A Population-Based Study of Genes Previously Implicated in Breast Cancer.

BACKGROUND: Population-based estimates of the risk of breast cancer associated with germline pathogenic variants in cancer-predisposition genes are critically needed for risk assessment and management in women with inherited pathogenic variants. METHODS: In a population-based case-control study, we performed sequencing using a custom multigene amplicon-based panel to identify germline pathogenic variants in 28 cancer-predisposition genes among 32,247 women with breast cancer (case patients) and 32,544 unaffected women (controls) from population-based studies in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Associations between pathogenic variants in each gene and the risk of breast cancer were assessed. RESULTS: Pathogenic variants in 12 established breast cancer-predisposition genes were detected in 5.03% of case patients and in 1.63% of controls. Pathogenic variants in BRCA1 and BRCA2 were associated with a high risk of breast cancer, with odds ratios of 7.62 (95% confidence interval [CI], 5.33 to 11.27) and 5.23 (95% CI, 4.09 to 6.77), respectively. Pathogenic variants in PALB2 were associated with a moderate risk (odds ratio, 3.83; 95% CI, 2.68 to 5.63). Pathogenic variants in BARD1, RAD51C, and RAD51D were associated with increased risks of estrogen receptor-negative breast cancer and triple-negative breast cancer, whereas pathogenic variants in ATM, CDH1, and CHEK2 were associated with an increased risk of estrogen receptor-positive breast cancer. Pathogenic variants in 16 candidate breast cancer-predisposition genes, including the c.657_661del5 founder pathogenic variant in NBN, were not associated with an increased risk of breast cancer. CONCLUSIONS: This study provides estimates of the prevalence and risk of breast cancer associated with pathogenic variants in known breast cancer-predisposition genes in the U.S. population. These estimates can inform cancer testing and screening and improve clinical management strategies for women in the general population with inherited pathogenic variants in these genes. (Funded by the National Institutes of Health and the Breast Cancer Research Foundation.).

A prospective case-cohort analysis of plasma metabolites and breast cancer risk.

BACKGROUND: Breast cancer incidence rates have not declined despite an improvement in risk prediction and the identification of modifiable risk factors, suggesting the need to identify novel risk factors and etiological pathways involved in this cancer. Metabolomics has emerged as a promising tool to find circulating metabolites associated with breast cancer risk. METHODS: Untargeted metabolomic analysis was done on prediagnostic plasma samples from a case-cohort study of 1695 incident breast cancer cases and a 1983 women subcohort drawn from Cancer Prevention Study 3. The associations of 868 named metabolites (per one standard deviation increase) with breast cancer were determined using Prentice-weighted Cox proportional hazards regression modeling. RESULTS: A total of 11 metabolites were associated with breast cancer at false discovery rate (FDR) < 0.05 with the majority having inverse association [ranging from RR = 0.85 (95% CI 0.80-0.92) to RR = 0.88 (95% CI 0.82-0.94)] and one having a positive association [RR = 1.14 (95% CI 1.06-1.23)]. An additional 50 metabolites were associated at FDR < 0.20 with inverse associations ranging from RR = 0.88 (95% CI 0.81-0.94) to RR = 0.91 (95% CI 0.85-0.98) and positive associations ranging from RR = 1.13 (95% CI 1.05-1.22) to RR = 1.11 (95% CI 1.02-1.20). Several of these associations validated the findings of previous metabolomic studies. These included findings that several progestogen and androgen steroids were associated with increased risk of breast cancer in postmenopausal women and four phospholipids, and the amino acids glutamine and asparagine were associated with decreased risk of this cancer in pre- and postmenopausal women. Several novel associations were also identified, including a positive association for syringol sulfate, a biomarker for smoked meat, and 3-methylcatechol sulfate and 3-hydroxypyridine glucuronide, which are metabolites of xenobiotics used for the production of pesticides and other products. CONCLUSIONS: Our study validated previous metabolite findings and identified novel metabolites associated with breast cancer risk, demonstrating the utility of large metabolomic studies to provide new leads for understanding breast cancer etiology. Our novel findings suggest that consumption of smoked meats and exposure to catechol and pyridine should be investigated as potential risk factors for breast cancer.

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Transcriptome-wide association study of breast cancer risk by estrogen-receptor status.

