DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Genome wide DNA methylation profiling of colon adenocarcinoma cell line HCT116 wild type and with a genetic disruption of DNMT3B and DNMT1 (DKO). The Illumina Infinium 27k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 27,000 CpGs. For this study, we used two DKO subclones, DKO8 and DKO1, which retain approximately 45% and 5% of the HCT116 wild type global DNA methylation levels, respectively Bisulphite converted DNA from the 3 samples were hybridised to the Illumina Infinium 27k Human Methylation Beadchip v1.2

Database to study interplay of DNA methylation, gene expression and cancer that hosts both highly integrated data of DNA methylation, cancer-related gene, mutation and cancer information from public resources, and the CpG Island (CGI) clones derived from our large-scale sequencing. Interconnections between different data types were analyzed and presented. Search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. The search tool and graphical MethyView are developed to help users access all the data and data connections and view DNA methylation in context of genomics and genetics data. As part of the Cancer Epigenomics Project in China, MethyCancer serves as a platform for sharing data and analytical results from the Cancer Genome/Epigenome Project in China with colleagues all over the world.

Ductal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma (IBC). DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development. Genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer were generated using Illumina HumanMethylation450. Validation of methylation changes between normal and DCIS was performed in an independent dataset of 15 normal and 40 DCIS samples, and validation of a prognostic signature was performed on 583 breast cancer samples from The Cancer Genome Atlas. Using two independent datasets of normal breast tissue and DCIS revealed that DNA methylation profiles of DCIS were radically altered compared to normal breast tissue, involving almost 7000 genes (including CUL7 and ICAM2). Changes between DCIS and IBC involved around 1000 genes. In tumors, DNA methylation was associated with gene expression of almost 3000 genes (p<0.05, Bonferroni corrected) including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs (representing genes such as IRF6, TBX5, ZNF259, KCTD21, EPN3, MACF1 and CSNK1G2) was associated to survival of breast cancer patients with invasive tumors, as well as to survival of patients with DCIS and mixed lesions of DCIS and IBC. This work demonstrates that changes in the epigenome occurs early in the neoplastic progression, provide evidence for the possible utilization of DNA methylation based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis. Bisulphite converted DNA from the 285 samples were hybridised to the Illumina Infinium 450k Human Methylation Beadchip

Background Abnormal DNA methylation is well established for breast cancer and contributes to its progression by silencing tumor suppressor genes. DNA methylation profiling platforms might provide an alternative approach to expression microarrays for accurate breast tumor subtyping. We sought to determine whether the distinction of the inflammatory breast cancer (IBC) phenotype from the non-IBC phenotype by transcriptomics could be sustained by methylomics. Methodology/principal findings We performed methylation profiling on a cohort of IBC (N?=?19) and non-IBC (N?=?43) samples using the Illumina Infinium Methylation Assay. These results were correlated with gene expression profiles. Methylation values allowed separation of breast tumor samples into high and low methylation groups. This separation was significantly related to DNMT3B mRNA levels. The high methylation group was enriched for breast tumor samples from patients with distant metastasis and poor prognosis, as predicted by the 70-gene prognostic signature. Furthermore, this tumor group tended to be enriched for IBC samples (54% vs. 24%) and samples with a high genomic grade index (67% vs. 38%). A set of 16 CpG loci (14 genes) correctly classified 97% of samples into the low or high methylation group. Differentially methylated genes appeared to be mainly related to focal adhesion, cytokine-cytokine receptor interactions, Wnt signaling pathway, chemokine signaling pathways and metabolic processes. Comparison of IBC with non-IBC led to the identification of only four differentially methylated genes (TJP3, MOGAT2, NTSR2 and AGT). A significant correlation between methylation values and gene expression was shown for 4,981 of 6,605 (75%) genes. Conclusions/significance A subset of clinical samples of breast cancer was characterized by high methylation levels, which coincided with increased DNMT3B expression. Furthermore, an association was observed with molecular signatures indicative of poor patient prognosis. The results of the current study also suggest that aberrant DNA methylation is not the main force driving the molecular biology of IBC.

