape with breast cancer risk, independent of body size, are unclear because waist and hip circumferences are correlated strongly positively with body mass index (BMI).METHODS: We evaluated body shape with the allometric "a body shape index" (ABSI) and hip index (HI), which compare waist and hip circumferences, correspondingly, among individuals with the same weight and height. We examined associations of ABSI, HI, and BMI (per one standard deviation increment) with breast cancer overall, and according to menopausal status at baseline, age at diagnosis, and oestrogen and progesterone receptor status (ER+/-PR+/-) in multivariable Cox proportional hazards models using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.RESULTS: During a mean follow-up of 14.0 years, 9011 incident breast cancers were diagnosed among 218,276 women. Although there was little evidence for association of ABSI with breast cancer overall (hazard ratio HR = 0.984; 95% confidence interval: 0.961-1.007), we found borderline inverse associations for post-menopausal women (HR = 0.971; 0.942-1.000; n = 5268 cases) and breast cancers diagnosed at age ≥ 55 years (HR = 0.976; 0.951-1.002; n = 7043) and clear inverse associations for ER + PR- subtypes (HR = 0.894; 0.822-0.971; n = 726) and ER-PR- subtypes (HR = 0.906; 0.835-0.983 n = 759). There were no material associations with HI. BMI was associated strongly positively with breast cancer overall (HR = 1.074; 1.049-1.098), for post-menopausal women (HR = 1.117; 1.085-1.150), for cancers diagnosed at age ≥ 55 years (HR = 1.104; 1.076-1.132), and for ER + PR + subtypes (HR = 1.122; 1.080-1.165; n = 3101), but not for PR- subtypes.CONCLUSIONS: In the EPIC cohort, abdominal obesity evaluated with ABSI was not associated with breast cancer risk overall but was associated inversely with the risk of post-menopausal PR- breast cancer. Our findings require validation in other cohorts and with a larger number of PR- breast cancer cases.

BACKGROUND: The Mediterranean diet has been associated with lower risk of breast cancer (BC) but evidence from prospective studies on the role of Mediterranean diet on BC survival remains sparse and conflicting. We aimed to investigate whether adherence to Mediterranean diet prior to diagnosis is associated with overall and BC-specific mortality. METHODS: A total of 13,270 incident breast cancer cases were identified from an initial sample of 318,686 women in 9 countries from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Adherence to Mediterranean diet was estimated through the adapted relative Mediterranean diet (arMED), a 16-point score that includes 8 key components of the Mediterranean diet and excludes alcohol. The degree of adherence to arMED was classified as low (score 0-5), medium (score 6-8), and high (score 9-16). Multivariable Cox proportional hazards models were used to analyze the association between the arMED score and overall mortality, and Fine-Gray competing risks models were applied for BC-specific mortality. RESULTS: After a mean follow-up of 8.6 years from diagnosis, 2340 women died, including 1475 from breast cancer. Among all BC survivors, low compared to medium adherence to arMED score was associated with a 13% higher risk of all-cause mortality (HR 1.13, 95%CI 1.01-1.26). High compared to medium adherence to arMED showed a non-statistically significant association (HR 0.94; 95% CI 0.84-1.05). With no statistically significant departures from linearity, on a continuous scale, a 3-unit increase in the arMED score was associated with an 8% reduced risk of overall mortality (HR3-unit 0.92, 95% CI: 0.87-0.97). This result sustained when restricted to postmenopausal women and was stronger among metastatic BC cases (HR3-unit 0.81, 95% CI: 0.72-0.91). CONCLUSIONS: Consuming a Mediterranean diet before BC diagnosis may improve long-term prognosis, particularly after menopause and in cases of metastatic breast cancer. Well-designed dietary interventions are needed to confirm these findings and define specific dietary recommendations.