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.

Genome-wide association study of germline variants and breast cancer-specific mortality.

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). RESULTS: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. CONCLUSIONS: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Anthropometric factors and risk of myeloid leukaemias and myelodysplastic syndromes: a prospective study and meta-analysis.

There is insufficient evidence linking excess body weight to risk of myeloid malignancies. We investigated this association using data from the Cancer Prevention Study-II (CPS-II), and a meta-analysis of published cohort studies. Among 152 090 CPS-II participants, 387 acute myeloid leukaemias (AML), 100 chronic myeloid leukaemias (CML) and 170 MDS were identified over 21 years of follow-up. In CPS-II, body mass index (BMI) was weakly associated with risk of CML (hazard ratio [HR] = 1·04, 95% confidence interval [CI]: 0·99-1·09 per 1 unit increase in BMI), AML (HR = 1·01, 95% CI: 0·98-1·03) and MDS (HR = 1·03, 95% CI: 0·99-1·07). After controlling for other anthropometric factors, no clear association was observed for height, BMI at age 18 years or weight change. In the meta-analysis (n = 7117 myeloid leukaemias), BMI 25-29·9 kg/m2 (HRpooled  = 1·36, 95% CI: 1·12-1·59) and BMI ≥30 kg/m2 (HRpooled  = 1·43, 95% CI: 1·18-1·69) were associated with higher risk of myeloid leukaemia overall, compared to a BMI <25 kg/m2 . Likewise, BMI ≥25 kg/m2 was positively associated with both AML and CML risk individually in the meta-analysis. These results underscore the need for large studies to detect associations with rare cancers, and show a modest, but positive association between excess body weight and myeloid malignancy risk.

Smoking and mortality--beyond established causes.

BACKGROUND: Mortality among current smokers is 2 to 3 times as high as that among persons who never smoked. Most of this excess mortality is believed to be explained by 21 common diseases that have been formally established as caused by cigarette smoking and are included in official estimates of smoking-attributable mortality in the United States. However, if smoking causes additional diseases, these official estimates may significantly underestimate the number of deaths attributable to smoking. METHODS: We pooled data from five contemporary U.S. cohort studies including 421,378 men and 532,651 women 55 years of age or older. Participants were followed from 2000 through 2011, and relative risks and 95% confidence intervals were estimated with the use of Cox proportional-hazards models adjusted for age, race, educational level, daily alcohol consumption, and cohort. RESULTS: During the follow-up period, there were 181,377 deaths, including 16,475 among current smokers. Overall, approximately 17% of the excess mortality among current smokers was due to associations with causes that are not currently established as attributable to smoking. These included associations between current smoking and deaths from renal failure (relative risk, 2.0; 95% confidence interval [CI], 1.7 to 2.3), intestinal ischemia (relative risk, 6.0; 95% CI, 4.5 to 8.1), hypertensive heart disease (relative risk, 2.4; 95% CI, 1.9 to 3.0), infections (relative risk, 2.3; 95% CI, 2.0 to 2.7), various respiratory diseases (relative risk, 2.0; 95% CI, 1.6 to 2.4), breast cancer (relative risk, 1.3; 95% CI, 1.2 to 1.5), and prostate cancer (relative risk, 1.4; 95% CI, 1.2 to 1.7). Among former smokers, the relative risk for each of these outcomes declined as the number of years since quitting increased. CONCLUSIONS: A substantial portion of the excess mortality among current smokers between 2000 and 2011 was due to associations with diseases that have not been formally established as caused by smoking. These associations should be investigated further and, when appropriate, taken into account when the mortality burden of smoking is investigated. (Funded by the American Cancer Society.).

Serum metabolomic profiles associated with postmenopausal hormone use.

