LINE-1 methylation in leukocyte DNA, interaction with phosphatidylethanolamine N-methyltransferase variants and bladder cancer risk

BACKGROUND: Aberrant global DNA methylation is shown to increase cancer risk. LINE-1 has been proven a measure of global DNA methylation. The objectives of this study were to assess the association between LINE-1 methylation level and bladder cancer risk and to evaluate effect modification by environmental and genetic factors. METHODS: Bisulphite-treated leukocyte DNA from 952 cases and 892 hospital controls was used to measure LINE-1 methylation level at four CpG sites by pyrosequencing. Logistic regression model was fitted to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs). Interactions between LINE-1 methylation levels and environmental and genetic factors were assessed. RESULTS: The risk of bladder cancer followed a nonlinear association with LINE-1 methylation. Compared with subjects in the middle tertile, the adjusted OR for subjects in the lower and the higher tertiles were 1.26 (95% CI 0.99–1.60, P=0.06) and 1.33 (95% CI 1.05–1.69, P=0.02), respectively. This association significantly increased among individuals homozygous for the major allele of five single-nucleotide polymorphisms located in the phosphatidylethanolamine N-methyltransferase gene (corrected P-interaction<0.05). CONCLUSIONS: The findings from this large-scale study suggest that both low and high levels of global DNA methylation are associated with the risk of bladder cancer

Polymorphisms in GSTT1, GSTZ1, and CYP2E1, Disinfection By-products, and Risk of Bladder Cancer in Spain

Background: Bladder cancer has been linked with long-term exposure to disinfection by-products (DBPs) in drinking water.Objectives: In this study we investigated the combined influence of DBP exposure and polymorphisms in glutathione S-transferase (GSTT1, GSTZ1) and cytochrome P450 (CYP2E1) genes in the metabolic pathways of selected by-products on bladder cancer in a hospital-based case–control study in Spain. Methods: Average exposures to trihalomethanes (THMs; a surrogate for DBPs) from 15 years of age were estimated for each subject based on residential history and information on municipal water sources among 680 cases and 714 controls. We estimated effects of THMs and GSTT1, GSTZ1, and CYP2E1 polymorphisms on bladder cancer using adjusted logistic regression models with and without interaction terms. Results: THM exposure was positively associated with bladder cancer: adjusted odds ratios (ORs) and 95% confidence intervals (CIs) were 1.2 (0.8–1.8), 1.8 (1.1–2.9), and 1.8 (0.9–3.5) for THM quartiles 2, 3, and 4, respectively, relative to quartile 1. Associations between THMs and bladder cancer were stronger among subjects who were GSTT1 +/+ or +/– versus GSTT1 null (pinteraction = 0.021), GSTZ1 rs1046428 CT/TT versus CC (pinteraction = 0.018), or CYP2E1 rs2031920 CC versus CT/TT (pinteraction = 0.035). Among the 195 cases and 192 controls with high-risk forms of GSTT1 and GSTZ1, the ORs for quartiles 2, 3, and 4 of THMs were 1.5 (0.7–3.5), 3.4 (1.4–8.2), and 5.9 (1.8–19.0), respectively. Conclusions: Polymorphisms in key metabolizing enzymes modified DBP-associated bladder cancer risk. The consistency of these findings with experimental observations of GSTT1, GSTZ1, and CYP2E1 activity strengthens the hypothesis that DBPs cause bladder cancer and suggests possible mechanisms as well as the classes of compounds likely to be implicated.This work was funded by the Intramural Research Program of the National Institutes of Health, National Cancer Institute (N02-CP-11015), the Fondo de Investigación Sanitaria (00/0745, G03/174, G03/160, C03/09, and C03/90), and the Instituto de Salud Carlos III, Spanish Health Ministry (CP06/00341

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

Epigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis

Interest in the potential of DNA methylation in peripheral blood as a biomarker of cancer risk is increasing. We aimed to assess whether epigenome-wide DNA methylation measured in peripheral blood samples obtained before onset of the disease is associated with increased risk of breast cancer. We report on three independent prospective nested case-control studies from the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy; n = 162 matched case-control pairs), the Norwegian Women and Cancer study (NOWAC; n = 168 matched pairs), and the Breakthrough Generations Study (BGS; n = 548 matched pairs). We used the Illumina 450k array to measure methylation in the EPIC and NOWAC cohorts. Whole-genome bisulphite sequencing (WGBS) was performed on the BGS cohort using pooled DNA samples, combined to reach 50× coverage across ~16 million CpG sites in the genome including 450k array CpG sites. Mean β values over all probes were calculated as a measurement for epigenome-wide methylation

Study protocol : the empirical investigation of methods to correct for measurement error in biobanks with dietary assessment