Background: Current epidemiologic evidence indicates that smoking is associated with a lower endometrial cancer risk. However, it is unknown if this association is causal or confounded. To further elucidate the role of smoking in endometrial cancer risk, we conducted complementary observational and Mendelian randomization (MR) analyses. Methods: The observational analyses included 286,415 participants enrolled in the European Prospective Investigation into Cancer and Nutrition and 179,271 participants in the UK Biobank, and multivariable Cox proportional hazards models were used. In twosampleMR analyses, genetic variants robustly associated with lifetime amount of smoking (n ¼ 126 variants) and ever having smoked regularly (n ¼ 112 variants) were selected and their association with endometrial cancer risk (12,906 cancer/108,979 controls from the Endometrial Cancer Association Consortium) was examined. Results: In the observational analysis, lifetime amount of smoking and ever having smoked regularly were associated with a lower endometrial cancer risk. In the MR analysis accounting for body mass index, a genetic predisposition to a higher lifetime amount of smoking was not associated with endometrial cancer risk (OR per 1-SD increment: 1.15; 95% confidence interval: 0.91–1.44). Genetic predisposition to ever having smoked regularly was not associated with risk of endometrial cancer. Conclusions: Smoking was inversely associated with endometrial cancer in the observational analyses, although unsupported by the MR. Additional studies are required to better understand the possible confounders and mechanisms underlying the observed associations between smoking and endometrial cancer. Impact: The results from this analysis indicate that smoking is unlikely to be causally linked with endometrial cancer risk., World Health Organization, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, NIHR Imperial Biomedical Research Centre (BRC), Danish Cancer Society, Ligue Contre le Cancer (France) Institut Gustave Roussy (France) MutuelleGenerale de l'Education Nationale (France), Institut National de la Sante et de la Recherche Medicale (Inserm), Deutsche Krebshilfe German Cancer Research Center (DKFZ) (Germany) German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) (Germany) Federal Ministry of Education & Research (BMBF), Fondazione AIRC per la ricerca sul cancro Compagnia di San Paolo Consiglio Nazionale delle Ricerche (CNR), Netherlands Government Netherlands Government, World Cancer Research Fund International (WCRF), Netherlands Government, Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII) (Spain), Junta de Andalucia, Principality of Asturias Regional Government of Basque Country (Spain) Regional Government of Murcia (Spain) Regional Government of Navarra (Spain) Catalan Institute of Oncology - ICO (Spain), Swedish Cancer Society Swedish Research Council County Council of Skane (Sweden) County Council of Vasterbotten (Sweden), Cancer Research UK 14136 C8221/A29017, UK Research & Innovation (UKRI), Medical Research Council UK (MRC) 1000143 MR/M012190/1 MR/N003284/1 MC-UU_12015/1 MC_UU_00006/ 1, Cancer Research UK C864/A14136 C18281/A29019

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers, imputation of missing data, (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis, (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74-0.81, p = 3.1 × 10-31). ispartof: AMERICAN JOURNAL OF HUMAN GENETICS vol:108 issue:7 pages:1190-1203 ispartof: location:United States status: published

Evidence suggests an influence of sex hormones on cutaneous melanoma risk, but epidemiologic findings are conflicting. We examined the associations between use of oral contraceptives (OCs) and menopausal hormone therapy (MHT) and melanoma risk in women participating in the European Prospective Investigation into Cancer and Nutrition (EPIC). EPIC is a prospective cohort study initiated in 1992 in 10 European countries. Information on exogenous hormone use at baseline was derived from country-specific self-administered questionnaires. We used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Over 1992–2015, 1,696 melanoma cases were identified among 334,483 women, whereof 770 cases among 134,758 postmenopausal women. There was a positive, borderline-significant association between OC use and melanoma risk (HR = 1.12, 95% CI = 1.00–1.26), with no detected heterogeneity across countries (phomogeneity = 0.42). This risk increased linearly with duration of use (ptrend = 0.01). Among postmenopausal women, ever use of MHT was associated with a nonsignificant increase in melanoma risk overall (HR = 1.14, 95% CI = 0.97–1.43), which was heterogeneous across countries (phomogeneity = 0.05). Our findings do not support a strong and direct association between exogenous hormone use and melanoma risk. In order to better understand these relations, further research should be performed using prospectively collected data including detailed information on types of hormone, and on sun exposure, which may act as an important confounder or effect modifier on these relations. © 2019 UICC

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.

