Linkage of DNA Methylation Quantitative Trait Loci to Human Cancer Risk

Cancer, metastasis, and the epigenome

Cancer is the second leading cause of death worldwide and disease burden is expected to increase globally throughout the next several decades, with the majority of cancer-related deaths occurring in metastatic disease. Cancers exhibit known hallmarks that endow them with increased survival and proliferative capacities, frequently as a result of de-stabilizing mutations. However, the genomic features that resolve metastatic clones from primary tumors are not yet well-characterized, as no mutational landscape has been identified as predictive of metastasis. Further, many cancers exhibit no known mutation signature. This suggests a larger role for non-mutational genome re-organization in promoting cancer evolution and dissemination. In this review, we highlight current critical needs for understanding cell state transitions and clonal selection advantages for metastatic cancer cells. We examine links between epigenetic states, genome structure, and misregulation of tumor suppressors and oncogenes, and discuss how recent technologies for understanding domain-scale regulation have been leveraged for a more complete picture of oncogenic and metastatic potential.

Cellular senescence gene TACC3 associated with colorectal cancer risk via genetic and DNA methylated alteration

Cell senescence genes play a vital role in the pathogenesis of colorectal cancer, a process that may involve the triggering of genetic variations and reversible phenotypes caused by epigenetic modifications. However, the specific regulatory mechanisms remain unclear. Using CellAge and The Cancer Genome Atlas databases and in-house RNA-seq data, DNA methylation-modified cellular senescence genes (DMCSGs) were validated by Support Vector Machine and correlation analyses. In 1150 cases and 1342 controls, we identified colorectal cancer risk variants in DMCSGs. The regulatory effects of gene, variant, and DNA methylation were explored through dual-luciferase and 5-azacytidine treatment experiments, complemented by multiple database analyses. Biological functions of key gene were evaluated via cell proliferation assays, SA-β-gal staining, senescence marker detection, and immune infiltration analyses. The genetic variant rs4558926 in the downstream of TACC3 was significantly associated with colorectal cancer risk (OR = 1.35, P = 3.22 × 10-4). TACC3 mRNA expression increased due to rs4558926 C > G and decreased DNA methylation levels. The CpG sites in the TACC3 promoter region were regulated by rs4558926. TACC3 knockdown decreased proliferation and senescence in colorectal cancer cells. In addition, subjects with high-TACC3 expression presented an immunosuppressive microenvironment. These findings provide insights into the involvement of genetic variants of cellular senescence genes in the development and progression of colorectal cancer.

Epigenetic biomarkers for disease susceptibility and preventative medicine

The development of molecular biomarkers for disease makes it possible for preventative medicine approaches to be considered. Therefore, therapeutics, treatments, or clinical management can be used to delay or prevent disease development. The problem with genetic mutations as biomarkers is the low frequency with genome-wide association studies (GWASs), generally at best a 1% association of the patients with the disease. In contrast, epigenetic alterations have a high-frequency association of greater than 90%-95% of individuals with pathology in epigenome-wide association studies (EWASs). A wide variety of human diseases have been shown to have epigenetic biomarkers that are disease specific and that detect pathology susceptibility. This review is focused on the epigenetic biomarkers for disease susceptibility, and it distinct from the large literature on epigenetics of disease etiology or progression. The development of efficient epigenetic biomarkers for disease susceptibility will facilitate a paradigm shift from reactionary medicine to preventative medicine.

Polymorphic variants involved in methylation regulation: a strategy to discover risk loci for pancreatic ductal adenocarcinoma

INTRODUCTION

Only a small number of risk factors for pancreatic ductal adenocarcinoma (PDAC) has been established. Several studies identified a role of epigenetics and of deregulation of DNA methylation. DNA methylation is variable across a lifetime and in different tissues; nevertheless, its levels can be regulated by genetic variants like methylation quantitative trait loci (mQTLs), which can be used as a surrogate.

MATERIALS AND METHODS

We scanned the whole genome for mQTLs and performed an association study in 14 705 PDAC cases and 246 921 controls. The methylation data were obtained from whole blood and pancreatic cancer tissue through online databases. We used the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium genome-wide association study (GWAS) data as discovery phase and the Pancreatic Disease Research consortium, the FinnGen project and the Japan Pancreatic Cancer Research consortium GWAS as replication phase.

RESULTS

The C allele of 15q26.1-rs12905855 showed an association with a decreased risk of PDAC (OR=0.90, 95% CI 0.87 to 0.94, p=4.93×10-8 in the overall meta-analysis), reaching genome-level statistical significance. 15q26.1-rs12905855 decreases the methylation of a 'C-phosphate-G' (CpG) site located in the promoter region of the RCCD1 antisense (RCCD1-AS1) gene which, when expressed, decreases the expression of the RCC1 domain-containing (RCCD1) gene (part of a histone demethylase complex). Thus, it is possible that the rs12905855 C-allele has a protective role in PDAC development through an increase of RCCD1 gene expression, made possible by the inactivity of RCCD1-AS1.

CONCLUSION

We identified a novel PDAC risk locus which modulates cancer risk by controlling gene expression through DNA methylation.

Multiple Germline Events Contribute to Cancer Development in Patients with Li-Fraumeni Syndrome

Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbor a pathogenic germline variant in the TP53 tumor suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers, approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumor detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of patients with LFS (n = 396) with variant (n = 374) or wildtype TP53 (n = 22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harbored a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the noncoding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6, and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in patients with LFS with an area under the receiver operator characteristic curve (AUROC) of 0.725 (0.633-0.810).

Significance

Our study clarifies the genomic basis for the phenotypic variability in LFS and highlights the immense benefits of expanding genetic and epigenetic testing of patients with LFS beyond TP53. More broadly, it necessitates the dissociation of hereditary cancer syndromes as single gene disorders and emphasizes the importance of understanding these diseases in a holistic manner as opposed to through the lens of a single gene.

A polygenic two-hit hypothesis for prostate cancer

Prostate cancer is one of the most heritable cancers. Hundreds of germline polymorphisms have been linked to prostate cancer diagnosis and prognosis. Polygenic risk scores can predict genetic risk of a prostate cancer diagnosis. Although these scores inform the probability of developing a tumor, it remains unknown how germline risk influences the tumor molecular evolution. We cultivated a cohort of 1250 localized European-descent patients with germline and somatic DNA profiling. Men of European descent with higher genetic risk were diagnosed earlier and had less genomic instability and fewer driver genes mutated. Higher genetic risk was associated with better outcome. These data imply a polygenic "two-hit" model where germline risk reduces the number of somatic alterations required for tumorigenesis. These findings support further clinical studies of polygenic risk scores as inexpensive and minimally invasive adjuncts to standard risk stratification. Further studies are required to interrogate generalizability to more ancestrally and clinically diverse populations.