Peer reviewedPublisher PD

Disinfection By-Products in Drinking Water and Bladder Cancer:Evaluation of Risk Modification by Common Genetic Polymorphisms in Two Case-Control Studies

BACKGROUND: By-products are formed when disinfectants react with organic matter in source water. The most common class of disinfection by-products, trihalomethanes (THMs), have been linked to bladder cancer. Several studies have shown exposure–response associations with THMs in drinking water and bladder cancer risk. Few epidemiologic studies have evaluated gene–environment interactions for total THMs (TTHMs) with known bladder cancer susceptibility variants. OBJECTIVES: In this study, we investigated the combined effect on bladder cancer risk contributed by TTHMs, bladder cancer susceptibility variants identified through genome-wide association studies, and variants in several candidate genes. METHODS: We analyzed data from two large case–control studies—the New England Bladder Cancer Study ([Formula: see text] cases/1,162 controls), a population-based study, and the Spanish Bladder Cancer Study ([Formula: see text] cases/772 controls), a hospital-based study. Because of differences in exposure distributions and metrics, we estimated effects of THMs and genetic variants within each study separately using adjusted logistic regression models to calculate odds ratios (ORs) and 95% confidence intervals (CI) with and without interaction terms, and then combined the results using meta-analysis. RESULTS: Of the 16 loci showing strong evidence of association with bladder cancer, rs907611 at 11p15.5 [leukocyte-specific protein 1 (LSP1 region)] showed the strongest associations in the highest exposure category in each study, with evidence of interaction in both studies and in meta-analysis. In the highest exposure category, we observed [Formula: see text] (95% CI: 1.17, 2.34, [Formula: see text]) for those with the rs907611-GG genotype and [Formula: see text]. No other genetic variants tested showed consistent evidence of interaction. DISCUSSION: We found novel suggestive evidence for a multiplicative interaction between a putative bladder carcinogen, TTHMs, and genotypes of rs907611. Given the ubiquitous exposure to THMs, further work is needed to replicate and extend this finding and to understand potential molecular mechanisms. https://doi.org/10.1289/EHP989

Genetic variation in five genes important in telomere biology and risk for breast cancer

Telomeres, consisting of TTAGGG nucleotide repeats and a protein complex at chromosome ends, are critical for maintaining chromosomal stability. Genomic instability, following telomere crisis, may contribute to breast cancer pathogenesis. Many genes critical in telomere biology have limited nucleotide diversity, thus, single nucleotide polymorphisms (SNPs) in this pathway could contribute to breast cancer risk. In a population-based study of 1995 breast cancer cases and 2296 controls from Poland, 24 SNPs representing common variation in POT1, TEP1, TERF1, TERF2 and TERT were genotyped. We did not identify any significant associations between individual SNPs or haplotypes and breast cancer risk; however, data suggested that three correlated SNPs in TERT (−1381C>T, −244C>T, and Ex2-659G>A) may be associated with reduced risk of breast cancer among individuals with a family history of breast cancer (odds ratios 0.73, 0.66, and 0.57, 95% confidence intervals 0.53–1.00, 0.46–0.95 and 0.39–0.84, respectively). In conclusion, our data do not support substantial overall associations between SNPs in telomere pathway genes and breast cancer risk. Intriguing associations with variants in TERT among women with a family history of breast cancer warrant follow-up in independent studies

Detection of Somatic Mutations by High-Resolution DNA Melting (HRM) Analysis in Multiple Cancers

Identification of somatic mutations in cancer is a major goal for understanding and monitoring the events related to cancer initiation and progression. High resolution melting (HRM) curve analysis represents a fast, post-PCR high-throughput method for scanning somatic sequence alterations in target genes. The aim of this study was to assess the sensitivity and specificity of HRM analysis for tumor mutation screening in a range of tumor samples, which included 216 frozen pediatric small rounded blue-cell tumors as well as 180 paraffin-embedded tumors from breast, endometrial and ovarian cancers (60 of each). HRM analysis was performed in exons of the following candidate genes known to harbor established commonly observed mutations: PIK3CA, ERBB2, KRAS, TP53, EGFR, BRAF, GATA3, and FGFR3. Bi-directional sequencing analysis was used to determine the accuracy of the HRM analysis. For the 39 mutations observed in frozen samples, the sensitivity and specificity of HRM analysis were 97% and 87%, respectively. There were 67 mutation/variants in the paraffin-embedded samples, and the sensitivity and specificity for the HRM analysis were 88% and 80%, respectively. Paraffin-embedded samples require higher quantity of purified DNA for high performance. In summary, HRM analysis is a promising moderate-throughput screening test for mutations among known candidate genomic regions. Although the overall accuracy appears to be better in frozen specimens, somatic alterations were detected in DNA extracted from paraffin-embedded samples

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study