DNA methylation is instrumental for gene regulation. Global changes in the epigenetic landscape have been recognized as a hallmark of cancer. However, the role of DNA methylation in epithelial ovarian cancer (EOC) remains unclear. In this study, high-density genetic and DNA methylation data in white blood cells from the Framingham Heart Study (N = 1,595) were used to build genetic models to predict DNA methylation levels. These prediction models were then applied to the summary statistics of a genome-wide association study (GWAS) of ovarian cancer including 22,406 EOC cases and 40,941 controls to investigate genetically predicted DNA methylation levels in association with EOC risk. Among 62,938 CpG sites investigated, genetically predicted methylation levels at 89 CpG were significantly associated with EOC risk at a Bonferroni-corrected threshold of P < 7.94 × 10−7. Of them, 87 were located at GWAS-identified EOC susceptibility regions and two resided in a genomic region not previously reported to be associated with EOC risk. Integrative analyses of genetic, methylation, and gene expression data identified consistent directions of associations across 12 CpG, five genes, and EOC risk, suggesting that methylation at these 12 CpG may influence EOC risk by regulating expression of these five genes, namely MAPT, HOXB3, ABHD8, ARHGAP27, and SKAP1. We identified novel DNA methylation markers associated with EOC risk and propose that methylation at multiple CpG may affect EOC risk via regulation of gene expression. Significance: Identification of novel DNA methylation markers associated with EOC risk suggests that methylation at multiple CpG may affect EOC risk through regulation of gene expression.

Several blood protein biomarkers have been associated with prostate cancer risk. However, most studies assessed only a small number of biomarkers and/or included a small sample size. To identify novel protein biomarkers of prostate cancer risk, we studied 79,194 cases and 61,112 controls of European ancestry, included in the PRACTICAL/ELLIPSE consortia, using genetic instruments of protein quantitative trait loci for 1,478 plasma proteins. A total of 31 proteins were associated with prostate cancer risk including proteins encoded by GSTP1, whose methylation level was shown previously to be associated with prostate cancer risk, and MSMB, SPINT2, IGF2R, and CTSS, which were previously implicated as potential target genes of prostate cancer risk variants identified in genome-wide association studies. A total of 18 proteins inversely correlated and 13 positively correlated with prostate cancer risk. For 28 of the identified proteins, gene somatic changes of short indels, splice site, nonsense, or missense mutations were detected in patients with prostate cancer in The Cancer Genome Atlas. Pathway enrichment analysis showed that relevant genes were significantly enriched in cancer-related pathways. In conclusion, this study identifies 31 candidates of protein biomarkers for prostate cancer risk and provides new insights into the biology and genetics of prostate tumorigenesis. Significance: Integration of genomics and proteomics data identifies biomarkers associated with prostate cancer risk.

Importance: The reasons underlying racial/ethnic mortality disparities for cancer patients remain poorly understood, especially regarding the role of access to care. Participants: Over five million patients with a primary diagnosis of lung, breast, prostate, colon/rectum, pancreas, ovary, or liver cancer during 2004–2014, were identified from the National Cancer Database. Cox proportional hazards models were applied to estimate hazard ratios (HR) and 95% confidence intervals (CI) for total mortality associated with race/ethnicity, and access to care related factors (i.e., socioeconomic status [SES], insurance, treating facility, and residential type) for each cancer. Results: Racial/ethnic disparities in total mortality were observed across seven cancers. Compared with non-Hispanic (NH)-white patients, NH-black patients with breast (HR = 1.27, 95% CI: 1.26 to 1.29), ovarian (HR = 1.20, 95% CI: 1.17 to 1.23), prostate (HR = 1.31, 95% CI: 1.30 to 1.33), colorectal (HR = 1.11, 95% CI: 1.10 to 1.12) or pancreatic (HR = 1.03, 95% CI: 1.02 to 1.05) cancers had significantly elevated mortality, while Asians (13–31%) and Hispanics (13–19%) had lower mortality for all cancers. Racial/ethnic disparities were observed across all strata of access to care related factors and modified by those factors. NH-black and NH-white disparities were most evident among patients with high SES or those with private insurance, while Hispanic/Asian versus NH-white disparities were more evident among patients with low SES or those with no/poor insurance. Conclusions and Relevance: Racial/ethnic mortality disparities for major cancers exist across all patient groups with different access to care levels. The influence of SES or insurance on mortality disparity follows different patterns for racial/ethnic minorities versus NH-whites. Impact: Our study highlights the need for racial/ethnic-specific strategies to reduce the mortality disparities for major cancers.

