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Background: Cannabis use is increasing, including among smokers, an at-risk population for cancer. Research is equivocal on whether using cannabis inhibits quitting cigarettes. The current longitudinal study investigated associations between smoking cannabis and subsequently quitting cigarettes. Methods: Participants were 4,535 adult cigarette smokers from a cohort enrolled in the American Cancer Society's Cancer Prevention Study-3 in 2009–2013. Cigarette quitting was assessed on a follow-up survey in 2015–2017, an average of 3.1 years later. Rates of quitting cigarettes at follow-up were examined by retrospectively assessed baseline cannabis smoking status (never, former, recent), and by frequency of cannabis smoking among recent cannabis smokers (low: ≤3 days/month; medium: 4–19 days/month; high: ≥20 days/month). Logistic regression models adjusted for sociodemographic factors, smoking- and health-related behaviors, and time between baseline and follow-up. Results: Adjusted cigarette quitting rates at follow-up did not differ significantly by baseline cannabis smoking status [never 36.2%, 95% confidence interval (CI), 34.5–37.8; former 34.1%, CI, 31.4–37.0; recent 33.6%, CI, 30.1–37.3], nor by frequency of cannabis smoking (low 31.4%, CI, 25.6–37.3; moderate 36.7%, CI, 30.7–42.3; high 34.4%, CI, 28.3–40.2) among recent baseline cannabis smokers. In cross-sectional analyses conducted at follow-up, the proportion of cigarette smokers intending to quit smoking cigarettes in the next 30 days did not differ by cannabis smoking status (P = 0.83). Conclusions: Results do not support the hypothesis that cannabis smoking inhibits quitting cigarette smoking among adults. Impact: Future longitudinal research should include follow-ups of >1 year, and assess effects of intensity/frequency of cannabis use and motivation to quit on smoking cessation.

Background Valid assessment of dietary intake in diverse populations is important for studies of chronic disease risk in the United States. Objectives We evaluated the reproducibility and validity of a food frequency questionnaire (FFQ) modified for the American Cancer Society's Cancer Prevention Study-3 (CPS-3) prospective cohort, among a racially/ethnically diverse subgroup. Methods The Diet Assessment Substudy included 677 CPS-3 participants (64% women; 61% non-Hispanic white, 24% non-Hispanic black, 15% Hispanic), aged 31-70 y, who completed 2 FFQs 1 y apart (FFQ1, FFQ2), 4-6 telephone-administered 24-h dietary recalls (24HRs), and 2 fasting blood samples and 24-h urine collections ∼6 mo apart in the interim. Spearman rank correlation coefficients (ρ) were used to evaluate FFQ reproducibility and validity compared with 24HRs for 67 nutrient exposures. For 18 of these nutrients, we used the method of triads to calculate validity coefficients (VCs, ρ) from pairwise correlations of FFQ2, 24HRs, and biomarkers. Analyses were stratified by sex, race/ethnicity, education, and BMI. Results Mean (range) FFQ reproducibility correlations were ρ = 0.65 (0.50-0.91) for men and ρ = 0.63 (0.37-0.89) for women; mean (range) energy-adjusted, deattenuated correlations of FFQ2 with 24HRs were ρ = 0.60 (0.33-0.84) for men and ρ = 0.55 (0.21-0.79) for women. FFQ2 VCs (ρ) among men ranged from 0.42 for β-cryptoxanthin to 0.91 for omega-3 (n-3) fatty acids and, among women, from 0.41 for sodium to 0.79 for total vitamin D. Mean FFQ reproducibility and validity were highest among whites (ρ = 0.68, ρ = 0.58, respectively) and slightly lower among blacks (ρ = 0.57, ρ = 0.49, respectively) and Hispanics (ρ = 0.59, 0.55, respectively). FFQ reproducibility and validity were slightly lower among those with less than a 4-y college degree, and those with a BMI ≥30 kg/m2. Conclusions Reproducibility and validity of the CPS-3 FFQ were comparable with similar studies for most nutrients, among all subgroups. These findings support future dietary analyses in the contemporary CPS-3 cohort and other similar cohorts.

Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10–8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82–0.93; former smokers 1.00, 95% CI, 0.91–1.07; current smokers 1.25, 95% CI 1.12–1.40, Pinteraction = 3.08 × 10–9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. Significance: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.

Little is known about the underlying molecular mechanisms of prostate cancer, especially advanced and fatal prostate cancer.To examine associations of prediagnostic plasma metabolomic profiles with advanced and fatal prostate cancer.In a case-cohort study of the Cancer Prevention Study-II Nutrition Cohort, of 14 210 cancer-free men with a blood sample in 1998-2001, 129 were diagnosed with advanced prostate cancer (T3-T4 or N1 or M1) through June 2013 and 112 died from prostate cancer through December 2014. Plasma samples from advanced and fatal cases, and a randomly selected subcohort of 347 men were metabolically profiled using untargeted mass spectroscopy-based platforms.Prentice-weighted Cox proportional hazards regression models were used to assess associations of 699 known metabolites with advanced and fatal prostate cancer.Two metabolites derived from fatty acid metabolism (ethylmalonate and butyrylcarnitine), aspartate, sphingomyelin (d18:1/18:0), and two γ-glutamyl amino acids (γ-glutamylmethionine and γ-glutamylglutamine) were statistically significantly associated (false discovery rate0.2) with fatal prostate cancer. One standard deviation (SD) increase in each γ-glutamyl amino acid was associated with 34-38% decreased risk, whereas one SD increase in each of the other metabolites was associated with 45-53% increased risk. A metabolic risk score based on four of these metabolites (excluding butyrylcarnitine and γ-glutamylglutamine, which were not independent predictors) was strongly associated with fatal prostate cancer (relative risk per SD: 2.72, 95% confidence interval: 2.05-3.60). No metabolites were statistically significantly associated with advanced prostate cancer. These results were observational and may not be causal.These findings identified metabolic pathways that are altered in the development of fatal prostate cancer. Further research into these pathways may provide insights into the etiology of fatal prostate cancer.In a large follow-up study of cancer-free men, those with a certain metabolomic profile had a higher risk of dying from prostate cancer.

Although genome-wide association studies have identified more than eighty genetic variants associated with non-small cell lung cancer (NSCLC) risk, biological mechanisms of these variants remain largely unknown. By integrating a large-scale genotype data of 15 581 lung adenocarcinoma (AD) cases, 8350 squamous cell carcinoma (SqCC) cases, and 27 355 controls, as well as multiple transcriptome and epigenomic databases, we conducted histology-specific meta-analyses and functional annotations of both reported and novel susceptibility variants. We identified 3064 credible risk variants for NSCLC, which were overrepresented in enhancer-like and promoter-like histone modification peaks as well as DNase I hypersensitive sites. Transcription factor enrichment analysis revealed that USF1 was AD-specific while CREB1 was SqCC-specific. Functional annotation and gene-based analysis implicated 894 target genes, including 274 specifics for AD and 123 for SqCC, which were overrepresented in somatic driver genes (ER = 1.95, P = 0.005). Pathway enrichment analysis and Gene-Set Enrichment Analysis revealed that AD genes were primarily involved in immune-related pathways, while SqCC genes were homologous recombination deficiency related. Our results illustrate the molecular basis of both well-studied and new susceptibility loci of NSCLC, providing not only novel insights into the genetic heterogeneity between AD and SqCC but also a set of plausible gene targets for post-GWAS functional experiments.

International audience; Although men of African ancestry have a high risk of prostate cancer (PCa), no genes or mutations have been identified that contribute to familial clustering of PCa in this population. We investigated whether the African ancestry-specific PCa risk variant at 8q24, rs72725854, is enriched in men with a PCa family history in 9052 cases, 143 cases from high-risk families, and 8595 controls of African ancestry. We found the risk allele to be significantly associated with earlier age at diagnosis, more aggressive disease, and enriched in men with a PCa family history (32% of high-risk familial cases carried the variant vs 23% of cases without a family history and 12% of controls). For cases with two or more first-degree relatives with PCa who had at least one family member diagnosed at age

Background: A substantial proportion of cancer driver genes (CDG) are also cancer predisposition genes. However, the associations between genetic variants in lung CDGs and the susceptibility to lung cancer have rarely been investigated. Methods: We selected expression-related single-nucleotide polymorphisms (eSNP) and nonsynonymous variants of lung CDGs, and tested their associations with lung cancer risk in two large-scale genome-wide association studies (20,871 cases and 15,971 controls of European descent). Conditional and joint association analysis was performed to identify independent risk variants. The associations of independent risk variants with somatic alterations in lung CDGs or recurrently altered pathways were investigated using data from The Cancer Genome Atlas (TCGA) project. Results: We identified seven independent SNPs in five lung CDGs that were consistently associated with lung cancer risk in discovery (P < 0.001) and validation (P < 0.05) stages. Among these loci, rs78062588 in TPM3 (1q21.3) was a new lung cancer susceptibility locus (OR = 0.86, P = 1.65 × 10−6). Subgroup analysis by histologic types further identified nine lung CDGs. Analysis of somatic alterations found that in lung adenocarcinomas, rs78062588[C] allele (TPM3 in 1q21.3) was associated with elevated somatic copy number of TPM3 (OR = 1.16, P = 0.02). In lung adenocarcinomas, rs1611182 (HLA-A in 6p22.1) was associated with truncation mutations of the transcriptional misregulation in cancer pathway (OR = 0.66, P = 1.76 × 10−3). Conclusions: Genetic variants can regulate functions of lung CDGs and influence lung cancer susceptibility. Impact: Our findings might help unravel biological mechanisms underlying lung cancer susceptibility.

Cadmium and lead are persistent environmental toxins that are known or probable carcinogens, based on evidence for causality for nonhematologic cancers. Associations of these metals with risk of non-Hodgkin lymphoma (NHL) and multiple myeloma (MM) are unknown but biologically plausible. To examine the associations of circulating levels of lead and cadmium exposure with risk of B-cell NHL (B-NHL) and multiple myeloma, we conducted a nested case-control study among 299 incident B-cell NHLs and 76 MM cases within the Cancer Prevention Study-II Nutrition Cohort (CPS-II NC). Each case was incidence-density matched to two eligible controls on age, race, sex and blood draw date. Conditional logistic regression was used to estimate relative risks (RR) and 95% confidence intervals (CI) for lymphoid malignancies overall and stratified by subtype. We observed a significant positive association between high erythrocyte lead concentration and risk of lymphoid malignancies overall (RR = 1.16, 95% CI: 1.02-1.33 per 17.6 μg/L (1 standard deviation [SD])) and follicular lymphoma in particular (RR = 1.80, 95% CI: 1.15-2.80 per SD). In contrast, there was no association between erythrocyte cadmium and risk of B-NHL (RR = 0.89, 95% CI: 0.75-1.06 per 0.37 μg/L [1 SD]), or any B-NHL subtypes; but a strong inverse association with MM risk (RR = 0.59, 95% CI: 0.38-0.89, per SD). Results from our study suggest a positive association between erythrocyte lead level and risk of lymphoid malignancies and a possible inverse association between cadmium and myeloma. Additional research is needed to confirm and further explore these findings.

PURPOSE To investigate the association of postdiagnosis body mass index (BMI) and weight change with prostate cancer–specific mortality (PCSM), cardiovascular disease–related mortality (CVDM), and all-cause mortality among survivors of nonmetastatic prostate cancer. METHODS Men in the Cancer Prevention Study II Nutrition Cohort diagnosed with nonmetastatic prostate cancer between 1992 and 2013 were followed for mortality through December 2016. Current weight was self-reported on follow-up questionnaires approximately every 2 years. Postdiagnosis BMI was obtained from the first survey completed 1 to < 6 years after diagnosis. Weight change was the difference in weight between the first and second postdiagnosis surveys. Deaths occurring within 4 years of the follow-up were excluded to reduce bias from reverse causation. Analyses of BMI and weight change included 8,330 and 6,942 participants, respectively. RESULTS Postdiagnosis BMI analyses included 3,855 deaths from all causes (PCSM, n = 500; CVDM, n = 1,155). Using Cox proportional hazards models, hazard ratios (HRs) associated with postdiagnosis obesity (BMI ≥ 30 kg/m2) compared with healthy weight (BMI 18.5 to < 25.0 kg/m2) were 1.28 for PCSM (95% CI, 0.96 to 1.67), 1.24 for CVDM (95% CI, 1.03 to 1.49), and 1.23 for all-cause mortality (95% CI, 1.11 to 1.35). Weight gain analyses included 2,973 deaths (PCSM, n = 375; CVDM, n = 881). Postdiagnosis weight gain (> 5% of body weight), compared with stable weight (± < 3%), was associated with a higher risk of PCSM (HR, 1.65; 95% CI, 1.21 to 2.25) and all-cause mortality (HR, 1.27; 95% CI, 1.12 to 1.45) but not CVDM. CONCLUSION Results suggest that among survivors of nonmetastatic prostate cancer with largely localized disease, postdiagnosis obesity is associated with higher CVDM and all-cause mortality, and possibly higher PCSM, and that postdiagnosis weight gain may be associated with a higher mortality as a result of all causes and prostate cancer.

