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DNA Methylation Patterns Separate Senescence from Transformation Potential and Indicate Cancer Risk.

ABSTRACT: Overall shared DNA methylation patterns between senescence (Sen) and cancers have led to the model that tumor-promoting epigenetic patterns arise through senescence. We show that transformation-associated methylation changes arise stochastically and independently of programmatic changes during senescence. Promoter hypermethylation events in transformation involve primarily pro-survival and developmental genes, similarly modified in primary tumors. Senescence-associated hypermethylation mainly involves metabolic regulators and appears early in proliferating "near-senescent" cells, which can be immortalized but are refractory to transformation. Importantly, a subset of transformation-associated hypermethylated developmental genes exhibits highest methylation gains at all age-associated cancer risk states across tissue types. These epigenetic changes favoring cell self-renewal and survival, arising during tissue aging, are fundamentally important for stratifying cancer risk and concepts for cancer prevention.

SUBMITTER: Xie W

PROVIDER: S-EPMC5813821 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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DNA Methylation Patterns Separate Senescence from Transformation Potential and Indicate Cancer Risk.

Xie Wenbing W Kagiampakis Ioannis I Pan Lixia L Zhang Yang W YW Murphy Lauren L Tao Yong Y Kong Xiangqian X Kang Byunghak B Xia Limin L Carvalho Filipe L F FLF Sen Subhojit S Chiu Yen Ray-Whay RW Zahnow Cynthia A CA Ahuja Nita N Baylin Stephen B SB Easwaran Hariharan H

Cancer cell 20180201 2

Overall shared DNA methylation patterns between senescence (Sen) and cancers have led to the model that tumor-promoting epigenetic patterns arise through senescence. We show that transformation-associated methylation changes arise stochastically and independently of programmatic changes during senescence. Promoter hypermethylation events in transformation involve primarily pro-survival and developmental genes, similarly modified in primary tumors. Senescence-associated hypermethylation mainly in ...[more]

PMID: 29438699

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Project description:AimsNatriuretic peptides are useful for diagnosis and prognostication of heart failure of any cause. Now, research aims to discover novel biomarkers that will more specifically define the heart failure phenotype. DNA methylation plays a critical role in the development of cardiovascular disease with the potential to predict fundamental pathogenic processes. There is a lack of data relating DNA methylation in heart failure that specifically focuses on patients with severe multi-vessel coronary artery disease. To begin to address this, we conducted a pilot study uniquely exploring the utility of powerful whole-genome methyl-binding domain-capture sequencing in a cohort of cardiac surgery patients, matched for the severity of their coronary artery disease, aiming to identify candidate peripheral blood DNA methylation markers of ischaemic cardiomyopathy and heart failure.Methods and resultsWe recruited a cohort of 20 male patients presenting for coronary artery bypass graft surgery with phenotypic extremes of heart failure but who otherwise share a similar coronary ischaemic burden, age, sex, and ethnicity. Methylation profiling in patient blood samples was performed using methyl-binding domain-capture sequencing. Differentially methylated regions were validated using targeted bisulfite sequencing. Gene set enrichment analysis was performed to identify differences in methylation at or near gene promoters in certain known Reactome pathways. We detected 567 188 methylation peaks of which our general linear model identified 68 significantly differentially methylated regions in heart failure with a false discovery rate <0.05. Of these regions, 48 occurred within gene bodies and 25 were located near enhancer elements, some within coding genes and some in non-coding genes. Gene set enrichment analyses identified 103 significantly enriched gene sets (false discovery rate <0.05) in heart failure. Validation analysis of regions with the strongest differential methylation data was performed for two genes: HDAC9 and the uncharacterized miRNA gene MIR3675. Genes of particular interest as novel candidate markers of the heart failure phenotype with reduced methylation were HDAC9, JARID2, and GREM1 and with increased methylation PDSS2.ConclusionsWe demonstrate the utility of methyl-binding domain-capture sequencing to evaluate peripheral blood DNA methylation markers in a cohort of cardiac surgical patients with severe multi-vessel coronary artery disease and phenotypic extremes of heart failure. The differential methylation status of specific coding genes identified are candidates for larger longitudinal studies. We have further demonstrated the value and feasibility of examining DNA methylation during the perioperative period to highlight biological pathways and processes contributing to complex phenotypes.

Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Dataset Information

DNA Methylation Patterns Separate Senescence from Transformation Potential and Indicate Cancer Risk.

Publications

DNA Methylation Patterns Separate Senescence from Transformation Potential and Indicate Cancer Risk.

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