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: Except for a documented increase in osteoprotegerin (OPG) concentrations with older age, data on determinants of soluble Receptor Activator of Nuclear Factor kB (sRANKL) and OPG concentrations in women are limited. We evaluated reproductive and lifestyle factors as potential sources of variation in circulating sRANKL and OPG concentrations in pre- and postmenopausal women. Methods: This study includes 2,016 controls [n = 1,552 (76%) postmenopausal, n = 757 (38%) using postmenopausal hormone therapy (PMH)] from a breast cancer case-control study nested in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Serum sRANKL was measured using an ELISA and serum OPG using an electrochemiluminescent assay. Generalized linear models were used to evaluate associations between these analytes and reproductive and lifestyle factors. Results: Older age at blood collection was associated with lower sRANKL concentrations in postmenopausal women (Ptrend < 0.03) and higher OPG concentrations in all women (Ptrend < 0.01). Longer duration of oral contraceptive use among premenopausal women and postmenopausal PMH users was associated with higher OPG (Ptrend < 0.04). In postmenopausal non-PMH users, sRANKL concentrations were lower with longer duration of oral contraceptive use and current (vs. never) smoking (P < 0.01). sRANKL concentrations were higher among women with higher BMI (Ptrend < 0.01). The evaluated factors accounted for 12% of the variation in sRANKL concentrations and 21% of the variation in OPG concentrations. Conclusions: Circulating sRANKL and OPG concentrations are minimally impacted by hormone-related factors in pre- and postmenopausal women. Impact: This study suggests circulating concentrations of sRANKL and OPG are unlikely to be strongly modified by hormone-related reproductive and lifestyle factors. © 2019 American Association for Cancer Research.

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.

Background: Metabolomics is a promising molecular tool to identify novel etiologic pathways leading to cancer. Using a targeted approach, we prospectively investigated the associations between metabolite concentrations in plasma and breast cancer risk. Methods: A nested case-control study was established within the European Prospective Investigation into Cancer cohort, which included 1624 first primary incident invasive breast cancer cases (with known estrogen and progesterone receptor and HER2 status) and 1624 matched controls. Metabolites (n = 127, acylcarnitines, amino acids, biogenic amines, glycerophospholipids, hexose, sphingolipids) were measured by mass spectrometry in pre-diagnostic plasma samples and tested for associations with breast cancer incidence using multivariable conditional logistic regression. Results: Among women not using hormones at baseline (n = 2248), and after control for multiple tests, concentrations of arginine (odds ratio [OR] per SD = 0.79, 95% confidence interval [CI] = 0.70-0.90), asparagine (OR = 0.83 (0.74-0.92)), and phosphatidylcholines (PCs) ae C36:3 (OR = 0.83 (0.76-0.90)), aa C36:3 (OR = 0.84 (0.77-0.93)), ae C34:2 (OR = 0.85 (0.78-0.94)), ae C36:2 (OR = 0.85 (0.78-0.88)), and ae C38:2 (OR = 0.84 (0.76-0.93)) were inversely associated with breast cancer risk, while the acylcarnitine C2 (OR = 1.23 (1.11-1.35)) was positively associated with disease risk. In the overall population, C2 (OR = 1.15 (1.06-1.24)) and PC ae C36:3 (OR = 0.88 (0.82-0.95)) were associated with risk of breast cancer, and these relationships did not differ by breast cancer subtype, age at diagnosis, fasting status, menopausal status, or adiposity. Conclusions: These findings point to potentially novel pathways and biomarkers of breast cancer development. Results warrant replication in other epidemiological studies. © 2019 The Author(s).

Background: Even though in situ breast cancer (BCIS) accounts for a large proportion of the breast cancers diagnosed, few studies have investigated potential risk factors for BCIS. Their results suggest that some established risk factors for invasive breast cancer have a similar impact on BCIS risk, but large population-based studies on lifestyle factors and BCIS risk are lacking. Thus, we investigated the association between lifestyle and BCIS risk within the European Prospective Investigation into Cancer and Nutrition cohort. Methods: Lifestyle was operationalized by a score reflecting the adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations. The recommendations utilized in these analyses were the ones pertinent to healthy body weight, physical activity, consumption of plant-based foods, energy-dense foods, red and processed meat, and sugary drinks and alcohol, as well as the recommendation on breastfeeding. Cox proportional hazards regression was used to assess the association between lifestyle score and BCIS risk. The results were presented as hazard ratios (HR) and corresponding 95% confidence intervals (CI). Results: After an overall median follow-up time of 14.9 years, 1277 BCIS cases were diagnosed. Greater adherence to the WCRF/AICR cancer prevention recommendations was not associated with BCIS risk (HR = 0.98, 95% CI 0.93-1.03; per one unit of increase; multivariable model). An inverse association between the lifestyle score and BCIS risk was observed in study centers, where participants were recruited mainly via mammographic screening and attended additional screening throughout follow-up (HR = 0.85, 95% CI 0.73-0.99), but not in the remaining ones (HR = 0.99, 95% CI 0.94-1.05). Conclusions: While we did not observe an overall association between lifestyle and BCIS risk, our results indicate that lifestyle is associated with BCIS risk among women recruited via screening programs and with regular screening participation. This suggests that a true inverse association between lifestyle habits and BCIS risk in the overall cohort may have been masked by a lack of information on screening attendance. The potential inverse association between lifestyle and BCIS risk in our analyses is consistent with the inverse associations between lifestyle scores and breast cancer risk reported from previous studies. © 2019 The Author(s).

