Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region.
Abstract Background Adiposity has been characterized as a modifiable risk factor for prostate cancer. Its association with outcomes after prostate cancer diagnosis, however, must be better understood, and more evidence is needed to facilitate the development of lifestyle guidance for patients with prostate cancer. Methods We investigated the associations between adiposity indices close to prostate cancer diagnosis (up to 2 years before or up to 5 years after diagnosis) and mortality in 1968 men of the European Prospective Investigation into Cancer and Nutrition cohort. Men were followed up for a median of 9.5 years. Cox proportional hazards models were adjusted for age and year of diagnosis, disease stage and grade, and smoking history and stratified by country. Results Each 5-unit increment in prediagnosis or postdiagnosis body mass index combined was associated with a 30% higher rate of all-cause mortality and a 49% higher rate of prostate cancer–specific mortality. Similarly, each 5-unit increment in prediagnosis body mass index was associated with a 35% higher rate of all-cause mortality and a 51% higher rate of prostate cancer–specific mortality. The associations were less strong for postdiagnosis body mass index, with a lower number of men in analyses. Less clear positive associations were shown for waist circumference, hip circumference, and waist to hip ratio, but data were limited. Conclusions Elevated levels of adiposity close to prostate cancer diagnosis could lead to higher risk of mortality; therefore, men are encouraged to maintain a healthy weight. Additional research is needed to confirm whether excessive adiposity after prostate cancer diagnosis could worsen prognosis.
Abstract The current epidemiologic evidence suggests that men with type 2 diabetes mellitus may be at lower risk of developing prostate cancer. More research is needed to clarify whether and how time since diabetes diagnosis, diabetes treatment, stage and grade of prostate cancer, age at recruitment, adiposity, and physical activity modify the risk profile. We assessed the association of self-reported history of diabetes mellitus diagnosis at baseline with risk of prostate cancer among 139,131 men recruited into the European Prospective Investigation into Cancer and Nutrition (EPIC), after excluding men with prevalent cancer at recruitment and men who did not return the baseline questionnaires or had missing information about their history of diabetes. After an average of 12 years follow-up, 4,531 prostate cancers were diagnosed, of which 894 men had advanced (T3-4, N+, M+) disease and 1,688 had localized disease (T0-2, N0, M0); 375 men had high-grade disease (Gleason score ≥ 8) disease and 1,811 had low-grade disease (Gleason score < 8). Hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated using Cox proportional hazards models stratified by EPIC recruitment center and age at enrolment, and adjusted for body mass index, waist circumference, cigarette smoking status, university education, physical activity, and daily dietary intake of energy, alcohol, fruits and vegetables, fish, calcium and protein from dairy sources. A total of 5,100 men (3.7%) stated at baseline that they had a history of diabetes mellitus. Men with diabetes had a 26% reduced risk of developing prostate cancer compared to men without diabetes (HR, 0.74; 95% CI, 0.63-0.86). There was no significant reduction in risk in the first two years after diabetes diagnosis (No. of cases, 39; HR, 0.83; 95% CI, 0.60-1.14), but was evident for men diagnosed between 3 to 6 years after diagnosis (No. of cases, 32; HR, 0.72; 95% CI, 0.51-1.02) and 7-12 years (No. of cases, 32; HR, 0.75; 95% CI, 0.53-1.07), and was even lower for men who had been diagnosed for more than 12 years (No. of cases, 31; HR, 0.64; 95% CI, 0.45-0.92). Compared to non-diabetics, diabetic men not receiving insulin treatment at baseline had a risk of prostate cancer (HR, 0.75; 95% CI, 0.60-0.94) similar to what of diabetic men who were receiving insulin (HR, 0.78; 95% CI, 0.59-1.03). These results did not differ by stage and grade of prostate cancer, or by age at recruitment, adiposity, or physical activity. Our results are compatible with the hypothesis that diabetes is associated with reduced prostate cancer risk. A potential biological explanation underlying this association is that men with severe type 2 diabetes may manifest lower testosterone concentrations, which provide an environment that is not conducive to proliferation of prostate cancer cells. Citation Format: Konstantinos K. Tsilidis, Naomi Allen, Ruth Travis, Sabine Rohrmann, Ute Nöthlings, Larraitz Arriola, Marc Gunter, Timothy Key. Diabetes mellitus and risk of prostate cancer in the European Prospective Investigation into Cancer and Nutrition. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3620. doi:10.1158/1538-7445.AM2013-3620