Genetic Variants in Telomerase Reverse Transcriptase Contribute to Solar Lentigines

Solar lentigines (SLs) are a hallmark of human skin aging. They result from chronic exposure to sunlight and other environmental stressors. Recent studies also imply genetic factors, but findings are partially conflicting and lack of replication. Through a multi-trait based analysis strategy, we discovered that genetic variants in telomerase reverse transcriptase were significantly associated with non-facial SL in two East Asian (Taizhou longitudinal cohort, n = 2,964 and National Survey of Physical Traits, n = 2,954) and one Caucasian population (SALIA, n = 462), top SNP rs2853672 (P-value for Taizhou longitudinal cohort = 1.32 × 10^‒28 and P-value for National Survey of Physical Traits = 3.66 × 10^‒17 and P-value for SALIA = 0.0007 and P_meta = 4.93 × 10^‒44). The same variants were nominally associated with facial SL but not with other skin aging or skin pigmentation traits. The SL-enhanced allele/haplotype upregulated the transcription of the telomerase reverse transcriptase gene. Of note, well-known telomerase reverse transcriptase‒related aging markers such as leukocyte telomere length and intrinsic epigenetic age acceleration were not associated with SL. Our results indicate a previously unrecognized role of telomerase reverse transcriptase in skin aging‒related lentigines formation.

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N_BrCa = 228,951, N_PrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N_BrCa = 621, N_PrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P_cond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)—a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

Methylome-wide association studies identify genetically-influenced CpGs associated with breast and prostate cancer risk and (epi)genome-transcriptome mechanistic relationships, with lipid metabolism genes implicated as potential therapeutic targets.

Cell-type-specific meQTLs extend melanoma GWAS annotation beyond eQTLs and inform melanocyte gene-regulatory mechanisms

Although expression quantitative trait loci (eQTLs) have been powerful in identifying susceptibility genes from genome-wide association study (GWAS) findings, most trait-associated loci are not explained by eQTLs alone. Alternative QTLs, including DNA methylation QTLs (meQTLs), are emerging, but cell-type-specific meQTLs using cells of disease origin have been lacking. Here, we established an meQTL dataset by using primary melanocytes from 106 individuals and identified 1,497,502 significant cis-meQTLs. Multi-QTL colocalization with meQTLs, eQTLs, and mRNA splice-junction QTLs from the same individuals together with imputed methylome-wide and transcriptome-wide association studies identified candidate susceptibility genes at 63% of melanoma GWAS loci. Among the three molecular QTLs, meQTLs were the single largest contributor. To compare melanocyte meQTLs with those from malignant melanomas, we performed meQTL analysis on skin cutaneous melanomas from The Cancer Genome Atlas (n = 444). A substantial proportion of meQTL probes (45.9%) in primary melanocytes is preserved in melanomas, while a smaller fraction of eQTL genes is preserved (12.7%). Integration of melanocyte multi-QTLs and melanoma meQTLs identified candidate susceptibility genes at 72% of melanoma GWAS loci. Beyond GWAS annotation, meQTL-eQTL colocalization in melanocytes suggested that 841 unique genes potentially share a causal variant with a nearby methylation probe in melanocytes. Finally, melanocyte trans-meQTLs identified a hotspot for rs12203592, a cis-eQTL of a transcription factor, IRF4, with 131 candidate target CpGs. Motif enrichment and IRF4 ChIP-seq analysis demonstrated that these target CpGs are enriched in IRF4 binding sites, suggesting an IRF4-mediated regulatory network. Our study highlights the utility of cell-type-specific meQTLs.

Navigating the DNA methylation landscape of cancer

DNA methylation is a chemical modification that defines cell type and lineage through the control of gene expression and genome stability. Disruption of DNA methylation control mechanisms causes a variety of diseases, including cancer. Cancer cells are characterized by aberrant DNA methylation (i.e., genome-wide hypomethylation and site-specific hypermethylation), mainly targeting CpG islands in gene expression regulatory elements. In particular, the early findings that a variety of tumor suppressor genes (TSGs) are targets of DNA hypermethylation in cancer led to the proposal of a model in which aberrant DNA methylation promotes cellular oncogenesis through TSGs silencing. However, recent genome-wide analyses have revealed that this classical model needs to be reconsidered. In this review, we will discuss the molecular mechanisms of DNA methylation abnormalities in cancer as well as their therapeutic potential.

Genetic impacts on DNA methylation: research findings and future perspectives

Multiple recent studies highlight that genetic variants can have strong impacts on a significant proportion of the human DNA methylome. Methylation quantitative trait loci, or meQTLs, allow for the exploration of biological mechanisms that underlie complex human phenotypes, with potential insights for human disease onset and progression. In this review, we summarize recent milestones in characterizing the human genetic basis of DNA methylation variation over the last decade, including heritability findings and genome-wide identification of meQTLs. We also discuss challenges in this field and future areas of research geared to generate insights into molecular processes underlying human complex traits.

Epigenome-wide association study of COVID-19 severity with respiratory failure

BACKGROUND

Patients infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for the coronavirus disease 2019 (COVID-19), exhibit a wide spectrum of disease behaviour. Since DNA methylation has been implicated in the regulation of viral infections and the immune system, we performed an epigenome-wide association study (EWAS) to identify candidate loci regulated by this epigenetic mark that could be involved in the onset of COVID-19 in patients without comorbidities.

METHODS

Peripheral blood samples were obtained from 407 confirmed COVID-19 patients ≤ 61 years of age and without comorbidities, 194 (47.7%) of whom had mild symptomatology that did not involve hospitalization and 213 (52.3%) had a severe clinical course that required respiratory support. The set of cases was divided into discovery (n = 207) and validation (n = 200) cohorts, balanced for age and sex of individuals. We analysed the DNA methylation status of 850,000 CpG sites in these patients.

FINDINGS

The DNA methylation status of 44 CpG sites was associated with the clinical severity of COVID-19. Of these loci, 23 (52.3%) were located in 20 annotated coding genes. These genes, such as the inflammasome component Absent in Melanoma 2 (AIM2) and the Major Histocompatibility Complex, class I C (HLA-C) candidates, were mainly involved in the response of interferon to viral infection. We used the EWAS-identified sites to establish a DNA methylation signature (EPICOVID) that is associated with the severity of the disease.

INTERPRETATION

We identified DNA methylation sites as epigenetic susceptibility loci for respiratory failure in COVID-19 patients. These candidate biomarkers, combined with other clinical, cellular and genetic factors, could be useful in the clinical stratification and management of patients infected with the SARS-CoV-2.

FUNDING

The Unstoppable campaign of the Josep Carreras Leukaemia Foundation, the Cellex Foundation and the CERCA Programme/Generalitat de Catalunya.

DNA Methylation in Ovarian Cancer Susceptibility

Epigenetic alterations are somatically acquired over the lifetime and during neoplastic transformation but may also be inherited as widespread 'constitutional' alterations in normal tissues that can cause cancer predisposition. Epithelial ovarian cancer (EOC) has an established genetic susceptibility and mounting epidemiological evidence demonstrates that DNA methylation (DNAm) intermediates as well as independently contributes to risk. Targeted studies of known EOC susceptibility genes (CSGs) indicate rare, constitutional BRCA1 promoter methylation increases familial and sporadic EOC risk. Blood-based epigenome-wide association studies (EWAS) for EOC have detected a total of 2846 differentially methylated probes (DMPs) with 71 genes replicated across studies despite significant heterogeneity. While EWAS detect both symptomatic and etiologic DMPs, adjustments and analytic techniques may enrich risk associations, as evidenced by the detection of dysregulated methylation of BNC2-a known CSG identified by genome-wide associations studies (GWAS). Integrative genetic-epigenetic approaches have mapped methylation quantitative trait loci (meQTL) to EOC risk, revealing DNAm variations that are associated with nine GWAS loci and, further, one novel risk locus. Increasing efforts to mapping epigenome variation across populations and cell types will be key to decoding both the genomic and epigenomic causal pathways to EOC.