INTRODUCTION: Postmenopausal hormone use is linked to several health outcomes and the risk associated with some may differ depending on whether estrogen is used alone or in combination with progestin. OBJECTIVE: Metabolomic analyses of postmenopausal hormone use and differences between hormone regimes was done to identify metabolites associated with each type of hormone treatment. METHODS: Untargeted metabolomics analysis was done on serum from 1336 women enrolled in the Cancer Prevention II Nutrition Cohort. Levels of 781 named metabolites were compared between 667 nonusers with 332 estrogen-only and with 337 estrogen plus progestin users using linear regression. Metabolite levels were also compared between estrogen-only and estrogen plus progestin users. RESULTS: Compared to nonusers, 276 metabolites were statistically significantly (P < 6.40 × 10- 5) associated with estrogen-only use and 222 were associated with estrogen plus progestin use. The metabolites associated with both types of hormones included numerous lipids, acyl carnitines, and amino acids as well as the thyroid hormone thyroxine and the oncometabolite fumarate. The 65 metabolites that differed significantly between estrogen-only and estrogen plus progestin users included 19 steroids and 12 lipids that contained the bioactive fatty acid arachidonic acid. CONCLUSIONS: These findings suggest that postmenopausal hormone use influences metabolic pathways linked to a variety of cellular processes, including the regulation of metabolism and stress responses, energy production, and inflammation. The differential association of numerous lipids which influence cellular signaling suggests that differences in signal transduction may contribute to the disparate risks for some diseases between estrogen-only and estrogen plus progestin users.

Reproducibility of non-fasting plasma metabolomics measurements across processing delays.

INTRODUCTION: Processing delays after blood collection is a common pre-analytical condition in large epidemiologic studies. It is critical to evaluate the suitability of blood samples with processing delays for metabolomics analysis as it is a potential source of variation that could attenuate associations between metabolites and disease outcomes. OBJECTIVES: We aimed to evaluate the reproducibility of metabolites over extended processing delays up to 48 h. We also aimed to test the reproducibility of the metabolomics platform. METHODS: Blood samples were collected from 18 healthy volunteers. Blood was stored in the refrigerator and processed for plasma at 0, 15, 30, and 48 h after collection. Plasma samples were metabolically profiled using an untargeted, ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) platform. Reproducibility of 1012 metabolites over processing delays and reproducibility of the platform were determined by intraclass correlation coefficients (ICCs) with variance components estimated from mixed-effects models. RESULTS: The majority of metabolites (approximately 70% of 1012) were highly reproducible (ICCs ≥ 0.75) over 15-, 30- or 48-h processing delays. Nucleotides, energy-related metabolites, peptides, and carbohydrates were most affected by processing delays. The platform was highly reproducible with a median technical ICC of 0.84 (interquartile range 0.68-0.93). CONCLUSION: Most metabolites measured by the UPLC-MS/MS platform show acceptable reproducibility up to 48-h processing delays. Metabolites of certain pathways need to be interpreted cautiously in relation to outcomes in epidemiologic studies with prolonged processing delays.

Untargeted Metabolomics Identifies Novel Potential Biomarkers of Habitual Food Intake in a Cross-Sectional Study of Postmenopausal Women.

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 < 4.63 × 10-7, |r| > 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.

The relationship between physical activity, obesity, and lung cancer risk by smoking status in a large prospective cohort of US adults.

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 <8.75 (reference), 8.75-17.4, 17.5+ MET-hours/week), baseline body mass index (BMI, weight (kg)/height (m2); <18.5, 18.5-22.0 (reference), 22.1-24.9, 25.0-29.9, 30.0+ kg/m2), and waist circumference (measured in 1997; sex-specific quartiles) in relation to lung cancer risk stratified by smoking status and years since quitting among former smokers (never, current, former <10 years, former, 10-19 years, former 20+ years). Cox proportional hazards modeling computed hazard rate ratios (RR) and 95% confidence intervals (CI) while adjusting for potential confounders. During 2,384,546 person years of follow-up time, 4,669 men and women were diagnosed with lung cancer (453 among never smokers; 1,452 among current smokers; 1,194 among former smokers <10 years since quitting; 725 among former 10-19 years; and 845 among former 20+ years). Physical activity was not associated with lung cancer risk within any of the smoking strata except in former smokers less than 10 years since quitting (RR = 0.77; 95% CI 0.67-0.90 for 17.5+ MET-hours/week). Similarly, BMI was inversely associated with lung cancer in former smokers less than 10 years since quitting (RR = 0.68; 95% CI 0.55-0.84 for 30+ kg/m2) and more modestly in former smokers who quit 10-19 and 20+ years ago. Waist circumference was not associated with lung cancer risk in any smoking category. While being physically active and maintaining a healthy body weight are important for prevention of various chronic diseases, including several types of cancer, our results suggest that physical activity, BMI, and waist circumference are not associated with lung cancer risk, regardless of smoking status.