Deficient intake of micronutrients involved in one-carbon metabolism (e.g., choline, methionine, vitamin B(12), and folic acid) leads to hepatocellular carcinoma (HCC) development in rodents, but it is under-investigated in humans. We investigated the association between one-carbon metabolism–related micronutrient intake and HCC risk in a prospective cohort of 494,860 participants with 16 years of follow-up in the NIH-AARP study. Dietary intakes and supplement use were ascertained at baseline using a food-frequency questionnaire. Total intake (diet plus supplements) of the following one-carbon metabolism–related micronutrients were calculated: folate, methionine, and vitamins B(2) (riboflavin), B(3) (niacin), B(6), and B(12). These micronutrients were examined both individually and simultaneously, with adjustment for covariates. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Over the 16-year follow-up period, 647 incident HCC cases were diagnosed. When examined individually, higher total vitamin B(3) intake was associated with a lower HCC risk (HR(Q5vsQ1)=0.60; 95%CI=0.42–0.85; P(trend)=0.008), and the association remained significant when all six micronutrients were examined simultaneously (HR(Q5vsQ1)=0.32; 95%CI=0.18–0.55; P(trend)3 years of follow-up, higher total vitamin B(3) intake was again associated with lower risk (HR(Q5vsQ1)=0.37; 95%CI=0.20–0.68; P(trend)=0.001), whereas higher total vitamin B(6) intake was associated with higher risk (HR(Q5vsQ1)=2.04; 95%CI=1.02–4.07; P(trend)=0.04). Restricted cubic spline analyses showed a dose-response inverse association between total vitamin B(3) intake and HCC risk, and dose-response positive association between total vitamin B(6) intake and HCC risk. The study suggests that higher vitamin B3 intake is associated with lower HCC risk, whereas higher vitamin B6 intake is associated with increased risk.

We investigated whether genetic susceptibility to tuberculosis (TB) influences lung adenocarcinoma development among never-smokers using TB genome-wide association study (GWAS) results within the Female Lung Cancer Consortium in Asia. Pathway analysis with the adaptive rank truncated product method was used to assess the association between a TB-related gene-set and lung adenocarcinoma using GWAS data from 5512 lung adenocarcinoma cases and 6277 controls. The gene-set consisted of 31 genes containing known/suggestive associations with genetic variants from previous TB-GWAS. Subsequently, we followed-up with Mendelian Randomization to evaluate the association between TB and lung adenocarcinoma using three genome-wide significant variants from previous TB-GWAS in East Asians. The TB-related gene-set was associated with lung adenocarcinoma (p = 0.016). Additionally, the Mendelian Randomization showed an association between TB and lung adenocarcinoma (OR = 1.31, 95% CI: 1.03, 1.66, p = 0.027). Our findings support TB as a causal risk factor for lung cancer development among never-smoking Asian women.

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs of MM in 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10−6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better capture MM risk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10−12). Our study shows that common genetic variation contributes to MM risk in individuals with AA.

Background: Advanced prostate cancer etiology is poorly understood. Few studies have examined associations of anthropometric factors (e.g. early adulthood obesity) with advanced prostate cancer risk.Patients and methods: We carried out pooled analyses to examine associations between body fatness, height, and prostate cancer risk. Among 830 772 men, 51 734 incident prostate cancer cases were identified, including 4762 advanced (T4/N1/M1 or prostate cancer deaths) cases, 2915 advanced restricted (same as advanced, but excluding localized cancers that resulted in death) cases, 9489 high-grade cases, and 3027 prostate cancer deaths. Cox proportional hazards models were used to calculate study-specific hazard ratios (HR) and 95% confidence intervals (CI); results were pooled using random effects models.Results: No statistically significant associations were observed for body mass index (BMI) in early adulthood for advanced, advanced restricted, and high-grade prostate cancer, and prostate cancer mortality. Positive associations were shown for BMI at baseline with advanced prostate cancer (HR = 1.30, 95% CI = 0.95-1.78) and prostate cancer mortality (HR = 1.52, 95% CI = 1.12-2.07) comparing BMI >= 35.0 kg/m(2) with 21-22.9 kg/m(2). When considering early adulthood and baseline BMI together, a 27% higher prostate cancer mortality risk (95% CI = 9% to 49%) was observed for men with BMI = 30.0 kg/m(2) at baseline compared with BMI = 110 cm with = 1.00 with = 1.90 m withConclusion: Our findings suggest that height and total and central adiposity in mid-to-later adulthood, but not early adulthood adiposity, are associated with risk of advanced forms of prostate cancer. Thus, maintenance of healthy weight may help prevent advanced prostate cancer.

ObjectiveTo examine the association between prediagnostic plasma polyunsaturated fatty acids levels (PUFA) and amyotrophic lateral sclerosis (ALS).MethodsWe identified 275 individuals who developed ALS while enrolled in 5 US prospective cohorts, and randomly selected 2 controls, alive at the time of the case diagnosis, matched on cohort, birth year, sex, ethnicity, fasting status, and time of blood draw. We measured PUFA, expressed as percentages of total fatty acids, using gas liquid chromatography and used conditional logistic regression to estimate risk ratios (RR) and 95% confidence intervals (CI) for the association between PUFA and ALS.ResultsThere was no association between total, n-3, and n-6 PUFA, eicosapentaenoic acid, or docosapentaenoic acid levels and ALS. Higher plasma α-linolenic acid (ALA) in men was associated with lower risk of ALS in age- and matching factor-adjusted analyses (top vs bottom quartile: RR = 0.21 [95% CI 0.07, 0.58], p for trend = 0.004). In women, higher plasma arachidonic acid was associated with higher risk (top vs bottom quartile: RR = 1.65 [95% CI 0.99, 2.76], p for trend = 0.052). Multivariable adjustment, including correlated PUFA, did not change the findings for ALA and arachidonic acid. In men and women combined, higher plasma docosahexaenoic acid (DHA) was associated with higher risk of ALS (top vs bottom quartile: RR = 1.56 [95% CI 1.01, 2.41], p for trend = 0.054), but in multivariable models the association was only evident in men.ConclusionsThe majority of individual PUFAs were not associated with ALS. In men, ALA was inversely and DHA was positively related to risk of ALS, while in women arachidonic acid was positively related. These findings warrant confirmation in future studies.

Objective This study was undertaken to identify metabolites associated with BMI and waist circumference (WC) in women and to determine whether these metabolites are associated with biomarkers of metabolic health. Methods Untargeted metabolomic analysis was done on serum from 1,534 women. Metabolites associated with BMI and WC were identified using linear regression with a Bonferroni-corrected P value. Clustered blocks of these metabolites were then defined whose association with the anthropometric measures could be represented by a single metabolite. The association of these representative metabolites with biomarkers for diabetes and inflammation was then determined. Results About one-third of 781 metabolites included in the analyses were associated with BMI and/or WC. Associations were found for some novel metabolites, including several sphingolipids, nucleotides, and modified fatty acids. Among metabolites most strongly inversely associated with BMI, the choline-containing plasmalogen (O-16:0/18:1) (β = -0.30, P = 6.62 × 10-32 ) was also inversely associated with c-peptide and positively associated with adiponectin. Adjustment for BMI attenuated the metabolite-biomarker associations more for hemoglobin A1c (> 100%) and c-peptide (58.8% to > 100%) than for C-reactive protein (10.5%-40.0%) and adiponectin (7.0%-30.4%). Conclusions These results add to the list of metabolites associated with adiposity and indicate that some may influence processes that contribute to the development of obesity-related diseases.

Vitamin B supplementation can have side effects for human health, including cancer risk. We aimed to elucidate the role of vitamin B12 in lung cancer aetiology via direct measurements of pre‐diagnostic circulating vitamin B12 concentrations in a nested case‐control study, complemented with a Mendelian randomization (MR) approach in an independent case‐control sample. We used pre‐diagnostic biomarker data from 5,183 case‐control pairs nested within 20 prospective cohorts, and genetic data from 29,266 cases and 56,450 controls.Exposures included directly measured circulating vitamin B12 in pre‐diagnostic blood samples from the nested case‐control study, and 8 single nucleotide polymorphisms associated with vitamin B12 concentrations in the MR study.Our main outcome of interest was increased risk for lung cancer, overall and by histological subtype, per increase in circulating vitamin B12 concentrations.We found circulating vitamin B12 to be positively associated with overall lung cancer risk in a dose response fashion (odds ratio for a doubling in B12 [ORlog2B12] = 1.15, 95% confidence interval (95%CI) = 1.06‐1.25). The MR analysis based on 8 genetic variants also indicated that genetically determined higher vitamin B12 concentrations were positively associated with overall lung cancer risk (OR per 150 pmol/L standard deviation increase in B12 [ORSD]= 1.08, 95%CI= 1.00‐1.16).Considering the consistency of these two independent and complementary analyses, these findings support the hypothesis that high vitamin B12 status increases the risk of lung cancer.

Background: Whether associations between circulating metabolites and prostate cancer are causal is unknown. We report on the largest study of metabolites and prostate cancer (2,291 cases and 2,661 controls) and appraise causality for a subset of the prostate cancer–metabolite associations using two-sample Mendelian randomization (MR). Methods: The case–control portion of the study was conducted in nine UK centers with men ages 50–69 years who underwent prostate-specific antigen screening for prostate cancer within the Prostate Testing for Cancer and Treatment (ProtecT) trial. Two data sources were used to appraise causality: a genome-wide association study (GWAS) of metabolites in 24,925 participants and a GWAS of prostate cancer in 44,825 cases and 27,904 controls within the Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) consortium. Results: Thirty-five metabolites were strongly associated with prostate cancer (P < 0.0014, multiple-testing threshold). These fell into four classes: (i) lipids and lipoprotein subclass characteristics (total cholesterol and ratios, cholesterol esters and ratios, free cholesterol and ratios, phospholipids and ratios, and triglyceride ratios); (ii) fatty acids and ratios; (iii) amino acids; (iv) and fluid balance. Fourteen top metabolites were proxied by genetic variables, but MR indicated these were not causal. Conclusions: We identified 35 circulating metabolites associated with prostate cancer presence, but found no evidence of causality for those 14 testable with MR. Thus, the 14 MR-tested metabolites are unlikely to be mechanistically important in prostate cancer risk. Impact: The metabolome provides a promising set of biomarkers that may aid prostate cancer classification.

The original version of this Article contained an error in the spelling of the author Manuela Gago-Dominguez, which was incorrectly given as Manuela G. Dominguez. This has now been corrected in both the PDF and HTML versions of the Article.

Previous cross-sectional metabolomics studies have identified many potential dietary biomarkers, mostly in blood. Few studies examined urine samples although urine is preferred for dietary biomarker discovery. Furthermore, little is known regarding the reproducibility of urinary metabolomic biomarkers over time. We aimed to identify urinary metabolomic biomarkers of diet and assess their reproducibility over time. We conducted a metabolomics analysis among 648 racially/ethnically diverse men and women in the Diet Assessment Sub-study of the Cancer Prevention Study-3 cohort to examine the correlation between &gt, 100 food groups/items [101 by a food frequency questionnaire (FFQ), and 105 by repeated 24 h diet recalls (24HRs)] and 1391 metabolites measured in 24 h urine sample replicates, six months apart. Diet–metabolite associations were examined by Pearson’s partial correlation analysis. Biomarkers were evaluated for prediction accuracy assessed using area under the curve (AUC) calculated from the receiver operating characteristic curve and for reproducibility assessed using intraclass correlation coefficients (ICCs). A total of 1708 diet–metabolite associations were identified after Bonferroni correction for multiple comparisons and restricting correlation coefficients to &gt, 0.2 or &lt, −0.2 (1570 associations using the FFQ and 933 using 24HRs), 513 unique metabolites correlated with 79 food groups/items. The median ICCs of the 513 putative biomarkers was 0.53 (interquartile range 0.42–0.62). In this study, with comprehensive dietary data and repeated 24 h urinary metabolic profiles, we identified a large number of diet–metabolite correlations and replicated many found in previous studies. Our findings revealed the promise of urine samples for dietary biomarker discovery in a large cohort study and provide important information on biomarker reproducibility, which could facilitate their utilization in future clinical and epidemiological studies.

Untargeted metabolomic studies have identified potential biomarkers of colorectal cancer risk, but evidence is still limited and broadly inconsistent. Among 39,239 Cancer Prevention Study II Nutrition cohort participants who provided a blood sample between 1998–2001, 517 newly diagnosed colorectal cancers were identified through 30 June 2015. In this nested case–control study, controls were matched 1:1 to cases on age, sex, race and date of blood draw. Mass spectroscopy-based metabolomic analyses of pre-diagnostic plasma identified 886 named metabolites, after quality control exclusions. Conditional logistic regression models estimated multivariable-adjusted odds ratios (OR) and 95% confidence intervals (CI) for 1 standard deviation (SD) increase in each metabolite with risk of colorectal cancer. Six metabolites were associated with colorectal cancer risk at a false discovery rate &lt, 0.20. These metabolites were of several classes, including cofactors and vitamins, nucleotides, xenobiotics, lipids and amino acids. Five metabolites (guanidinoacetate, 2’-O-methylcytidine, vanillylmandelate, bilirubin (E,E) and N-palmitoylglycine) were positively associated (OR per 1 SD = 1.29 to 1.32), and one (3-methylxanthine) was inversely associated with CRC risk (OR = 0.79, 95% CI, 0.69–0.89). We did not replicate findings from two earlier prospective studies of 250 cases each after adjusting for multiple comparisons. Large pooled prospective analyses are warranted to confirm or refute these findings and to discover and replicate metabolites associated with colorectal cancer risk.