Co-occurrence and mutual exclusivity (COME) of DNA methylation refer to two or more genes that tend to be positively or negatively correlated in DNA methylation among different samples. Although COME of gene mutations in pan-cancer have been well explored, little is known about the COME of DNA methylation in pan-cancer. Here, we systematically explored the COME of DNA methylation profile in diverse human cancer. A total of 5,128,332 COME events were identified in 14 main cancers types in The Cancer Genome Atlas (TCGA). We also identified functional epigenetic modules of the zinc finger gene family in six cancer types by integrating the gene expression and DNA methylation data and the frequently occurred COME network. Interestingly, most of the genes in those functional epigenetic modules are epigenetically repressed. Strikingly, those frequently occurred COME events could be used to classify the patients into several subtypes with significant different clinical outcomes in six cancers as well as pan-cancer (p-value ≤ = 0.05). Moreover, we observed significant associations between different COME subtypes and clinical features (e.g., age, gender, histological type, neoplasm histologic grade, and pathologic stage) in distinct cancers. Taken together, we identified millions of COME events of DNA methylation in pan-cancer and detected functional epigenetic COME events that could separate tumor patients into different subtypes, which may benefit the diagnosis and prognosis of pan-cancer.

Genome wide DNA methylation profiling of 20 PDAC cell lines and an immortalized non-malignant pancreatic duct cell line (HPDE) to facilitate identification of novel tumor suppressor genes using an integrative genomics approach Genome wide DNA methylation profiling of 20 PDAC cell lines and an immortalized non-malignant pancreatic duct cell line (HPDE) to identify novel tumor suppressor genes Bisulphite converted DNA from the 21 samples were hybridised to the Illumina Infinium 27k Human Methylation Beadchip

The DNA methylation patterns associated with the development and progression of cancer. The aim of the present study was to identify novel methylation markers that can discriminate between normal and Non-muscle-invasive bladder cancer (NMIBC), and between good and poor prognosis using microarray analysis of DNA methylation and RNA expression patterns in NMIBC patients. From the 24 matched microarray-based DNA methylation and gene expression profiling data set, tumor specific hypermethylated genes were selected and clinical relevance of these genes were verified by quantitative PQS analyses. Methylation statues of several genes were significantly associated with decreased gene expression levels and aggressive clinicopathological characteristics. In multivariate regression analyses, hypermethylation of these genes were the independent predictors of recurrence and progression. Genomic DNA was extracted by standard methods using the Wizard Genomic DNA Purification System (Promega, Madison, WI). Total RNA was extracted with TRIzol reagent (Life Technologies, NY) according to the manufacturer’s protocol. Methylation patterns were assayed using the genome-wide Illumina Infinium HumanMethylation27 BeadChip array (Illumina Inc., San Diego, CA). Methylation assays were carried out according to the manufacturer’s protocol. Bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation Kit (Zymo Research, Orange, CA). Fluorescence signals corresponding to C or T nucleotides were measured and the data were used to assign a quantitative measure of methylation level (β value). The β value is a quantitative measure of DNA methylation levels of specific CpGs and ranges from 0 for completely unmethylated to 1 for completely methylated.

This dataset contains supplemental tables and tracks for the study entitled: "Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation".

