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Additional file 1: Circulating inflammatory biomarkers and breast cancer risk in the EPIC prospective study: Supplementary Tables S1- S7 and Figures S1-S3. Table S1. Geometric mean values for inflammatory biomarkers in breast cancer cases and matched controls. Table S2. Associations between inflammatory biomarkers and breast cancer risk by age at diagnosis. Table S3. Associations between inflammatory biomarkers and breast cancer risk by breast cancer molecular subtypes. Table S4. Associations between inflammatory biomarkers and breast cancer risk by time between blood collection and diagnosis. Table S5. Associations between inflammatory biomarkers and breast cancer risk by body mass index. Table S6. Associations between inflammatory biomarkers and breast cancer risk by waist circumference. Table S7. Associations between inflammatory biomarkers and breast cancer risk among non-hormone users at blood collection. Table S8. Associations between IL-10 and breast cancer risk overall and according to menopausal status at blood collection, after excluding women with values of IL-10 below the LOQ. Table S9. Associations between TNF-α and breast cancer risk overall and according to menopausal status at blood collection, after excluding women with values of TNF-α below the LOQ. Figure S1. Flow chart of the study population. Figure S2. Association between leptin and breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline). Figure S3. Association between leptin-to-adiponectin ratio and breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline). Figure S4. Association between CRP with breast cancer risk, overall and by menopausal status, allowing for nonlinear effects (restricted cubic spline).

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated 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 selection 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 etiologic markers on independent pathways and genetic markers may further improve discrimination.

Recent cohort studies suggest that increased breast cancer risks were associated with longer smoking duration, higher pack-years and a dose-response relationship with increasing pack-years of smoking between menarche and first full-term pregnancy (FFTP). Studies with comprehensive quantitative life-time measures of passive smoking suggest an association between passive smoking dose and breast cancer risk. We conducted a study within the European Prospective Investigation into Cancer and Nutrition to examine the association between passive and active smoking and risk of invasive breast cancer and possible effect modification by known breast cancer risk factors. Among the 322,988 women eligible for the study, 9,822 developed breast cancer (183,608 women with passive smoking information including 6,264 cases). When compared to women who never smoked and were not being exposed to passive smoking at home or work at the time of study registration, current, former and currently exposed passive smokers were at increased risk of breast cancer (hazard ratios (HR) [95% confidence interval (CI)] 1.16 [1.05-1.28], 1.14 [1.04-1.25] and 1.10 [1.01-1.20], respectively). Analyses exploring associations in different periods of life showed the most important increase in risk with pack-years from menarche to FFTP (1.73 [1.29-2.32] for every increase of 20 pack-years) while pack-years smoked after menopause were associated with a significant decrease in breast cancer risk (HR = 0.53, 95% CI: 0.34-0.82 for every increase of 20 pack-years). Our results provide an important replication, in the largest cohort to date, that smoking (passively or actively) increases breast cancer risk and that smoking between menarche and FFTP is particularly deleterious.

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.

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.

BACKGROUND: Although smoking and HPV infection are recognized as important risk factors for oropharyngeal cancer, how their joint exposure impacts on oropharyngeal cancer risk is unclear. Specifically, whether smoking confers any additional risk to HPV-positive oropharyngeal cancer is not understood.METHODS: Using HPV serology as a marker of HPV-related cancer, we examined the interaction between smoking and HPV16 in 459 oropharyngeal (and 1445 oral cavity and laryngeal) cancer patients and 3024 control participants from two large European multi-centre studies. Odds ratios and credible intervals [CrI], adjusted for potential confounders, were estimated using Bayesian logistic regression.RESULTS: Both smoking [odds ratio (OR [CrI]: 6.82 [4.52, 10.29]) and HPV seropositivity (OR [CrI]: 235.69 [99.95, 555.74]) were independently associated with oropharyngeal cancer. The joint association of smoking and HPV seropositivity was consistent with that expected on the additive scale (synergy index [CrI]: 1.32 [0.51, 3.45]), suggesting they act as independent risk factors for oropharyngeal cancer.CONCLUSIONS: Smoking was consistently associated with increase in oropharyngeal cancer risk in models stratified by HPV16 seropositivity. In addition, we report that the prevalence of oropharyngeal cancer increases with smoking for both HPV16-positive and HPV16-negative persons. The impact of smoking on HPV16-positive oropharyngeal cancer highlights the continued need for smoking cessation programmes for primary prevention of head and neck cancer.

Endometrial cancer (EC) is the fourth most frequent cancer in women in Europe, and as its incidence is increasing, prevention strategies gain further pertinence. Risk prediction models can be a useful tool for identifying women likely to benefit from targeted prevention measures. On the basis of data from 201,811 women (mostly aged 30–65 years) including 855 incident EC cases from eight countries in the European Prospective Investigation into Cancer and Nutrition cohort, a model to predict EC was developed. A step-wise model selection process was used to select confirmed predictive epidemiologic risk factors. Piece-wise constant hazard rates in 5-year age-intervals were estimated in a cause-specific competing risks model, five-fold-cross-validation was applied for internal validation. Risk factors included in the risk prediction model were body-mass index (BMI), menopausal status, age at menarche and at menopause, oral contraceptive use, overall and by different BMI categories and overall duration of use, parity, age at first full-term pregnancy, duration of menopausal hormone therapy and smoking status (specific for pre, peri- and post-menopausal women). These variables improved the discriminating capacity to predict risk over 5 years from 71 % for a model based on age alone to 77 % (overall C statistic), and the model was well-calibrated (ratio of expected to observed cases = 0.99). Our model could be used for the identification of women at increased risk of EC in Western Europe. To achieve an EC-risk model with general validity, a large-scale cohort-consortium approach would be needed to assess and adjust for population variation.