Breast cancer is the most common cancer in women, but its incidence can only be partially explained through established risk factors. Our aim was to use metabolomics to identify novel risk factors for breast cancer and to validate recently reported metabolite-breast cancer findings. We measured levels of 1275 metabolites in prediagnostic serum in a nested case-control study of 782 postmenopausal breast cancer cases and 782 matched controls. Metabolomics analysis was performed by Metabolon Inc using ultra-performance liquid chromatography and a Q-Exactive high resolution/accurate mass spectrometer. Controls were matched by birth date, date of blood draw, and race/ethnicity. Odds ratios (ORs) and 95% confidence intervals (CIs) of breast cancer at the 90th versus 10th percentile (modeled on a continuous basis) of metabolite levels were estimated using conditional logistic regression, with adjustment for age. Twenty-four metabolites were significantly associated with breast cancer risk at a false discovery rate &lt, 0.20. For the nine metabolites positively associated with risk, the ORs ranged from 1.75 (95% CI: 1.29–2.36) to 1.45 (95% CI: 1.13–1.85), and for the 15 metabolites inversely associated with risk, ORs ranged from 0.59 (95% CI: 0.43–0.79) to 0.69 (95% CI: 0.55–0.87). These metabolites largely comprised carnitines, glycerolipids, and sex steroid metabolites. Associations for three sex steroid metabolites validated findings from recent studies and the remainder were novel. These findings contribute to growing data on metabolite-breast cancer associations by confirming prior findings and identifying novel leads for future validation efforts.

In the version of this article originally published, the names of the equally contributing authors and jointly supervising authors were switched. The correct affiliations are: “These authors contributed equally: David V. Conti, Burcu F. Darst. These authors jointly supervised this work: David V. Conti, Rosalind A. Eeles, Zsofia Kote-Jarai, Christopher A. Haiman.” The error has been corrected in the HTML and PDF versions of the article.

Imbalances of blood biomarkers are associated with disease, and biomarkers may also vary non-pathologically across population groups. We described variation in concentrations of biomarkers of one-carbon metabolism, vitamin status, inflammation including tryptophan metabolism, and endothelial and renal function among cancer-free older adults. We analyzed 5167 cancer-free controls aged 40–80 years from 20 cohorts in the Lung Cancer Cohort Consortium (LC3). Centralized biochemical analyses of 40 biomarkers in plasma or serum were performed. We fit multivariable linear mixed effects models to quantify variation in standardized biomarker log-concentrations across four factors: age, sex, smoking status, and body mass index (BMI). Differences in most biomarkers across most factors were small, with 93% (186/200) of analyses showing an estimated difference lower than 0.25 standard-deviations, although most were statistically significant due to large sample size. The largest difference was for creatinine by sex, which was − 0.91 standard-deviations lower in women than men (95%CI − 0.98; − 0.84). The largest difference by age was for total cysteine (0.40 standard-deviation increase per 10-year increase, 95%CI 0.36; 0.43), and by BMI was for C-reactive protein (0.38 standard-deviation increase per 5-kg/m2 increase, 95%CI 0.34; 0.41). For 31 of 40 markers, the mean difference between current and never smokers was larger than between former and never smokers. A statistically significant (p

International audience; Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Background There is an urgent need to identify factors specifically associated with aggressive prostate cancer (PCa) risk. We investigated whether rare pathogenic, likely pathogenic, or deleterious (P/LP/D) germline variants in DNA repair genes are associated with aggressive PCa risk in a case-case study of aggressive vs nonaggressive disease. Methods Participants were 5545 European-ancestry men, including 2775 nonaggressive and 2770 aggressive PCa cases, which included 467 metastatic cases (16.9%). Samples were assembled from 12 international studies and germline sequenced together. Rare (minor allele frequency Results BRCA2 and PALB2 had the most statistically significant gene-based associations, with 2.5% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D BRCA2 alleles (odds ratio [OR] = 3.19, 95% confidence interval [CI] = 1.94 to 5.25, P = 8.58 × 10-7) and 0.65% of aggressive and 0.11% of nonaggressive cases carrying P/LP/D PALB2 alleles (OR = 6.31, 95% CI = 1.83 to 21.68, P = 4.79 × 10-4). ATM had a nominal association, with 1.6% of aggressive and 0.8% of nonaggressive cases carrying P/LP/D ATM alleles (OR = 1.88, 95% CI = 1.10 to 3.22, P = .02). In aggregate, P/LP/D alleles within 24 literature-curated candidate PCa DNA repair genes were more common in aggressive than nonaggressive cases (carrier frequencies = 14.2% vs 10.6%, respectively; P = 5.56 × 10-5). However, this difference was non-statistically significant (P = .18) on excluding BRCA2, PALB2, and ATM. Among these 24 genes, P/LP/D carriers had a 1.06-year younger diagnosis age (95% CI = -1.65 to 0.48, P = 3.71 × 10-4). Conclusions Risk conveyed by DNA repair genes is largely driven by rare P/LP/D alleles within BRCA2, PALB2, and ATM. These findings support the importance of these genes in both screening and disease management considerations.

Previous metabolomic studies have identified putative blood biomarkers of dietary intake. These biomarkers need to be replicated in other populations and tested for reproducibility over time for the potential use in future epidemiological studies. We conducted a metabolomics analysis among 671 racially/ethnically diverse men and women included in a diet validation study to examine the correlation between &gt, 100 food groups/items (101 by a food frequency questionnaire (FFQ), 105 by 24-h diet recalls (24HRs)) with 1141 metabolites measured in fasting plasma sample replicates, six months apart. Diet&ndash, metabolite associations were examined by Pearson&rsquo, s partial correlation analysis. Biomarker reproducibility was assessed using intraclass correlation coefficients (ICCs). A total of 677 diet&ndash, metabolite associations were identified after Bonferroni adjustment for multiple comparisons and restricting absolute correlation coefficients to greater than 0.2 (601 associations using the FFQ and 395 using 24HRs). The median ICCs of the 238 putative biomarkers was 0.56 (interquartile range 0.46&ndash, 0.68). In this study, with repeated FFQs, 24HRs and plasma metabolic profiles, we identified several potentially novel food biomarkers and replicated others found in our previous study. Our findings contribute to the growing literature on food-based biomarkers and provide important information on biomarker reproducibility which could facilitate their utilization in future nutritional epidemiological studies.

Background: Some evidence suggests the association between body mass index (BMI) and pancreatic cancer risk is weaker among current smokers than among never smokers. Methods: We examined the association between BMI and pancreatic cancer mortality among adults who reported smoking status at enrollment into Cancer Prevention Study-II in 1982, including 420,543 never smokers, 282,244 former cigarette smokers, and 219,885 current cigarette smokers. After excluding the first 3 years of follow-up to reduce reverse causation, we calculated multivariable-adjusted hazard ratios (HR). Results: During the full follow-up period from 1985 to 2014, 7,904 participants died of pancreatic cancer. The HR per 5 BMI units was lower among current smokers [HR = 1.14; 95% confidence interval (CI), 1.07–1.20] than never smokers (HR = 1.22; 95% CI, 1.17–1.27), although this difference was not statistically significant (P = 0.06). BMI was significantly less strongly associated with pancreatic cancer mortality among current smokers reporting ≥20 cigarettes/day (HR = 1.10; 95% CI, 1.03–1.18) than among never smokers. During follow-up within 10 years of enrollment, when current smokers at enrollment were the most likely to have still been smoking, BMI was not associated with pancreatic cancer mortality among current smokers (HR = 1.02; 95% CI, 0.90–1.16, P = 0.03 for difference between current and never smokers). BMI HRs were similar among former and never smokers. Conclusions: These results support a weaker association between BMI and pancreatic cancer among current smokers than among never smokers. Impact: In populations with low smoking prevalence, the pancreatic cancer burden due to BMI is likely to be higher than that predicted by risk estimates from studies including substantial numbers of smokers.

We have recently completed the largest GWAS on lung cancer including 29,266 cases and 56,450 controls of European descent. The goal of this study has been to integrate the complete GWAS results with a large-scale expression quantitative trait loci (eQTL) mapping study in human lung tissues (n=1,038) to identify candidate causal genes for lung cancer. We performed transcriptome-wide association study (TWAS) for lung cancer overall, by histology (adenocarcinoma, squamous cell carcinoma, small cell lung cancer) and smoking subgroups (never- and ever-smokers). We performed replication analysis using lung data from the Genotype-Tissue Expression (GTEx) project. DNA damage assays were performed in human lung fibroblasts for selected TWAS genes. As expected, the main TWAS signal for all histological subtypes and ever-smokers was on chromosome 15q25. The gene most strongly associated with lung cancer at this locus using the TWAS approach was IREB2 (PTWAS =1.09E-99), where lower predicted expression increased lung cancer risk. A new lung adenocarcinoma susceptibility locus was revealed on 9p13.3 and associated with higher predicted expression of AQP3 (PTWAS =3.72E-6). Among the 45 previously described lung cancer GWAS loci, we mapped candidate target gene for 17 of them. The association AQP3-adenocarcinoma on 9p13.3 was replicated using GTEx (PTWAS =6.55E-5). Consistent with the effect of risk alleles on gene expression levels, IREB2 knockdown and AQP3 overproduction promote endogenous DNA damage. These findings indicate genes whose expression in lung tissue directly influence lung cancer risk. This article is protected by copyright. All rights reserved.

The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN (...), Ji, X., Mukherjee, S., Landi, M.T., Bosse, Y., Joubert, P., Zhu, D., Gorlov, I., Xiao, X., Han, Y., Gorlova, O., Hung, R.J., Brhane, Y., Carreras-Torres, R., Christiani, D.C., Caporaso, N., Johansson, M., Liu, G., Bojesen, S.E., Le Marchand, L., Albanes, D., Bickeböller, H., Aldrich, M.C., Bush, W.S., Tardon, A., Rennert, G., Chen, C., Byun, J., Dragnev, K.H., Field, J.K., Kiemeney, L.F., Lazarus, P., Zienolddiny, S., Lam, S., Schabath, M.B., Andrew, A.S., Bertazzi, P.A., Pesatori, A.C., Diao, N., Su, L., Song, L., Zhang, R., Leighl, N., Johansen, J.S., Mellemgaard, A., Saliba, W., Haiman, C., Wilkens, L., Fernandez-Somoano, A., Fernandez-Tardon, G., Heijden, E.H.F.M., Kim, J.H., Davies, M.P.A., Marcus, M.W., Brunnström, H., Manjer, J., Melander, O., Muller, D.C., Overvad, K., Trichopoulou, A., Tumino, R., Goodman, G.E., Cox, A., Taylor, F., Woll, P., Wichmann, E., Muley, T., Risch, A., Rosenberger, A., Grankvist, K., Johansson, M., Shepherd, F., Tsao, M.-S., Arnold, S.M., Haura, E.B., Bolca, C., Holcatova, I., Janout, V., Kontic, M., Lissowska, J., Mukeria, A., Ognjanovic, S., Orlowski, T.M., Scelo, G., Swiatkowska, B., Zaridze, D., Bakke, P., Skaug, V., Butler, L.M., Offit, K., Srinivasan, P., Bandlamudi, C., Hellmann, M.D., Solit, D.B., Robson, M.E., Rudin, C.M., Stadler, Z.K., Taylor, B.S., Berger, M.F., Houlston, R., McLaughlin, J., Stevens, V., Nickle, D.C., Obeidat, M., Timens, W., Artigas, M.S., Shete, S., Brenner, H., Chanock, S., Brennan, P., McKay, J.D., Amos, C.I.

Background Recent studies suggest that untargeted metabolomics is a promising tool to identify novel biomarkers of individual foods. However, few large cross-sectional studies with comprehensive data on habitual diet and circulating metabolites have been conducted. Objective We aimed to identify potential food biomarkers and evaluate their predictive accuracy. Methods We conducted a cross-sectional analysis of consumption of 91 food groups or items, assessed by a 152-item food-frequency questionnaire, in relation to 1186 serum metabolites measured by mass spectrometry-based platforms from 1369 nonsmoking postmenopausal women (mean age = 68.3 y). Diet-metabolite associations were selected by Pearson's partial correlation analysis (P 0.2). The predictive accuracy of the selected food metabolites was evaluated from the area under the curve (AUC) calculated from receiver operating characteristic analysis conducted among women in the top and bottom quintiles of dietary intake. Results We identified 379 diet-metabolite associations. Forty-two food groups or items were correlated with 199 serum metabolites. We replicated 63 metabolites as biomarkers of habitual food intake reported in previous cross-sectional studies. Among those not previously shown to be associated with habitual diet, several are biologically plausible and were reported in acute feeding studies including: banana and dopamine 3-O-sulfate (r = 0.34, AUC = 76%) and dopamine 4-O-sulfate (r = 0.33, AUC = 74%), garlic and alliin (r = 0.24, AUC = 69%), N-acetylalliin (r = 0.27, AUC = 70%), and S-allylcysteine (r = 0.23, AUC = 69). Two unannotated metabolites were the strongest predictors for dark fish (X-02269, r = 0.51, AUC = 94%) and coffee intake (X-21442, r = 0.62, AUC = 98%). Conclusion In this comprehensive, cross-sectional analysis of habitual food intake and serum metabolites among postmenopausal women, we identified several potentially novel food biomarkers and replicated others. Our findings contribute to the limited literature on food-based biomarkers and highlight the significant and promising role that large cohort studies with archived blood samples could play in this field. This study was registered at clinicaltrials.gov as NCT03282812.