• Files
  • PMDs_CGIs.zip
    • The included files contain
    • Genome positions of detected PMDs with their mean methylation (weighted mean, see Methods)
    • Genome positions of CpG islands with their mean methylation (weighted mean)
    • The "Brinkman" directory contains files from breast cancer data produced in this study
    • The "normals" directory contains files from normal tissues (external data) analyzed in this study
    • The "tumors" directory contains files from tumors (external data) analyzed in this study
    • All genome positions are based on GRCh37/hg19
    • All files are TAB-delimited text files (.tsv)
  • DNAme_bigwigs.zip
    • The included files are BIGWIG files (http://genome.ucsc.edu/goldenPath/help/bigWig.html) for viewing the DNA methylation profiles in a genome browser such as UCSC (http://genome.ucsc.edu). Each file represents a whole-Genome Bisulfite Sequencing (WGBS) DNA methylation profile from one tumor used in this study. The used genome build was GRCh37/hg19. For every CpG with a coverage of at least 4 reads, the DNA methylation value (range: 0-1) is included.
• Methods
  • Detection of partially methylated domains (PMDs) in all whole-genome bisulfite sequencing (WGBS) methylation profiles throughout this study was done using the MethylSeekR package for R (1). Before PMD calling, CpGs overlapping common SNPs (dbSNP build 137) were removed. The alpha distribution (1) was used to determine whether PMDs were present at all, along with visual inspection of WGBS profiles. After PMD calling, the resulting PMDs were further filtered by removing regions overlapping with centromers (undetermined sequence content).
  • Mean methylation values from WGBS inside CGIs were calculated using the ‘weighted methylation level’ (2).
  • Mean methylation values from WGBS inside PMDs were calculated using the ‘weighted methylation level’ (2). Calculation of mean methylation within PMDs involved removing all CpGs overlapping with CpG island(-shores) and promoters, as the high CpG densities within these elements yield unbalanced mean methylation values, not representative of PMD methylation.
• References
  • (1) Burger, L., Gaidatzis, D., Schübeler, D. & Stadler, M. B. Identification of active regulatory regions from DNA methylation data. Nucleic Acids Research 41, (2013).
  • (2) Schultz, M. D., Schmitz, R. J. & Ecker, J. R. ’Leveling’ the playing field for analyses of single-base resolution DNA methylomes. Trends in Genetics 28, 583–585 (2012).

A microarray targeting promoters of cancer-related genes was used to evaluate DNA methylation at 935 CpG sites in 517 invasive breast tumors from the Carolina Breast Cancer Study (CBCS), a population-based study of invasive breast cancer. Concensus clustering using methylation (β) values for the 167 most variant CpG loci defined 4 clusters differing most distinctly in hormone receptor (HR) status, intrinsic subtype (luminal versus basal-like) and p53 mutation status. Supervised analyses for HR status, subtype, and p53 status identified differentially methylated CpG loci with considerable overlap (n=266). Concensus clustering also defined a hypermethylated luminal-enriched tumor cluster 3; gene ontology analysis of cluster 3 hypermethylated loci revealed enrichment for developmental genes, including homeobox domain genes (HOXB13, PAX6, IPF1, EYA4, DLK1, IHH, ISL1, TBX1, SOX1, SOX17). The hypermethylated luminal-enriched cluster 3 independently predicted poorer survival in multivariate Cox proportional hazard analysis, and this finding was confirmed in analysis of luminal A tumors. This study demonstrates epigenetic heterogeneity among breast tumors of a single intrinsic subtype, and shows that epigenetic patterns are strongly associated with HR status, subtype, and p53 mutation status. Among HR+ tumors, a gene signature characterized by hypermethylation of developmental genes may have prognostic value. Genes differentially methylated between clinically-important tumor subsets have roles in differentiation, development, and tumor growth and may be critical to inducing and maintaining tumor phenotypes and clinical outcomes. Overall design: 517 breast tumors, 9 normal breast tissues

Microarray-based DNA methylation and gene expression profiling was carried out using a panel of prostate cancer cell lines (LNCaP-FGC, DU-145, and PC-3) and the control normal prostate RWPE1 cell line. The identification of prostate cancer-specific methylation markers was based on the following criteria: a difference in DNA methylation level (β) of at least 0.5, and at least a 2-fold difference in expression level between cancer and control cells. Using highly stringent selection criteria, we identified novel hypermethylated genes whose expression was silenced in prostate cancer cells. Genomic DNA was extracted by standard methods using the Wizard Genomic DNA Purification System (Promega, Madison, WI). Total RNA was extracted with TRIzol reagent (Life Technologies, NY) according to the manufacturer’s protocol. Methylation patterns were assayed using the genome-wide Illumina Infinium HumanMethylation27 BeadChip array (Illumina Inc., San Diego, CA). Methylation assays were carried out according to the manufacturer’s protocol. Bisulfite conversion of genomic DNA was performed using the EZ DNA Methylation Kit (Zymo Research, Orange, CA). Fluorescence signals corresponding to C or T nucleotides were measured and the data were used to assign a quantitative measure of methylation level (β value). The β value is a quantitative measure of DNA methylation levels of specific CpGs and ranges from 0 for completely unmethylated to 1 for completely methylated. For the integrated analysis of global methylation status and gene expression levels, we used the genome-wide HumanHT-12 Gene Expression BeadChip (Illumina Inc., San Diego, CA). Gene expression analysis was performed according to the manufacturer’s protocol. Five hundred nanograms of total RNA were used for labeling hybridization according to the manufacturer’s protocol. Arrays were scanned with an Illumina Bead Array Reader confocal scanner (BeadStation 500GXDW; Illumina Inc., San Diego, CA), according to the manufacturer's instructions. Initial microarray gene expression data were obtained using the gene expression analysis module of Bead Studio software (version 3.1.3, Illumina Inc., San Diego, CA).

