Epigenetic variability in cells of normal cytology is associated with the risk of future morphological transformation

DNA methylation variability in Alzheimer's disease

DNA methylation plays a critical role in brain aging and Alzheimer's disease (AD). While prior studies have largely focused on testing mean DNA methylation, DNA methylation instability (quantified by DNA methylation variability) may also affect disease susceptibility. Using DNA methylation data collected by the Religious Orders Study and the Rush Memory and Aging Project, we identified 249 and 115 variably methylated probes (VMPs) associated with amyloid-β and neurofibrillary tangles, respectively. These VMPs clustered into 133 and 14 regions, respectively. Notably, we found that most of these VMPs did not overlap with differentially methylated probes, indicating that VMPs and differentially methylated probes may capture different sets of genes associated with AD pathology. Overall, our results demonstrated that DNA methylation instability affects AD neuropathology and highlights the importance of testing methylation variability in epigenetic research.

An integrative module analysis of DNA methylation landscape in aging

To investigate the molecular mechanism of aging, the combination of module analysis and DNA methylation data was used to detect dynamically controlled modules for aging. Multiple differential expression networks (DENs) were constructed based on the microarray profiles across different aging groups (<70 years, 70–80 years, and >80 years). Next, a module-based approach was utilized to extract the common candidate modules across all age groups. We used Module Connectivity Dynamic Score (MCDS) to quantify the connectivity change of the common modules among the different age groups. Functional analyses were implemented for the genes in the common modules to further identify the significant biological processes. A total of two DENs were constructed. Overall 657 informative genes were screened out. When false discovery rate (FDR) was set as 0.05, we found that 148 modules were significant. Only 1 significant 2-differential modules (DMs) (module 493) with dynamic changes was discovered. Significantly, the genes in the module 493 participated in 7 significant pathways, including pentose phosphate pathway, carbon metabolism, and citrate cycle (TCA cycle). In conclusion, pathway functions [pentose phosphate pathway, carbon metabolism, citrate cycle (TCA cycle), chromosomal instability, ateroid biosynthesis, PPAR signaling pathway, and immune response] may serve as potential therapeutic targets in aging.

Highly variant DNA methylation in normal tissues identifies a distinct subclass of cancer patients

The "CpG Island Methylator Phenotype" (CIMP) has been found to be a useful concept in stratifying several types of human cancer into molecularly and clinically distinguishable subgroups. We have identified an additional epigenetic stratification category, the "Outlier Methylation Phenotype" (OMP). Whereas CIMP is defined on the basis of hyper-methylation in tumor genomes, OMP is defined on the basis of highly variant (either or both hyper- and hypo-methylation) methylation at many sites in normal tissues. OMP was identified and defined, originally, as being more common among low birth weight individuals conceived in vitro but we have also identified OMP individuals among colon cancer patients profiled by us, as well as multiple types of cancer patients in the TCGA database. The cause(s) of OMP are unknown, as is whether these individuals identify a clinically useful subgroup of patients, but both the causes of, and potential consequences to, this epigenetically distinct group are of great interest.

ZFP57 suppress proliferation of breast cancer cells through down-regulation of MEST-mediated Wnt/β-catenin signalling pathway

Activation of oncogenes by promoter hypomethylation plays an important role in tumorigenesis. Zinc finger protein 57 (ZFP57), a member of KRAB-ZFPs, could maintain DNA methylation in embryonic stem cells (ESCs), although its role and underlying mechanisms in breast cancer are not well understood. In this study, we found that ZFP57 had low expression in breast cancer, and overexpression of ZFP57 could inhibit the proliferation of breast cancer cells by inhibiting the Wnt/β-catenin pathway. MEST was validated as the direct target gene of ZFP57 and MEST may be down-regulated by ZFP57 through conserving DNA methylation. Furthermore, overexpression of MEST could restore the tumour-suppressed and the Wnt/β-catenin pathway inactivated effects of ZFP57. ZFP57-MEST and the Wnt/β-catenin pathway axis are involved in breast tumorigenesis, which may represent a potential diagnostic biomarker, and provide a new insight into a novel therapeutic strategy for breast cancer patients.

Methylation and ovarian cancer: Can DNA methylation be of diagnostic use?

Ovarian cancer is a silent killer and, due to late diagnosis and frequent chemo resistance in patients, the primary cause of fatality amongst the various types of gynecological cancer. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, thereby saving lives. Biomarkers could also improve treatment, by predicting which patients will benefit from specific treatment strategies. DNA methylation is an epigenetic mechanism, and 'methylation imbalance' is characteristic of cancer. Previous research suggests that changes in DNA methylation can be used diagnostically, and that they may predict resistance to treatment. This paper gives an up-to-date overview of research investigating the potential of DNA methylation-based markers for diagnostics, prognostics, screening and prediction of drug resistance for ovarian cancer patients. DNA methylation cancer-biomarkers may be useful for cancer treatment, particularly since they are chemically stable and since cancer-associated changes in methylation typically precedes tumor growth. DNA methylation markers could improve diagnosis and treatment and might even be used for screening in the future. Furthermore, DNA methylation biomarkers could facilitate the development of precision medicine. However, at this point no biomarkers for ovarian cancer have a sufficient combination of sensitivity and specificity in a clinical setting. A reason for this is that most studies have focused on a single or a few methylation sites. More large screenings and genome-wide studies must be performed to increase the chance of identifying a DNA methylation marker which can identify ovarian cancer.

Novel epigenetic markers for gastric cancer risk stratification in individuals after Helicobacter pylori eradication

BACKGROUND

The risk stratification of healthy individuals after Helicobacter pylori eradication is an urgent issue. The assessment of aberrant DNA methylation accumulated in gastric tissues with normal appearance, which can reflect overall epigenomic damage, is a promising strategy. We aimed to establish novel epigenetic cancer risk markers for H. pylori-eradicated individuals.

METHODS

Gastric mucosa was collected from eight healthy volunteers without H. pylori infection (G1), 75 healthy individuals with gastric atrophy (G2), and 94 gastric cancer patients (G3) after H. pylori eradication. Genome-wide analysis was conducted using Infinium 450 K and differentially methylated probes were screened using large difference and iEVORA-based methods. Bisulfite pyrosequencing was used for validation.

