LINE1 methylation levels associated with increased bladder cancer risk in pre-diagnostic blood DNA among US (PLCO) and European (ATBC) cohort study participants

Global methylation in blood DNA has been associated with bladder cancer risk in case-control studies, but has not been examined prospectively. We examined the association between LINE1 total percent 5-methylcytosine and bladder cancer risk using pre-diagnostic blood DNA from the United States-based, Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial (PLCO) (299 cases/676 controls), and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) cohort of Finnish male smokers (391 cases/778 controls). Logistic regression adjusted for age at blood draw, study center, pack-years of smoking, and sex was used to estimate odd ratios (ORs) and 95% confidence intervals (CIs) using study- and sex-specific methylation quartiles. In PLCO, higher, although non-significant, bladder cancer risks were observed for participants in the highest three quartiles (Q2–Q4) compared with the lowest quartile (Q1) (OR = 1.36, 95% CI: 0.96 -1.92). The association was stronger in males (Q2–Q4 vs. Q1 OR = 1.48, 95% CI: 1.00–2.20) and statistically significant among male smokers (Q2–Q4 vs. Q1 OR = 1.83, 95% CI: 1.14–2.95). No association was found among females or female smokers. Findings for male smokers were validated in ATBC (Q2–Q4 vs. Q1: OR = 2.31, 95% CI: 1.62–3.30) and a highly significant trend was observed (P = 8.7 × 10^?7). After determining that study data could be combined, pooled analysis of PLCO and ATBC male smokers (580 cases/1119 controls), ORs were significantly higher in Q2-Q4 compared with Q1 (OR = 2.03, 95% CI: 1.52–2.72), and a trend across quartiles was observed (P = 0.0001). These findings suggest that higher global methylation levels prior to diagnosis may increase bladder cancer risk, particularly among male smokers.     

An epigenome-wide association meta-analysis of prenatal maternal stress in neonates: A model approach for replication

Prenatal maternal stress exposure has been associated with neonatal differential DNA methylation. However, the available evidence in humans is largely based on candidate gene methylation studies, where only a few CpG sites were evaluated. The aim of this study was to examine the association between prenatal exposure to maternal stress and offspring genome-wide cord blood methylation using different methods. First, we conducted a meta-analysis and follow-up pathway analyses. Second, we used novel region discovery methods [i.e., differentially methylated regions (DMRs) analyses]. To this end, we used data from two independent population-based studies, the Generation R Study (n = 912) and the Avon Longitudinal Study of Parents and Children (ALSPAC, n = 828), to (i) measure genome-wide DNA methylation in cord blood and (ii) extract a prenatal maternal stress composite. The meta-analysis (n_total = 1,740) revealed no epigenome-wide (meta P <1.00e-07) associations of prenatal maternal stress exposure with neonatal differential DNA methylation. Follow-up analyses of the top hits derived from our epigenome-wide meta-analysis (meta P <1.00e-04) indicated an over-representation of the methyltransferase activity pathway. We identified no Bonferroni-corrected (P <1.00e-06) DMRs associated with prenatal maternal stress exposure. Combining data from two independent population-based samples in an epigenome-wide meta-analysis, the current study indicates that there are no large effects of prenatal maternal stress exposure on neonatal DNA methylation. Such replication efforts are essential in the search for robust associations, whether derived from candidate gene methylation or epigenome-wide studies.     

Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls

Pancreatic adenocarcinoma (PaC) is one of most difficult tumors to treat. Much of this is attributed to the late diagnosis. To identify biomarkers for early detection, we examined DNA methylation differences in leukocyte DNA between PaC cases and controls in a two-phase study. In phase I, we measured methylation levels at 1,505 CpG sites in treatment-naïve leukocyte DNA from 132 never-smoker PaC patients and 60 never-smoker healthy controls. We found significant differences in 110 CpG sites (false discovery rate <0.05). In phase II, we tested and validated 88 of 96 phase I selected CpG sites in 240 PaC cases and 240 matched controls (p≤0.05). Using penalized logistic regression, we built a prediction model consisting of five CpG sites (IL10_P348, LCN2_P86, ZAP70_P220, AIM2_P624, TAL1_P817) that discriminated PaC patients from controls (C-statistic = 0.85 in phase I; 0.76 in phase II). Interestingly, one CpG site (LCN2_P86) alone could discriminate resectable patients from controls (C-statistic  = 0.78 in phase I; 0.74 in phase II). We also performed methylation quantitative trait loci (methQTL) analysis and identified three CpG sites (AGXT_P180_F, ALOX12_E85_R, JAK3_P1075_R) where the methylation levels were significantly associated with single nucleotide polymorphisms (SNPs) (false discovery rate <0.05). Our results demonstrate that epigenetic variation in easily obtainable leukocyte DNA, manifested by reproducible methylation differences, may be used to detect PaC patients. The methylation differences at certain CpG sites are partially attributable to genetic variation. This study strongly supports future epigenome-wide association study using leukocyte DNA for biomarker discovery in human diseases.     

Peripheral blood DNA methylation profiles are indicative of head and neck squamous cell carcinoma: An epigenome-wide association study

Head and neck cancer accounts for an estimated 47,560 new cases and 11,480 deaths annually in the United States, the majority of which are squamous cell carcinomas (HNSCC). The overall 5 year survival is approximately 60% and declines with increasing stage at diagnosis, indicating a need for non-invasive tests that facilitate the detection of early disease. DNA methylation is a stable epigenetic modification that is amenable to measurement and readily available in peripheral blood. We used a semi-supervised recursively partitioned mixture model (SS-RPMM) approach to identify novel blood DNA methylation markers of HNSCC using genome-wide methylation array data for peripheral blood samples from 92 HNSCC cases and 92 cancer-free control subjects. To assess the performance of the resultant markers, we constructed receiver operating characteristic (ROC) curves and calculated the corresponding area under the curve (AUC). Cases and controls were best differentiated by a methylation profile of six CpG loci (associated with FGD4, SERPINF1, WDR39, IL27, HYAL2 and PLEKHA6), with an AUC of 0.73 (95% CI: 0.62–0.82). After adjustment for subject age, gender, smoking, alcohol consumption and HPV16 serostatus, the AUC increased to 0.85 (95% CI: 0.76–0.92). We have identified a novel blood-based methylation profile that is indicative of HNSCC with a high degree of accuracy. This profile demonstrates the potential of DNA methylation measured in blood for development of non-invasive applications for detection of head and neck cancer.     

Genome-scale screen for DNA methylation-based detection markers for ovarian cancer

Background

The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer.

Methodology/Principal Findings

We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels.We identified one marker, IFFO1 promoter methylation (IFFO1-M), that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients.

Conclusions/Significance

We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers.     

LINE1 methylation levels associated with increased bladder cancer risk in pre-diagnostic blood DNA among US (PLCO) and European (ATBC) cohort study participants

Global methylation in blood DNA has been associated with bladder cancer risk in case-control studies, but has not been examined prospectively. We examined the association between LINE1 total percent 5-methylcytosine and bladder cancer risk using pre-diagnostic blood DNA from the United States-based, Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial (PLCO) (299 cases/676 controls), and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) cohort of Finnish male smokers (391 cases/778 controls). Logistic regression adjusted for age at blood draw, study center, pack-years of smoking, and sex was used to estimate odd ratios (ORs) and 95% confidence intervals (CIs) using study- and sex-specific methylation quartiles. In PLCO, higher, although non-significant, bladder cancer risks were observed for participants in the highest three quartiles (Q2–Q4) compared with the lowest quartile (Q1) (OR = 1.36, 95% CI: 0.96 -1.92). The association was stronger in males (Q2–Q4 vs. Q1 OR = 1.48, 95% CI: 1.00–2.20) and statistically significant among male smokers (Q2–Q4 vs. Q1 OR = 1.83, 95% CI: 1.14–2.95). No association was found among females or female smokers. Findings for male smokers were validated in ATBC (Q2–Q4 vs. Q1: OR = 2.31, 95% CI: 1.62–3.30) and a highly significant trend was observed (P = 8.7 × 10⁻⁷). After determining that study data could be combined, pooled analysis of PLCO and ATBC male smokers (580 cases/1119 controls), ORs were significantly higher in Q2-Q4 compared with Q1 (OR = 2.03, 95% CI: 1.52–2.72), and a trend across quartiles was observed (P = 0.0001). These findings suggest that higher global methylation levels prior to diagnosis may increase bladder cancer risk, particularly among male smokers.     