Background: Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction. Methods: We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention. Results: Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail. Conclusions: Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention. © 2018 The Author(s).

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

Background: Neoplastic and non-neoplastic events may raise levels of mucins, CA15.3, and CA125, and generate antibodies against them, but their impact on epithelial ovarian cancer (EOC) risk has not been fully defined. Methods: CA15.3, CA125, and IgG1 antibodies against them were measured in 806 women who developed EOC and 1,927 matched controls from the European Prospective Investigation of Nutrition and Cancer. Associations between epidemiologic factors and anti-mucin antibodies were evaluated using generalized linear models; EOC risks associated with anti-mucin antibodies, by themselves or in combination with respective antigens, were evaluated using conditional logistic regression. Results: In controls, lower antibodies against both mucins were associated with current smoking; and, in postmenopausal women, higher levels with longer oral contraceptive use and later-age-at and shorter-interval-since last birth. Lower anti-CA15.3 antibodies were associated with higher body mass and, in premenopausal women, more ovulatory cycles. Higher anti-CA15.3 and anti-CA125 antibodies were associated with higher risk for mucinous EOC occurring ≥ 3 years from enrollment. Long-term risk for serous EOC was reduced in women with low CA125 and high anti-CA125 antibodies relative to women with low concentrations of both. Conclusions: We found general support for the hypothesis that anti-mucin antibody levels correlate with risk factors for EOC. Antibodies alone or in combinations with their antigen may predict longer term risk of specific EOC types. Impact: Anti-CA125 and anti-CA15.3 antibodies alone or in perspective of antigens may be informative in the pathogenesis of EOC subtypes, but less useful for informing risk for all EOC. © 2018 American Association for Cancer Research.

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: Receptor activator of nuclear factor kappa-B (RANK)-signaling is involved in tumor growth and spread in experimental models. Binding of RANK ligand (RANKL) to RANK activates signaling, which is inhibited by osteoprotegerin (OPG). We have previously shown that circulating soluble RANKL (sRANKL) and OPG are associated with breast cancer risk. Here we extend these findings to provide the first data on pre-diagnosis concentrations of sRANKL and OPG and risk of breast cancer-specific and overall mortality after a breast cancer diagnosis. Methods: Two thousand six pre- and postmenopausal women with incident invasive breast cancer (1620 (81%) with ER+ disease) participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort were followed-up for mortality. Pre-diagnosis concentrations of sRANKL and OPG were quantified in baseline serum samples using an enzyme-linked immunosorbent assay and electrochemiluminescent assay, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) for breast cancer-specific and overall mortality were calculated using Cox proportional hazards regression models. Results: Especially in women with ER+ disease, higher circulating OPG concentrations were associated with higher risk of breast cancer-specific (quintile 5 vs 1 HR 1.77 [CI 1.03, 3.04]; ptrend 0.10) and overall mortality (q5 vs 1 HR 1.39 [CI 0.94, 2.05]; ptrend 0.02). sRANKL and the sRANKL/OPG ratio were not associated with mortality following a breast cancer diagnosis. Conclusions: High pre-diagnosis endogenous concentrations of OPG, the decoy receptor for RANKL, were associated with increased risk of death after a breast cancer diagnosis, especially in those with ER+ disease. These results need to be confirmed in well-characterized patient cohorts. © 2018 The Author(s).

High blood concentrations of C-reactive protein (CRP) have been associated with elevated risk of colorectal cancer in several prospective studies including the European Prospective Investigation into Cancer and Nutrition (EPIC), but it is unknown whether these observations reflect a causal relationship. We aimed to investigate whether CRP genetic variants associated with lifelong higher CRP concentrations translate into higher colorectal cancer risk. We conducted a prospective nested case-control study within EPIC including 727 cases diagnosed between 1992 and 2003 and 727 matched controls selected according to an incidence-density sampling protocol. Baseline CRP concentrations were measured in plasma samples by a high sensitivity assay. Tagging single nucleotide polymorphisms (SNPs) in the CRP gene (rs1205, rs1800947, rs1130864, rs2808630, rs3093077) were identified via HapMap. The causal effect of CRP on colorectal cancer risk was examined in a Mendelian Randomization approach utilizing multiple CRP genetic variants as instrumental variables. The SNPs rs1205, rs1800947, rs1130864 and rs3093077 were significantly associated with CRP concentrations and were incorporated in a CRP allele score which was associated with 13% higher CRP concentrations per allele count (95% confidence interval 8%, 19%). Using the CRP-score as instrumental variable, genetically 2-fold higher CRP concentrations were associated with higher risk of colorectal cancer (odds ratio 1.74, 95% confidence interval 1.06, 2.85). Similar observations were made using alternative definitions of instrumental variables. Our findings give support to the hypothesis that elevated circulating CRP may play a direct role in the etiology of colorectal cancer.