RESULTS

Screening, using 8 G1, 12 G2, and 12 G3 samples, isolated 57 candidates unmethylated in G1 and differentially methylated in G3 compared with G2. Validation for nine candidate markers (FLT3, LINC00643, RPRM, JAM2, ELMO1, BHLHE22, RIMS1, GUSBP5, and ZNF3) in 63 G2 and 82 G3 samples showed that all of them had significantly higher methylation levels in G3 than in G2 (P < 0.0001). Their methylation levels were highly correlated, which indicated that they reflect overall epigenomic damage. The candidates had sufficient performance (AUC: 0.70-0. 80) and high odds ratios (5.43-23.41), some of which were superior to a previous marker, miR-124a-3. The methylation levels of our novel markers were not associated with gastric atrophy, gender, or age.

CONCLUSIONS

Novel epigenetic markers for gastric cancer risk optimized for H. pylori-eradicated individuals were established.

Identification of DAPK1 Promoter Hypermethylation as a Biomarker for Intra-Epithelial Lesion and Cervical Cancer: A Meta-Analysis of Published Studies, TCGA, and GEO Datasets

Background: Promoter hypermethylation in death-associated protein kinase 1 (DAPK1) gene has been long linked to cervical neoplasia, but the established results remained controversial. Here, we performed a meta-analysis to assess the associations of DAPK1 promoter hypermethylation with low-grade intra-epithelial lesion (HSIL), high-grade intra-epithelial lesion (HSIL), cervical cancer (CC), and clinicopathological features of CC.

Methods: Published studies with qualitative methylation data were initially searched from PubMed, Web of Science, EMBASE, and China National Knowledge Infrastructure databases (up to March 2018). Then, quantitative methylation datasets, retrieved from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, were pooled to validate the results of published studies.

Results: In a meta-analysis of 37 published studies, DAPK1 promoter hypermethylation progressively increased the risk of LSIL by 2.41-fold (P = 0.012), HSIL by 7.62-fold (P < 0.001), and CC by 23.17-fold (P < 0.001). Summary receiver operating characteristic curves suggested a potential diagnostic value of DAPK1 promoter hypermethylation in CC, with a large area-under-the-curve of 0.83, a high specificity of 97%, and a moderate sensitivity of 59%. There were significant impacts of DAPK1 promoter hypermethylation on histological type (odds ratio (OR) = 3.53, P < 0.001) and FIGO stage of CC (OR = 2.15, P = 0.003). Then, a pooled analysis of nine TCGA and GEO datasets, covering 13 CPG sites within DAPK1 promoter, identified eight CC-associated sites, six sites with diagnostic values for CC (pooled specificities: 74–90%; pooled sensitivities: 70–81%), nine loci associated with the histological type of CC, and all 13 loci with down-regulated effects on DAPK1 mRNA expression.

Conclusion: The meta-analysis suggests that DAPK1 promoter hypermethylation is significantly associated with the disease severity of cervical neoplasia. DAPK1 methylation detection exhibits a promising ability to discriminate CC from cancer-free controls.

A novel cell-type deconvolution algorithm reveals substantial contamination by immune cells in saliva, buccal and cervix

Aim: An outstanding challenge in epigenome studies is the estimation of cell-type proportions in complex epithelial tissues. Materials & methods: Here, we construct and validate a DNA methylation reference and algorithm for complex tissues that contain epithelial, immune and nonimmune stromal cells. Results: Using this reference, we show that easily accessible tissues such as saliva, buccal and cervix exhibit substantial variation in immune cell (IC) contamination. We further validate our reference in the context of oral cancer, where it correctly predicts an increased IC infiltration in cancer but suppressed in patients with highest smoking exposure. Finally, our method can improve the specificity of differentially methylated CpG calls in epithelial cancer. Conclusion: The degree and variation of IC contamination in complex epithelial tissues is substantial. We provide a valuable resource and tool for assessing the epithelial purity and IC contamination of samples and for identifying differential methylation in such complex tissues.

Robust joint score tests in the application of DNA methylation data analysis

Background

Recently differential variability has been showed to be valuable in evaluating the association of DNA methylation to the risks of complex human diseases. The statistical tests based on both differential methylation level and differential variability can be more powerful than those based only on differential methylation level. Anh and Wang (2013) proposed a joint score test (AW) to simultaneously detect for differential methylation and differential variability. However, AW’s method seems to be quite conservative and has not been fully compared with existing joint tests.

Results

We proposed three improved joint score tests, namely iAW.Lev, iAW.BF, and iAW.TM, and have made extensive comparisons with the joint likelihood ratio test (jointLRT), the Kolmogorov-Smirnov (KS) test, and the AW test. Systematic simulation studies showed that: 1) the three improved tests performed better (i.e., having larger power, while keeping nominal Type I error rates) than the other three tests for data with outliers and having different variances between cases and controls; 2) for data from normal distributions, the three improved tests had slightly lower power than jointLRT and AW. The analyses of two Illumina HumanMethylation27 data sets GSE37020 and GSE20080 and one Illumina Infinium MethylationEPIC data set GSE107080 demonstrated that three improved tests had higher true validation rates than those from jointLRT, KS, and AW.

Conclusions

The three proposed joint score tests are robust against the violation of normality assumption and presence of outlying observations in comparison with other three existing tests. Among the three proposed tests, iAW.BF seems to be the most robust and effective one for all simulated scenarios and also in real data analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2185-3) contains supplementary material, which is available to authorized users.

DNA Methylation Patterns in Normal Tissue Correlate more Strongly with Breast Cancer Status than Copy-Number Variants

Normal tissue at risk of neoplastic transformation is characterized by somatic mutations, copy-number variation and DNA methylation changes. It is unclear however, which type of alteration may be more informative of cancer risk. We analyzed genome-wide DNA methylation and copy-number calls from the same DNA assay in a cohort of healthy breast samples and age-matched normal samples collected adjacent to breast cancer. Using statistical methods to adjust for cell type heterogeneity, we show that DNA methylation changes can discriminate normal-adjacent from normal samples better than somatic copy-number variants. We validate this important finding in an independent dataset. These results suggest that DNA methylation alterations in the normal cell of origin may offer better cancer risk prediction and early detection markers than copy-number changes.