Physical activity has been associated with lower lung cancer risk in numerous studies with estimates ranging from 20 to 50% lower risk in the most versus the least active study participants. Underweight and obesity have also been associated with lower lung cancer risk, with a nonlinear, inverted U-shaped relationship. However, associations of physical activity and obesity with lung cancer are likely significantly confounded by smoking since individuals who smoke are generally less active and leaner than non-smokers, but few studies have examined these associations stratified by smoking status. Using data from 162,679 men and women who were cancer-free at enrollment (1992–1993) in the American Cancer Society Cancer Prevention Study-II Nutrition Cohort, we examined associations of baseline recreational physical activity (MET-hours per week; none, 0.1 to

Background: Circulating concentrations of B vitamins and factors related to one-carbon metabolism have been found to be strongly inversely associated with lung cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. The extent to which these associations are present in other study populations is unknown. Methods: Within 20 prospective cohorts from the National Cancer Institute Cohort Consortium, a nested case-control study was designed including 5364 incident lung cancer case patients and 5364 control subjects who were individually matched to case patients by age, sex, cohort, and smoking status. Centralized biochemical analyses were performed to measure circulating concentrations of vitamin B6, folate, and methionine, as well as cotinine as an indicator of recent tobacco exposure. The association between these biomarkers and lung cancer risk was evaluated using conditional logistic regression models. Results: Participants with higher circulating concentrations of vitamin B6 and folate had a modestly decreased risk of lung cancer risk overall, the odds ratios when comparing the top and bottom fourths (OR 4vs1 ) being 0.88 (95% confidence interval [CI] = 0.78 to 1.00) and 0.86 (95% CI = 0.74 to 0.99), respectively. We found stronger associations among men (vitamin B6: OR 4vs1 = 0.74, 95% CI = 0.62 to 0.89; folate: OR 4vs1 = 0.75, 95% CI = 0.61 to 0.93) and ever smokers (vitamin B6: OR 4vs1 = 0.78, 95% CI = 0.67 to 0.91; folate: OR 4vs1 = 0.87, 95% CI = 0.73 to 1.03). We further noted that the association of folate was restricted to Europe/Australia and Asia, whereas no clear association was observed for the United States. Circulating concentrations of methionine were not associated with lung cancer risk overall or in important subgroups. Conclusions: Although confounding by tobacco exposure or reverse causation cannot be ruled out, these study results are compatible with a small decrease in lung cancer risk in ever smokers who avoid low concentrations of circulating folate and vitamin B6.

BACKGROUND Prospective cohort studies contribute importantly to understanding the role of lifestyle, genetic, and other factors in chronic disease etiology. METHODS The American Cancer Society (ACS) recruited a new prospective cohort study, Cancer Prevention Study 3 (CPS-3), between 2006 and 2013 from 35 states and Puerto Rico. Enrollment took place primarily at ACS community events and at community enrollment “drives.” At enrollment sites, participants completed a brief survey that included an informed consent, identifying information necessary for follow-up, and key exposure information. They also provided a waist measure and a nonfasting blood sample. Most participants also completed a more comprehensive baseline survey at home that included extensive medical, lifestyle, and other information. Participants will be followed for incident cancers through linkage with state cancer registries and for cause-specific mortality through linkage with the National Death Index. RESULTS In total, 303,682 participants were enrolled. Of these, 254,650 completed the baseline survey and are considered “fully” enrolled; they will be sent repeat surveys periodically for at least the next 20 years to update exposure information. The remaining participants (n = 49,032) will not be asked to update exposure information but will be followed for outcomes. Twenty-three percent of participants were men, 17.3% reported a race or ethnicity other than “white,” and the median age at enrollment was 47 years. CONCLUSIONS CPS-3 will be a valuable resource for studies of cancer and other outcomes because of its size; its diversity with respect to age, ethnicity, and geography; and the availability of blood samples and detailed questionnaire information collected over time. Cancer 2017;123:2014–2024. © 2017 American Cancer Society.

Introduction We sought to determine whether survival motor neuron (SMN) protein blood levels correlate with denervation and SMN2 copies in spinal muscular atrophy (SMA). Methods Using a mixed‐effect model, we tested associations between SMN levels, compound muscle action potential (CMAP), and SMN2 copies in a cohort of 74 patients with SMA. We analyzed a subset of 19 of these patients plus four additional patients who had been treated with received gene therapy to examine SMN trajectories early in life. Results Patients with SMA who had lower CMAP values had lower circulating SMN levels (P = .04). Survival motor neuron protein levels were different between patients with two and three SMN2 copies (P < .0001) and between symptomatic and presymptomatic patients (P < .0001), with the highest levels after birth and progressive decline over the first 3 years. Neither nusinersen nor gene therapy clearly altered SMN levels. Discussion These data provide evidence that whole blood SMN levels correlate with SMN2 copy number and severity of denervation.

Background: Research on the relationship of meat, fish, and egg consumption and mortality among prostate cancer survivors is limited. Methods: In the Cancer Prevention Study-II Nutrition Cohort, men diagnosed with nonmetastatic prostate cancer between baseline in 1992/1993 and 2015 were followed for mortality until 2016. Analyses of pre- and postdiagnosis intakes of red and processed meat, poultry, fish, and eggs included 9,286 and 4,882 survivors, respectively. Multivariable-adjusted RRs and 95% confidence intervals (CI) were estimated using Cox proportional hazards models. Results: A total of 4,682 and 2,768 deaths occurred during follow-up in pre- and postdiagnosis analyses, respectively. Both pre- and postdiagnosis intakes of total red and processed meat were positively associated with all-cause mortality (quartile 4 vs. 1: RR = 1.13; 95% CI, 1.03–1.25; Ptrend = 0.02; RR = 1.22; 95% CI, 1.07–1.39; Ptrend = 0.03, respectively), and both pre- and postdiagnosis poultry intakes were inversely associated with all-cause mortality (quartile 4 vs. 1 RR = 0.90; 95% CI, 0.82–0.98; Ptrend = 0.04; RR = 0.84; 95% CI, 0.75–0.95; Ptrend = 0.01, respectively). No associations were seen for prostate cancer–specific mortality, except that higher postdiagnosis unprocessed red meat intake was associated with lower risk. Conclusions: Higher red and processed meat, and lower poultry, intakes either before or after prostate cancer diagnosis were associated with higher risk of all-cause mortality. Impact: Our findings provide additional evidence that prostate cancer survivors should follow the nutrition guidelines limiting red and processed meat consumption to improve overall survival. Additional research on the relationship of specific meat types and mortality is needed.

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Cell-mediated immune suppression may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), quinolinic acid (QA) and neopterin as markers of immune regulation and inflammation with lung cancer risk in 5,364 smoking-matched case-control pairs from 20 prospective cohorts included in the international Lung Cancer Cohort Consortium. All biomarkers were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with individual biomarkers were calculated using conditional logistic regression with adjustment for circulating cotinine. Compared to the lowest quintile, the highest quintiles of kynurenine, KTR, QA and neopterin were associated with a 20-30% higher risk, and tryptophan with a 15% lower risk of lung cancer (all ptrend < 0.05). The strongest associations were seen for current smokers, where the adjusted ORs (95% CIs) of lung cancer for the highest quintile of KTR, QA and neopterin were 1.42 (1.15-1.75), 1.42 (1.14-1.76) and 1.45 (1.13-1.86), respectively. A stronger association was also seen for KTR and QA with risk of lung squamous cell carcinoma followed by adenocarcinoma, and for lung cancer diagnosed within the first 2 years after blood draw. This study demonstrated that components of the tryptophan-kynurenine pathway with immunomodulatory effects are associated with risk of lung cancer overall, especially for current smokers. Further research is needed to evaluate the role of these biomarkers in lung carcinogenesis and progression.