DNA methylation levels and long-term trihalomethane exposure in drinking water: an epigenome-wide association study

Trihalomethanes (THM) are undesired disinfection byproducts (DBPs) formed during water treatment. Mice exposed to DBPs showed global DNA hypomethylation and c-myc and c-jun gene-specific hypomethylation, while evidence of epigenetic effects in humans is scarce. We explored the association between lifetime THM exposure and DNA methylation through an epigenome-wide association study. We selected 138 population-based controls from a case-control study of colorectal cancer conducted in Barcelona, Spain, exposed to average lifetime THM levels ≤85 μg/L vs. >85 μg/L (N = 68 and N = 70, respectively). Mean age of participants was 70 years, and 54% were male. Average lifetime THM level in the exposure groups was 64 and 130 µg/L, respectively. DNA was extracted from whole blood and was bisulphite converted to measure DNA methylation levels using the Illumina HumanMethylation450 BeadChip. Data preprocessing was performed using RnBeads. Methylation was compared between exposure groups using empirical Bayes moderated linear regression for CpG sites and Gaussian kernel for CpG regions. ConsensusPathDB was used for gene set enrichment. Statistically significant differences in methylation between exposure groups was found in 140 CpG sites and 30 gene-related regions, after false discovery rate <0.05 and adjustment for age, sex, methylation first principal component, and blood cell proportion. The annotated genes were localized to several cancer pathways. Among them, 29 CpGs had methylation levels associated with THM levels (|Δβ|≥0.05) located in 11 genes associated with cancer in other studies. Our results suggest that THM exposure may affect DNA methylation in genes related to tumors, including colorectal and bladder cancers. Future confirmation studies are required.     

Blood-based profiles of DNA methylation predict the underlying distribution of cell types

The potential influence of underlying differences in relative leukocyte distributions in studies involving blood-based profiling of DNA methylation is well recognized and has prompted development of a set of statistical methods for inferring changes in the distribution of white blood cells using DNA methylation signatures. However, the extent to which this methodology can accurately predict cell-type proportions based on blood-derived DNA methylation data in a large-scale epigenome-wide association study (EWAS) has yet to be examined. We used publicly available data deposited in the Gene Expression Omnibus (GEO) database (accession number GSE37008), which consisted of both blood-derived epigenome-wide DNA methylation data assayed using the Illumina Infinium HumanMethylation27 BeadArray and complete blood cell (CBC) counts among a community cohort of 94 non-diseased individuals. Constrained projection (CP) was used to obtain predictions of the proportions of lymphocytes, monocytes and granulocytes for each of the study samples based on their DNA methylation signatures. Our findings demonstrated high consistency between the average CBC-derived and predicted percentage of monocytes and lymphocytes (17.9% and 17.6% for monocytes and 82.1% and 81.4% for lymphocytes), with root mean squared error (rMSE) of 5% and 6%, for monocytes and lymphocytes, respectively. Similarly, there was moderate-high correlation between the CP-predicted and CBC-derived percentages of monocytes and lymphocytes (0.60 and 0.61, respectively), and these results were robust to the number of leukocyte differentially methylated regions (L-DMRs) used for CP prediction. These results serve as further validation of the CP approach and highlight the promise of this technique for EWAS where DNA methylation is profiled using whole-blood genomic DNA.     