A polycomb-mediated epigenetic field defect precedes invasive cervical carcinoma

Human papillomavirus (HPV)-associated cervical carcinoma is preceded by stages of cervical intra-epithelial neoplasia (CIN) that can variably progress to malignancy. Understanding the different molecular processes involved in the progression of pre-malignant CIN is critical to the development of improved predictive and interventional capabilities. We tested the role of regulators of transcription in both the development and the progression of HPV-associated CIN, performing the most comprehensive genomic survey to date of DNA methylation in HPV-associated cervical neoplasia, testing ~2 million loci throughout the human genome in biopsies from 78 HPV+ women, identifying changes starting in early CIN and maintained through carcinogenesis. We identified loci at which DNA methylation is consistently altered, beginning early in the course of neoplastic disease and progressing with disease advancement. While the loss of DNA methylation occurs mostly at intergenic regions, acquisition of DNA methylation is at sites involved in transcriptional regulation, with strong enrichment for targets of polycomb repression. Using an independent cohort from The Cancer Genome Atlas, we validated the loci with increased DNA methylation and found that these regulatory changes were associated with locally decreased gene expression. Secondary validation using immunohistochemistry showed that the progression of neoplasia was associated with increasing polycomb protein expression specifically in the cervical epithelium. We find that perturbations of genomic regulatory processes occur early and persist in cervical carcinoma. The results indicate a polycomb-mediated epigenetic field defect in cervical neoplasia that may represent a target for early, topical interventions using polycomb inhibitors.

Epigenetic alterations to Polycomb targets precede malignant transition in a mouse model of breast cancer

Malignant breast cancer remains a major health threat to women of all ages worldwide and epigenetic variations on DNA methylation have been widely reported in cancers of different types. We profiled DNA methylation with ERRBS (Enhanced Reduced Representation Bisulfite Sequencing) across four main stages of tumor progression in the MMTV-PyMT mouse model (hyperplasia, adenoma/mammary intraepithelial neoplasia, early carcinoma and late carcinoma), during which malignant transition occurs. We identified a large number of differentially methylated cytosines (DMCs) in tumors relative to age-matched normal mammary glands from FVB mice. Despite similarities, the methylation differences of the premalignant stages were distinct from the malignant ones. Many differentially methylated loci were preserved from the first to the last stage throughout tumor progression. Genes affected by methylation gains were enriched in Polycomb repressive complex 2 (PRC2) targets, which may present biomarkers for early diagnosis and targets for treatment.

Suppression of STING signaling through epigenetic silencing and missense mutation impedes DNA damage mediated cytokine production

The production of cytokines in response to DNA-damage events may be an important host defense response to help prevent the escape of pre-cancerous cells. The innate immune pathways involved in these events are known to be regulated by cellular molecules such as stimulator of interferon genes (STING), which controls type I interferon and pro-inflammatory cytokine production in response to the presence of microbial DNA or cytosolic DNA that has escaped from the nucleus. STING signaling has been shown to be defective in a variety of cancers, such as colon cancer and melanoma, actions that may enable damaged cells to escape the immunosurveillance system. Here, we report through examination of databases that STING signaling may be commonly suppressed in a greater variety of tumors due to loss-of-function mutation or epigenetic silencing of the STING/cGAS promoter regions. In comparison, RNA activated innate immune pathways controlled by RIG-I/MDA5 were significantly less affected. Examination of reported missense STING variants confirmed that many exhibited a loss-of-function phenotype and could not activate cytokine production following exposure to cytosolic DNA or DNA-damage events. Our data imply that the STING signaling pathway may be recurrently suppressed by a number of mechanisms in a considerable variety of malignant disease and be a requirement for cellular transformation.

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.

miRNA-Processing Gene Methylation and Cancer Risk

Background: Dysregulation of miRNA and methylation levels are epigenetic hallmarks of cancer, potentially linked via miRNA-processing genes. Studies have found genetic alterations to miRNA-processing genes in cancer cells and human population studies. Our objective was to prospectively examine changes in DNA methylation of miRNA-processing genes and their associations with cancer risk.Methods: We examined cohort data from the Department of Veterans' Affairs Normative Aging Study. Participants were assessed every 3 to 5 years starting in 1999 through 2013 including questionnaires, medical record review, and blood collection. Blood from 686 consenting participants was analyzed using the Illumina 450K BeadChip array to measure methylation at CpG sites throughout the genome. We selected 19 genes based on a literature review, with 519 corresponding CpG sites. We then used Cox proportional hazards models to examine associations with cancer incidence, and generalized estimating equations to examine associations with cancer prevalence. Associations at false discovery rate < 0.05 were considered statistically significant.Results: Methylation of three CpGs (DROSHA: cg23230564, TNRC6B: cg06751583, and TNRC6B: cg21034183) was prospectively associated with time to cancer development (positively for cg06751583, inversely for cg23230564 and cg21034183), whereas methylation of one CpG site (DROSHA: cg16131300) was positively associated with cancer prevalence.Conclusions: DNA methylation of DROSHA, a key miRNA-processing gene, and TNRC6B may play a role in early carcinogenesis.Impact: Changes in miRNA processing may exert multiple effects on cancer development, including protecting against it via altered global miRNAs, and may be a useful early detection biomarker of cancer. Cancer Epidemiol Biomarkers Prev; 27(5); 550-7. ©2018 AACR.

Genetic and epigenetic alterations in normal tissues have differential impacts on cancer risk among tissues

Genetic and epigenetic alterations are both involved in carcinogenesis, and their low-level accumulation in normal tissues constitutes cancer risk. However, their relative importance has never been examined, as measurement of low-level mutations has been difficult. Here, we measured low-level accumulations of genetic and epigenetic alterations in normal tissues with low, intermediate, and high cancer risk and analyzed their relative effects on cancer risk in the esophagus and stomach. Accumulation of genetic alterations, estimated as a frequency of rare base substitution mutations, significantly increased according to cancer risk in esophageal mucosae, but not in gastric mucosae. The mutation patterns reflected the exposure to lifestyle risk factors. In contrast, the accumulation of epigenetic alterations, measured as DNA methylation levels of marker genes, significantly increased according to cancer risk in both tissues. Patients with cancer (high-risk individuals) were precisely discriminated from healthy individuals with exposure to risk factors (intermediate-risk individuals) by a combination of alterations in the esophagus (odds ratio, 18.2; 95% confidence interval, 3.69-89.9) and by only epigenetic alterations in the stomach (odds ratio, 7.67; 95% confidence interval, 2.52-23.3). The relative importance of epigenetic alterations upon genetic alterations was 1.04 in the esophagus and 2.31 in the stomach. The differential impacts among tissues will be critically important for effective cancer prevention and precision cancer risk diagnosis.