Transcriptome Profile of Next Generation Sequence Data Related to Inflammation on Nasopharyngeal Carcinoma Cases in Indonesia

Objective:

Transcriptomic Profile Analysis Related to Inflammation in Nasopharyngeal Carcinoma Cases.

Methods:

This study used 2 control samples taken using the brushing technique and 7 cancer samples with tissue biopsy. Isolate total RNA using Rneasy^® RNA Extraction Mini Kit. Measurement of total RNA concentration and purity using a fluorometer and nanodrop Qubit. Synthesis of cDNA library uses TruSeq® RNA Library Preparation Kit V2 and concentration is measured using qPCR. Sequencing samples using NGS Illumina NextSeq 550 platform engine. Quality control results of sequencing using FASTQC, and raw data processing using HISAT2. Differential analysis of gene expression (DEGs) using edgeR and pathway analysis using DAVID and PANTHER.

Results:

From the 25,493 genes that experienced a significant change in expression level (P <0.05) from DEG analysis there were 13 genes that play a role in the inflammatory process. Based on DAVID pathway analysis software, there are 8 genes detected based on the KEGG pathway database found in 2 pathways, namely Inflammatory Mediator Regulation of TRP Channels pathway with genes that play HTR2A, NGF, TRPA1, PRKCG, and ADCY8. CXCL9, CXCL10, and CXCL11 genes are found in the Toll-Like Receptor Signaling pathway. Based on PANTHER pathway analysis software, 6 genes were found, namely CXCL10, MYLK2, COL20A1, MYH2, ACTC1, and ALOX15 in the Inflammation Mediated by Chemokine and Cytokine Signaling pathways. Almost all genes found from DEGs are upregulated, except the ALOX15 gene that is downregulated.

Conclusion:

There are 13 genes that play a role in the inflammatory process in Nasopharyngeal Carcinomafrom a sample of the Indonesian population. Genes CXCL9, CXCL10, CXCL11, MYLK2, COL20A1, MYH2, ACTC1, HTR2A, NGF, TRPA1, PRKCG, and ADCY8 have been upregulated and ALOX15 has been downregulated. These genes play a role in the Inflammation Mediated by Chemokine and Cytokine Signaling pathways, Inflammatory Mediator Regulation of TRP Channels, and Toll-Like Receptor Signaling.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Genome-wide germline correlates of the epigenetic landscape of prostate cancer

Oncogenesis is driven by germline, environmental and stochastic factors. It is unknown how these interact to produce the molecular phenotypes of tumors. We therefore quantified the influence of germline polymorphisms on the somatic epigenome of 589 localized prostate tumors. Predisposition risk loci influence a tumor's epigenome, uncovering a mechanism for cancer susceptibility. We identified and validated 1,178 loci associated with altered methylation in tumoral but not nonmalignant tissue. These tumor methylation quantitative trait loci influence chromatin structure, as well as RNA and protein abundance. One prominent tumor methylation quantitative trait locus is associated with AKT1 expression and is predictive of relapse after definitive local therapy in both discovery and validation cohorts. These data reveal intricate crosstalk between the germ line and the epigenome of primary tumors, which may help identify germline biomarkers of aggressive disease to aid patient triage and optimize the use of more invasive or expensive diagnostic assays.

Integration of miRNA and mRNA Co-Expression Reveals Potential Regulatory Roles of miRNAs in Developmental and Immunological Processes in Calf Ileum during Early Growth

This study aimed to investigate the potential regulatory roles of miRNAs in calf ileum developmental transition from the pre- to the post-weaning period. For this purpose, ileum tissues were collected from eight calves at the pre-weaning period and another eight calves at the post-weaning period and miRNA expression characterized by miRNA sequencing, followed by functional analyses. A total of 388 miRNAs, including 81 novel miRNAs, were identified. A total of 220 miRNAs were differentially expressed (DE) between the two periods. The potential functions of DE miRNAs in ileum development were supported by significant enrichment of their target genes in gene ontology terms related to metabolic processes and transcription factor activities or pathways related to metabolism (peroxisomes), vitamin digestion and absorption, lipid and protein metabolism, as well as intracellular signaling. Integration of DE miRNAs and DE mRNAs revealed several DE miRNA-mRNA pairs with crucial roles in ileum development (bta-miR-374a-FBXO18, bta-miR-374a-GTPBP3, bta-miR-374a-GNB2) and immune function (bta-miR-15b-IKBKB). This is the first integrated miRNA-mRNA analysis exploring the potential roles of miRNAs in calf ileum growth and development during early life.

Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data

Human tumors undergo massive changes in DNA methylation. Recent studies showed that site-specific methylation of CpG sites is determined by the DNA sequence context surrounding the CpG site, which alludes to a possible mechanism for site-specific aberrant DNA methylation in cancer through DNA-binding proteins. In this paper, DNA methylation data and RNA-Seq data of breast tumors and normal tissues in the database of The Cancer Genome Atlas (TCGA) were integrated with information of DNA motifs in seven databases to find DNA-binding proteins and their binding motifs that were involved in aberrant DNA methylation in breast cancer. A total of 42,850 differentially methylated regions (DMRs) that include 77,298 CpG sites were detected in breast cancer. One hundred eight DNA motifs were found to be enriched in DMRs, and 109 genes encoding proteins binding to these motifs were determined. Based on these motifs and genes, 63 methylation modulator genes were identified to regulate differentially methylated CpG sites in breast cancer. A network of these 63 modulator genes and 645 transcription factors was constructed, and 20 network modules were determined. A number of pathways and gene sets related to breast cancer were found to be enriched in these network modules. The 63 methylation modulator genes identified may play an important role in aberrant methylation of CpG sites in breast cancer. They may help to understand site-specific dysregulation of DNA methylation and provide epigenetic markers for breast cancer.

Early detection of gastric cancer using global, genome-wide and IRF4, ELMO1, CLIP4 and MSC DNA methylation in endoscopic biopsies

Clinically useful molecular tools to triage gastric cancer patients are not currently available. We aimed to develop a molecular tool to predict gastric cancer risk in endoscopy-driven biopsies obtained from high-risk gastric cancer clinics in low resource settings.

We discovered and validated a DNA methylation biomarker panel in endoscopic samples obtained from 362 patients seen between 2004 and 2009 in three high-risk gastric cancer clinics in Lima, Perú, and validated it in 306 samples from the Cancer Genome Atlas project (“TCGA”). Global, epigenome wide and gene-specific DNA methylation analyses were used in a Phase I Biomarker Development Trial to identify a continuous biomarker panel that combines a Global DNA Methylation Index (GDMI) and promoter DNA methylation levels of IRF4, ELMO1, CLIP4 and MSC.

We observed an inverse association between the GDMI and histological progression to gastric cancer, when comparing gastritis patients without metaplasia (mean = 5.74, 95% CI, 4.97−6.50), gastritis patients with metaplasia (mean = 4.81, 95% CI, 3.77−5.84), and gastric cancer cases (mean = 3.38, 95% CI, 2.82−3.94), respectively (p < 0.0001). Promoter methylation of IRF4 (p < 0.0001), ELMO1 (p < 0.0001), CLIP4 (p < 0.0001), and MSC (p < 0.0001), is also associated with increasing severity from gastritis with no metaplasia to gastritis with metaplasia and gastric cancer.

