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Since genetic alteration only accounts for 20%-30% in the drug effect-related factors, the role of epigenetic regulation mechanisms in drug response is gradually being valued. However, how epigenetic changes and abnormal gene expression affect the chemotherapy response remains unclear. Therefore, we constructed a variety of mathematical models based on the integrated DNA methylation, gene expression, and anticancer drug response data of cancer cell lines from pan-cancer levels to identify genes whose DNA methylation is associated with drug response and then to assess the impact of epigenetic regulation of gene expression on the sensitivity of anticancer drugs. The innovation of the mathematical models lies in: Linear regression model is followed by logistic regression model, which greatly shortens the calculation time and ensures the reliability of results by considering the covariates. Second, reconstruction of prediction models based on multiple dataset partition methods not only evaluates the model stability but also optimizes the drug-gene pairs. For 368,520 drug-gene pairs with P  < 0.05 in linear models, 999 candidate pairs with both AUC ≥ 0.8 and P  < 0.05 were obtained by logistic regression models between drug response and DNA methylation. Then 931 drug-gene pairs with 45 drugs and 491 genes were optimized by model stability assessment. Integrating both DNA methylation and gene expression markedly increased predictive power for 732 drug-gene pairs where 598 drug-gene pairs including 44 drugs and 359 genes were prioritized. Several drug target genes were enriched in the modules of the drug-gene-weighted interaction network. Besides, for cancer driver genes such as EGFR , MET , and TET2 , synergistic effects of DNA methylation and gene expression can predict certain anticancer drugs' responses. In summary, we identified potential drug sensitivity-related markers from pan-cancer levels and concluded that synergistic regulation of DNA methylation and gene expression affect anticancer drug response.

Aim: To comprehensively identify allele-specific DNA methylation (ASM) at the genome-wide level. Methods: Here, we propose a new method, called GeneASM, to identify ASM using high-throughput bisulfite sequencing data in the absence of haplotype information. Results: A total of 2194 allele-specific DNA methylated genes were identified in the GM12878 lymphocyte lineage using GeneASM. These genes are mainly enriched in cell cytoplasm function, subcellular component movement or cellular linkages. GM12878 methylated DNA immunoprecipitation sequencing, and methylation sensitive restriction enzyme sequencing data were used to evaluate ASM. The relationship between ASM and disease was further analyzed using the The Cancer Genome Atlas (TCGA) data of lung adenocarcinoma (LUAD), and whole genome bisulfite sequencing data. Conclusion: GeneASM, which recognizes ASM by high-throughput bisulfite sequencing and heterozygous single-nucleotide polymorphisms, provides new perspective for studying genomic imprinting.

Aim: To discover CpG island methylator phenotype (CIMP) as a predictor for cancer drug-response mechanism., Materials & Methods: CIMP classification of 966 cancer cell lines was determined according to identified copy number alteration and differential methylation by DNA methylation profiles. CIMP-related drugs were analyzed by analysis of variance. Tissue-cell-drug networks were developed to predict drug response of individual samples., Results & Conclusion: One hundred and thirty-six copy number gain and 142 copy number loss cell lines were classified into CIMP-high and CIMP-low groups, meanwhile 9 and 24 CIMP-associated drugs were identified, respectively. Specially, breast invasive carcinoma samples primarily composed by HCC1419 were predicted to be sensitive to GSK690693. The study provides guidance for drug response in cancer therapy through genome-wide DNA methylation.

Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis-subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal-like subtype into two different prognosis-subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes., (© 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

Different human genes often exhibit different degrees of stability in their DNA methylation levels between tissues, samples or cell types. This may be related to the evolution of human genome. Thus, we compared the evolutionary conservation between two types of genes: genes with stable DNA methylation levels (SM genes) and genes with fluctuant DNA methylation levels (FM genes). For long-term evolutionary characteristics between species, we compared the percentage of the orthologous genes, evolutionary rate dn/ds and protein sequence identity. We found that the SM genes had greater percentages of the orthologous genes, lower dn/ds, and higher protein sequence identities in all the 21 species. These results indicated that the SM genes were more evolutionarily conserved than the FM genes. For short-term evolutionary characteristics among human populations, we compared the single nucleotide polymorphism (SNP) density, and the linkage disequilibrium (LD) degree in HapMap populations and 1000 genomes project populations. We observed that the SM genes had lower SNP densities, and higher degrees of LD in all the 11 HapMap populations and 13 1000 genomes project populations. These results mean that the SM genes had more stable chromosome genetic structures, and were more conserved than the FM genes.

The aim of this study was to compare nonrandom associations between physically adjacent single methylation polymorphism loci among rheumatoid arthritis (RA) and normal subjects for investigating RA‐risk methylation haplotypes (meplotype). With 354 ACPA‐positive RA patients and 335 normal controls selected from a case–control study based on Swedish population, we conducted the first RA epigenome‐wide meplotype association study using our software EWAS2.0, mainly including (i) converted the β value to methylation genotype (menotype) data, (ii) identified methylation disequilibrium (MD) block, (iii) calculated frequent of each meplotypes in MD block and performed case–control association test and (iv) screened for RA‐risk meplotypes by odd ratio (OR) and p‐values. Ultimately, 545 meplotypes on 334 MD blocks were identified significantly associated with RA (p‐value <.05). These meplotypes were mapped to 329 candidate genes related to RA. Subsequently, combined with gene optimization, eight RA‐risk meplotypes were identified on three risk genes: HLA‐DRB1, HLA‐DRB5 and HLA‐DQB1. Our results reported the relationship between DNA methylation pattern on HLA‐DQB1 and the risk of RA for the first time, demonstrating the co‐demethylation of 'cg22984282' and 'cg13423887' on HLA‐DQB1 gene (meplotype UU, p‐value = 2.90E − 6, OR = 1.68, 95% CI = [1.35, 2.10]) may increase the risk of RA. Our results demonstrates the potential of methylation haplotype analysis to identify RA‐related genes from a new perspective and its applicability to the study of other disease. [ABSTRACT FROM AUTHOR]