Background Obesity is positively associated with colorectal cancer. Recently, body size subtypes categorised by the prevalence of hyperinsulinaemia have been defined, and metabolically healthy overweight/obese individuals (without hyperinsulinaemia) have been suggested to be at lower risk of cardiovascular disease than their metabolically unhealthy (hyperinsulinaemic) overweight/obese counterparts. Whether similarly variable relationships exist for metabolically defined body size phenotypes and colorectal cancer risk is unknown. Methods and Findings The association of metabolically defined body size phenotypes with colorectal cancer was investigated in a case–control study nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Metabolic health/body size phenotypes were defined according to hyperinsulinaemia status using serum concentrations of C-peptide, a marker of insulin secretion. A total of 737 incident colorectal cancer cases and 737 matched controls were divided into tertiles based on the distribution of C-peptide concentration amongst the control population, and participants were classified as metabolically healthy if below the first tertile of C-peptide and metabolically unhealthy if above the first tertile. These metabolic health definitions were then combined with body mass index (BMI) measurements to create four metabolic health/body size phenotype categories: (1) metabolically healthy/normal weight (BMI < 25 kg/m2), (2) metabolically healthy/overweight (BMI ≥ 25 kg/m2), (3) metabolically unhealthy/normal weight (BMI < 25 kg/m2), and (4) metabolically unhealthy/overweight (BMI ≥ 25 kg/m2). Additionally, in separate models, waist circumference measurements (using the International Diabetes Federation cut-points [≥80 cm for women and ≥94 cm for men]) were used (instead of BMI) to create the four metabolic health/body size phenotype categories. Statistical tests used in the analysis were all two-sided, and a p-value of, Gunter and colleagues analyse a large European dataset to determine how body size and metabolic profile associates with the risk of developing colorectal cancer., Editors' Summary Background Colorectal cancer is the third most common cancer worldwide and is a leading cause of cancer-related death, killing around 700,000 people every year. It develops when cells in the colon (the final part of the digestive system, which is also known as the large intestine or large bowel) or the rectum (the lower end of the colon) acquire genetic changes that allow them to divide uncontrollably to form a tumor and to move around the body (metastasize). Symptoms of colorectal cancer include blood in the stool, a change in bowel habits, and unexplained weight loss. Treatments for colorectal cancer include surgery, chemotherapy, and radiation. As with other types of cancer, these treatments are more likely to be successful if started when the tumor is very small. Consequently, many countries run screening programs that use colonoscopy, the fecal occult blood test, and other tests to detect the earliest signs of colorectal cancer in apparently healthy people. Why Was This Study Done? Being obese—having too much body fat—is associated with an increased colorectal cancer risk (other risk factors include age, having a family history of colorectal cancer, and eating a high-fat, low-fiber diet). Obesity is also associated with several other chronic diseases, and recent evidence suggests that some obese individuals have a higher risk of developing these diseases than others. For example, overweight/obese individuals who have hyperinsulinemia (abnormally high blood levels of insulin; “metabolically unhealthy”) seem to have a higher risk of cardiovascular disease than their non-hyperinsulinemic (“metabolically healthy”) overweight counterparts. If certain combinations of metabolic health status and body size (“metabolically defined body size phenotypes”) are also associated with colorectal cancer, measurement of insulin levels in conjunction with body fat (adiposity) measurements such as body mass index (BMI; an indicator of body fat calculated by dividing a person’s weight in kilograms by their height in meters squared) might improve colorectal cancer risk assessment. In this nested case–control study, the researchers assess the associations between metabolically defined body size phenotypes and colorectal cancer risk. A nested case–control study identifies everyone in a group (here, participants in the European Prospective Investigation into Cancer and Nutrition [EPIC] study) who has a specific condition, identifies matched individuals in the same group without the condition, and asks whether these controls and the cases differ in terms of a specific characteristic or outcome. What Did the Researchers Do and Find? The researchers matched 737 participants in the EPIC study who developed colorectal cancer after study enrollment with 737 controls and used serum concentrations of C-peptide, a marker of insulin secretion, and BMI measurements to classify each individual as metabolically healthy/normal weight, metabolically healthy/overweight, metabolically unhealthy/normal weight, or metabolically unhealthy/overweight. Specifically, the researchers categorized people as metabolically unhealthy if they had a C-peptide level above an arbitrarily chosen cut-off value based on the distribution of C-peptide levels in the control participants and as overweight if they had a BMI of ≥25 kg/m2 (the standard definition of overweight). Compared to metabolically healthy normal weight individuals, metabolically unhealthy normal weight and overweight individuals had an increased colorectal cancer risk; metabolically healthy overweight individuals had a similar colorectal cancer risk to metabolically healthy normal weight individuals. Among overweight individuals, metabolically healthy individuals had a lower colorectal cancer risk than metabolically unhealthy individuals. Finally, similar associations were seen when the researchers used waist circumference instead of BMI as the measure of adiposity. What Do These Findings Mean? These findings suggest that normal weight individuals with hyperinsulinemia (the metabolically unhealthy normal weight phenotype) have a higher risk of colorectal cancer than normal weight individuals without hyperinsulinemia. They also suggest that metabolically unhealthy overweight individuals have a higher risk of colorectal cancer than metabolically healthy overweight individuals. The accuracy of these findings may be limited by the method the researchers used to classify individuals as hyperinsulinemic—there is no universally accepted clinical definition for using C-peptide level to diagnose hyperinsulinemia. Nevertheless, these findings suggest that the assessment of insulin levels in conjunction with adiposity measures might be a better way to assess an individual’s colorectal cancer risk than simply measuring adiposity, and might help to identify those individuals at high risk of colorectal cancer who are most likely to benefit from targeted interventions designed to prevent the onset of clinical disease. Additional Information This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at http://dx.doi.org/10.1371/journal.pmed.1001988. The US National Cancer Institute provides information for patients about all aspects of colorectal cancer; it also provides more detailed information colorectal cancer for health professionals and information on cancer risk and obesity The UK National Health Service Choices website has information and personal stories about colorectal cancer and information on obesity The not-for-profit organization Cancer Research UK provides information about colorectal cancer and about the association between cancer and obesity MedlinePlus provides links to further resources about colorectal cancer and about obesity Wikipedia has a page on hyperinsulinemia (note that Wikipedia is a free online encyclopedia that anyone can edit; available in several languages) More information about the EPIC study is available