Our findings suggest that IRF4, ELMO1, CLIP4 and MSC promoter methylation coupled with a GDMI>4 are useful molecular tools for gastric cancer risk stratification in endoscopic biopsies.

Epigenome-based cancer risk prediction: rationale, opportunities and challenges

The incidence of cancer is continuing to rise and risk-tailored early diagnostic and/or primary prevention strategies are urgently required. The ideal risk-predictive test should: integrate the effects of both genetic and nongenetic factors and aim to capture these effects using an approach that is both biologically stable and technically reproducible; derive a score from easily accessible biological samples that acts as a surrogate for the organ in question; and enable the effectiveness of risk-reducing measures to be monitored. Substantial evidence has accumulated suggesting that the epigenome and, in particular, DNA methylation-based tests meet all of these requirements. However, the development and implementation of DNA methylation-based risk-prediction tests poses considerable challenges. In particular, the cell type specificity of DNA methylation and the extensive cellular heterogeneity of the easily accessible surrogate cells that might contain information relevant to less accessible tissues necessitates the use of novel methods in order to account for these confounding issues. Furthermore, the engagement of the scientific community with health-care professionals, policymakers and the public is required in order to identify and address the organizational, ethical, legal, social and economic challenges associated with the routine use of epigenetic testing.

Meta-analysis of DNA methylation biomarkers in hepatocellular carcinoma

DNA methylation is an epigenetic mechanism in the pathogenesis of hepatocellular carcinoma (HCC). Here, we conducted a systematic meta-analysis to evaluate the contribution of DNA methylation to the risk of HCC. A total of 2109 publications were initially retrieved from PubMed, Web of Science, Cochrane Library, Embase, CNKI and Wanfang literature database. After a four-step filtration, we harvested 144 case-control articles in the meta-analysis. Our results revealed that 24 genes (carcinoma tissues vs adjacent tissues), 17 genes (carcinoma tissues vs normal tissues) and six genes (carcinoma serums vs normal serums) were significantly hypermethylated in HCC. Subgroup meta-analysis by geographical populations showed that six genes (carcinoma tissues vs adjacent tissues) and four genes (carcinoma tissues vs normal tissues) were significantly hypermethylated in HCC. Our meta-analysis identified the correlations between a number of aberrant methylated genes (p16, RASSF1A, GSTP1, p14, CDH1, APC, RUNX3, SOCS1, p15, MGMT, SFRP1, WIF1, PRDM2, DAPK1, RARβ, hMLH1, p73, DLC1, p53, SPINT2, OPCML and WT1) and HCC. Aberrant DNA methylation might become useful biomarkers for the prediction and diagnosis of HCC.

Heavy metals effect on breast cancer progression

Background

Breast cancer is the most frequent localization of malignant process in American women and women of European countries. To date it is not possible to control the morbidity growth due to lack of effective ways of primary prevention. Comparing the incidence of breast cancer in developed countries with the countries of Asia and Africa, there is the fact of population predominance lesion in more urbanized countries. This suggests that the environment along with other factors, occupies a significant place in the initiation and progression of breast neoplasia. The impressive rates of industrial development led to the pollution of soil, surface water and, as a consequence, food by heavy metal salts.

The purposes of this paper are as follows: the chemical composition determination of neoplastic breast tissue, evaluation of the DNA methylation level, study of prognostic-important receptors expression in the breast cancer cells, establishing linkages between all the derived indicators.

Methods

In our study we used the following methods: studying of the chemical composition of breast cancer tissue by atomic absorption spectrophotometry and energy-dispersion spectrometer; іmmunohistochemical study of ER, PR, HER2/neu, p53, Ki-67, E-cadherin and MGMT receptors; DNA extraction and investigation by oscillating infrared spectroscopy method.

Results

The total amount of heavy metals in breast cancer tissue ranged from 51.21 × 10⁻³ to 84.86 × 10⁻³ μg/kg. We have got the following results: the growth of heavy metals in neoplastic tissue is accompanied with the increase of HER2/neu, p53, Ki-67, MGMT expression and decrease of ER and PR expression. The increment of pathological DNA methylation is accompanied with the increasing amount of heavy metals in tumor tissue.

Conclusions

Heavy metals through different pathogenetic links stimulate the progression of breast cancer and reduce its sensitivity to treatment. DNA of tumor tissue has a different level of methylation which changes with the amount of heavy metals in cancer cells. This is displayed on the synthesis of prognostically important receptors in neoplastic tissue.

pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms

Background

Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different “omics” layers. Existing tools only consider single-nucleotide polymorphism (SNP)–SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables.

Results

We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different “omics” layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels.

Conclusions

The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1838-y) contains supplementary material, which is available to authorized users.

Aberrant DNA Methylation in Colorectal Cancer: What Should We Target?

Colorectal cancers (CRCs) are characterized by global hypomethylation and promoter-specific DNA methylation. A subset of CRCs with extensive and co-ordinate patterns of promoter methylation has also been identified, termed the CpG-island methylator phenotype. Some genes methylated in CRC are established tumor suppressors; however, for the majority, direct roles in disease initiation or progression have not been established. Herein, we examine functional evidence of specific methylated genes contributing to CRC pathogenesis, focusing on components of commonly deregulated signaling pathways. We also review current knowledge of the mechanisms underpinning promoter methylation in CRC, including genetic events, altered transcription factor binding, and DNA damage. Finally, we summarize clinical trials of DNA methyltransferase inhibitors in CRC, and propose strategies for enhancing their efficacy.

Linking the Epigenome with Exposure Effects and Susceptibility: The Epigenetic Seed and Soil Model

The epigenome is a dynamic mediator of gene expression that shapes the way that cells, tissues, and organisms respond to their environment. Initial studies in the emerging field of "toxicoepigenetics" have described either the impact of an environmental exposure on the epigenome or the association of epigenetic signatures with the onset or progression of disease; however, the majority of these pioneering studies examined the relationship between discrete epigenetic modifications and the effects of a single environmental factor. Although these data provide critical blocks with which we construct our understanding of the role of the epigenome in susceptibility and disease, they are akin to individual letters in a complex alphabet that is used to compose the language of the epigenome. Advancing the use of epigenetic data to gain a more comprehensive understanding of the mechanisms underlying exposure effects, identify susceptible populations, and inform the next generation risk assessment depends on our ability to integrate these data in a way that accounts for their cumulative impact on gene regulation. Here we will review current examples demonstrating associations between the epigenetic impacts of intrinsic factors, such as such as age, genetics, and sex, and environmental exposures shape the epigenome and susceptibility to exposure effects and disease. We will also demonstrate how the "epigenetic seed and soil" model can be used as a conceptual framework to explain how epigenetic states are shaped by the cumulative impacts of intrinsic and extrinsic factors and how these in turn determine how an individual responds to subsequent exposure to environmental stressors.

Identification of Genetic and Epigenetic Variants Associated with Breast Cancer Prognosis by Integrative Bioinformatics Analysis

INTRODUCTION

Breast cancer being a multifaceted disease constitutes a wide spectrum of histological and molecular variability in tumors. However, the task for the identification of these variances is complicated by the interplay between inherited genetic and epigenetic aberrations. Therefore, this study provides an extrapolate outlook to the sinister partnership between DNA methylation and single-nucleotide polymorphisms (SNPs) in relevance to the identification of prognostic markers in breast cancer. The effect of these SNPs on methylation is defined as methylation quantitative trait loci (meQTL).