Background: Proteomics-based technologies are emerging tools used for cancer biomarker discovery. Limited prospective studies have been conducted to evaluate the role of circulating proteins in colorectal cancer (CRC) development. Methods: A two-stage case-control proteomics study nested in the Shanghai Women’s Health Study was conducted. A total of 1104 circulating proteins were measured in the discovery phase, consisting of 100 incident CRC cases and 100 individually matched controls. An additional 60 case-control pairs were selected for validation. Protein profiling at both stages was completed using the Olink platforms. Conditional logistic regression was used to evaluate the associations between circulating proteins and CRC risk. The elastic net method was employed to develop a protein score for CRC risk. Results: In the discovery set, 27 proteins showed a nominally significant association with CRC risk, among which 22 were positively and 5 were inversely associated. Six of the 27 protein markers were significantly associated with CRC risk in the validation set. In the analysis of pooled discovery and validation sets, odds ratios (ORs) per standard deviation (SD) increase in levels of these proteins were 1.54 (95% confidence interval (CI): 1.15–2.06) for CD79B; 1.71 (95% CI: 1.24–2.34) for DDR1; 2.04 (95% CI: 1.39–3.01) for EFNA4; 1.54 (95% CI: 1.16–2.02) for FLRT2; 2.09 (95% CI: 1.47–2.98) for LTA4H and 1.88 (95% CI: 1.35–2.62) for NCR1. Sensitivity analyses showed consistent associations for all proteins with the exclusion of cases diagnosed within the first two years after the cohort enrollment, except for CD79B. Furthermore, a five-protein score was developed based on the six proteins identified and showed significant associations with CRC risk in both discovery and validation sets (Discovery: OR_1-SD = 2.46, 95% CI: 1.53–3.95; validation: OR_1-SD = 4.16, 95% CI: 1.92–8.99). Conclusions: A panel of five protein markers was identified as potential biomarkers for CRC risk. Our findings provide novel insights into the etiology of CRC and may facilitate the risk assessment of the malignancy.

Background: The role of methylation in pancreatic cancer risk remains unclear. We integrated genome and methylome data to identify CpG sites (CpG) with the genetically predicted methylation to be associated with pancreatic cancer risk. We also studied gene expression to understand the identified associations.Methods: Using genetic data and white blood cell methylation data from 1,595 subjects of European descent, we built genetic models to predict DNA methylation levels. After internal and external validation, we applied prediction models with satisfactory performance to the genetic data of 8,280 pancreatic cancer cases and 6,728 controls of European ancestry to investigate the associations of predicted methylation with pancreatic cancer risk. For associated CpGs, we compared their measured levels in pancreatic tumor versus benign tissue.Results: We identified 45 CpGs at nine loci showing an association with pancreatic cancer risk, including 15 CpGs showing an association independent from identified risk variants. We observed significant correlations between predicted methylation of 16 of the 45 CpGs and predicted expression of eight adjacent genes, of which six genes showed associations with pancreatic cancer risk. Of the 45 CpGs, we were able to compare measured methylation of 16 in pancreatic tumor versus benign pancreatic tissue. Of them, six showed differentiated methylation.Conclusions: We identified methylation biomarker candidates associated with pancreatic cancer using genetic instruments and added additional insights into the role of methylation in regulating gene expression in pancreatic cancer development.Impact: A comprehensive study using genetic instruments identifies 45 CpG sites at nine genomic loci for pancreatic cancer risk.