Obesity and associated metabolic disorders have been implicated in liver carcinogenesis; however, there are little data on the role of obesity-related biomarkers on liver cancer risk. We studied prospectively the association of inflammatory and metabolic biomarkers with risks of hepatocellular carcinoma (HCC), intrahepatic bile duct (IBD), and gallbladder and biliary tract cancers outside of the liver (GBTC) in a nested case-control study within the European Prospective Investigation into Cancer and Nutrition. Over an average of 7.7 years, 296 participants developed HCC (n=125), GBTC (n=137), or IBD (n=34). Using risk-set sampling, controls were selected in a 2:1 ratio and matched for recruitment center, age, sex, fasting status, and time of blood collection. Baseline serum concentrations of C-reactive protein (CRP), interleukin-6 (IL-6), C-peptide, total high-molecular-weight (HMW) adiponectin, leptin, fetuin-a, and glutamatdehydrogenase (GLDH) were measured, and incidence rate ratios (IRRs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. After adjustment for lifestyle factors, diabetes, hepatitis infection, and adiposity measures, higher concentrations of CRP, IL-6, C-peptide, and non-HMW adiponectin were associated with higher risk of HCC (IRR per doubling of concentrations=1.22; 95% CI=1.02-1.46; P = 0.03; 1.90; 95% CI=1.30-2.77; P = 0.001; 2.25; 95% CI=1.43-3.54; P = 0.0005; and 2.09; 95% CI=1.19-3.67; P = 0.01, respectively). CRP was associated also with risk of GBTC (IRR=1.22; 95% CI=1.05-1.42; P = 0.01). GLDH was associated with risks of HCC (IRR=1.62; 95% CI=1.25-2.11; P = 0.0003) and IBD (IRR=10.5; 95% CI=2.20-50.90; P = 0.003). The continuous net reclassification index was 0.63 for CRP, IL-6, C-peptide, and non-HMW adiponectin and 0.46 for GLDH, indicating good predictive ability of these biomarkers. Conclusion: Elevated levels of biomarkers of inflammation and hyperinsulinemia are associated with a higher risk of HCC, independent of obesity and established liver cancer risk factors. © 2014 The Authors. Hepatology published by Wiley on behalf of the American Association for the Study of Liver Diseases.

Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models. Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case–control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone–binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting. Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor–positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection. Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. ©2017 AACR.

Background: Ovarian cancer early detection markers CA125, CA15.3, HE4, and CA72.4 vary between healthy women, limiting their utility for screening. Methods: We evaluated cross-sectional relationships between lifestyle and reproductive factors and these markers among controls (n = 1910) from a nested case-control study in the European Prospective Investigation into Cancer and Nutrition (EPIC). Improvements in discrimination of prediction models adjusting for correlates of the markers were evaluated among postmenopausal women in the nested case-control study (n = 590 cases). Generalized linear models were used to calculate geometric means of CA125, CA15.3, and HE4. CA72.4 above vs. below limit of detection was evaluated using logistic regression. Early detection prediction was modeled using conditional logistic regression. Results: CA125 concentrations were lower, and CA15.3 higher, in post- vs. premenopausal women (p ≤ 0.02). Among postmenopausal women, CA125 was higher among women with higher parity and older age at menopause (ptrend ≤ 0.02), but lower among women reporting oophorectomy, hysterectomy, ever use of estrogen-only hormone therapy, or current smoking (p < 0.01). CA15.3 concentrations were higher among heavier women and in former smokers (p ≤ 0.03). HE4 was higher with older age at blood collection and in current smokers, and inversely associated with OC use duration, parity, and older age at menopause (≤ 0.02). No associations were observed with CA72.4. Adjusting for correlates of the markers in prediction models did not improve the discrimination. Conclusions: This study provides insights into sources of variation in ovarian cancer early detection markers in healthy women and informs about the utility of individualizing marker cutpoints based on epidemiologic factors. © 2017 The Author(s).

Invasive epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy. The etiology of EOC remains elusive; however, experimental and epidemiologic data suggest a role for hormone-related exposures in ovarian carcinogenesis and risk factor differences by histologic phenotypes and developmental pathways. Research on prediagnosis androgen concentrations and EOC risk has yielded inconclusive results, and analyses incorporating EOC subtypes are sparse. We conducted a pooled analysis of 7 nested case-control studies in the Ovarian Cancer Cohort Consortium to investigate the association between prediagnosis circulating androgens [testosterone, free testosterone, androstenedione, dehydroepiandrosterone sulfate (DHEAS)], sex hormone binding globulin (SHBG), and EOC risk by tumor characteristics (i.e., histology, grade, and stage). The final study population included 1,331 EOC cases and 3,017 matched controls. Multivariable conditional logistic regression was used to assess risk associations in pooled individual data. Testosterone was positively associated with EOC risk (all subtypes combined, ORlog2 = 1.12; 95% confidence interval 1.02-1.24); other endogenous androgens and SHBG were not associated with overall risk. Higher concentrations of testosterone and androstenedione associated with an increased risk in endometrioid andmucinous tumors [e.g., testosterone, endometrioid tumors, ORlog2 = 1.40 (1.03-1.91)], but not serous or clear cell. An inverse association was observed between androstenedione and high grade serous tumors [ORlog2 = 0.76 (0.60-0.96)]. Our analyses provide further evidence for a role of hormonerelated pathways in EOC risk, with differences in associations between androgens and histologic subtypes of EOC. © 2017 American Association for Cancer Research.

Receptor activator of nuclear factor-kappa B (RANK)-RANK ligand (RANKL) signaling promotes mammary tumor development in experimental models. Circulating concentrations of soluble RANKL (sRANKL) may influence breast cancer risk via activation of RANK signaling; this may be modulated by osteoprotegerin (OPG), the decoy receptor for RANKL. sRANKL and breast cancer risk by hormone receptor subtype has not previously been investigated. A case-control study was nested in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. This study included 1, 976 incident invasive breast cancer cases [estrogen receptor positive (ER+), n=1, 598], matched 1:1 to controls. Women were pre- or postmenopausal at blood collection. Serum sRANKL was quantified using an ELISA, serum OPG using an electrochemiluminescent assay. Risk ratios (RR) and95% confidence intervals (95% CI) were calculated using conditional logistic regression. Associations between sRANKL and breast cancer risk differed by tumor hormone receptor status (Phet = 0.05). Higher concentrations of sRANKL were positively associated with risk of ER+ breast cancer [5th vs. 1st quintile RR 1.28 (95% CI, 1.01-1.63); Ptrend=0.20], but not ER-disease. For both ER+and estrogen and progesterone receptor positive (ER+PR+) breast cancer, results considering the sRANKL/OPG ratio were similar to those for sRANKL; we observed a suggestive inverse association between the ratio and ER-PR-disease [5th vs. 1st quintile RR = 0.60 (0.31-1.14); Ptrend=0.03]. This study provides the first largescale prospective data on circulating sRANKLand breast cancer. We observed limited evidence for an association between sRANKLand breast cancer risk. Cancer Prev Res; 10(9); 525-34. © 2017 American Association for Cancer Research.