OBJECTIVES: Colonic microbiota can be modified by diet and other environmental exposures that are associated with risk of cancer and other diseases. In addition, the stool metabolome represents products formed by gut microbial utilization of foods, nutrients, and other compounds. The collection of stool samples for analyses of the microbiome and metabolome in prospective cohort studies may lead to a better understanding of diet and other factors in disease etiology. Most studies of the gut microbiome and metabolome have been small clinical studies and relied on the collection of stool samples that were quickly flash-frozen and stored with no preservative. However, this method of collection is not feasible in large epidemiologic studies in which participants would self-collect and ship samples to a central repository. Other methods may preclude metabolomic analyses or result in a limited amount of stool. METHODS: We designed a pilot study to inform stool collection procedures for participants enrolled in a nationwide prospective cohort study. In this pilot study, stool samples will be collected by 10 volunteers who will aliquot their sample into: 1) OMNIgene Gut collection kit, 2) 95% ethanol collection kit, (3) Fecal Occult Blood Test cards, and (4) a vial for fresh-frozen sample with no solution (i.e., the gold standard in this analysis). Fresh-frozen samples will be shipped overnight on dry ice to a central biorepository. The remaining samples will be: 1) shipped overnight on cold packs; 2) shipped overnight at ambient temperature; 3) shipped overnight after 4 days at ambient temperature; and 4) shipped overnight at ambient temperature, followed by 4-day incubation at 50 °C to mimic maximum shipment temperature. The reproducibility, stability, and accuracy of 16S sequencing and metabolomics in stool will be compared among the collection kits, and shipping and storage conditions. RESULTS: Initial results are anticipated by June 2019. CONCLUSIONS: The findings of this pilot study will inform future stool collection among Cancer Prevention Study-3 (CPS-3) participants, which, when analyzed with food frequency questionnaire data, will broaden our understanding of the role of diet in cancer risk. FUNDING SOURCES: The American Cancer Society funds the creation, maintenance, and updating of CPS-3.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Publisher's version (útgefin grein)., Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10−8), breast and ovarian cancer (rg = 0.24, p = 7 × 10−5), breast and lung cancer (rg = 0.18, p =1.5 × 10−6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10−4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis., The authors in this manuscript were working on behalf of BCAC, CCFR, CIMBA, CORECT, GECCO, OCAC, PRACTICAL, CRUK, BPC3, CAPS, PEGASUS, TRICL-ILCCO, ABCTB, APCB, BCFR, CONSIT TEAM, EMBRACE, GC-HBOC, GEMO, HEBON, kConFab/AOCS Mod SQuaD, and SWE-BRCA. The breast cancer genome-wide association analyses: BCAC is funded by Cancer Research UK [C1287/A16563, C1287/A10118], the European Union’s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST, respectively), and by the European Community’s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSR-SIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978), and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112—the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. The Australian Breast Cancer Family Study (ABCFS) was supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The ABCFS was also supported by the National Health and Medical Research Council of Australia, the New South Wales Cancer Council, the Victorian Health Promotion Foundation (Australia), and the Victorian Breast Cancer Research Consortium. J.L.H. is a National Health and Medical Research Council (NHMRC) Senior Principal Research Fellow. M.C.S. is a NHMRC Senior Research Fellow. The ABCS study was supported by the Dutch Cancer Society [grants NKI 2007-3839; 2009 4363]. The Australian Breast Cancer Tissue Bank (ABCTB) is generously supported by the National Health and Medical Research Council of Australia, The Cancer Institute NSW and the National Breast Cancer Foundation. The ACP study is funded by the Breast Cancer Research Trust, UK. The AHS study is supported by the intramural research program of the National Institutes of Health, the National Cancer Institute (grant number Z01-CP010119), and the National Institute of Environmental Health Sciences (grant number Z01-ES049030). The work of the BBCC was partly funded by ELAN-Fond of the University Hospital of Erlangen. The BBCS is funded by Cancer Research UK and Breast Cancer Now and acknowledges NHS funding to the NIHR Biomedical Research Centre, and the National Cancer Research Network (NCRN). The BCEES was funded by the National Health and Medical Research Council, Australia and the Cancer Council Western Australia and acknowledges funding from the National Breast Cancer Foundation (JS). For the BCFR-NY, BCFR-PA, and BCFR-UT this work was supported by grant UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. For BIGGS, ES is supported by NIHR Comprehensive Biomedical Research Centre, Guy’s & St. Thomas’ NHS Foundation Trust in partnership with King’s College London, United Kingdom. IT is supported by the Oxford Biomedical Research Centre. BOCS is supported by funds from Cancer Research UK (C8620/A8372/A15106) and the Institute of Cancer Research (UK). BOCS acknowledges NHS funding to the Royal Marsden/Institute of Cancer Research NIHR Specialist Cancer Biomedical Research Centre. The BREast Oncology GAlician Network (BREOGAN) is funded by Acción Estratégica de Salud del Instituto de Salud Carlos III FIS PI12/02125/Cofinanciado FEDER; Acción Estratégica de Salud del Instituto de Salud Carlos III FIS Intrasalud (PI13/01136); Programa Grupos Emergentes, Cancer Genetics Unit, Instituto de Investigacion Biomedica Galicia Sur. Xerencia de Xestion Integrada de Vigo-SERGAS, Instituto de Salud Carlos III, Spain; Grant 10CSA012E, Consellería de Industria Programa Sectorial de Investigación Aplicada, PEME I + D e I + D Suma del Plan Gallego de Investigación, Desarrollo e Innovación Tecnológica de la Consellería de Industria de la Xunta de Galicia, Spain; Grant EC11-192. Fomento de la Investigación Clínica Independiente, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain; and Grant FEDER-Innterconecta. Ministerio de Economia y Competitividad, Xunta de Galicia, Spain. The BSUCH study was supported by the Dietmar-Hopp Foundation, the Helmholtz Society and the German Cancer Research Center (DKFZ). The CAMA study was funded by Consejo Nacional de Ciencia y Tecnología (CONACyT) (SALUD-2002-C01-7462). Sample collection and processing was funded in part by grants from the National Cancer Institute (NCI R01CA120120 and K24CA169004). CBCS is funded by the Canadian Cancer Society (grant # 313404) and the Canadian Institutes of Health Research. CCGP is supported by funding from the University of Crete. The CECILE study was supported by Fondation de France, Institut National du Cancer (INCa), Ligue Nationale contre le Cancer, Agence Nationale de Sécurité Sanitaire, de l’Alimentation, de l’Environnement et du Travail (ANSES), Agence Nationale de la Recherche (ANR). The CGPS was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council, and Herlev and Gentofte Hospital. The CNIO-BCS was supported by the Instituto de Salud Carlos III, the Red Temática de Investigación Cooperativa en Cáncer and grants from the Asociación Española Contra el Cáncer and the Fondo de Investigación Sanitario (PI11/00923 and PI12/00070). COLBCCC is supported by the German Cancer Research Center (DKFZ), Heidelberg, Germany. D.T. was in part supported by a postdoctoral fellowship from the Alexander von Humboldt Foundation. The American Cancer Society funds the creation, maintenance, and updating of the CPS-II cohort. The CTS was initially supported by the California Breast Cancer Act of 1993 and the California Breast Cancer Research Fund (contract 97-10500) and is currently funded through the National Institutes of Health (R01 CA77398, UM1 CA164917, and U01 CA199277). Collection of cancer incidence data was supported by the California Department of Public Health as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885. H.A.C eceives support from the Lon V Smith Foundation (LVS39420). The University of Westminster curates the DietCompLyf database funded by Against Breast Cancer Registered Charity No. 1121258 and the NCRN. The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by: Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation, the Stavros Niarchos Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom). The ESTHER study was supported by a grant from the Baden Württemberg Ministry of Science, Research and Arts. Additional cases were recruited in the context of the VERDI study, which was supported by a grant from the German Cancer Aid (Deutsche Krebshilfe). FHRISK is funded from NIHR grant PGfAR 0707-10031. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: Rita K. Schmutzler, Cologne). This work was also funded by the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, and 14575/2470). The GENICA was funded by the Federal Ministry of Education and Research (BMBF) Germany grants 01KW9975/5, 01KW9976/8, 01KW9977/0, and 01KW0114, the Robert Bosch Foundation, Stuttgart, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, the Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr University Bochum (IPA), Bochum, as well as the Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter Krankenhaus, Bonn, Germany. The GEPARSIXTO study was conducted by the German Breast Group GmbH. The GESBC was supported by the Deutsche Krebshilfe e. V. [70492] and the German Cancer Research Center (DKFZ). GLACIER was supported by Breast Cancer Now, CRUK and Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The HABCS study was supported by the Claudia von Schilling Foundation for Breast Cancer Research, by the Lower Saxonian Cancer Society, and by the Rudolf-Bartling Foundation. The HEBCS was financially supported by the Helsinki University Central Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society, and the Sigrid Juselius Foundation. The HERPACC was supported by MEXT Kakenhi (No. 170150181 and 26253041) from the Ministry of Education, Science, Sports, Culture and Technology of Japan, by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from Ministry Health, Labour and Welfare of Japan, by Health and Labour Sciences Research Grants for Research on Applying Health Technology from Ministry Health, Labour and Welfare of Japan, by National Cancer Center Research and Development Fund, and “Practical Research for Innovative Cancer Control (15ck0106177h0001)” from Japan Agency for Medical Research and development, AMED, and Cancer Bio Bank Aichi. The HMBCS was supported by a grant from the Friends of Hannover Medical School and by the Rudolf Bartling Foundation. The HUBCS was supported by a grant from the German Federal Ministry of Research and Education (RUS08/017), and by the Russian Foundation for Basic Research and the Federal Agency for Scientific Organizations for support the Bioresource collections and RFBR grants 14-04-97088, 17-29-06014, and 17-44-020498. ICICLE was supported by Breast Cancer Now, CRUK, and Biomedical Research Centre at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. Financial support for KARBAC was provided through the regional agreement on medical training and clinical research (A.L.F.) between Stockholm County Council and Karolinska Institutet, the Swedish Cancer Society, The Gustav V Jubilee foundation and Bert von Kantzows foundation. The KARMA study was supported by Märit and Hans Rausings Initiative Against Breast Cancer. The KBCP was financially supported by the special Government Funding (E.V.O.) of Kuopio University Hospital grants, Cancer Fund of North Savo, the Finnish Cancer Organizations, and by the strategic funding of the University of Eastern Finland. kConFab is supported by a grant from the National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. Financial support for the AOCS was provided by the United States Army Medical Research and Materiel Command [DAMD17-01-1-0729], Cancer Council Victoria, Queensland Cancer Fund, Cancer Council New South Wales, Cancer Council South Australia, The Cancer Foundation of Western Australia, Cancer Council Tasmania and the National Health and Medical Research Council of Australia (NHMRC; 400413, 400281, 199600). G.C.-T. and P.W. are supported by the NHMRC. RB was a Cancer Institute NSW Clinical Research Fellow. The KOHBRA study was partially supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 1020350; 1420190). LAABC is supported by grants (1RB-0287, 3PB-0102, 5PB-0018, 10PB-0098) from the California Breast Cancer Research Program. Incident breast cancer cases were collected by the USC Cancer Surveillance Program (CSP) which is supported under subcontract by the California Department of Health. The CSP is also part of the National Cancer Institute’s Division of Cancer Prevention and Control Surveillance, Epidemiology, and End Results Program, under contract number N01CN25403. L.M.B.C. is supported by the ‘Stichting tegen Kanker’. D.L. is supported by the FWO. The MABCS study is funded by the Research Centre for Genetic Engineering and Biotechnology “Georgi D. Efremov” and supported by the German Academic Exchange Program, DAAD. The MARIE study was supported by the Deutsche Krebshilfe e.V. [70-2892-BR I, 106332, 108253, 108419, 110826, 110828], the Hamburg Cancer Society, the German Cancer Research Center (DKFZ) and the Federal Ministry of Education and Research (BMBF) Germany [01KH0402]. MBCSG is supported by grants from the Italian Association for Cancer Research (AIRC) and by funds from the Italian citizens who allocated the 5/1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects “5 × 1000”). The MCBCS was supported by the NIH grants CA192393, CA116167, CA176785 an NIH Specialized Program of Research Excellence (SPORE) in Breast Cancer [CA116201], and the Breast Cancer Research Foundation and a generous gift from the David F. and Margaret T. Grohne Family Foundation. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057 and 396414, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. The MEC was support by NIH grants CA63464, CA54281, CA098758, CA132839, and CA164973. The MISS study is supported by funding from ERC-2011-294576 Advanced grant, Swedish Cancer Society, Swedish Research Council, Local hospital funds, Berta Kamprad Foundation, Gunnar Nilsson. The MMHS study was supported by NIH grants CA97396, CA128931, CA116201, CA140286, and CA177150. MSKCC is supported by grants from the Breast Cancer Research Foundation and Robert and Kate Niehaus Clinical Cancer Genetics Initiative. The work of MTLGEBCS was supported by the Quebec Breast Cancer Foundation, the Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. MYBRCA is funded by research grants from the Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Malaysia. MYMAMMO is supported by research grants from Yayasan Sime Darby LPGA Tournament and Malaysian Ministry of Higher Education (RP046B-15HTM). The NBCS has been supported by the Research Council of Norway grant 193387/V50 (to A.-L. Børresen-Dale and V.N. Kristensen) and grant 193387/H10 (to A.-L. Børresen-Dale and V.N. Kristensen), South Eastern Norway Health Authority (grant 39346 to A.-L. Børresen-Dale and 27208 to V.N. Kristensen) and the Norwegian Cancer Society (to A.-L. Børresen-Dale and 419616 - 71248 - PR-2006-0282 to V.N. Kristensen). It has received funding from the K.G. Jebsen Centre for Breast Cancer Research (2012-2015). The NBHS was supported by NIH grant R01CA100374. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The Northern California Breast Cancer Family Registry (NC-BCFR) and Ontario Familial Breast Cancer Registry (OFBCR) were supported by grant UM1 CA164920 from the National Cancer Institute (USA). The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the USA Government or the BCFR. The Carolina Breast Cancer Study was funded by Komen Foundation, the National Cancer Institute (P50 CA058223, U54 CA156733, and U01 CA179715), and the North Carolina University Cancer Research Fund. The NGOBCS was supported by Grants-in-Aid for the Third Term Comprehensive Ten-Year Strategy for Cancer Control from the Ministry of Health, Labor and Welfare of Japan, and for Scientific Research on Priority Areas, 17015049 and for Scientific Research on Innovative Areas, 221S0001, from the Ministry of Education, Culture, Sports, Science, and Technology of Japan. The NHS was supported by NIH grants P01 CA87969, UM1 CA186107, and U19 CA148065. The NHS2 was supported by NIH grants UM1 CA176726 and U19 CA148065. The OBCS was supported by research grants from the Finnish Cancer Foundation, the Academy of Finland (grant number 250083, 122715 and Center of Excellence grant number 251314), the Finnish Cancer Foundation, the Sigrid Juselius Foundation, the University of Oulu, the University of Oulu Support Foundation, and the special Governmental EVO funds for Oulu University Hospital-based research activities. The ORIGO study was supported by the Dutch Cancer Society (RUL 1997-1505) and the Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL CP16). The PBCS was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. Genotyping for PLCO was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by Cancer Research UK (grants C1275/A11699, C1275/C22524, C1275/A19187, C1275/A15956, and Breast Cancer Campaign 2010PR62, 2013PR044. PROCAS is funded from NIHR grant PGfAR 0707-10031. The RBCS was funded by the Dutch Cancer Society (DDHK 2004-3124, DDHK 2009-4318). The SASBAC study was supported by funding from the Agency for Science, Technology and Research of Singapore (A*STAR), the US National Institute of Health (NIH) and the Susan G. Komen Breast Cancer Foundation. The SBCGS was supported primarily by NIH grants R01CA64277, R01CA148667, UMCA182910, and R37CA70867. Biological sample preparation was conducted the Survey and Biospecimen Shared Resource, which is supported by P30 CA68485. The scientific development and funding of this project were, in part, supported by the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network U19 CA148065. The SBCS was supported by Sheffield Experimental Cancer Medicine Centre and Breast Cancer Now Tissue Bank. The SCCS is supported by a grant from the National Institutes of Health (R01 CA092447). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (CDC). The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by Cancer Research UK [C490/A10124, C490/A16561] and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academic Reserve. SEBCS was supported by the BRL (Basic Research Laboratory) program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2012-0000347). SGBCC is funded by the NUS start-up Grant, National University Cancer Institute Singapore (NCIS) Centre Grant and the NMRC Clinician Scientist Award. Additional controls were recruited by the Singapore Consortium of Cohort Studies-Multi-ethnic cohort (SCCS-MEC), which was funded by the Biomedical Research Council, grant number: 05/1/21/19/425. The Sister Study (SISTER) is supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES049033). The Two Sister Study (2SISTER) was supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES044005 and Z01-ES102245), and, also by a grant from Susan G. Komen for the Cure, grant FAS0703856. SKKDKFZS is supported by the DKFZ. The SMC is funded by the Swedish Cancer Foundation. The SZBCS was supported by Grant PBZ_KBN_122/P05/2004. The TBCS was funded by The National Cancer Institute, Thailand. The TNBCC was supported by a Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), a grant from the Breast Cancer Research Foundation, a generous gift from the David F. and Margaret T. Grohne Family Foundation. The TWBCS is supported by the Taiwan Biobank project of the Institute of Biomedical Sciences, Academia Sinica, Taiwan. The UCIBCS component of this research was supported by the NIH [CA58860, CA92044] and the Lon V Smith Foundation [LVS39420]. The UKBGS is funded by Breast Cancer Now and the Institute of Cancer Research (ICR), London. ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. The US3SS study was supported by Massachusetts (K.M.E., R01CA47305), Wisconsin (P.A.N., R01 CA47147) and New Hampshire (L.T.-E., R01CA69664) centers, and Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The USRT Study was funded by Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA. The WAABCS study was supported by grants from the National Cancer Institute of the National Institutes of Health (R01 CA89085 and P50 CA125183 and the D43 TW009112 grant), Susan G. Komen (SAC110026), the Dr. Ralph and Marian Falk Medical Research Trust, and the Avon Foundation for Women. The WHI program is funded by the National Heart, Lung, and Blood Institute, the US National Institutes of Health and the US Department of Health and Human Services (HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C). This work was also funded by NCI U19 CA148065-01. D.G.E. is supported by the all Manchester NIHR Biomedical research center Manchester (IS-BRC-1215-20007). HUNBOCS, Hungarian Breast and Ovarian Cancer Study was supported by Hungarian Research Grant KTIA-OTKA CK-80745, NKFI_OTKA K-112228. C.I. received support from the Nontherapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI P30-CA-51008) and the Jess and Mildred Fisher Center for Hereditary Cancer and Clinical Genomics Research. K.M. is supported by CRUK C18281/A19169. City of Hope Clinical Cancer Community Research Network and the Hereditary Cancer Research Registry, supported in part by Award Number RC4CA153828 (PI: J Weitzel) from the National Cancer Institute and the office of the Directory, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The colorectal cancer genome-wide association analyses: Colorectal Transdisciplinary Study (CORECT): The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the CORECT Consortium, nor does mention of trade names, commercial products or organizations imply endorsement by the US Government or the CORECT Consortium. We are incredibly grateful for the contributions of Dr. Brian Henderson and Dr. Roger Green over the course of this study and acknowledge them in memoriam. We are also grateful for support from Daniel and Maryann Fong. ColoCare: we thank the many investigators and staff who made this research possible in ColoCare Seattle and ColoCare Heidelberg. ColoCare was initiated and developed at the Fred Hutchinson Cancer Research Center by Drs. Ulrich and Grady. CCFR: the Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and financial support from the U.S. National Cancer Institute, without which this important registry would not exist. Galeon: GALEON wishes to thank the Department of Surgery of University Hospital of Santiago (CHUS), Sara Miranda Ponte, Carmen M Redondo, and the staff of the Department of Pathology and Biobank of CHUS, Instituto de Investigación Sanitaria de Santiago (IDIS), Instituto de Investigación Sanitaria Galicia Sur (IISGS), SERGAS, Vigo, Spain, and Programa Grupos Emergentes, Cancer Genetics Unit, CHUVI Vigo Hospital, Instituto de Salud Carlos III, Spain. MCCS: this study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited participants and continue to work on follow-up. We would also like to express our gratitude to the many thousands of Melbourne residents who took part in the study and provided blood samples. SEARCH: We acknowledge the contributions of Mitul Shah, Val Rhenius, Sue Irvine, Craig Luccarini, Patricia Harrington, Don Conroy, Rebecca Mayes, and Caroline Baynes. The Swedish low-risk colorectal cancer study: we thank Berith Wejderot and the Swedish low-risk colorectal cancer study group. Genetics & Epidemiology of Colorectal Cancer Consortium (GECCO): we thank all those at the GECCO Coordinating Center for helping bring together the data and people that made this project possible. ASTERISK: we are very grateful to Dr. Bruno Buecher without whom this project would not have existed. We also thank all those who agreed to participate in this study, including the patients and the healthy control persons, as well as all the physicians, technicians and students. DACHS: we thank all participants and cooperating clinicians, and Ute Handte-Daub, Renate Hettler-Jensen, Utz Benscheid, Muhabbet Celik, and Ursula Eilber for excellent technical assistance. HPFS, NHS and PHS: we acknowledge Patrice Soule and Hardeep Ranu of the Dana-Farber Harvard Cancer Center High-Throughput Polymorphism Core who assisted in the genotyping for NHS, HPFS, and PHS under the supervision of Dr. Immaculata Devivo and Dr. David Hunter, Qin (Carolyn) Guo, and Lixue Zhu who assisted in programming for NHS and HPFS and Haiyan Zhang who assisted in programming for the PHS. We thank the participants and staff of the Nurses’ Health Study and the Health Professionals Follow-Up Study, for their valuable contributions as well as the following state cancer registries for their help: A.L., A.Z., A.R., C.A., C.O., C.T., D.E., F.L., G.A., I.D., I.L., I.N., I.A., K.Y., L.A., M.E., M.D., M.A., M.I., N.E., N.H., N.J., N.Y., N.C., N.D., O.H., O.K., O.R., P.A., R.I., S.C., T.N., T.X., V.A., W.A., W.Y. In addition, this study was approved by the Connecticut Department of Public Health (DPH) Human Investigations Committee. Certain data used in this publication were obtained from the DPH. We assume full responsibility for analyses and interpretation of these data. PLCO: we thank Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention, National Cancer Institute, the Screening Center investigators and staff or the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial, Mr. Tom Riley and staff, Information Management Services Inc., Ms. Barbara O’Brien and staff, Westat Inc. and Drs. Bill Kopp, Wen Shao and staff, SAIC-Frederick. Most importantly, we acknowledge the study participants for their contributions for making this study possible. The statements contained herein are solely those of the authors and do not represent or imply concurrence or endorsement by NCI. PMH: we thank the study participants and staff of the Hormones and Colon Cancer study. WHI: we thank the WHI investigators and staff for their dedication, and the study participants for making the program possible. A full listing of WHI investigators can be found at https://cleo.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Short20List.pdf. CORECT: The CORECT Study was supported by the National Cancer Institute, National Institutes of Health (NCI/NIH), U.S. Department of Health and Human Services (grant numbers U19 CA148107, R01 CA81488, P30 CA014089, R01 CA197350; P01 CA196569; and R01 CA201407) and National Institutes of Environmental Health Sciences, National Institutes of Health (grant number T32 ES013678). The ATBC Study was supported by the US Public Health Service contracts (N01-CN-45165, N01-RC-45035, N01-RC-37004, and HHSN261201000006C) from the National Cancer Institute. The Cancer Prevention Study-II Nutrition Cohort is funded by the American Cancer Society. ColoCare: This work was supported by the National Institutes of Health (grant numbers R01 CA189184, U01 CA206110, 2P30CA015704-40 (Gilliland)), the Matthias Lackas-Foundation, the German Consortium for Translational Cancer Research, and the EU TRANSCAN initiative. Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): funding for GECCO was provided by the National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (grant numbers U01 CA137088, R01 CA059045, and U01 CA164930). This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704. The Colon Cancer Family Registry (CFR) Illumina GWAS was supported by funding from the National Cancer Institute, National Institutes of Health (grant numbers U01 CA122839, R01 CA143247). The Colon CFR/CORECT Affymetrix Axiom GWAS and OncoArray GWAS were supported by funding from National Cancer Institute, National Institutes of Health (grant number U19 CA148107 to S.G.). The Colon CFR participant recruitment and collection of data and biospecimens used in this study were supported by the National Cancer Institute, National Institutes of Health (grant number UM1 CA167551) and through cooperative agreements with the following Colon CFR centers: Australasian Colorectal Cancer Family Registry (NCI/NIH grant numbers U01 CA074778 and U01/U24 CA097735), USC Consortium Colorectal Cancer Family Registry (NCI/NIH grant numbers U01/U24 CA074799), Mayo Clinic Cooperative Family Registry for Colon Cancer Studies (NCI/NIH grant number U01/U24 CA074800), Ontario Familial Colorectal Cancer Registry (NCI/NIH grant number U01/U24 CA074783), Seattle Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074794), and University of Hawaii Colorectal Cancer Family Registry (NCI/NIH grant number U01/U24 CA074806), Additional support for case ascertainment was provided from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute to Fred Hutchinson Cancer Research Center (Control Nos. N01-CN-67009 and N01-PC-35142, and Contract No. HHSN2612013000121), the Hawai’i Department of Health (Control Nos. N01-PC-67001 and N01-PC-35137, and Contract No. HHSN26120100037C, and the California Department of Public Health (contracts HHSN261201000035C awarded to the University of Southern California, and the following state cancer registries: A.Z., C.O., M.N., N.C., N.H., and by the Victoria Cancer Registry and Ontario Cancer Registry. ESTHER/VERDI was supported by grants from the Baden–Württemberg Ministry of Science, Research and Arts and the German Cancer Aid. MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. GALEON: FIS Intrasalud (PI13/01136). The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MSKCC: the work at Sloan Kettering in New York was supported by the Robert and Kate Niehaus Center for Inherited Cancer Genomics and the Romeo Milio Foundation. Moffitt: This work was supported by funding from the National Institutes of Health (grant numbers R01 CA189184, P30 CA076292), Florida Department of Health Bankhead-Coley Grant 09BN-13, and the University of South Florida Oehler Foundation. Moffitt contributions were supported in part by the Total Cancer Care Initiative, Collaborative Data Services Core, and Tissue Core at the H. Lee Moffitt Cancer Center & Research Institute, a National Cancer Institute-designated Comprehensive Cancer Center (grant number P30 CA076292). SEARCH: Cancer Research UK (C490/A16561). The Spanish study was supported by Instituto de Salud Carlos III, co-funded by FEDER funds –a way to build Europe– (grants PI14-613 and PI09-1286), Catalan Government DURSI (grant 2014SGR647), and Junta de Castilla y León (grant LE22A10-2). The Swedish Low-risk Colorectal Cancer Study: the study was supported by grants from the Swedish research council; K2015-55 × -22674-01-4, K2008-55 × -20157-03-3, K2006-72 × -20157-01-2 and the Stockholm County Council (ALF project). CIDR genotyping for the Oncoarray was conducted under contract 268201200008I (to K.D.), through grant 101HG007491-01 (to C.I.A.). The Norris Cotton Cancer Center - P30CA023108, The Quantitative Biology Research Institute - P20GM103534, and the Coordinating Center for Screen Detected Lesions - U01CA196386 also supported efforts of C.I.A. This work was also supported by the National Cancer Institute (grant numbers U01 CA1817700, R01 CA144040). ASTERISK: a Hospital Clinical Research Program (PHRC) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC). COLO2&3: National Institutes of Health (grant number R01 CA060987). DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1, and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A, and 01ER1505B). DALS: National Institutes of Health (grant number R01 CA048998 to M.L.S). HPFS is supported by National Institutes of Health (grant numbers P01 CA055075, UM1 CA167552, R01 137178, and P50 CA127003), NHS by the National Institutes of Health (grant numbers UM1 CA186107, R01 CA137178, P01 CA087969, and P50 CA127003), NHSII by the National Institutes of Health (grant numbers R01 050385CA and UM1 CA176726), and PHS by the National Institutes of Health (grant number R01 CA042182). MEC: National Institutes of Health (grant numbers R37 CA054281, P01 CA033619, and R01 CA063464). OFCCR: National Institutes of Health, through funding allocated to the Ontario Registry for Studies of Familial Colorectal Cancer (grant number U01 CA074783); see Colon CFR section above. As subset of ARCTIC, OFCCR is supported by a GL2 grant from the Ontario Research Fund, the Canadian Institutes of Health Research, and the Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society Research Institute. T.J.H. and B.W.Z. are recipients of Senior Investigator Awards from the Ontario Institute for Cancer Research, through generous support from the Ontario Ministry of Research and Innovation. PLCO: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. Additionally, a subset of control samples was genotyped as part of the Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer GWAS, Colon CGEMS pancreatic cancer scan (PanScan), and the Lung Cancer and Smoking study. The prostate and PanScan study datasets were accessed with appropriate approval through the dbGaP online resource (http://cgems.cancer.gov/data/) accession numbers phs000207.v1.p1 and phs000206.v3.p2, respectively, and the lung datasets were accessed from the dbGaP website (http://www.ncbi.nlm.nih.gov/gap) through accession number phs000093.v2.p2. Funding for the Lung Cancer and Smoking study was provided by National Institutes of Health (NIH), Genes, Environment and Health Initiative (GEI) Z01 CP 010200, NIH U01 HG004446, and NIH GEI U01 HG 004438. For the lung study, the GENEVA Coordinating Center provided assistance with genotype cleaning and general study coordination, 23 and the Johns Hopkins University Center for Inherited Disease Research conducted genotyping. PMH: National Institutes of Health (grant number R01 CA076366). VITAL: National Institutes of Health (grant number K05-CA154337). WHI: The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C, and HHSN268201600004C. The head and neck cancer genome-wide association analyses: The study was supported by NIH/NCI: P50 CA097190, and P30 CA047904, Canadian Cancer Society Research Institute (no. 020214) and Cancer Care Ontario Research Chair to R.H. The Princess Margaret Hospital Head and Neck Cancer Translational Research Program is funded by the Wharton family, Joe’s Team, Gordon Tozer, Bruce Galloway and the Elia family. Geoffrey Liu was supported by the Posluns Family Fund and the Lusi Wong Family Fund at the Princess Margaret Foundation, and the Alan B. Brown Chair in Molecular Genomics. This publication presents data from Head and Neck 5000 (H&N5000). H&N5000 was a component of independent research funded by the UK National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034). The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. Human papillomavirus (HPV) in H&N5000 serology was supported by a Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant number: C18281/A19169). National Cancer Institute (R01-CA90731); National Institute of Environmental Health Sciences (P30ES10126). The authors thank all the members of the GENCAPO team/The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). CPS-II recruitment and maintenance is supported with intramural research funding from the American Cancer Society. Genotyping performed at the Center for Inherited Disease Research (CIDR) was funded through the U.S. National Institute of Dental and Craniofacial Research (NIDCR) grant 1 × 01HG007780-0. The University of Pittsburgh head and neck cancer case-control study is supported by National Institutes of Health grants P50 CA097190 and P30 CA047904. The Carolina Head and Neck Cancer Study (CHANCE) was supported by the National Cancer Institute (R01-CA90731). The Head and Neck Genome Project (GENCAPO) was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (Grant numbers 04/12054-9 and 10/51168-0). The authors thank all the members of the GENCAPO team. The HN5000 study was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research scheme (RP-PG-0707-10034), the views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Toronto study was funded by the Canadian Cancer Society Research Institute (020214) and the National Cancer Institute (U19-CA148127) and the Cancer Care Ontario Research Chair. The alcohol-related cancers and genetic susceptibility study in Europe (ARCAGE) was funded by the European Commission’s 5th Framework Program (QLK1-2001-00182), the Italian Association for Cancer Research, Compagnia di San Paolo/FIRMS, Region Piemonte, and Padova University (CPDA057222). The Rome Study was supported by the Associazione Italiana per la Ricerca sul Cancro (AIRC) IG 2011 10491 and IG2013 14220 to S.B., and Fondazione Veronesi to S.B. The IARC Latin American study was funded by the European Commission INCO-DC programme (IC18-CT97-0222), with additional funding from Fondo para la Investigacion Cientifica y Tecnologica (Argentina) and the Fundação de Amparo à Pesquisa do Estado de São Paulo (01/01768-2). We thank Leticia Fernandez, Instituto Nacional de Oncologia y Radiobiologia, La Habana, Cuba and Sergio and Rosalina Koifman, for their efforts with the IARC Latin America study São Paulo center. The IARC Central Europe study was supported by European Commission’s INCO-COPERNICUS Program (IC15- CT98-0332), NIH/National Cancer Institute grant CA92039, and the World Cancer Research Foundation grant WCRF 99A28. The IARC Oral Cancer Multicenter study was funded by grant S06 96 202489 05F02 from Europe against Cancer; grants FIS 97/0024, FIS 97/0662, and BAE 01/5013 from Fondo de Investigaciones Sanitarias, Spain; the UICC Yamagiwa-Yoshida Memorial International Cancer Study; the National Cancer Institute of Canada; Associazione Italiana per la Ricerca sul Cancro; and the Pan-American Health Organization. Coordination of the EPIC study is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The lung cancer genome-wide association analyses: Transdisciplinary Research for Cancer in Lung (TRICL) of the International Lung Cancer Consortium (ILCCO) was supported by (U19-CA148127, CA148127S1, U19CA203654, and Cancer Prevention Research Institute of Texas RR170048). The ILCCO data harmonization is supported by Cancer Care Ontario Research Chair of Population Studies to R. H. and Lunenfeld-Tanenbaum Research Institute, Sinai Health System. The TRICL-ILCCO OncoArray was supported by in-kind genotyping by the Centre for Inherited Disease Research (26820120008i-0-26800068-1). The CAPUA study was supported by FIS-FEDER/Spain grant numbers FIS-01/310, FIS-PI03-0365, and FIS-07-BI060604, FICYT/Asturias grant numbers FICYT PB02-67 and FICYT IB09-133, and the University Institute of Oncology (IUOPA), of the University of Oviedo and the Ciber de Epidemiologia y Salud Pública. CIBERESP, SPAIN. The work performed in the CARET study was supported by the National Institute of Health/National Cancer Institute: UM1 CA167462 (PI: Goodman), National Institute of Health UO1-CA6367307 (PIs Omen, Goodman); National Institute of Health R01 CA111703 (PI Chen), National Institute of Health 5R01 CA151989-01A1(PI Doherty). The Liverpool Lung project is supported by the Roy Castle Lung Cancer Foundation. The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578, CA074386. The Multi-ethnic Cohort Study was partially supported by NIH Grants CA164973, CA033619, CA63464, and CA148127. The work performed in MSH-PMH study was supported by The Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to R.J.H. and G.L. and the Alan Brown Chair and Lusi Wong Programs at the Princess Margaret Hospital Foundation. NJLCS was funded by the State Key Program of National Natural Science of China (81230067), the National Key Basic Research Program Grant (2011CB503805), the Major Program of the National Natural Science Foundation of China (81390543). The Norway study was supported by Norwegian Cancer Society, Norwegian Research Council. The Shanghai Cohort Study (SCS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The Singapore Chinese Health Study (SCHS) was supported by National Institutes of Health R01 CA144034 (PI: Yuan) and UM1 CA182876 (PI: Yuan). The work in TLC study has been supported in part the James & Esther King Biomedical Research Program (09KN-15), National Institutes of Health Specialized Programs of Research Excellence (SPORE) Grant (P50 CA119997), and by a Cancer Center Support Grant (CCSG) at the H. Lee Moffitt Cancer Center and Research Institute, an NCI designated Comprehensive Cancer Center (grant number P30-CA76292). The Vanderbilt Lung Cancer Study—BioVU dataset used for the analyses described was obtained from Vanderbilt University Medical Center’s BioVU, which is supported by institutional funding, the 1S10RR025141-01 instrumentation award, and by the Vanderbilt CTSA grant UL1TR000445 from NCATS/NIH. Dr. Aldrich was supported by NIH/National Cancer Institute K07CA172294 (PI: Aldrich) and Dr. Bush was supported by NHGRI/NIH U01HG004798 (PI: Crawford). The Copenhagen General Population Study (CGPS) was supported by the Chief Physician Johan Boserup and Lise Boserup Fund, the Danish Medical Research Council and Herlev Hospital. The NELCS study: Grant Number P20RR018787 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). The Kentucky Lung Cancer Research Initiative was supported by the Department of Defense [Congressionally Directed Medical Research Program, U.S. Army Medical Research and Materiel Command Program] under award number: 10153006 (W81XWH-11-1-0781). Views and opinions of, and endorsements by the author(s) do not reflect those of the US Army or the Department of Defense. This research was also supported by unrestricted infrastructure funds from the UK Center for Clinical and Translational Science, NIH grant UL1TR000117 and Markey Cancer Center NCI Cancer Center Support Grant (P30 CA177558) Shared Resource Facilities: Cancer Research Informatics, Biospecimen and Tissue Procurement, and Biostatistics and Bioinformatics. The M.D. Anderson Cancer Center study was supported in part by grants from the NIH (P50 CA070907, R01 CA176568) (to X.W.), Cancer Prevention & Research Institute of Texas (RP130502) (to X.W.), and The University of Texas MD Anderson Cancer Center institutional support for the Center for Translational and Public Health Genomics. The deCODE study of smoking and nicotine dependence was funded in part by a grant from NIDA (R01- DA017932). The study in Lodz center was partially funded by Nofer Institute of Occupational Medicine, under task NIOM 10.13: Predictors of mortality from non-small cell lung cancer—field study. Genetic sharing analysis was funded by NIH grant CA194393. The research undertaken by M.D.T., L.V.W., and M.S.A. was partly funded by the National Institute for Health Research (NIHR). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. M.D.T. holds a Medical Research Council Senior Clinical Fellowship (G0902313). The work to assemble the FTND GWAS meta-analysis was supported by the National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA) grant number R01 DA035825 (Principal Investigator [PI]: DBH). The study populations included COGEND (dbGaP phs000092.v1.p1 and phs000404.v1.p1), COPDGene (dbGaP phs000179.v3.p2), deCODE Genetics, EAGLE (dbGaP phs000093.vs.p2), and SAGE. dbGaP phs000092.v1.p1). See Hancock et al. Transl Psychiatry 2015 (PMCID: PMC4930126) for the full listing of funding sources and other acknowledgments. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT)study was funded by the Sheffield Hospitals Charity, Sheffield Experimental Cancer Medicine Centre and Weston Park Hospital Cancer Charity. The ovarian cancer genome-wide association analysis: The Ovarian Cancer Association Consortium (OCAC) is supported by a grant from the Ovarian Cancer Research Fund thanks to donations by the family and friends of Kathryn Sladek Smith (PPD/RPCI.07). The scientific development and funding for this project were in part supported by the US National Cancer Institute GAME-ON Post-GWAS Initiative (U19-CA148112). This study made use of data generated by the Wellcome Trust Case Control consortium that was funded by the Wellcome Trust under award 076113. The results published here are in part based upon data generated by The Cancer Genome Atlas Pilot Project established by the National Cancer Institute and National Human Genome Research Institute (dbGap accession number phs000178.v8.p7). The OCAC OncoArray genotyping project was funded through grants from the U.S. National Institutes of Health (CA1X01HG007491-01 (C.I.A.), U19-CA148112 (T.A.S.), R01-CA149429 (C.M.P.), and R01-CA058598 (M.T.G.); Canadian Institutes of Health Research (MOP-86727 (L.E.K.) and the Ovarian Cancer Research Fund (A.B.). The COGS project was funded through a European Commission’s Seventh Framework Programme grant (agreement number 223175 - HEALTH-F2-2009-223175) and through a grant from the U.S. National Institutes of Health (R01-CA122443 (E.L.G)). Funding for individual studies: AAS: National Institutes of Health (RO1-CA142081); AOV: The Canadian Institutes for Health Research (MOP-86727); AUS: The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), National Health & Medical Research Council of Australia (199600, 400413 and 400281), Cancer Councils of New South Wales, Victoria, Queensland, South Australia and Tasmania and Cancer Foundation of Western Australia (Multi-State Applications 191, 211, and 182). The Australian Ovarian Cancer Study gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Foundation; BAV: ELAN Funds of the University of Erlangen-Nuremberg; BEL: National Kankerplan; BGS: Breast Cancer Now, Institute of Cancer Research; BVU: Vanderbilt CTSA grant from the National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) (ULTR000445); CAM: National Institutes of Health Research Cambridge Biomedical Research Centre and Cancer Research UK Cambridge Cancer Centre; CHA: Innovative Research Team in University (PCSIRT) in China (IRT1076); CNI: Instituto de Salud Carlos III (PI12/01319); Ministerio de Economía y Competitividad (SAF2012); COE: Department of Defense (W81XWH-11-2-0131); CON: National Institutes of Health (R01-CA063678, R01-CA074850; and R01-CA080742); DKE: Ovarian Cancer Research Fund; DOV: National Institutes of Health R01-CA112523 and R01-CA87538; EMC: Dutch Cancer Society (EMC 2014-6699); EPC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), Federal Ministry of Education and Research (BMBF) (Germany); the Hellenic Health Foundation (Greece); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); ERC-2009-AdG 232997 and Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway); Health Research Fund (FIS), PI13/00061 to Granada, PI13/01162 to EPIC-Murcia, Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC (RD06/0020) (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C570/A16491 and C8221/A19170 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk, MR/M012190/1 to EPIC-Oxford) (United Kingdom); GER: German Federal Ministry of Education and Research, Programme of Clinical Biomedical Research (01 GB 9401) and the German Cancer Research Center (DKFZ); GRC: This research has been co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund; GRR: Roswell Park Cancer Institute Alliance Foundation, P30 CA016056; HAW: U.S. National Institutes of Health (R01-CA58598, N01-CN-55424, and N01-PC-67001); HJO: Intramural funding; Rudolf-Bartling Foundation; HMO: Intramural funding; Rudolf-Bartling Foundation; HOC: Helsinki University Research Fund; HOP: Department of Defense (DAMD17-02-1-0669) and NCI (K07-CA080668, R01-CA95023, P50-CA159981 MO1-RR000056 R01-CA126841); HUO: Intramural funding; Rudolf-Bartling Foundation; JGO: JSPS KAKENHI grant; JPN: Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare; KRA: This study (Ko-EVE) was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 0920010); LAX: American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124; LUN: ERC-2011-AdG 294576-risk factors cancer, Swedish Cancer Society, Swedish Research Council, Beta Kamprad Foundation; MAC: National Institutes of Health (R01-CA122443, P30-CA15083, P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; Fraternal Order of Eagles; MAL: Funding for this study was provided by research grant R01- CA61107 from the National Cancer Institute, Bethesda, MD, research grant 94 222 52 from the Danish Cancer Society, Copenhagen, Denmark; and the Mermaid I project; MAS: Malaysian Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation; MAY: National Institutes of Health (R01-CA122443, P30-CA15083, and P50-CA136393); Mayo Foundation; Minnesota Ovarian Cancer Alliance; Fred C. and Katherine B. Andersen Foundation; MCC: Cancer Council Victoria, National Health and Medical Research Council of Australia (NHMRC) grants number 209057, 251533, 396414, and 504715; MDA: DOD Ovarian Cancer Research Program (W81XWH-07-0449); MEC: NIH (CA54281, CA164973, CA63464); MOF: Moffitt Cancer Center, Merck Pharmaceuticals, the state of Florida, Hillsborough County, and the city of Tampa; NCO: National Institutes of Health (R01-CA76016) and the Department of Defense (DAMD17-02-1-0666); NEC: National Institutes of Health R01-CA54419 and P50-CA105009 and Department of Defense W81XWH-10-1-02802; NHS: UM1 CA186107, P01 CA87969, R01 CA49449, R01-CA67262, UM1 CA176726; NJO: National Cancer Institute (NIH-K07 CA095666, R01-CA83918, NIH-K22-CA138563, and P30-CA072720) and the Cancer Institute of New Jersey; If Sara Olson and/or Irene Orlow is a co-author, please add NCI CCSG award (P30-CA008748) to the funding sources; NOR: Helse Vest, The Norwegian Cancer Society, The Research Council of Norway; NTH: Radboud University Medical Centre; OPL: National Health and Medical Research Council (NHMRC) of Australia (APP1025142) and Brisbane Women’s Club; ORE: OHSU Foundation; OVA: This work was supported by Canadian Institutes of Health Research grant (MOP-86727) and by NIH/NCI 1 R01CA160669-01A1; PLC: Intramural Research Program of the National Cancer Institute; POC: Pomeranian Medical University; POL: Intramural Research Program of the National Cancer Institute; PVD: Canadian Cancer Society and Cancer Research Society GRePEC Program; RBH: National Health and Medical Research Council of Australia; RMH: Cancer Research UK, Royal Marsden Hospital; RPC: National Institute of Health (P50-CA159981, R01-CA126841); SEA: Cancer Research UK (C490/A10119 C490/A10124); UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; SIS: NIH, National Institute of Environmental Health Sciences, Z01-ES044005 and Z01-ES049033; SMC: The bbSwedish Research Council-SIMPLER infrastructure; the Swedish Cancer Foundation; SON: National Health Research and Development Program, Health Canada, grant 6613-1415-53; SRO: Cancer Research UK (C536/A13086, C536/A6689) and Imperial Experimental Cancer Research Centre (C1312/A15589); STA: NIH grants U01 CA71966 and U01 CA69417; SWE: Swedish Cancer foundation, WeCanCureCancer and VårKampMotCancer foundation; SWH: NIH (NCI) grant R37-CA070867; TBO: National Institutes of Health (R01-CA106414-A2), American Cancer Society (CRTG-00-196-01-CCE), Department of Defense (DAMD17-98-1-8659), Celma Mastery Ovarian Cancer Foundation; TOR: NIH grants R01-CA063678 and R01 CA063682; UCI: NIH R01-CA058860 and the Lon V Smith Foundation grant LVS39420; UHN: Princess Margaret Cancer Centre Foundation-Bridge for the Cure; UKO: The UKOPS study was funded by The Eve Appeal (The Oak Foundation) and supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre; UKR: Cancer Research UK (C490/A6187), UK National Institute for Health Research Biomedical Research Centres at the University of Cambridge; USC: P01CA17054, P30CA14089, R01CA61132, N01PC67010, R03CA113148, R03CA115195, N01CN025403, and California Cancer Research Program (00-01389V-20170, 2II0200); VAN: BC Cancer Foundation, VGH & UBC Hospital Foundation; VTL: NIH K05-CA154337; WMH: National Health and Medical Research Council of Australia, Enabling Grants ID 310670 & ID 628903. Cancer Institute NSW Grants 12/RIG/1-17 & 15/RIG/1-16; WOC: National Science Centren (N N301 5645 40). The Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Warsaw, Poland. The University of Cambridge has received salary support for PDPP from the NHS in the East of England through the Clinical Academia Reserve. The prostate cancer genome-wide association analyses: we pay tribute to Brian Henderson, who was a driving force behind the OncoArray project, for his vision and leadership, and who sadly passed away before seeing its fruition. We also thank the individuals who participated in these studies enabling this work. The ELLIPSE/PRACTICAL (http//:practical.icr.ac.uk) prostate cancer consortium and his collaborating partners were supported by multiple funding mechanisms enabling this current work. ELLIPSE/PRACTICAL Genotyping of the OncoArray was funded by the US National Institutes of Health (NIH) (U19 CA148537 for ELucidating Loci Involved in Prostate Cancer SuscEptibility (ELLIPSE) project and X01HG007492 to the Center for Inherited Disease Research (CIDR) under contract number HHSN268201200008I). Additional analytical support was provided by NIH NCI U01 CA188392 (F.R.S.). Funding for the iCOGS infrastructure came from the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, and C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065, and 1U19 CA148112; the GAME-ON initiative), the Department of Defense (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. This work was supported by the Canadian Institutes of Health Research, European Commission’s Seventh Framework Programme grant agreement n° 223175 (HEALTH-F2-2009-223175), Cancer Research UK Grants C5047/A7357, C1287/A10118, C1287/A16563, C5047/A3354, C5047/A10692, C16913/A6135, C5047/A21332 and The National Institute of Health (NIH) Cancer Post-Cancer GWAS initiative grant: No. 1 U19 CA148537-01 (the GAME-ON initiative). We also thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, and The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The Prostate Cancer Program of Cancer Council Victoria also acknowledge grant support from The National Health and Medical Research Council, Australia (126402, 209057, 251533, 396414, 450104, 504700, 504702, 504715, 623204, 940394, and 614296), VicHealth, Cancer Council Victoria, The Prostate Cancer Foundation of Australia, The Whitten Foundation, PricewaterhouseCoopers, and Tattersall’s. E.A.O., D.M.K., and E.M.K. acknowledge the Intramural Program of the National Human Genome Research Institute for their support. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233 to D.J.H., U01-CA98710 to S.M.G., U01-CA98216 to E.R., and U01-CA98758 to B.E.H., and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). CAPS GWAS study was supported by the Swedish Cancer Foundation (grant no 09-0677, 11-484, 12-823), the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant no K2010-70 × -20430-04-3, 2014-2269). The Hannover Prostate Cancer Study was supported by the Lower Saxonian Cancer Society. PEGASUS was supported by the Intramural Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health. RAPPER was supported by the NIHR Manchester Biomedical Research Center, Cancer Research UK (C147/A25254, C1094/A18504) and the EU’s 7th Framework Programme Grant/Agreement no 60186. Overall: this research has been conducted using the UK Biobank Resource (application number 16549). NHS is supported by UM1 CA186107 (NHS cohort infrastructure grant), P01 CA87969, and R01 CA49449. NHSII is supported by UM1 CA176726 (NHSII cohort infrastructure grant), and R01-CA67262. A.L.K. is supported by R01 MH107649. We would like to thank the participants and staff of the NHS and NHSII for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (PC, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10(−9); T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55–2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04–6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa(1).

