Genetic variability of the forkhead box O3 and prostate cancer risk in the European Prospective Investigation on Cancer

Forkhead box O3 (FOXO3) has a wide range of functions: it promotes tumor suppression, cell cycle arrest, repair of damaged DNA, detoxification of reactive oxygen species, apoptosis and plays a pivotal role in promoting longevity. FOXO3 is a key downstream target of the PI3K-Akt pathway in response to cellular stimulation by growth factors or insulin and has been proposed as a bridge between ageing and tumor suppression. Three SNPs in the FOXO3 gene (rs3800231, rs9400239 and rs479744) that have been shown to be strongly and consistently associated with longevity, were examined in relation to PC risk in a case control study of 1571 incident PC cases and 1840 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). There was no statistically significant association between the SNPs and PC risk regardless of the model of inheritance (dominant, codominant and recessive). The associations were not modified by disease aggressiveness, circulating levels of steroid sex hormones, or IGFs or BMI. We conclude that polymorphisms in the FOXO3 gene that are associated with longevity are not major risk factors for PC risk, in this population of Caucasian men

Genetic Variability of the mTOR Pathway and Prostate Cancer Risk in the European Prospective Investigation on Cancer (EPIC)

The mTOR (mammalian target of rapamycin) signal transduction pathway integrates various signals, regulating ribosome biogenesis and protein synthesis as a function of available energy and amino acids, and assuring an appropriate coupling of cellular proliferation with increases in cell size. In addition, recent evidence has pointed to an interplay between the mTOR and p53 pathways. We investigated the genetic variability of 67 key genes in the mTOR pathway and in genes of the p53 pathway which interact with mTOR. We tested the association of 1,084 tagging SNPs with prostate cancer risk in a study of 815 prostate cancer cases and 1,266 controls nested within the European Prospective Investigation into Cancer and Nutrition (EPIC). We chose the SNPs (n = 11) with the strongest association with risk (p<0.01) and sought to replicate their association in an additional series of 838 prostate cancer cases and 943 controls from EPIC. In the joint analysis of first and second phase two SNPs of the PRKCI gene showed an association with risk of prostate cancer (ORallele = 0.85, 95% CI 0.78-0.94, p = 1.3 x 10(-3) for rs546950 and ORallele = 0.84, 95% CI 0.76-0.93, p = 5.6 x 10(-4) for rs4955720). We confirmed this in a meta-analysis using as replication set the data from the second phase of our study jointly with the first phase of the Cancer Genetic Markers of Susceptibility (CGEMS) project. In conclusion, we found an association with prostate cancer risk for two SNPs belonging to PRKCI, a gene which is frequently overexpressed in various neoplasms, including prostate cancer

An epidemiological model for prediction of endometrial cancer risk in Europe

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

Genetic variability of the fatty acid synthase pathway is not associated with prostate cancer risk in the European Prospective Investigation on Cancer (EPIC)

A western lifestyle, characterised by low rates of energy expenditure and a high-energy diet rich in animal protein, saturated fats and refined carbohydrates, is associated with high incidence of prostate cancer in men. A high-energy nutritional status results in insulin/IGF signalling in cells, which in turn stimulates synthesis of fatty acids. We investigated whether the genetic variability of the genes belonging to the fatty acid synthesis pathway is related to prostate cancer risk in 815 prostate cancer cases and 1266 controls from the European Prospective Investigation on Cancer (EPIC). Using a tagging approach and selecting 252 SNPs in 22 genes, we covered all the common genetic variation of this pathway. None of the SNPs reached statistical significance after adjusting for multiple comparisons. Common SNPs in the fatty acid synthase pathway are not major contributors to prostate cancer risk. (C) 2010 Elsevier Ltd. All rights reserved

Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies

Objective To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts. Design Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability). Results The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC. Conclusion Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained

Endometrial Cancer Risk Prediction Including Serum-Based Biomarkers: Results From The Epic Cohort

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.Wo

Prediagnostic plasma testosterone, sex hormone-binding globulin, IGF-I and hepatocellular carcinoma: Etiological factors or risk markers?