MATERIALS AND METHODS

We developed a novel method to identify prognostic gene signatures for breast cancer by integrating genomic and epigenomic data. This is based on the hypothesis that multiple sources of evidence pointing to the same gene or pathway are likely to lead to reduced false positives. We also apply random resampling to reduce overfitting noise by dividing samples into training and testing data sets. Specifically, the common samples between Illumina 450 DNA methylation, Affymetrix SNP array, and clinical data sets obtained from the Cancer Genome Atlas (TCGA) for breast invasive carcinoma (BRCA) were randomly divided into training and test models. An intensive statistical analysis based on log-rank test and Cox proportional hazard model has established a significant association between differential methylation and the stratification of breast cancer patients into high- and low-risk groups, respectively.

RESULTS

The comprehensive assessment based on the conjoint effect of CpG–SNP pair has guided in delaminating the breast cancer patients into the high- and low-risk groups. In particular, the most significant association was found with respect to cg05370838–rs2230576, cg00956490–rs940453, and cg11340537–rs2640785 CpG–SNP pairs. These CpG–SNP pairs were strongly associated with differential expression of ADAM8, CREB5, and EXPH5 genes, respectively. Besides, the exclusive effect of SNPs such as rs10101376, rs140679, and rs1538146 also hold significant prognostic determinant.

CONCLUSIONS

Thus, the analysis based on DNA methylation and SNPs have resulted in the identification of novel susceptible loci that hold prognostic relevance in breast cancer.

The impact of methylation quantitative trait loci (mQTLs) on active smoking-related DNA methylation changes

BACKGROUND

Methylation quantitative trait loci (mQTLs) are the genetic variants that may affect the DNA methylation patterns of CpG sites. However, their roles in influencing the disturbances of smoking-related epigenetic changes have not been well established. This study was conducted to address whether mQTLs exist in the vicinity of smoking-related CpG sites (± 50 kb) and to examine their associations with smoking exposure and all-cause mortality in older adults.

RESULTS

We obtained DNA methylation profiles in whole blood samples by Illumina Infinium Human Methylation 450 BeadChip array of two independent subsamples of the ESTHER study (discovery set, n = 581; validation set, n = 368) and their corresponding genotyping data using the Illumina Infinium OncoArray BeadChip. After correction for multiple testing (FDR), we successfully identified that 70 out of 151 previously reported smoking-related CpG sites were significantly associated with 192 SNPs within the 50 kb search window of each locus. The 192 mQTLs significantly influenced the active smoking-related DNA methylation changes, with percentage changes ranging from 0.01 to 18.96%, especially for the weakly/moderately smoking-related CpG sites. However, these identified mQTLs were not directly associated with active smoking exposure or all-cause mortality.

CONCLUSIONS

Our findings clearly demonstrated that if not dealt with properly, the mQTLs might impair the power of epigenetic-based models of smoking exposure to a certain extent. In addition, such genetic variants could be the key factor to distinguish between the heritable and smoking-induced impact on epigenome disparities. These mQTLs are of special importance when DNA methylation markers measured by Illumina Infinium assay are used for any comparative population studies related to smoking-related cancers and chronic diseases.

Correlation between the germline methylation status in ERβ promoter and the risk in prostate cancer: a prospective study

Familial aggregation of cancer may reflect an overall contribution of inherited genes or a shared mechanism for the manipulation of gene function. DNA methylation in the promoter regions is considered to be a mechanism through which tumor suppressor genes are inhibited, which will lead to tumorigenesis and tumor progression. To evaluate the association between the methylation status in the promoter of estrogen receptor (ER) β,possibly a tumor suppressor gene specific for prostate cancer, and the risk in prostate cancer in a Chinese population, a case-control study that included 56 sporadic prostate cancer cases and 60 healthy controls was conducted. Genomic DNA was extracted from peripheral blood of all the subjects for analyzing the methylation status of the ERβ promoter by methylation-specific PCR, which was verified by bisulfite genomic sequencing PCR. A significant difference was observed in the methylation frequencies of the ERβ promoter between cancer patients (12/56, 21.4%) and healthy controls (5/60, 8.3%). Prostate cancer (PC-3 and DU-145) and prostatic epithelial (RWPE-1) cell lines were treated with various concentrations of the methyltransferase inhibitor 5-Aza-2'-dC. Expression of ERβ was detected at both transcriptional and translational levels. As a result, both mRNA and protein of ERβ were elevated following treatment with increasing concentrations of the demethylating agent. Taken together, our results support the conclusion that abnormal methylation of the ERβ promoter may increase genetic susceptibility to prostate cancer.

Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

The incidence of type 1 diabetes (T1D) has substantially increased over the past decade, suggesting a role for non-genetic factors such as epigenetic mechanisms in disease development. Here we present an epigenome-wide association study across 406,365 CpGs in 52 monozygotic twin pairs discordant for T1D in three immune effector cell types. We observe a substantial enrichment of differentially variable CpG positions (DVPs) in T1D twins when compared with their healthy co-twins and when compared with healthy, unrelated individuals. These T1D-associated DVPs are found to be temporally stable and enriched at gene regulatory elements. Integration with cell type-specific gene regulatory circuits highlight pathways involved in immune cell metabolism and the cell cycle, including mTOR signalling. Evidence from cord blood of newborns who progress to overt T1D suggests that the DVPs likely emerge after birth. Our findings, based on 772 methylomes, implicate epigenetic changes that could contribute to disease pathogenesis in T1D.

A childhood acute lymphoblastic leukemia genome-wide association study identifies novel sex-specific risk variants

Childhood acute lymphoblastic leukemia (ALL) occurs more frequently in males. Reasons behind sex differences in childhood ALL risk are unknown. In the present genome-wide association study (GWAS), we explored the genetic basis of sex differences by comparing genotype frequencies between male and female cases in a case-only study to assess effect-modification by sex.The case-only design included 236 incident cases of childhood ALL consecutively recruited at the Texas Children's Cancer Center in Houston, Texas from 2007 to 2012. All cases were non-Hispanic whites, aged 1 to 10 years, and diagnosed with confirmed B-cell precursor ALL. Genotyping was performed using the Illumina HumanCoreExome BeadChip on the Illumina Infinium platform. Besides the top 100 statistically most significant results, results were also analyzed by the top 100 highest effect size with a nominal statistical significance (P <0.05).The statistically most significant sex-specific association (P = 4 × 10) was with the single nucleotide polymorphism (SNP) rs4813720 (RASSF2), an expression quantitative trait locus (eQTL) for RASSF2 in peripheral blood. rs4813720 is also a strong methylation QTL (meQTL) for a CpG site (cg22485289) within RASSF2 in pregnancy, at birth, childhood, and adolescence. cg22485289 is one of the hypomethylated CpG sites in ALL compared with pre-B cells. Two missense SNPs, rs12722042 and 12722039, in the HLA-DQA1 gene yielded the highest effect sizes (odds ratio [OR] ∼ 14; P <0.01) for sex-specific results. The HLA-DQA1 SNPs belong to DQA1*01 and confirmed the previously reported male-specific association with DQA1*01. This finding supports the proposed infection-related etiology in childhood ALL risk for males. Further analyses revealed that most SNPs (either direct effect or through linkage disequilibrium) were within active enhancers or active promoter regions and had regulatory effects on gene expression levels.Cumulative data suggested that RASSF2 rs4813720, which correlates with increased RASSF2 expression, may counteract the suppressor effect of estrogen-regulated miR-17-92 on RASSF2 resulting in protection in males. Given the amount of sex hormone-related mechanisms suggested by our findings, future studies should examine prenatal or early postnatal programming by sex hormones when hormone levels show a large variation.