This SuperSeries is composed of the SubSeries listed below.

Cancer is an aging-associated disease but the underlying molecular links between these processes are still largely unknown. Gene promoters that become hypermethylated in aging and cancer share a common chromatin signature in ES cells. In addition, there is also global DNA hypomethylation in both processes. However, any similarities of the regions where this loss of DNA methylation occurs is currently not well characterized, nor is it known whether such regions also share a common chromatin signature in aging and cancer. To address this issue we analysed TCGA DNA methylation data from a total of 2,311 samples, including control and cancer cases from patients with breast, kidney, thyroid, skin, brain and lung tumors and healthy blood, and integrated the results with histone, chromatin state and transcription factor binding site data from the NIH Roadmap Epigenomics and ENCODE projects. We identified 98,857 CpG sites differentially methylated in aging, and 286,746 in cancer. Hyper- and hypomethylated changes in both processes each had a similar genomic distribution across tissues and displayed tissue-independent alterations. The identified hypermethylated regions in aging and cancer shared a similar bivalent chromatin signature. In contrast, hypomethylated DNA sequences occurred in very different chromatin contexts. DNA hypomethylated sequences were enriched at genomic regions marked with the activating histone posttranslational modification H3K4me1 in aging, whilst in cancer, loss of DNA methylation was primarily associated with the repressive H3K9me3 mark.

Genome wide DNA methylation profiling of invasive breast cancer samples isolated from an ethnically diverse group of 80 patients in the Breast Cancer Care in Chicago (BCCC) study. DNA was extracted from formalin fixed, paraffin-embedded samples on 80 patients (21 White, 31 African-American, 23 Hispanic and 5 not reported) (training dataset) enrolled in the BCCC. Hormone receptor status was defined as negative if tumors were negative for both estrogen and progesterone (ER/PR) receptors (N=22/75). Formalin-fixed, paraffin-embedded (FFPE) tumor samples came from the Breast Cancer Care in Chicago (BCCC) study (Dookeran KA, Silva A, Warnecke RB, Rauscher GH: Race/ethnicity and disparities in mastectomy practice in the Breast Cancer Care in Chicago study. Ann Surg Oncol 2015, 22:66–74). Copies of pathology reports and the corresponding set of Hematoxylin and Eosin (H&E) stained slides were requested from the pathology department at each diagnosing institution, and a single pathologist selected tumor blocks representative of the tumor. Two recuts (at 4 µm each) were made from each selected block for H&E staining. The recuts were then examined in order to identify invasive components of the sample, and areas were marked according to tissue component. Cores of invasive tissue (2 mm in diameter) were obtained from the marked areas and DNA was extracted for the DNA methylation study. Bisulphite converted DNA from the 80 samples were hybridised to the Illumina GoldenGate Methylation Cancer Panel I