Background Increased vitamin B6 catabolism related to inflammation, as measured by the PAr index (the ratio of 4-pyridoxic acid over the sum of pyridoxal and pyridoxal-5'-phosphate), has been positively associated with lung cancer risk in two prospective European studies. However, the extent to which this association translates to more diverse populations is not known. Materials and methods For this study, we included 5323 incident lung cancer cases and 5323 controls individually matched by age, sex, and smoking status within each of 20 prospective cohorts from the Lung Cancer Cohort Consortium. Cohort-specific odds ratios (ORs) and 95% confidence intervals (CIs) for the association between PAr and lung cancer risk were calculated using conditional logistic regression and pooled using random-effects models. Results PAr was positively associated with lung cancer risk in a dose-response fashion. Comparing the fourth versus first quartiles of PAr resulted in an OR of 1.38 (95% CI: 1.19–1.59) for overall lung cancer risk. The association between PAr and lung cancer risk was most prominent in former smokers (OR: 1.69, 95% CI: 1.36–2.10), men (OR: 1.60, 95% CI: 1.28–2.00), and for cancers diagnosed within 3 years of blood draw (OR: 1.73, 95% CI: 1.34–2.23). Conclusion Based on pre-diagnostic data from 20 cohorts across 4 continents, this study confirms that increased vitamin B6 catabolism related to inflammation and immune activation is associated with a higher risk of developing lung cancer. Moreover, PAr may be a pre-diagnostic marker of lung cancer rather than a causal factor. (c) The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology. Annals of Oncology 30: 478–485, 2019 doi:10.1093/annonc/mdz002 Published online 30 January 2019 This is an Open Access article under the CC-BY-NC-ND license.