Evaluation of a Novel Difficulty of Smoking Cessation Phenotype Based on Number of Quit Attempts.

BACKGROUND: Numerous studies have sought to identify genes that influence the ability to quit smoking, but none found any that are consistently associated with smoking cessation. METHODS: We developed a novel difficulty of quitting smoking phenotype based on the extremes of the number of quit attempts needed to achieve successful abstinence: Easy quitters were defined as having achieved long-term (>1 year) abstinence after their first quit attempt and difficult quitters as having reported 10 or more quit attempts. We conducted a two-stage study to determine if this phenotype could be useful for identifying single nucleotide polymorphisms (SNPs) that influence smoking cessation. In stage 1, 82 SNPs in 26 genes involved in nicotine signaling and metabolism were genotyped in 1357 easy quitters and 1321 difficult quitters from Cancer Prevention Study 3 (CPS-3). In stage 2, the 11 SNPs associated with difficult quitting in stage 1 (p < .1) were genotyped in an independent sample of 1300 easy quitters and 1299 difficult quitters from CPS-3. RESULTS: Three of 11 SNPs (HTR1B rs6298, NR4A2 rs834829, and CYP2A65 rs8192729) were significantly associated with the difficult quitting phenotype in stage 2 (p < .05). In addition, a polygenic risk score based on the 11 SNPs identified in stage 1 was significantly associated with the difficult quitting phenotype in stage 2 (odds ratio = 1.08, 95% confidence interval: 1.03-1.14 per quintile, p trend = 4.5×10-3). CONCLUSIONS: Using a novel difficulty of quitting phenotype, three gene variants and a polygenic risk score based on 11 SNPs were found to be significantly associated with smoking cessation. IMPLICATIONS: Our results provide evidence that a difficulty of quitting smoking phenotype based on the extremes of number of quit attempts could be a useful tool for identifying genetic variants that influence difficulty of smoking cessation. Knowledge of these genetic variants will indicate biological pathways that could be targeted for the development of novel smoking cessation aids and could be used to determine which smokers are most likely to benefit from such smoking cessation aids.

50-year trends in smoking-related mortality in the United States.

BACKGROUND: The disease risks from cigarette smoking increased in the United States over most of the 20th century, first among male smokers and later among female smokers. Whether these risks have continued to increase during the past 20 years is unclear. METHODS: We measured temporal trends in mortality across three time periods (1959-1965, 1982-1988, and 2000-2010), comparing absolute and relative risks according to sex and self-reported smoking status in two historical cohort studies and in five pooled contemporary cohort studies, among participants who became 55 years of age or older during follow-up. RESULTS: For women who were current smokers, as compared with women who had never smoked, the relative risks of death from lung cancer were 2.73, 12.65, and 25.66 in the 1960s, 1980s, and contemporary cohorts, respectively; corresponding relative risks for male current smokers, as compared with men who had never smoked, were 12.22, 23.81, and 24.97. In the contemporary cohorts, male and female current smokers also had similar relative risks for death from chronic obstructive pulmonary disease (COPD) (25.61 for men and 22.35 for women), ischemic heart disease (2.50 for men and 2.86 for women), any type of stroke (1.92 for men and 2.10 for women), and all causes combined (2.80 for men and 2.76 for women). Mortality from COPD among male smokers continued to increase in the contemporary cohorts in nearly all the age groups represented in the study and within each stratum of duration and intensity of smoking. Among men 55 to 74 years of age and women 60 to 74 years of age, all-cause mortality was at least three times as high among current smokers as among those who had never smoked. Smoking cessation at any age dramatically reduced death rates. CONCLUSIONS: The risk of death from cigarette smoking continues to increase among women and the increased risks are now nearly identical for men and women, as compared with persons who have never smoked. Among men, the risks associated with smoking have plateaued at the high levels seen in the 1980s, except for a continuing, unexplained increase in mortality from COPD.

Waist circumference, body mass index, and postmenopausal breast cancer incidence in the Cancer Prevention Study-II Nutrition Cohort.