The potential of circulating tumor DNA methylation analysis for the early detection and management of ovarian cancer

Background

Despite a myriad of attempts in the last three decades to diagnose ovarian cancer (OC) earlier, this clinical aim still remains a significant challenge. Aberrant methylation patterns of linked CpGs analyzed in DNA fragments shed by cancers into the bloodstream (i.e. cell-free DNA) can provide highly specific signals indicating cancer presence.

Methods

We analyzed 699 cancerous and non-cancerous tissues using a methylation array or reduced representation bisulfite sequencing to discover the most specific OC methylation patterns. A three-DNA-methylation-serum-marker panel was developed using targeted ultra-high coverage bisulfite sequencing in 151 women and validated in 250 women with various conditions, particularly in those associated with high CA125 levels (endometriosis and other benign pelvic masses), serial samples from 25 patients undergoing neoadjuvant chemotherapy, and a nested case control study of 172 UKCTOCS control arm participants which included serum samples up to two years before OC diagnosis.

Results

The cell-free DNA amount and average fragment size in the serum samples was up to ten times higher than average published values (based on samples that were immediately processed) due to leakage of DNA from white blood cells owing to delayed time to serum separation. Despite this, the marker panel discriminated high grade serous OC patients from healthy women or patients with a benign pelvic mass with specificity/sensitivity of 90.7% (95% confidence interval [CI] = 84.3–94.8%) and 41.4% (95% CI = 24.1–60.9%), respectively. Levels of all three markers plummeted after exposure to chemotherapy and correctly identified 78% and 86% responders and non-responders (Fisher’s exact test, p = 0.04), respectively, which was superior to a CA125 cut-off of 35 IU/mL (20% and 75%). 57.9% (95% CI 34.0–78.9%) of women who developed OC within two years of sample collection were identified with a specificity of 88.1% (95% CI = 77.3–94.3%). Sensitivity and specificity improved further when specifically analyzing CA125 negative samples only (63.6% and 87.5%, respectively).

Conclusions

Our data suggest that DNA methylation patterns in cell-free DNA have the potential to detect a proportion of OCs up to two years in advance of diagnosis and may potentially guide personalized treatment. The prospective use of novel collection vials, which stabilize blood cells and reduce background DNA contamination in serum/plasma samples, will facilitate clinical implementation of liquid biopsy analyses.

Electronic supplementary material

The online version of this article (doi:10.1186/s13073-017-0500-7) contains supplementary material, which is available to authorized users.

Epigenetic and Transcriptional Variability Shape Phenotypic Plasticity

Epigenetic and transcriptional variability contribute to the vast diversity of cellular and organismal phenotypes and are key in human health and disease. In this review, we describe different types, sources, and determinants of epigenetic and transcriptional variability, enabling cells and organisms to adapt and evolve to a changing environment. We highlight the latest research and hypotheses on how chromatin structure and the epigenome influence gene expression variability. Further, we provide an overview of challenges in the analysis of biological variability. An improved understanding of the molecular mechanisms underlying epigenetic and transcriptional variability, at both the intra- and inter-individual level, provides great opportunity for disease prevention, better therapeutic approaches, and personalized medicine.

Searching for new biomarkers in ovarian cancer patients: Rationale and design of a retrospective study under the Mermaid III project

Ovarian cancer is a silent killer and, due to late diagnosis, the primary cause of death amongst gynecological cancers, killing approximately 376 women annually in Denmark. The discovery of a specific and sensitive biomarker for ovarian cancer could improve early diagnosis, but also treatment, by predicting which patients will benefit from specific treatment strategies. The Mermaid III project is consisting of 3 parts including "Early detection, screening and long-term survival," "Biomarkers and/or prognostic markers" and "The infection theory." The present paper gives an overview of the part regarding biomarkers and/or prognostic markers, with a focus on rationale and design. The study described has 3 major branches: microRNAs, epigenetics and Next Generation Sequencing. Tissue and blood from ovarian cancer patients, already enrolled in the prospective ongoing pelvic mass cohort, will be examined. Relevant microRNAs and DNA methylation patterns will be investigated using array technology. Patient exomes will be fully sequenced, and identified genetic variations will be validated with Next Generation Sequencing. In all cases, data will be correlated with clinical information on the patient, in order to identify possible biomarkers. A thorough investigation of biomarkers in ovarian cancer, including large numbers of different markers, has never been done before. Besides from improving diagnosis and treatment, other outcomes could be markers for screening, knowledge of the molecular aspects of cancer and the discovery of new drugs. Moreover, biomarkers are a prerequisite for the development of precision medicine. This study will attack the ovarian cancer problem from several angles, thereby increasing the chance of successfully contributing to saving lives.

Statistical and integrative system-level analysis of DNA methylation data

Epigenetics plays a key role in cellular development and function. Alterations to the epigenome are thought to capture and mediate the effects of genetic and environmental risk factors on complex disease. Currently, DNA methylation is the only epigenetic mark that can be measured reliably and genome-wide in large numbers of samples. This Review discusses some of the key statistical challenges and algorithms associated with drawing inferences from DNA methylation data, including cell-type heterogeneity, feature selection, reverse causation and system-level analyses that require integration with other data types such as gene expression, genotype, transcription factor binding and other epigenetic information.

Smoking is associated with hypermethylation of the APC 1A promoter in colorectal cancer: the ColoCare Study

Smoking tobacco is a known risk factor for the development of colorectal cancer and for mortality associated with the disease. Smoking has been reported to be associated with changes in DNA methylation in blood and in lung tumour tissues, although there has been scant investigation of how epigenetic factors may be implicated in the increased risk of developing colorectal cancer. To identify epigenetic changes associated with smoking behaviours, we performed epigenome-wide analysis of DNA methylation in colorectal tumours from 36 never-smokers, 47 former smokers, and 13 active smokers, and in adjacent mucosa from 49 never-smokers, 64 former smokers, and 18 active smokers. Our analyses identified 15 CpG sites within the APC 1A promoter that were significantly hypermethylated and 14 CpG loci within the NFATC1 gene body that were significantly hypomethylated (pLIS < 1 × 10^-5 ) in the tumours of active smokers. The APC 1A promoter was hypermethylated in 7 of 36 tumours from never-smokers (19%), 12 of 47 tumours from former smokers (26%), and 8 of 13 tumours from active smokers (62%). Promoter hypermethylation was positively associated with duration of smoking (Spearman rank correlation, ρ = 0.26, p = 0.03) and was confined to tumours, with hypermethylation never being observed in adjacent mucosa. Further analysis of adjacent mucosa revealed significant hypomethylation of four loci associated with the TNXB gene in tissue from active smokers. Our findings provide exploratory evidence for hypermethylation of the key tumour suppressor gene APC being implicated in smoking-associated colorectal carcinogenesis. Further work is required to establish the validity of our observations in independent cohorts. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Establishment of a Strong Link Between Smoking and Cancer Pathogenesis through DNA Methylation Analysis