Purpose In a recent large prospective study, vasectomy was associated with modestly higher risk of prostate cancer, especially high-grade and lethal prostate cancer. However, evidence from prospective studies remains limited. Therefore, we assessed the associations of vasectomy with prostate cancer incidence and mortality in a large cohort in the United States. Patients and Methods We examined the association between vasectomy and prostate cancer mortality among 363,726 men in the Cancer Prevention Study II (CPS-II) cohort, of whom 7,451 died as a result of prostate cancer during follow-up from 1982 to 2012. We also examined the association between vasectomy and prostate cancer incidence among 66,542 men in the CPS-II Nutrition Cohort, a subgroup of the CPS-II cohort, of whom 9,133 were diagnosed with prostate cancer during follow-up from 1992 to 2011. Cox proportional hazards regression modeling was used to estimate multivariable-adjusted hazard ratios (HRs) and 95% CIs. Results In the CPS-II cohort, vasectomy was not associated with prostate cancer mortality (HR, 1.01; 95% CI, 0.93 to 1.10). In the CPS-II Nutrition Cohort, vasectomy was not associated with either overall prostate cancer incidence (HR, 1.02; 95% CI, 0.96 to 1.08) or high-grade prostate cancer incidence (HR, 0.91; 95% CI, 0.78 to 1.07 for cancers with Gleason score ≥ 8). Conclusion Results from these large prospective cohorts do not support associations of vasectomy with either prostate cancer incidence or prostate cancer mortality.