Genetic contribution to variation in DNA methylation at maternal smoking-sensitive loci in exposed neonates

Epigenome-wide DNA methylation association studies have identified highly replicable genomic loci sensitive to maternal smoking during gestation. The role of inter-individual genetic variation in influencing DNA methylation, leading to the possibility of confounding or bias of such associations, has not been assessed. We investigated whether the DNA methylation levels at the top 10 CpG sites previously associated with exposure to maternal smoking during gestation were associated with individual genetic variation at the genome-wide level. Genome-wide association tests between DNA methylation at the top 10 candidate CpG and genome-wide SNPs were performed in 736 case and control participants of the California Childhood Leukemia Study. Three of the strongest maternal-smoking sensitive CpG sites in newborns were significantly associated with SNPs located proximal to each gene: cg18146737 in the GFI1 gene with rs141819830 (P = 8.2×10^?44), cg05575921 in the AHRR gene with rs148405299 (P = 5.3×10^?10), and cg12803068 in the MYO1G gene with rs61087368 (P = 1.3×10^?18). For the GFI1 CpG cg18146737, the underlying genetic variation at rs141819830 confounded the association between maternal smoking and DNA methylation in our data (the regression coefficient changed from ?0.02 [P = 0.139] to ?0.03 [P = 0.015] after including the genotype). Our results suggest that further studies using DNA methylation at cg18146737, cg05575921, or cg12803068 that aim to assess exposure to maternal smoking during gestation should include genotype at the corresponding SNP. New methods are required for adequate and routine inclusion of genotypic influence on DNA methylation in epigenome-wide association studies to control for potential confounding.     

Peripheral blood DNA methylation profiles are indicative of head and neck squamous cell carcinoma: An epigenome-wide association study

Head and neck cancer accounts for an estimated 47,560 new cases and 11,480 deaths annually in the United States, the majority of which are squamous cell carcinomas (HNSCC). The overall 5 year survival is approximately 60% and declines with increasing stage at diagnosis, indicating a need for non-invasive tests that facilitate the detection of early disease. DNA methylation is a stable epigenetic modification that is amenable to measurement and readily available in peripheral blood. We used a semi-supervised recursively partitioned mixture model (SS-RPMM) approach to identify novel blood DNA methylation markers of HNSCC using genome-wide methylation array data for peripheral blood samples from 92 HNSCC cases and 92 cancer-free control subjects. To assess the performance of the resultant markers, we constructed receiver operating characteristic (RJC) curves and calculated the corresponding area under the curve (AUC). Cases and controls were best differentiated by a methylation profile of six CpG loci (associated with FGD4, SERPINF1, WDR39, IL27, HYAL2 and PLEKHA6), with an AUC of 0.73 (95% CI: 0.62–0.82). After adjustment for subject age, gender, smoking, alcohol consumption and HPV16 serostatus, the AUC increased to 0.85 (95% CI: 0.76–0.92). We have identified a novel blood-based methylation profile that is indicative of HNSCC with a high degree of accuracy. This profile demonstrates the potential of DNA methylation measured in blood for development of non-invasive applications for detection of head and neck cancer.Key words: semi-supervised RPMM, HNSCC, epigenetics, biomarkers, Infinium, methylation array     

Effect of prenatal arsenic exposure on DNA methylation and leukocyte subpopulations in cord blood