<p>Supplementary Figure 1: Electrophoretic mobility shift assays (EMSA) for CCNE1 SNPs rs8102137 and rs7257330. Supplementary Figure 2: Alignment of cyclin E protein isoforms - WT1, WT2 and ES and ET. Supplementary Figure 3: Functional analysis of cyclin E isoforms.Supplementary Table 1: Description of sub-studies included in NCI-GWAS1 and GWAS2 of bladder cancer. Supplementary Table 2: Characteristics of bladder tissue samples used for mRNA expression analysis. Supplementary Table 3: PCR primers, genotyping and gene expression assays, EMSA probes and antibodies. Supplementary Table 4: Bladder cancer stage and grade information for patients in the combined GWAS1+2 set. Supplementary Table 6: Association with bladder cancer risk with mutual adjustment for CCNE1 variants. Supplementary Table 7: Association with bladder cancer risk for CCNE1 variants previously associated with other cancers and for two non-synonymous coding variants. Supplementary Table 8: Association between cyclin E protein expression (IHC scores), bladder cancer patient characteristics and CCNE1 variants.</p>
Other than the influence of ionizing radiation and benign thyroid disease, little is known about the risk factors for differentiated thyroid cancer (TC) which is an increasing common cancer worldwide. Consistent evidence shows that body mass is positively associated with TC risk. As excess weight is a state of chronic inflammation, we investigated the relationship between concentrations of leptin, adiponectin, C‐reactive protein, interleukin (IL)‐6, IL‐10 and tumor necrosis factor (TNF)‐α and the risk of TC. A case‐control study was nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) study and included 475 first primary incident TC cases (399 women and 76 men) and 1,016 matched cancer‐free cohort participants. Biomarkers were measured in serum samples using validated and highly sensitive commercially available immunoassays. Odds ratios (ORs) of TC by levels of each biomarker were estimated using conditional logistic regression models, adjusting for BMI and alcohol consumption. Adiponectin was inversely associated with TC risk among women (OR T3vs.T1 = 0.69, 95% CI: 0.49–0.98, P trend = 0.04) but not among men (OR T3vs.T1 = 1.36, 95% CI: 0.67–2.76, P trend = 0.37). Increasing levels of IL‐10 were positively associated with TC risk in both genders and significantly so in women (OR T3vs.T1 = 1.59, 95% CI: 1.13–2.25, P trend = 0.01) but not in men (OR T3vs.T1 = 1.78, 95% CI: 0.80–3.98, P trend = 0.17). Leptin, CRP, IL‐6 and TNF‐α were not associated with TC risk in either gender. These results indicate a positive association of TC risk with IL‐10 and a negative association with adiponectin that is probably restricted to women. Inflammation may play a role in TC in combination with or independently of excess weight.
Several susceptibility loci for classical Hodgkin lymphoma have been reported. However, much of the heritable risk is unknown. Here, we perform a meta-analysis of two existing genome-wide association studies, a new genome-wide association study, and replication totalling 5,314 cases and 16,749 controls. We identify risk loci for all classical Hodgkin lymphoma at 6q22.33 (rs9482849, P = 1.52 × 10
<p>Supplementary Tables S1-3 and Figures S1-3. Table S1: Median, interquartile range (IQR), minimum (Min.) and maximum (max.) of follow-up time by cancer site. Table S2: Hazard ratios (HRs) and 95% confidence intervals (CIs) of cancer risk according to circulating insulin-like growth factor-I concentrations. Table S3: Hazard ratios (HRs) and 95% confidence intervals (CIs) of cancer risk according to circulating insulin-like growth factor-I concentrations stratified by follow-up time, sex and age-group. Figure S1: Flow diagram for reaching the analytical sample. Figure S2: Hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer risk per 5 nmol/l higher insulin-like growth factor-I concentration by cancer site and sex. Figure S3: Hazard ratios (HRs) and 95% confidence intervals (CIs) for cancer risk per 5 nmol/l higher insulin-like growth factor-I concentration by cancer site and age (<55, {greater than or equal to}55 years) at biomarker assessment.</p>
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