Smoking is a well-documented risk factor in various cancers, especially lung cancer. In the current study, we tested the hypothesis that abnormal DNAm loci associated with smoking are enriched in genes and pathways that convey a risk of cancer by determining whether smoking-related methylated genes led to enrichment in cancer-related pathways. We analyzed two sets of smoking-related methylated genes from 28 studies originating from blood and buccal samples. By analyzing 320 methylated genes from 26 studies on blood samples (N = 17,675), we found 57 enriched pathways associated with different types of cancer (FDR < 0.05). Of these, 11 were also significantly overrepresented in the 661 methylated genes from two studies of buccal samples (N = 1,002). We further found the aryl hydrocarbon receptor signaling pathway plays an important role in the initiation of smoking-attributable cancer. Finally, we constructed a subnetwork of genes important for smoking-attributable cancer from the 48 non-redundant genes in the 11 oncogenic pathways. Of these, genes such as DUSP4 and AKT3 are well documented as being involved in smoking-related lung cancer. In summary, our findings provide robust and systematic evidence in support of smoking’s impact on the epigenome, which may be an important contributor to cancer.

Isolation and Characterization of Mesenchymal Stromal Cells from Human Umbilical Cord and Fetal Placenta

The human umbilical cord (UC) and placenta are non-invasive, primitive and abundant sources of mesenchymal stromal cells (MSCs) that have increasingly gained attention because they do not pose any ethical or moral concerns. Current methods to isolate MSCs from UC yield low amounts of cells with variable proliferation potentials. Since UC is an anatomically-complex organ, differences in MSC properties may be due to the differences in the anatomical regions of their isolation. In this study, we first dissected the cord/placenta samples into three discrete anatomical regions: UC, cord-placenta junction (CPJ), and fetal placenta (FP). Second, two distinct zones, cord lining (CL) and Wharton's jelly (WJ), were separated. The explant culture technique was then used to isolate cells from the four sources. The time required for the primary culture of cells from the explants varied depending on the source of the tissue. Outgrowth of the cells occurred within 3 - 4 days of the CPJ explants, whereas growth was observed after 7 - 10 days and 11 - 14 days from CL/WJ and FP explants, respectively. The isolated cells were adherent to plastic and displayed fibroblastoid morphology and surface markers, such as CD29, CD44, CD73, CD90, and CD105, similarly to bone marrow (BM)-derived MSCs. However, the colony-forming efficiency of the cells varied, with CPJ-MSCs and WJ-MSCs showing higher efficiency than BM-MSCs. MSCs from all four sources differentiated into adipogenic, chondrogenic, and osteogenic lineages, indicating that they were multipotent. CPJ-MSCs differentiated more efficiently in comparison to other MSC sources. These results suggest that the CPJ is the most potent anatomical region and yields a higher number of cells, with greater proliferation and self-renewal capacities in vitro. In conclusion, the comparative analysis of the MSCs from the four sources indicated that CPJ is a more promising source of MSCs for cell therapy, regenerative medicine, and tissue engineering.

PLMET: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalilzed Exponential Tilt Mixture Models

Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and non-differentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g. mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and non-identifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, the test with simple asymptotic distribution has computational advantages compared with permutation-based test for high-dimensional genetic or epigenetic data. We propose a pseudolikelihood based expectation-maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. In particular, the proposed test outperforms the commonly used tests under all simulation settings considered, especially when there are variance differences between two groups. The proposed test is applied to a real data set to identify differentially methylated sites between ovarian cancer subjects and normal subjects.

Multiple network algorithm for epigenetic modules via the integration of genome-wide DNA methylation and gene expression data

Background

With the increase in the amount of DNA methylation and gene expression data, the epigenetic mechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene expression data into a network by specifying the anti-correlation between them. However, the correlation between methylation and expression is usually unknown and difficult to determine.

Results

To address this issue, we present a novel multiple network framework for epigenetic modules, namely, Epigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation and gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation and expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of The Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively and negatively correlated modules and these modules are significantly more enriched in the known pathways than those obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by using methylation profiles, where positively and negatively correlated modules are of equal importance in the classification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is critical for cancer therapy.

Conclusions

The proposed model and algorithm provide an effective method for the integrative analysis of DNA methylation and gene expression. The algorithm is freely available as an R-package at https://github.com/william0701/EMDN.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1490-6) contains supplementary material, which is available to authorized users.

Genome-wide analysis of differential transcriptional and epigenetic variability across human immune cell types

Background

A healthy immune system requires immune cells that adapt rapidly to environmental challenges. This phenotypic plasticity can be mediated by transcriptional and epigenetic variability.

Results

We apply a novel analytical approach to measure and compare transcriptional and epigenetic variability genome-wide across CD14⁺CD16⁻ monocytes, CD66b⁺CD16⁺ neutrophils, and CD4⁺CD45RA⁺ naïve T cells from the same 125 healthy individuals. We discover substantially increased variability in neutrophils compared to monocytes and T cells. In neutrophils, genes with hypervariable expression are found to be implicated in key immune pathways and are associated with cellular properties and environmental exposure. We also observe increased sex-specific gene expression differences in neutrophils. Neutrophil-specific DNA methylation hypervariable sites are enriched at dynamic chromatin regions and active enhancers.

Conclusions

Our data highlight the importance of transcriptional and epigenetic variability for the key role of neutrophils as the first responders to inflammatory stimuli. We provide a resource to enable further functional studies into the plasticity of immune cells, which can be accessed from: http://blueprint-dev.bioinfo.cnio.es/WP10/hypervariability.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1156-8) contains supplementary material, which is available to authorized users.