Prenatal arsenic exposure is associated with increased risk of disease in adulthood. This has led to considerable interest in arsenic’s ability to disrupt fetal programming. Many studies report that arsenic exposure alters DNA methylation in whole blood but these studies did not adjust for cell mixture. In this study, we examined the relationship between arsenic in maternal drinking water collected ≤ 16 weeks gestational age and DNA methylation in cord blood (n = 44) adjusting for leukocyte-tagged differentially methylated regions. DNA methylation was quantified using the Infinium HumanMethylation 450 BeadChip array. Recursively partitioned mixture modeling examined the relationship between arsenic and methylation at 473,844 CpG sites. Median arsenic concentration in water was 12 µg/L (range < 1- 510 µg/L). Log₁₀ arsenic was associated with altered DNA methylation across the epigenome (P = 0.002); however, adjusting for leukocyte distributions attenuated this association (P = 0.013). We also observed that arsenic had a strong effect on the distribution of leukocytes in cord blood. In adjusted models, every log₁₀ increase in maternal drinking water arsenic exposure was estimated to increase CD8+ T cells by 7.4% (P = 0.0004) and decrease in CD4+ T cells by 9.2% (P = 0.0002). These results show that prenatal exposure to arsenic had an exposure-dependent effect on specific T cell subpopulations in cord blood and altered DNA methylation in cord blood. Future research is needed to determine if these small changes in DNA methylation alter gene expression or are associated with adverse health effects.     

Fetal lung and placental methylation is associated with in utero nicotine exposure

In utero smoke exposure has been shown to have detrimental effects on lung function and to be associated with persistent wheezing and asthma in children. One potential mechanism of IUS effects could be alterations in DNA methylation, which may have life-long implications. The goal of this study was to examine the association between DNA methylation and nicotine exposure in fetal lung and placental tissue in early development; nicotine exposure in this analysis represents a likely surrogate for in-utero smoke. We performed an epigenome-wide analysis of DNA methylation in fetal lung tissue (n = 85, 41 smoke exposed (48%), 44 controls) and the corresponding placental tissue samples (n = 80, 39 smoke exposed (49%), 41 controls) using the Illumina HumanMethylation450 BeadChip array. Differential methylation analyses were conducted to evaluate the variation associated with nicotine exposure. The most significant CpG sites in the fetal lung analysis mapped to the PKP3 (P = 2.94 × 10^?03), ANKRD33B (P = 3.12 × 10^?03), CNTD2 (P = 4.9 × 10^?03) and DPP10 (P = 5.43 × 10^?03) genes. In the placental methylome, the most significant CpG sites mapped to the GTF2H2C and GTF2H2D genes (P = 2.87 × 10^?06 ? 3.48 × 10^?05). One hundred and one unique CpG sites with P-values < 0.05 were concordant between lung and placental tissue analyses. Gene Set Enrichment Analysis demonstrated enrichment of specific disorders, such as asthma and immune disorders. Our findings demonstrate an association between in utero nicotine exposure and variable DNA methylation in fetal lung and placental tissues, suggesting a role for DNA methylation variation in the fetal origins of chronic diseases.     

Global DNA hypomethylation in peripheral blood leukocytes as a biomarker for cancer risk: a meta-analysis

Background

Good biomarkers for early detection of cancer lead to better prognosis. However, harvesting tumor tissue is invasive and cannot be routinely performed. Global DNA methylation of peripheral blood leukocyte DNA was evaluated as a biomarker for cancer risk.

Methods

We performed a meta-analysis to estimate overall cancer risk according to global DNA hypomethylation levels among studies with various cancer types and analytical methods used to measure DNA methylation. Studies were systemically searched via PubMed with no language limitation up to July 2011. Summary estimates were calculated using a fixed effects model.

Results

The subgroup analyses by experimental methods to determine DNA methylation level were performed due to heterogeneity within the selected studies (p<0.001, I²: 80%). Heterogeneity was not found in the subgroup of %5-mC (p = 0.393, I²: 0%) and LINE-1 used same target sequence (p = 0.097, I²: 49%), whereas considerable variance remained in LINE-1 (p<0.001, I²: 80%) and bladder cancer studies (p = 0.016, I²: 76%). These results suggest that experimental methods used to quantify global DNA methylation levels are important factors in the association study between hypomethylation levels and cancer risk. Overall, cancer risks of the group with the lowest DNA methylation levels were significantly higher compared to the group with the highest methylation levels [OR (95% CI): 1.48 (1.28–1.70)].