Incidence rates for liver cancer have increased 3-fold since the mid-1970s in the United States in parallel with increasing trends for obesity and type II diabetes mellitus. We conducted an analysis of baseline body mass index (BMI), waist circumference (WC), and type II diabetes mellitus with risk of liver cancer. The Liver Cancer Pooling Project maintains harmonized data from 1.57 million adults enrolled in 14 U.S.-based prospective studies. Cox regression estimated HRs and 95% confidence intervals (CI) adjusted for age, sex, study center, alcohol, smoking, race, and BMI (for WC and type II diabetes mellitus). Stratified analyses assessed whether the BMI–liver cancer associations differed by hepatitis sera-positivity in nested analyses for a subset of cases (n = 220) and controls (n = 547). After enrollment, 2,162 incident liver cancer diagnoses were identified. BMI, per 5 kg/m2, was associated with higher risks of liver cancer, more so for men (HR = 1.38; 95% CI, 1.30–1.46) than women (HR = 1.25; 95% CI, 1.17–1.35; Pinteraction = 0.02). WC, per 5 cm, was associated with higher risks of liver cancer, approximately equally by sex (overall, HR = 1.08; 95% CI, 1.04–1.13). Type II diabetes mellitus was associated with higher risk of liver cancer (HR = 2.61; 95% CI, 2.34–2.91). In stratified analyses, there was a null association between BMI and liver cancer risk for participants who were sera-positive for hepatitis. This study suggests that high BMI, high WC, and type II diabetes mellitus are associated with higher risks of liver cancer and that the association may differ by status of viral hepatitis infection. Cancer Res; 76(20); 6076–83. ©2016 AACR.

Reports relating meat intake to prostate cancer risk are inconsistent. Associations between these dietary factors and prostate cancer were examined in a consortium of 15 cohort studies. During follow-up, 52,683 incident prostate cancer cases, including 4,924 advanced cases, were identified among 842,149 men. Cox proportional hazard models were used to calculate study-specific relative risks (RR) and then pooled using random effects models. Results do not support a substantial effect of total red, unprocessed red and processed meat for all prostate cancer outcomes, except for a modest positive association for tumors identified as advanced stage at diagnosis (advanced(r)). For seafood, no substantial effect was observed for prostate cancer regardless of stage or grade. Poultry intake was inversely associated with risk of advanced and fatal cancers (pooled multivariable RR [MVRR], 95% confidence interval, comparing ≥ 45 vs.

Loss of 13q14.3 is a chromosomal event found in approximately 50 percent of B-cell chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis (MBL) cases. Surveys of somatic alterations in solid tumors have shown sporadic 13q14.3 loss in many different tumor types, but not at high frequency in any specific tumor type. In our recent survey of the single nucleotide polymorphism (SNP) microarray data from 127,000 cancer free or solid tumor cases, we observed mosaic 13q14.3 loss as a common autosomal somatic large structural events (>2 Mb in size) in blood and buccal-derived DNA. Herein, we examined this region more closely investigating structural mosaic events

Background: The relationship between excess body weight and prostate cancer risk is unclear. However, some evidence suggests that waist circumference, which provides a measure of central adiposity, may be positively associated with more advanced stages or grades of prostate cancer. Methods: The association between waist circumference and prostate cancer was investigated among 46,094 men enrolled in the Cancer Prevention Study II Nutrition Cohort, of whom 5,711 were diagnosed with this cancer between 1997 and 2013. Using Cox proportional hazards regression, we examined associations of weight circumference with total and high-grade prostate cancer incidence and with prostate cancer mortality. Results: In both categorical and continuous analyses, waist circumference was not associated with total or high-grade (Gleason score ≥ 8) prostate cancer incidence or with prostate cancer mortality regardless of whether body mass index was adjusted for in the statistical model. Waist circumference was inversely associated with low-grade (Gleason score < 8) prostate cancer, but the association was not statistically significant after adjustment for body mass index. Conclusions: Our results suggest that central adiposity, as measured by waist circumference, is not significantly associated with prostate cancer incidence or mortality. Impact: Compared with men in other studies with significant results, men in our study were considerably older, suggesting that age may influence the association between waist circumference and prostate cancer. Cancer Epidemiol Biomarkers Prev; 26(12); 1812–4. ©2017 AACR.