Parenclitic Network Analysis of Methylation Data for Cancer Identification

We make use of ideas from the theory of complex networks to implement a machine learning classification of human DNA methylation data, that carry signatures of cancer development. The data were obtained from patients with various kinds of cancers and represented as parenclictic networks, wherein nodes correspond to genes, and edges are weighted according to pairwise variation from control group subjects. We demonstrate that for the 10 types of cancer under study, it is possible to obtain a high performance of binary classification between cancer-positive and negative samples based on network measures. Remarkably, an accuracy as high as 93−99% is achieved with only 12 network topology indices, in a dramatic reduction of complexity from the original 15295 gene methylation levels. Moreover, it was found that the parenclictic networks are scale-free in cancer-negative subjects, and deviate from the power-law node degree distribution in cancer. The node centrality ranking and arising modular structure could provide insights into the systems biology of cancer.

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.

Tumour biomarkers: homeostasis as a novel prognostic indicator

The term 'personalized medicine' refers to a medical procedure that consists in the grouping of patients based on their predicted individual response to therapy or risk of disease. In oncologic patients, a 'tailored' therapeutic approach may potentially improve their survival and well-being by not only reducing the tumour, but also enhancing therapeutic response and minimizing the adverse effects. Diagnostic tests are often used to select appropriate and optimal therapies that rely both on patient genome and other molecular/cellular analysis. Several studies have shown that lifestyle and environmental factors can influence the epigenome and that epigenetic events may be involved in carcinogenesis. Thus, in addition to traditional biomarkers, epigenetic factors are raising considerable interest, because they could potentially be used as an excellent tool for cancer diagnosis and prognosis. In this review, we summarize the role of conventional cancer genetic biomarkers and their association with epigenomics. Furthermore, we will focus on the so-called 'homeostatic biomarkers' that result from the physiological response to cancer, emphasizing the concept that an altered 'new' homeostasis influence not only tumour environment, but also the whole organism.

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.

Correlation of an epigenetic mitotic clock with cancer risk

Background

Variation in cancer risk among somatic tissues has been attributed to variations in the underlying rate of stem cell division. For a given tissue type, variable cancer risk between individuals is thought to be influenced by extrinsic factors which modulate this rate of stem cell division. To date, no molecular mitotic clock has been developed to approximate the number of stem cell divisions in a tissue of an individual and which is correlated with cancer risk.

Results

Here, we integrate mathematical modeling with prior biological knowledge to construct a DNA methylation-based age-correlative model which approximates a mitotic clock in both normal and cancer tissue. By focusing on promoter CpG sites that localize to Polycomb group target genes that are unmethylated in 11 different fetal tissue types, we show that increases in DNA methylation at these sites defines a tick rate which correlates with the estimated rate of stem cell division in normal tissues. Using matched DNA methylation and RNA-seq data, we further show that it correlates with an expression-based mitotic index in cancer tissue. We demonstrate that this mitotic-like clock is universally accelerated in cancer, including pre-cancerous lesions, and that it is also accelerated in normal epithelial cells exposed to a major carcinogen.

Conclusions

Unlike other epigenetic and mutational clocks or the telomere clock, the epigenetic clock proposed here provides a concrete example of a mitotic-like clock which is universally accelerated in cancer and precancerous lesions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-016-1064-3) contains supplementary material, which is available to authorized users.

Defining, distinguishing and detecting the contribution of heterogeneous methylation to cancer heterogeneity

DNA methylation is a fundamental means of epigenetic gene regulation that occurs in virtually all cell types. In many higher organisms, including humans, it plays vital roles in cell differentiation and homeostatic maintenance of cell phenotype. The control of DNA methylation has traditionally been attributed to a highly coordinated, linear process, whose dysregulation has been associated with numerous pathologies including cancer, where it occurs early in, and even prior to, the development of neoplastic tissues. Recent experimental evidence has demonstrated that, contrary to prevailing paradigms, methylation patterns are actually maintained through inexact, dynamic processes. These processes normally result in minor stochastic differences between cells that accumulate with age. However, various factors, including cancer itself, can lead to substantial differences in intercellular methylation patterns, viz. methylation heterogeneity. Advancements in molecular biology techniques are just now beginning to allow insight into how this heterogeneity contributes to clonal evolution and overall cancer heterogeneity. In the current review, we begin by presenting a didactic overview of how the basal bimodal methylome is established and maintained. We then provide a synopsis of some of the factors that lead to the accrual of heterogeneous methylation and how this heterogeneity may lead to gene silencing and impact the development of cancerous phenotypes. Lastly, we highlight currently available methylation assessment techniques and discuss their suitability to the study of heterogeneous methylation.

Tobacco smoking-associated genome-wide DNA methylation changes in the EPIC study

AIM

Epigenetic changes may occur in response to environmental stressors, and an altered epigenome pattern may represent a stable signature of environmental exposure.

MATERIALS & METHODS

Here, we examined the potential of DNA methylation changes in 910 prediagnostic peripheral blood samples as a marker of exposure to tobacco smoke in a large multinational cohort.

RESULTS

We identified 748 CpG sites that were differentially methylated between smokers and nonsmokers, among which we identified novel regionally clustered CpGs associated with active smoking. Importantly, we found a marked reversibility of methylation changes after smoking cessation, although specific genes remained differentially methylated up to 22 years after cessation.

CONCLUSION

Our study has comprehensively cataloged the smoking-associated DNA methylation alterations and showed that these alterations are reversible after smoking cessation.

Stochastic epigenetic outliers can define field defects in cancer

Background

There is growing evidence that DNA methylation alterations may contribute to carcinogenesis. Recent data also suggest that DNA methylation field defects in normal pre-neoplastic tissue represent infrequent stochastic “outlier” events. This presents a statistical challenge for standard feature selection algorithms, which assume frequent alterations in a disease phenotype. Although differential variability has emerged as a novel feature selection paradigm for the discovery of outliers, a growing concern is that these could result from technical confounders, in principle thus favouring algorithms which are robust to outliers.

Results

Here we evaluate five differential variability algorithms in over 700 DNA methylomes, including two of the largest cohorts profiling precursor cancer lesions, and demonstrate that most of the novel proposed algorithms lack the sensitivity to detect epigenetic field defects at genome-wide significance. In contrast, algorithms which recognise heterogeneous outlier DNA methylation patterns are able to identify many sites in pre-neoplastic lesions, which display progression in invasive cancer. Thus, we show that many DNA methylation outliers are not technical artefacts, but define epigenetic field defects which are selected for during cancer progression.