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.

Experimental evidence shows cross-talk in mammary cells between estrogen, insulin-like growth factor I (IGF-I) and their respective receptors and possible synergistic effects of estrogen receptor (ER) activation and increased IGF-I signaling with regard to breast tumor development, and epidemiological evidence suggests that circulating IGF-I levels may be related more to the risk of ER-positive than ER-negative breast cancer. Using a case-control study nested within the prospective European EPIC cohort (938 breast cancer cases and 1,394 matched control subjects), we analyzed the relationships of prediagnostic serum IGF-I levels with the risk of estrogen and progesterone receptor-positive and -negative breast tumors. IGF-I levels were positively associated with the risk of ER+ breast tumors overall (pre- and postmenopausal women combined, odds ratio (OR)Q4-Q1 = 1.41 [95% confidence interval (CI) 1.01-1.98] for the highest vs. lowest quartile; OR = 1.17 [95% CI 1.04-1.33] per 1-standard deviation (SD) increase in IGF-I, ptrend = 0.01) and among women who were diagnosed with breast cancer at 50 years or older (ORQ3-Q1 = 1.38 [95% CI 1.01-1.89]; OR = 1.19 [95% CI 1.04-1.36] per 1-SD increase in IGF-I, ptrend = 0.01) but not with receptor-positive disease diagnosed at an earlier age. No statistically significant associations were observed for ER- breast tumors overall and by age at diagnosis. Tests for heterogeneity by receptor status of the tumor were not statistically significant, except for women diagnosed with breast cancer at 50 years or older (phet = 0.03 for ER+/PR+ vs. ER-/PR- disease). Our data add to a global body of evidence indicating that higher circulating IGF-I levels may increase risk specifically of receptor-positive, but not receptor-negative, breast cancer diagnosed at 50 years or older. What's new Both estrogen and insulin-like growth factor (IGF-I) promote breast cancer formation, and evidence suggests the two may work together. Some breast tumors express estrogen receptor, others don't. Does the presence of estrogen receptor allow IGF-I to stimulate tumor formation To address this question, the authors compared women's IGF-I levels before diagnosis with their risk of developing breast cancer, with or without estrogen receptor. They found a direct relationship between IGF levels and risk of ER-positive breast tumors diagnosed after age 50. They found no association with ER-positive tumors diagnosed at an earlier age, nor with ER-negative tumors. © 2013 UICC.

Purpose: Alcohol intake may adversely affect the concentrations of endogenous sex hormones, and thus increase the risk of endometrial cancer. However, epidemiologic studies have provided conflicting results. Therefore, we investigated the association between alcohol intake and endometrial cancer risk a large, multicenter, prospective study. Methods: From 1992 through 2010, 301,051 women in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort were followed for incident endometrial cancer (n = 1382). Baseline alcohol consumption was assessed by country-specific, validated dietary questionnaires. Information on past alcohol consumption was collected by lifestyle questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated from Cox proportional hazard models. Results: The multivariable HRs (and 95% CIs) compared with light drinkers (0.1-6 g/d) were 1.03 (0.88-1.20) for 0 g of alcohol per day at baseline, 1.01 (0.86-1.17) for 6.1-12 g/d, 1.03 (0.87-1.22) for 12.1-24 g/d, 1.07 (0.87-1.38) for 24.1-36 g/d, and 0.85 (0.61-1.18) for more than 36 g/d (ptrend = 0.77). No association was observed among former drinkers (OR, 1.28; 95% CI, 0.98-1.68 compared with light drinkers). Null associations were also found between alcohol consumption at age 20 years, lifetime pattern of alcohol drinking, and baseline alcohol intake from specific alcoholic beverages and endometrial cancer risk. Conclusions: Our findings suggest no association between alcohol intake and endometrial cancer risk. © 2013 Elsevier Inc.