Elevated prediagnostic testosterone and insulin-like growth factor I (IGF-I) concentrations have been proposed to increase risk of hepatocellular carcinoma (HCC). However, the metabolism of these hormones is altered as a consequence of liver damage and they may have clinical utility as HCC risk markers. A case-control study was nested within the European Prospective Investigation into Cancer and Nutrition cohort and included 125 incident HCC cases and 247 individually matched controls. Testosterone, sex hormone-binding globulin (SHBG) and IGF-I were analyzed by immunoassays. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by conditional logistic regression. The area under the receiver operating curves (AUC) was calculated to assess HCC predictive ability of the tested models. After adjustments for epidemiological variables (body mass index, smoking, ethanol intake, hepatitis and diabetes) and liver damage (a score based on albumin, bilirubin, aspartate aminotransaminase, alanine aminotransaminase, gamma-glutamyltransferase and alkaline phosphatase concentrations), only SHBG remained significantly associated with risk [OR for top versus bottom tertile of 3.86 (1.32-11.3), p(trend) = 0.009]. As a single factor SHBG had an AUC of 0.81 (0.75-0.86). A small, but significant increase in AUC was observed when SHBG was added to a model including the liver damage score and epidemiological variables (from 0.89 to 0.91, p = 0.02) and a net reclassification of 0.47% (0.45-0.48). The observed associations of HCC with prediagnostic SHBG, free testosterone and IGF-I concentrations are in directions opposite to that expected under the etiological hypotheses. SHBG has a potential to be tested as prediagnostic risk marker for HCC. (c) 2013 UICC What's new? Testosterone and insulin-like growth factor-1 (IGF-1) are implicated in the development of hepatocellular carcinoma (HCC), though their involvement may be more complex than previously thought. Here, in a unique study population with low prevalence of hepatitis infections, an association was detected between HCC risk and increased levels of sex hormone binding globulin (SHBG) and IGF-1 prior to diagnosis. Neither testosterone nor IGF-1, however, was found to have an etiological influence in the decade before diagnosis. The results suggest that SHBG and IGF-I should be considered in the clinical evaluation of patients at increased risk of HCC

Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort

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 &lt; 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

A Prospective Evaluation of Early Detection Biomarkers for Ovarian Cancer in the European EPIC Cohort

Purpose: About 60% of ovarian cancers are diagnosed at late stage, when 5-year survival is less than 30% in contrast to 90% for local disease. This has prompted search for early detection biomarkers. For initial testing, specimens taken months or years before ovarian cancer diagnosis are the best source of information to evaluate earlydetection biomarkers. Here we evaluate the most promising ovarian cancer screening biomarkers in prospectively collected samples from the European Prospective Investigation into Cancer and Nutrition study. Experimental Design: We measured CA125, HE4, CA72.4, and CA15.3 in 810 invasive epithelial ovarian cancer cases and 1,939 controls. We calculated the sensitivity at 95% and 98% specificity as well as area under the receiver operator curve (C-statistic) for each marker individually and in combination. In addition, we evaluated marker performance by stage at diagnosis and time between blood draw and diagnosis. Results: We observed the best discrimination between cases and controls within 6 months of diagnosis for CA125 (C-statistic = 0.92), then HE4 (0.84), CA72.4 (0.77), and CA15.3 (0.73). Marker performance declined with longer time between blood draw and diagnosis and for earlier staged disease. However, assessment of discriminatory ability at early stage was limited by small numbers. Combinations of markers performed modestly, but significantly better than any single marker. Conclusions: CA125 remains the single best marker for the early detection of invasive epithelial ovarian cancer, but can be slightly improved by combining with other markers. Identifying novel markers for ovarian cancer will require studies including larger numbers of early-stage cases. (C) 2016 AACR