Conclusions

Given that cancer studies aiming to find epigenetic field defects are likely to be limited by sample size, adopting the novel feature selection paradigm advocated here will be critical to increase assay sensitivity.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1056-z) contains supplementary material, which is available to authorized users.

Epigenetic drift, epigenetic clocks and cancer risk

It is well-established that the DNA methylation landscape of normal cells undergoes a gradual modification with age, termed as 'epigenetic drift'. Here, we review the current state of knowledge of epigenetic drift and its potential role in cancer etiology. We propose a new terminology to help distinguish the different components of epigenetic drift, with the aim of clarifying the role of the epigenetic clock, mitotic clocks and active changes, which accumulate in response to environmental disease risk factors. We further highlight the growing evidence that epigenetic changes associated with cancer risk factors may play an important causal role in cancer development, and that monitoring these molecular changes in normal cells may offer novel risk prediction and disease prevention strategies.

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

This year is the tenth anniversary of the publication in this journal of a model suggesting the existence of 'tumour progenitor genes'. These genes are epigenetically disrupted at the earliest stages of malignancies, even before mutations, and thus cause altered differentiation throughout tumour evolution. The past decade of discovery in cancer epigenetics has revealed a number of similarities between cancer genes and stem cell reprogramming genes, widespread mutations in epigenetic regulators, and the part played by chromatin structure in cellular plasticity in both development and cancer. In the light of these discoveries, we suggest here a framework for cancer epigenetics involving three types of genes: 'epigenetic mediators', corresponding to the tumour progenitor genes suggested earlier; 'epigenetic modifiers' of the mediators, which are frequently mutated in cancer; and 'epigenetic modulators' upstream of the modifiers, which are responsive to changes in the cellular environment and often linked to the nuclear architecture. We suggest that this classification is helpful in framing new diagnostic and therapeutic approaches to cancer.

Integration of genetic and epigenetic markers for risk stratification: opportunities and challenges

Common genetic susceptibility variants could be used for risk stratification in risk-tailored cancer screening and prevention programmes. Combining genetic variants with environmental risk factors would improve risk stratification. Epigenetic changes are surrogate markers of environmental exposures during individual's lifetime. Integrating epigenetic markers, in lieu of environmental exposure data, with genetic markers would potentially improve risk stratification. Epigenetic changes are reversible and acquired gradually, providing potentials for prevention and early detection strategies. The epigenetic changes are tissue-specific and stage-of-development-specific, raising challenges in choice of sample and timing for evaluation of cancer-associated changes. The Horizon 2020 funded research programme, FORECEE, using empirical data, will investigate the value of integration of epigenomics with genomics for risk prediction and prevention of women-specific cancers.

DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer

Identifying molecular alterations in normal tissue adjacent to cancer is important for understanding cancer aetiology and designing preventive measures. Here we analyse the DNA methylome of 569 breast tissue samples, including 50 from cancer-free women and 84 from matched normal cancer pairs. We use statistical algorithms for dissecting intra- and inter-sample cellular heterogeneity and demonstrate that normal tissue adjacent to breast cancer is characterized by tens to thousands of epigenetic alterations. We show that their genomic distribution is non-random, being strongly enriched for binding sites of transcription factors specifying chromatin architecture. We validate the field defects in an independent cohort and demonstrate that over 30% of the alterations exhibit increased enrichment within matched cancer samples. Breast cancers highly enriched for epigenetic field defects, exhibit adverse clinical outcome. Our data support a model where clonal epigenetic reprogramming towards reduced differentiation in normal tissue is an important step in breast carcinogenesis.

Altered epigenetics is a feature of cancer but whether these changes occur early in tumour development is unclear. Here, the authors analyse methylation events in breast cancer and adjacent normal pairs, and show that methylation changes in the normal tissue are also found in the tumour, suggesting that some of these events occur early in cancer.

A Comparative Study of Tests for Homogeneity of Variances with Application to DNA Methylation Data

Variable DNA methylation has been associated with cancers and complex diseases. Researchers have identified many DNA methylation markers that have different mean methylation levels between diseased subjects and normal subjects. Recently, researchers found that DNA methylation markers with different variabilities between subject groups could also have biological meaning. In this article, we aimed to help researchers choose the right test of equal variance in DNA methylation data analysis. We performed systematic simulation studies and a real data analysis to compare the performances of 7 equal-variance tests, including 2 tests recently proposed in the DNA methylation analysis literature. Our results showed that the Brown-Forsythe test and trimmed-mean-based Levene's test had good performance in testing for equality of variance in our simulation studies and real data analyses. Our results also showed that outlier profiles could be biologically very important.

DNA methylation and healthy human aging

The process of aging results in a host of changes at the cellular and molecular levels, which include senescence, telomere shortening, and changes in gene expression. Epigenetic patterns also change over the lifespan, suggesting that epigenetic changes may constitute an important component of the aging process. The epigenetic mark that has been most highly studied is DNA methylation, the presence of methyl groups at CpG dinucleotides. These dinucleotides are often located near gene promoters and associate with gene expression levels. Early studies indicated that global levels of DNA methylation increase over the first few years of life and then decrease beginning in late adulthood. Recently, with the advent of microarray and next‐generation sequencing technologies, increases in variability of DNA methylation with age have been observed, and a number of site‐specific patterns have been identified. It has also been shown that certain CpG sites are highly associated with age, to the extent that prediction models using a small number of these sites can accurately predict the chronological age of the donor. Together, these observations point to the existence of two phenomena that both contribute to age‐related DNA methylation changes: epigenetic drift and the epigenetic clock. In this review, we focus on healthy human aging throughout the lifetime and discuss the dynamics of DNA methylation as well as how interactions between the genome, environment, and the epigenome influence aging rates. We also discuss the impact of determining ‘epigenetic age’ for human health and outline some important caveats to existing and future studies.