Relation between Established Glioma Risk Variants and DNA Methylation in the Tumor

Genome-wide association studies and candidate gene studies have identified several genetic variants that increase glioma risk. The majority of these variants are non-coding and the mechanisms behind the increased risk in carriers are not known. In this study, we hypothesize that some of the established glioma risk variants induce aberrant DNA methylation in the developing tumor, either locally (gene-specific) or globally (genome-wide). In a pilot data set including 77 glioma patients, we used Illumina beadchip technology to analyze genetic variants in blood and DNA methylation in matched tumor samples. To validate our findings, we used data from the Cancer Genome Atlas, including 401 glioblastoma patients. Consensus clustering identified the glioma CpG island methylator phenotype (gCIMP) and two additional subgroups with distinct patterns of global DNA methylation. In the pilot dataset, gCIMP was associated with two genetic variants in CDKN2B-AS1, rs1412829 and rs4977756 (9p21.3, p = 8.1 x 10-7 and 4.8 x 10-5, respectively). The association was in the same direction in the TCGA dataset, although statistically significant only when combining individuals with AG and GG genotypes. We also investigated the relation between glioma risk variants and DNA methylation in the promoter region of genes located within 30 kb of each variant. One association in the pilot dataset, between the TERT risk variant rs2736100 and lower methylation of cg23827991 (in TERT; p = 0.001), was confirmed in the TCGA dataset (p = 0.001). In conclusion, we found an association between rs1412829 and rs4977756 (9p21.3, CDKN2B-AS1) and global DNA methylation pattern in glioma, for which a trend was seen also in the TCGA glioblastoma dataset. We also found an association between rs2736100 (in TERT) and levels of methylation at cg23827991 (localized in the same gene, 3.3 kbp downstream of the risk variant), which was validated in the TCGA dataset. Except for this one association, we did not find strong evidence for gene-specific DNA methylation mediated by glioma risk variants.

Interplay of genetic and epigenetic alterations in hepatocellular carcinoma

Genetic and epigenetic alterations play prominent roles in hepatocarcinogenesis and their appearance varies depending on etiological factors, race and tumor progression. Intriguingly, distinct patterns of these genetic and epigenetic mutations are coupled not only to affect each other, but to trigger different types of tumorigenesis. The patterns and frequencies of somatic variations vary depending on the nature of the surrounding chromatin. On the other hand, epigenetic alterations often induce genomic instability prone to mutation. Therefore, genetic mutations and epigenetic alterations in hepatocellular carcinoma appear to be inseparable factors that accelerate tumorigenesis synergistically. We have summarized recent findings on genetic and epigenetic modifications, their influences on each other's alterations and putative roles in liver tumorigenesis.

Role of ATG10 expression quantitative trait loci in non-small cell lung cancer survival

The aim of this article was to evaluate whether genetic variants in autophagy-related genes affect the overall survival (OS) of non-small cell lung cancer (NSCLC) patients. We analyzed 14 single nucleotide polymorphisms (SNPs) in core autophagy-related genes for OS in 1,001 NSCLC patients. Three promising SNPs in ATG10 were subsequently annotated by the expression quantitative trait loci (eQTL) and methylation quantitative trait loci (meQTL) analyses based on Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) datasets. We observed that the variants of rs10514231, rs1864182 and rs1864183 were associated with poor lung cancer survival (HR = 1.33, 95% CI = 1.07-1.65; HR = 1.43, 95% CI = 1.13-1.81; HR = 1.38, 95% CI = 1.14-1.68, respectively) and positively correlated with ATG10 expression (all p < 0.05) from GTEx and TCGA datasets. The elevated expression of ATG10 may predict shorter survival time in lung cancer patients in TCGA dataset (HR = 2.10, 95% CI = 1.33-3.29). Moreover, the variants of rs10514231 and rs1864182 were associated with the increased methylation levels of cg17942617 (meQTL), which in turn contributed to the elevated ATG10 expression and decreased survival time. Further functional assays revealed that ATG10 facilitated lung cancer cell proliferation and migration. Our findings suggest that eQTL/meQTL variations of ATG10 could influence lung cancer survival through regulating ATG10 expression.

SNP rs16906252C>T Is an Expression and Methylation Quantitative Trait Locus Associated with an Increased Risk of Developing MGMT-Methylated Colorectal Cancer

PURPOSE

Methylation of the MGMT promoter is the major cause of O⁶-methylguanine methyltransferase deficiency in cancer and has been associated with the T variant of the promoter enhancer SNP rs16906252C>T. We sought evidence for an association between the rs16906252C>T genotype and increased risk of developing a subtype of colorectal cancer featuring MGMT methylation, mediated by genotype-dependent epigenetic silencing within normal tissues.

EXPERIMENTAL DESIGN

By applying a molecular pathologic epidemiology case-control study design, associations between rs16906252C>T and risk for colorectal cancer overall, and colorectal cancer stratified by MGMT methylation status, were estimated using multinomial logistic regression in two independent retrospective series of colorectal cancer cases and controls. The test sample comprised 1,054 colorectal cancer cases and 451 controls from Sydney, Australia. The validation sample comprised 612 colorectal cancer cases and 245 controls from the Australasian Colon Cancer Family Registry (ACCFR). To determine whether rs16906252C>T was linked to a constitutively altered epigenetic state, quantitative allelic expression and methylation analyses were performed in normal tissues.

RESULTS

An association between rs16906252C>T and increased risk of developing MGMT-methylated colorectal cancer in the Sydney sample was observed [OR, 3.3; 95% confidence interval (CI), 2.0-5.3; P < 0.0001], which was replicated in the ACCFR sample (OR, 4.0; 95% CI, 2.4-6.8; P < 0.0001). The T allele demonstrated about 2.5-fold reduced transcription in normal colorectal mucosa from cases and controls and was selectively methylated in a minority of normal cells, indicating that rs16906252C>T represents an expression and methylation quantitative trait locus.

CONCLUSIONS

We provide evidence that rs16906252C>T is associated with elevated risk for MGMT-methylated colorectal cancer, likely mediated by constitutive epigenetic repression of the T allele. Clin Cancer Res; 22(24); 6266-77. ©2016 AACR.