PURPOSE: High body mass index (BMI) is an established risk factor for postmenopausal breast cancer. However, less is known about associations with waist circumference. In particular, it is unclear whether a larger waist circumference is associated with risk more than would be expected based solely on its contribution to BMI. METHODS: We examined the associations of BMI and waist circumference with risk of postmenopausal breast cancer, with and without mutual adjustment, in the Cancer Prevention Study-II Nutrition Cohort. Analyses included 28,965 postmenopausal women who reported weight and waist circumference on a questionnaire in 1997 and were not taking menopausal hormones. RESULTS: During a median follow-up of 11.58 years, 1,088 invasive breast cancer cases were identified. Hazard ratios (HR) and 95 % confidence intervals (CI) were estimated from multivariable-adjusted Cox proportional hazard regression models. Without adjustment for BMI, a larger waist circumference was associated with higher risk of breast cancer (per 10 cm increase in waist circumference, HR = 1.13, 95 % CI 1.08-1.19). However, adjustment for BMI eliminated the association with waist circumference (per 10 cm HR = 1.00, 95 % CI 0.92-1.08). BMI was associated with risk unadjusted for waist circumference (per 1 kg/m(2) HR = 1.04, 95 % CI 1.03-1.05) and adjusted for waist circumference (per 1 kg/m(2) HR = 1.04, 95 % CI 1.02-1.06). CONCLUSIONS: Our study of predominantly white women provides evidence that a larger waist circumference is associated with higher risk of postmenopausal breast cancer, but not beyond its contribution to BMI.

Plasma Metabolomic Profiles and Risk of Advanced and Fatal Prostate Cancer.

BACKGROUND: Little is known about the underlying molecular mechanisms of prostate cancer, especially advanced and fatal prostate cancer. OBJECTIVE: To examine associations of prediagnostic plasma metabolomic profiles with advanced and fatal prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: 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. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Prentice-weighted Cox proportional hazards regression models were used to assess associations of 699 known metabolites with advanced and fatal prostate cancer. RESULTS AND LIMITATIONS: 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 rate <0.2) 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. CONCLUSIONS: 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. PATIENT SUMMARY: 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.

Association between Smoking Cannabis and Quitting Cigarettes in a Large American Cancer Society Cohort.

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.

A Metabolomics Analysis of Postmenopausal Breast Cancer Risk in the Cancer Prevention Study II.

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 <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.

Identification of Genetic Risk Factors for Familial Urinary Bladder Cancer: An Exome Sequencing Study.

PURPOSE: Previous studies have shown an approximately two-fold elevation in the relative risk of urinary bladder cancer (UBC) among people with a family history that could not be entirely explained by shared environmental exposures, thus suggesting a genetic component in its predisposition. Multiple genome-wide association studies and recent gene panel sequencing studies identified several genetic loci that are associated with UBC risk; however, the list of UBC-associated variants and genes is incomplete. MATERIALS AND METHODS: We exome sequenced eight patients from three multiplex UBC pedigrees and a group of 77 unrelated familial UBC cases matched to 241 cancer-free controls. In addition, we examined pathogenic germline variation in 444 candidate genes in 392 The Cancer Genome Atlas UBC cases. RESULTS: In the pedigrees, segregating variants were family-specific although the identified genes clustered in common pathways, most notably DNA repair (MLH1 and MSH2) and cellular metabolism (IDH1 and ME1). In the familial UBC group, the proportion of pathogenic and likely pathogenic variants was significantly higher in cases compared with controls (P = .003). Pathogenic and likely pathogenic variant load was also significantly increased in genes involved in cilia biogenesis (P = .001). In addition, a pathogenic variant in CHEK2 (NM_007194.4:c.1100del; p.T367Mfs*15) was over-represented in cases (variant frequency = 2.6%; 95% CI, 0.71 to 6.52) compared with controls (variant frequency = 0.21%; 95% CI, 0.01 to 1.15), but was not statistically significant. CONCLUSION: These results point to a complex polygenic predisposition to UBC. Despite heterogeneity, the genes cluster in several biologically relevant pathways and processes, for example, DNA repair, cilia biogenesis, and cellular metabolism. Larger studies are required to determine the importance of CHEK2 in UBC etiology.