The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Breastfeeding is inversely associated with breast cancer risk but the associations of breastfeeding with mammographic breast density (MBD) are not clear. We investigated the association between breastfeeding and volumetric measures of MBD [volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)] and evaluated whether it differs by race, menopausal status, and body mass index (BMI). The study population was comprised of 964 women (67% non-Hispanic White, 29% non-Hispanic Black) who had screening mammography at Washington University School of Medicine, St. Louis, MO. VPD, DV and NDV were log10 transformed. We performed multivariable linear regression models adjusted for age, BMI, family history of breast cancer, race, and age at menarche among all participants and exclusively in parous women. Mean age was 50.7 years. VPD was 12% lower among women who breastfed 0–6 months, [10β = 0.88, 95% confidence interval (CI; 0.79–0.98)] compared with nulliparous women. Breastfeeding was not associated with VPD among women who breastfed >7 months. Breastfeeding was inversely associated with DV [parous never breastfed: 10β = 0.93; 95% CI (0.83–1.04), breastfed 0–6 months: 10β = 0.91, 95% CI (0.79–1.05), breastfed 7–12 months: 10β = 0.94; 95% CI (0.81–1.10), breastfed >12 months: 10β = 0.87, 95% CI (0.78–0.98), Ptrend = 0.03]. BMI modified the association between breastfeeding and VPD. Women who breastfed for 0–6 months and had a BMI < 25 kg/m2 had lower VPD compared with nulliparous women, but among women with a BMI ≥ 25 kg/m2 there was no association (Pinteraction = 0.04). In this diverse study population, the association of breastfeeding with VPD appears to be modified by BMI, but not by race or menopausal status. Future research exploring the associations of breastfeeding with other mammographic features are needed. Prevention Relevance: Breastfeeding for up to 6 months may be associated with lower VPD among women with a BMI < 25 kg/m2. The potential role of MBD in mediating the associations of breastfeeding with breast cancer risk in a select group of women deserves further evaluation. See related Spotlight, p. 309

Background: Emerging evidence suggests metabolic dysregulation may contribute to colorectal cancer (CRC) aetiology. We aimed to identify pre-diagnostic metabolic biomarkers for CRC risk in 230,420 UK Biobank participants. Methods: Nuclear magnetic resonance spectroscopy was used to quantify 249 metabolic biomarkers in plasma samples collected at baseline. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals (CIs) for associations of metabolic biomarkers with CRC risk after adjusting for potential confounders. To infer the potential causality of biomarkers that were associated with CRC independent of the others, we performed genome-wide association analyses among 199,732 UK Biobank participants of European ancestry to identify biomarker-associated genetic variants, followed by two-sample Mendelian randomization (MR) analyses using summary statistics of 78,473 CRC cases and 107,143 controls of European ancestry. Results: During a median follow-up time of 9.7 years, 2,410 incident primary CRC cases were identified. Among 43 CRC-associated (P-value < 0.001) metabolic biomarkers, ten biomarkers including fatty acids (FAs), inflammation, ketone bodies, and lipoprotein lipids were associated with CRC risk after mutual adjustment. MR analyses provided strong evidence for potential causal associations of CRC risk with percentages of linolic acid [odds ratio (OR) = 0.89, 95% CI = 0.83-0.96, P-value = 3 × 10^-3] and saturated FAs (OR = 1.14, 95% CI = 1.03–1.25, P-value = 9 × 10^-3) to total FAs. Conclusions: We identified multiple CRC-associated metabolic biomarkers. Perturbed lipid and lipoprotein metabolism may promote colorectal carcinogenesis.

Background: Genome‐wide association studies (GWAS) have identified over 80 susceptibility loci for lung cancer risk. However, the genes underlying these associations remain largely unknown. Methods: We conducted a large transcriptome‐wide association study (TWAS) to identify lung cancer susceptibility genes. We leveraged gene expression data from lungs and 48 other tissue types and whole‐genome sequencing data from up to 706 samples of European ancestry in the GTEx (version 8) to build lung‐tissue and joint‐tissue gene expression prediction models. These models were applied to GWAS data, including 29,266 lung cancer cases and 56,450 controls, to assess the associations of genetically predicted gene expression levels with lung cancer risk. Results: A total of 8624 genes were successfully built for single‐tissue models, and 11,341 genes for joint‐tissue models (12,133 unique genes altogether). Among 40 genes whose expression levels were associated with the risk of lung cancer at a Bonferroni‐corrected significance level, ZKSCAN4 was located more than 2 Mb away from the GWAS‐identified variants linked to lung cancer. Among the remaining 39 genes within 2 Mb of GWAS‐identified variants, seven genes were independent of these. Among 53 genes associated with the risk of lung cancer subtypes, 13 genes were beyond 2 Mb of GWAS‐identified variants, and four genes were independent of the GWAS‐identified variants within 2 Mb regions. Conclusion: Our TWAS identified over 50 candidate susceptibility genes for lung cancer, providing new insights into lung cancer genetics.

Mammographic breast density (MBD) is a risk factor for breast cancer, but its molecular basis is poorly understood. Growth factors stimulate cellular and epithelial proliferation and could influence MBD via these mechanisms. Studies investigating the associations of circulating growth factors with MBD have, however, yielded conflicting results especially in postmenopausal women. We, therefore, investigated the associations of plasma growth factor gene expression [insulin-like growth factor (IGF)-1, IGF-binding protein 3, FGF-1, FGF-12, TGFβ1 and bone morphogenetic protein (BMP)-2] with MBD in postmenopausal women. We used NanoString nCounter platform to quantify plasma growth factor gene expression and Volpara to evaluate volumetric MBD measures. We investigated the associations of growth factor gene expression with MBD using both multiple linear regression (fold change) and multinomial logistic regression models, adjusted for potential confounders. The mean age of the 368 women enrolled was 58 years (range, 50–64). In analyses using linear regression models, one unit increase in IGF-1 gene expression was associated with a 35% higher volumetric percent density (VPD, 1.35; 95% confidence interval (CI), 1.13–1.60; P = 0.001). There were suggestions that TGFβ1 gene expression was positively associated with VPD while BMP-2 gene expression was inversely associated with VPD, but these were not statistically significant. In analyses using multinomial logistic regression, TGFβ1 gene expression was 33% higher (OR = 1.33; 95% CI, 1.13–1.56; P = 0.0008) in women with extremely dense breasts than those with almost entirely fatty breasts. There were no associations between growth factor gene expression and dense volume or nondense volume. Our study provides insights into the associations of growth factors with MBD in postmenopausal women and requires confirmation in other study populations. Prevention Relevance: Mammographic breast density is a strong risk factor for breast cancer. Understanding its underlying biological mechanisms could have utility in breast cancer prevention.