A study of the role of Notch1 and JAG1 gene methylation in development of breast cancer

This study is to explore the roles of gene methylation of Notch1 and JAG1 in development of invasive ductal carcinoma of breast. Quantitative analysis the DNA methylation levels of Notch1 and JAG1 gene by the MassARRAY method in invasive ductal carcinoma of breast (IDC; n = 89), atypical ductal hyperplasia of breast (ADH; n = 11), and ordinary ductal hyperplasia of breast (UDH; n = 20). The expressions of JAG1 and Notch1 protein in four breast tissues were detected by immunohistochemistry SP method. (1) Positive expression rates of Notch1 protein in IDC and DCIS were 88.7 % (79/89) and 70.0 % (14/20), respectively, which were significantly higher than the levels in ADH (36.0 %, 4/11) and UDH (25.0 %, 5/20; P < 0.05). Notch1 protein expression was significant positively correlated with lymph node metastasis, pathological grades, and TNM stages of IDC. (2) Positive expression rates of JAG1 protein in IDC and DCIS were 89.9 % (80/89) and 75.0 % (15/20), respectively, which were significantly higher than those of ADH (45.0 %, 5/11) and UDH (30.0 %, 6/20; P < 0.05). JAG1 protein expression was significant positive correlation with lymph node metastasis, pathological grades and TNM stages of IDC. There is an overall hypomethylation alteration of Notch1 and JAG gene in IDC, with corresponding over-expression of Notch1 and JAG1 protein. This inverse correlation shows that the alteration of protein expression results from hypomethylation oncogene Notch1 and JAG1, and this change may play an important role in occurrence and progression of breast cancer.

Genetic variation in one-carbon metabolism in relation to genome-wide DNA methylation in breast tissue from heathy women

Single nucleotide polymorphisms (SNPs) in one-carbon metabolism genes and lifestyle factors (alcohol drinking and breast folate) may be determinants of whole-genome methylation in the breast. DNA methylation profiling was performed using the Illumina Infinium HumanMethylation450 BeadChip in 81 normal breast tissues from women undergoing reduction mammoplasty and no history of cancer. ANCOVA, adjusting for age, race and BMI, was used to identify differentially-methylated (DM) CpGs. Gene expression, by the Affymetrix GeneChip Human Transcriptome Array 2.0, was correlated with DM. Biological networks of DM genes were assigned using Ingenuity Pathway Analysis. Fifty-seven CpG sites were DM in association with eight SNPs in FTHFD, MTHFD1, MTHFR, MTR, MTRR, and TYMS (P <5.0 x 10^-5); 56% of the DM CpGs were associated with FTHFD SNPs, including DM within FTHFD. Gene expression was negatively correlated with FTHFD methylation (r=-0.25, P=0.017). Four DM CpGs identified by SNPs in MTRR, MTHFR, and FTHFD were significantly associated with alcohol consumption and/or breast folate. The top biological network of DM CpGs was associated with Energy Production, Molecular Transportation, and Nucleic Acid Metabolism. This is the first comprehensive study of the association between SNPs in one-carbon metabolism genes and genome-wide DNA methylation in normal breast tissues. These SNPs, especially FTHFD, as well as alcohol intake and folate exposure, appear to affect DM in breast tissues of healthy women. The finding that SNPs in FTHFD and MTR are associated with their own methylation is novel and highlights a role for these SNPs as cis-methylation quantitative trait loci.

Quantitative Trait Loci Identify Functional Noncoding Variation in Cancer

The interpretation of noncoding alterations in cancer genomes presents an unresolved problem in cancer studies. While the impact of somatic variations in protein-coding regions is widely accepted, noncoding aberrations are mostly considered as passenger events. However, with the advance of genome-wide profiling strategies, alterations outside the coding context entered the focus, and multiple examples highlight the role of gene deregulation as cancer-driving events. This review describes the implication of noncoding alterations in oncogenesis and provides a theoretical framework for the identification of causal somatic variants using quantitative trait loci (QTL) analysis. Assuming that functional noncoding alterations affect quantifiable regulatory processes, somatic QTL studies constitute a valuable strategy to pinpoint cancer gene deregulation. Eventually, the comprehensive identification and interpretation of coding and noncoding alterations will guide our future understanding of cancer biology.

The heritability and patterns of DNA methylation in normal human colorectum

DNA methylation (DNAm) has been linked to changes in chromatin structure, gene expression and disease. The DNAm level can be affected by genetic variation; although, how this differs by CpG dinucleotide density and genic location of the DNAm site is not well understood. Moreover, the effect of disease causing variants on the DNAm level in a tissue relevant to disease has yet to be fully elucidated. To this end, we investigated the phenotypic profiles, genetic effects and regional genomic heritability for 196080 DNAm sites in healthy colorectum tissue from 132 unrelated Colombian individuals. DNAm sites in regions of low-CpG density were more variable, on average more methylated and were more likely to be significantly heritable when compared with DNAm sites in regions of high-CpG density. DNAm sites located in intergenic regions had a higher mean DNAm level and were more likely to be heritable when compared with DNAm sites in the transcription start site (TSS) of a gene expressed in colon tissue. Within CpG-dense regions, the propensity of the DNAm level to be heritable was lower in the TSS of genes expressed in colon tissue than in the TSS of genes not expressed in colon tissue. In addition, regional genetic variation was associated with variation in local DNAm level no more frequently for DNAm sites within colorectal cancer risk regions than it was for DNAm sites outside such regions. Overall, DNAm sites located in different genomic contexts exhibited distinguishable profiles and may have a different biological function.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer's disease

BACKGROUND

Alzheimer's disease affects ~13% of people in the United States 65 years and older, making it the most common neurodegenerative disorder. Recent work has identified roles for environmental, genetic, and epigenetic factors in Alzheimer's disease risk.

METHODS

We performed a genome-wide screen of DNA methylation using the Illumina Infinium HumanMethylation450 platform on bulk tissue samples from the superior temporal gyrus of patients with Alzheimer's disease and non-demented controls. We paired a sliding window approach with multivariate linear regression to characterize Alzheimer's disease-associated differentially methylated regions (DMRs).

RESULTS

We identified 479 DMRs exhibiting a strong bias for hypermethylated changes, a subset of which were independently associated with aging. DMR intervals overlapped 475 RefSeq genes enriched for gene ontology categories with relevant roles in neuron function and development, as well as cellular metabolism, and included genes reported in Alzheimer's disease genome-wide and epigenome-wide association studies. DMRs were enriched for brain-specific histone signatures and for binding motifs of transcription factors with roles in the brain and Alzheimer's disease pathology. Notably, hypermethylated DMRs preferentially overlapped poised promoter regions, marked by H3K27me3 and H3K4me3, previously shown to co-localize with aging-associated hypermethylation. Finally, the integration of DMR-associated single nucleotide polymorphisms with Alzheimer's disease genome-wide association study risk loci and brain expression quantitative trait loci highlights multiple potential DMRs of interest for further functional analysis.

CONCLUSION

We have characterized changes in DNA methylation in the superior temporal gyrus of patients with Alzheimer's disease, highlighting novel loci that facilitate better characterization of pathways and mechanisms underlying Alzheimer's disease pathogenesis, and improve our understanding of epigenetic signatures that may contribute to the development of disease.

Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome

Considerable progress towards an understanding of complex diseases has been made in recent years due to the development of high-throughput genotyping technologies. Using microarrays that contain millions of single-nucleotide polymorphisms (SNPs), Genome Wide Association Studies (GWASs) have identified SNPs that are associated with many complex diseases or traits. For example, as of February 2015, 2111 association studies have identified 15,396 SNPs for various diseases and traits, with the number of identified SNP-disease/trait associations increasing rapidly in recent years. However, it has been difficult for researchers to understand disease risk from GWAS results. This is because most GWAS-identified SNPs are located in non-coding regions of the genome. It is important to consider that the GWAS-identified SNPs serve only as representatives for all SNPs in the same haplotype block, and it is equally likely that other SNPs in high linkage disequilibrium (LD) with the array-identified SNPs are causal for the disease. Because it was hoped that disease-associated coding variants would be identified if the true casual SNPs were known, investigators have expanded their analyses using LD calculation and fine-mapping. However, such analyses also identified risk-associated SNPs located in non-coding regions. Thus, the GWAS field has been left with the conundrum as to how a single-nucleotide change in a non-coding region could confer increased risk for a specific disease. One possible answer to this puzzle is that the variant SNPs cause changes in gene expression levels rather than causing changes in protein function. This review provides a description of (1) advances in genomic and epigenomic approaches that incorporate functional annotation of regulatory elements to prioritize the disease risk-associated SNPs that are located in non-coding regions of the genome for follow-up studies, (2) various computational tools that aid in identifying gene expression changes caused by the non-coding disease-associated SNPs, and (3) experimental approaches to identify target genes of, and study the biological phenotypes conferred by, non-coding disease-associated SNPs.

Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences

Aim:

DNA methylation is the best known epigenetic mark. Cancer and other pathologies show an altered DNA methylome. However, delivering complete DNA methylation maps is compromised by the price and labor-intensive interpretation of single nucleotide methods.

Material & methods:

Following the success of the HumanMethylation450 BeadChip (Infinium) methylation microarray (450K), we report the technical and biological validation of the newly developed MethylationEPIC BeadChip (Infinium) microarray that covers over 850,000 CpG methylation sites (850K). The 850K microarray contains >90% of the 450K sites, but adds 333,265 CpGs located in enhancer regions identified by the ENCODE and FANTOM5 projects.

Results & conclusion:

The 850K array demonstrates high reproducibility at the 450K CpG sites, is consistent among technical replicates, is reliable in the matched study of fresh frozen versus formalin-fixed paraffin-embeded samples and is also useful for 5-hydroxymethylcytosine. These results highlight the value of the MethylationEPIC BeadChip as a useful tool for the analysis of the DNA methylation profile of the human genome.

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.

At present, it is already possible to generate different type of omics–high throughput–data in the same individuals. However, we lack methodology to adequately combine them. Many challenges arise while the amount of data increases and we need to find the way to identify and understand the complex relationships when integrating data. In this regard, new statistical approaches are needed, such as the ones we propose and apply here to integrate three types of omics data (genomics, epigenomics, and transcriptomics) generated using bladder cancer tumor samples. These innovative approaches (LASSO and ENET combined with a permutation-based MaxT method) allowed us to find 48 genes whose expression levels were significantly associated with genomics and epigenomics markers. The adequacy of this approach was confirmed by the use of an independent data set from The Cancer Genome Atlas Consortium: 75% of the genes were replicated. Previous sound biological evidences further support the results obtained.

The role of epigenetics in genetic and environmental epidemiology

Epidemiology is the branch of science that investigates the causes and distribution of disease in populations in order to provide preventative measures and promote human health. The fields of genetic and environmental epidemiology primarily seek to identify genetic and environmental risk factors for disease, respectively. Epigenetics is emerging as an important piece of molecular data to include in these studies because it can provide mechanistic insights into genetic and environmental risk factors for disease, identify potential intervention targets, provide biomarkers of exposure, illuminate gene-environment interactions and help localize disease-relevant genomic regions. Here, we describe the importance of including epigenetics in genetic and environmental epidemiology studies, provide a conceptual framework when considering epigenetic data in population-based studies and touch upon the many challenges that lie ahead.

Methylation quantitative trait locus analysis of osteoarthritis links epigenetics with genetic risk

Osteoarthritis (OA) is a common, painful and debilitating disease of articulating joints resulting from the age-associated loss of cartilage. Well-powered genetic studies have identified a number of DNA polymorphisms that are associated with OA susceptibility. Like most complex trait loci, these OA loci are thought to influence disease susceptibility through the regulation of gene expression, so-called expression quantitative loci, or eQTLs. One mechanism through which eQTLs act is epigenetic, by modulating DNA methylation. In such cases, there are quantitative differences in DNA methylation between the two alleles of the causal polymorphism, with the association signal referred to as a methylation quantitative trait locus, or meQTL. In this study, we aimed to investigate whether the OA susceptibility loci identified to date are functioning as meQTLs by integrating genotype data with whole genome methylation data of cartilage DNA. We investigated potential genotype-methylation correlations within a 1.0-1.5 Mb region surrounding each of 16 OA-associated single-nucleotide polymorphisms (SNPs) in 99 cartilage samples and identified four that function as meQTLs. Three of these replicated in an additional cohort of up to 62 OA patients. These observations suggest that OA susceptibility loci regulate the level of DNA methylation in cis and provide a mechanistic explanation as to how these loci impact upon OA susceptibility, further increasing our understanding of the role of genetics and epigenetics in this common disease.

Epigenetics in Legg-Calvé-Perthes disease: A study of global DNA methylation

OBJECTIVE

To examine the global methylation status of DNA in blood cells of children with Legg-Calvé-Perthes disease (LCPD), since the aetiopathogenesis of LCPD remains unclear, and many factors closely associated with DNA methylation may be linked to the occurrence of LCPD.

METHODS

Children with LCPD and age-, sex- and body mass index-matched controls were evaluated. Methylation levels of the long interspersed nuclear element 1 (LINE-1), a biomarker of global DNA methylation, were quantified by methylation-specific polymerase chain reaction.

RESULTS

Of 82 children with LCPD (68 male/14 female) and 120 matched controls (98 male/22 female), methylation of the LINE-1 promoter was significantly lower in patients with LCPD compared with controls. Subgroup analyses showed that methylation of the LINE-1 promoter was significantly lower in male patients with LCPD compared with male controls. No significant between-group differences were observed in female participants.

CONCLUSIONS

Reduced global DNA methylation may be associated with increased risk of LCPD in male children. Further research is required to understand whether detection of global DNA methylation may provide a basis for clinical diagnosis and early intervention of LCPD.

Constitutional epimutation as a mechanism for cancer causality and heritability?

Constitutional epimutation, which is an aberration in gene expression due to an altered epigenotype that is widely distributed in normal tissues (albeit frequently mosaic), provides an alternative mechanism to genetic mutation for cancer predisposition. Observational studies in cancer-affected families have revealed intergenerational inheritance of constitutional epimutation, providing unique insights into the heritability of epigenetic traits in humans. In this Opinion article, the potential contribution of constitutional epimutation to the 'missing' causality and heritability of cancer is explored.

DNA methylation changes in epithelial ovarian cancer histotypes

Survival after a diagnosis of ovarian cancer has not improved, and despite histological differences, treatment is similar for all cases. Understanding the molecular basis for ovarian cancer risk and prognosis is fundamental, and to this end much has been gleaned about genetic changes contributing to risk, and to a lesser extent, survival. There's considerable evidence for genetic differences between the four pathologically defined histological subtypes; however, the contribution of epigenetics is less well documented. In this report, we review alterations in DNA methylation in ovarian cancer, focusing on histological subtypes, and studies examining the roles of methylation in determining therapy response. As epigenetics is making its way into clinical care, we review the application of cell free DNA methylation to ovarian cancer diagnosis and care. Finally, we comment on recurrent limitations in the DNA methylation literature for ovarian cancer, which can and should be addressed to mature this field.