Cigarette smoking reduces DNA methylation levels at multiple genomic loci but the effect is partially reversible upon cessation

Smoking is a major risk factor in many diseases. Genome wide association studies have linked genes for nicotine dependence and smoking behavior to increased risk of cardiovascular, pulmonary, and malignant diseases. We conducted an epigenome wide association study in peripheral-blood DNA in 464 individuals (22 current smokers and 263 ex-smokers), using the Human Methylation 450 K array. Upon replication in an independent sample of 356 twins (41 current and 104 ex-smokers), we identified 30 probes in 15 distinct loci, all of which reached genome-wide significance in the combined analysis P < 5 × 10(-8). All but one probe (cg17024919) remained significant after adjusting for blood cell counts. We replicated all 9 known loci and found an independent signal at CPOX near GPR15. In addition, we found 6 new loci at PRSS23, AVPR1B, PSEN2, LINC00299, RPS6KA2, and KIAA0087. Most of the lead probes (13 out of 15) associated with cigarette smoking, overlapped regions of open chromatin (FAIRE and DNaseI hypersensitive sites) or/and H3K27Ac peaks (ENCODE data set), which mark regulatory elements. The effect of smoking on DNA methylation was partially reversible upon smoking cessation for longer than 3 months. We report the first statistically significant interaction between a SNP (rs2697768) and cigarette smoking on DNA methylation (cg03329539). We provide evidence that the metSNP for cg03329539 regulates expression of the CHRND gene located circa 95 Kb downstream of the methylation site. Our findings suggest the existence of dynamic, reversible site-specific methylation changes in response to cigarette smoking , which may contribute to the extended health risks associated with cigarette smoking.

Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis

Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a specific cancer type relative to the normal tissue, their degree of deviation from normal tissue expression in specific genes involved in tissue differentiation is a stable tumor mark that is reproducible across experiments and cancer types. Here we show that constructing gene expression signatures based on variability and the anti-profile approach yields classifiers capable of successfully distinguishing benign growths from cancerous growths based on deviation from normal expression. We then show that this same approach generates stable and reproducible signatures that predict probability of relapse and survival based on tumor gene expression. These results suggest that using the anti-profile framework for the discovery of genomic signatures is an avenue leading to the development of reproducible signatures suitable for adoption in clinical settings.

Genetic Determinants of Epigenetic Patterns: Providing Insight into Disease

The field of epigenetics and our understanding of the mechanisms that regulate the establishment, maintenance and heritability of epigenetic patterns continue to grow at a remarkable rate. This information is providing increased understanding of the role of epigenetic changes in disease, insight into the underlying causes of these epigenetic changes and revealing new avenues for therapeutic intervention. Epigenetic modifiers are increasingly being pursued as therapeutic targets in a range of diseases, with a number of agents targeting epigenetic modifications already proving effective in diseases such as cancer. Although it is well established that DNA mutations and aberrant expression of epigenetic modifiers play a key role in disease, attention is now turning to the interplay between genetic and epigenetic factors in complex disease etiology. The role of genetic variability in determining epigenetic profiles, which can then be modified by environmental and stochastic factors, is becoming more apparent. Understanding the interplay between genetic and epigenetic factors is likely to aid in identifying individuals most likely to benefit from epigenetic therapies. This goal is coming closer to realization because of continual advances in laboratory and statistical tools enabling improvements in the integration of genomic, epigenomic and phenotypic data.

An integrative multi-scale analysis of the dynamic DNA methylation landscape in aging

Recent studies have demonstrated that the DNA methylome changes with age. This epigenetic drift may have deep implications for cellular differentiation and disease development. However, it remains unclear how much of this drift is functional or caused by underlying changes in cell subtype composition. Moreover, no study has yet comprehensively explored epigenetic drift at different genomic length scales and in relation to regulatory elements.

Here we conduct an in-depth analysis of epigenetic drift in blood tissue. We demonstrate that most of the age-associated drift is independent of the increase in the granulocyte to lymphocyte ratio that accompanies aging and that enrichment of age-hypermethylated CpG islands increases upon adjustment for cellular composition. We further find that drift has only a minimal impact on in-cis gene expression, acting primarily to stabilize pre-existing baseline expression levels. By studying epigenetic drift at different genomic length scales, we demonstrate the existence of mega-base scale age-associated hypomethylated blocks, covering approximately 14% of the human genome, and which exhibit preferential hypomethylation in age-matched cancer tissue. Importantly, we demonstrate the feasibility of integrating Illumina 450k DNA methylation with ENCODE data to identify transcription factors with key roles in cellular development and aging. Specifically, we identify REST and regulatory factors of the histone methyltransferase MLL complex, whose function may be disrupted in aging.

In summary, most of the epigenetic drift seen in blood is independent of changes in blood cell type composition, and exhibits patterns at different genomic length scales reminiscent of those seen in cancer. Integration of Illumina 450k with appropriate ENCODE data may represent a fruitful approach to identify transcription factors with key roles in aging and disease.

Two well-known features of aging are the gradual decline of the body’s ability to regenerate tissues, as well as an increased incidence of diseases like cancer and Alzheimers. One of the most recent exciting findings which may underlie the aging process is a gradual modification of DNA, called epigenetic drift, which is effected by the covalent addition and removal of methyl groups, which in turn can deregulate the activity of nearby genes. However, this study presents the most convincing evidence to date that epigenetic drift acts to stabilize the activity levels of nearby genes. This study shows that instead, epigenetic drift may act primarly to disrupt DNA binding patterns of proteins which regulate the activity of many genes, and moreover identifies specific regulatory proteins with key roles in cancer and Alzheimers. The study also performs the most comprehensive analysis of epigenetic drift at different spatial scales, demonstrating that epigenetic drift on the largest length scales is highly reminiscent of those seen in cancer. In summary, this work substantially supports the view that epigenetic drift may contribute to the age-associated increased risk of diseases like cancer and Alzheimers, by disrupting master regulators of genomewide gene activity.

Discordance of DNA methylation variance between two accessible human tissues

Population epigenetic studies have been seeking to identify differences in DNA methylation between specific exposures, demographic factors, or diseases in accessible tissues, but relatively little is known about how inter-individual variability differs between these tissues. This study presents an analysis of DNA methylation differences between matched peripheral blood mononuclear cells (PMBCs) and buccal epithelial cells (BECs), the two most accessible tissues for population studies, in 998 promoter-located CpG sites. Specifically we compared probe-wise DNA methylation variance, and how this variance related to demographic factors across the two tissues. PBMCs had overall higher DNA methylation than BECs, and the two tissues tended to differ most at genomic regions of low CpG density. Furthermore, although both tissues showed appreciable probe-wise variability, the specific regions and magnitude of variability differed strongly between tissues. Lastly, through exploratory association analysis, we found indication of differential association of BEC and PBMC with demographic variables. The work presented here offers insight into variability of DNA methylation between individuals and across tissues and helps guide decisions on the suitability of buccal epithelial or peripheral mononuclear cells for the biological questions explored by epigenetic studies in human populations.