Conclusions

Global DNA hypomethylation in peripheral blood leukocytes may be a suitable biomarker for cancer risk. However, the association between global DNA methylation and cancer risk may be different based on experimental methods, and region of DNA targeted for measuring global hypomethylation levels as well as the cancer type. Therefore, it is important to select a precise and accurate surrogate marker for global DNA methylation levels in the association studies between global DNA methylation levels in peripheral leukocyte and cancer risk.     

Differential DNA methylation and PM2.5 species in a 450K epigenome-wide association study

Although there is growing evidence that exposure to ambient particulate matter is associated with global DNA methylation and gene-specific methylation, little is known regarding epigenome-wide changes in DNA methylation in relation to particles and, especially, particle components. Using the Illumina Infinium HumanMethylation450 BeadChip, we examined the relationship between one-year moving averages of PM_2.5 species (Al, Ca, Cu, Fe, K, Na, Ni, S, Si, V, and Zn) and DNA methylation at 484,613 CpG probes in a longitudinal cohort that included 646 subjects. Bonferroni correction was applied to adjust for multiple comparisons. Bioinformatics analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was also performed. We observed 20 Bonferroni significant (P-value < 9.4× 10^?9) CpGs for Fe, 8 for Ni, and 1 for V. Particularly, methylation at Schlafen Family Member 11 (SLFN11) cg10911913 was positively associated with measured levels of all 3 species. The SLFN11 gene codes for an interferon-induced protein that inhibits retroviruses and sensitizes cancer cells to DNA-damaging agents. Bioinformatics analysis suggests that gene targets may be relevant to pathways including cancers, signal transduction, and cell growth and death. Ours is the first study to examine the epigenome-wide association between ambient particles species and DNA methylation. We found that long-term exposures to specific components of ambient particle pollution, especially particles emitted during oil combustion, were associated with methylation changes in genes relevant to immune responses. Our findings provide insight into potential biologic mechanisms on an epigenetic level.     

Epigenetics,heritability and longitudinal analysis

Background

Longitudinal data and repeated measurements in epigenome-wide association studies (EWAS) provide a rich resource for understanding epigenetics. We summarize 7 analytical approaches to the GAW20 data sets that addressed challenges and potential applications of phenotypic and epigenetic data. All contributions used the GAW20 real data set and employed either linear mixed effect (LME) models or marginal models through generalized estimating equations (GEE). These contributions were subdivided into 3 categories: (a) quality control (QC) methods for DNA methylation data; (b) heritability estimates pretreatment and posttreatment with fenofibrate; and (c) impact of drug response pretreatment and posttreatment with fenofibrate on DNA methylation and blood lipids.

Results

Two contributions addressed QC and identified large statistical differences with pretreatment and posttreatment DNA methylation, possibly a result of batch effects. Two contributions compared epigenome-wide heritability estimates pretreatment and posttreatment, with one employing a Bayesian LME and the other using a variance-component LME. Density curves comparing these studies indicated these heritability estimates were similar. Another contribution used a variance-component LME to depict the proportion of heritability resulting from a genetic and shared environment. By including environmental exposures as random effects, the authors found heritability estimates became more stable but not significantly different. Two contributions investigated treatment response. One estimated drug-associated methylation effects on triglyceride levels as the response, and identified 11 significant cytosine-phosphate-guanine (CpG) sites with or without adjusting for high-density lipoprotein. The second contribution performed weighted gene coexpression network analysis and identified 6 significant modules of at least 30 CpG sites, including 3 modules with topological differences pretreatment and posttreatment.

Conclusions

Four conclusions from this GAW20 working group are: (a) QC measures are an important consideration for EWAS studies that are investigating multiple time points or repeated measurements; (b) application of heritability estimates between time points for individual CpG sites is a useful QC measure for DNA methylation studies; (c) drug intervention demonstrated strong epigenome-wide DNA methylation patterns across the 2 time points; and (d) new statistical methods are required to account for the environmental contributions of DNA methylation across time. These contributions demonstrate numerous opportunities exist for the analysis of longitudinal data in future epigenetic studies.