Beginning with precursor lesions, aberrant DNA methylation marks the entire spectrum of prostate cancer progression. We mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). A Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak) was used to locate peaks from mapped reads obtained in each sequencing run. The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. While approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues, promoter CGI methylation gradually increased from ~12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues. We found distinct patterns in promoter methylation around transcription start sites, where methylation occurred directly on the CGIs, flanking regions and on CGI sparse promoters. Among the 6,691 methylated promoters in prostate tissues, 2481 differentially methylated regions (DMRs) are cancer specific and several previously studied targets were among them. A novel cancer specific DMR in WFDC2 promoter showed 77% methylation in cancer (17/22), 100% methylation in transformed prostate cell lines (6/6), none in the benign tissues (0/10) and normal PrEC cells. Integration of LNCaP DNA methylation and H3K4me3 data suggested a role for DNA methylation in alternate transcription start site utilization. While methylated promoters containing CGIs had mutually exclusive H3K4me3 modification, the histone mark was absent in CGI sparse promoters. Finally, we observed difference in methylation of LINE-1 elements between transcription factor ERG positive and negative cancers. The comprehensive methylome map presented here will further our understanding of epigenetic regulation of the prostate cancer genome. We mapped the global DNA methylation patterns in prostate tissues (n=17; data not available in GEO - being deposited in dbGaP for controlled access) and cells (n=2) from fifty nanograms of genomic DNA using Methylplex-Next Generation Sequencing (M-NGS). For replicate analysis in cell lines, a total of 4 runs were completed for PrEC prostate normal cell line, and 5 runs were completed for LNCaP prostate cancer cell line. For tissue samples, 2 benign prostate samples were ran twice on illumina next generation sequencing platform to access overall repeatability of M-NGS.

Aberrant DNA methylation is a frequent epigenetic alteration in cancer cells that has emerged as a pivotal mechanism for tumorigenesis. Accordingly, novel therapies targeting the epigenome are being explored with the aim to restore normal DNA methylation patterns on oncogenes and tumor suppressor genes. A limited number of studies indicate that dietary compound resveratrol modulates DNA methylation of several cancer-related genes; however a complete view of changes in methylome by resveratrol has not been reported yet. In this study we performed a genome-wide survey of DNA methylation signatures in triple negative breast cancer cells exposed to resveratrol. Our data showed that resveratrol treatment for 24 h and 48 h decreased gene promoter hypermethylation and increased DNA hypomethylation. Of 2476 hypermethylated genes in control cells, 1,459 and 1,547 were differentially hypomethylated after 24 h and 48 h, respectively. Remarkably, resveratrol did not induce widespread non-specific DNA hyper- or hypomethylation as changes in methylation were found in only 12.5% of 27,728 CpG loci. Moreover, resveratrol restores the hypomethylated and hypermethylated status of key tumor suppressor genes and oncogenes, respectively. Importantly, the integrative analysis of methylome and transcriptome profiles in response to resveratrol showed that methylation alterations were concordant with changes in mRNA expression. Our findings reveal for the first time the impact of resveratrol on the methylome of breast cancer cells and identify novel potential targets for epigenetic therapy. We propose that resveratrol may be considered as a dietary epidrug as it may exert its anti-tumor activities by modifying the methylation status of cancer -related genes which deserves further in vivo characterization.

This repository contains the code to perform all the analyses and the data generated by the author of the MiMeSis paper (Romagnoli et al., 2023; 10.1093/bib/bbad015)

Immune response to a given antigen, particularly in cancer patients, is complex and is controlled by various genetic and environmental factors. Identifying biomarkers that can predict robust response to immunization is an urgent need in clinical cancer vaccine development. Given the involvement of DNA methylation in the development of lymphocytes, tumorigenicity and tumor progression, we aimed to analyze pre-vaccination DNA methylation profiles of peripheral blood mononuclear cells (PBMCs) from breast cancer subjects vaccinated with a novel peptide-based vaccine referred to as P10s-PADRE.

The underlying biology through which established breast cancer risk factors contribute to disease risk is not well characterized. One key risk factor for breast cancer is age, and age-related DNA methylation alterations may contribute to increased risk of disease. Here we assessed normal breast tissues and tested the relation of DNA methylation with known breast cancer risk factors. Cancer-free women donated breast tissue biopsy specimens through the Susan G. Komen Foundation and provided detailed risk factor data (n=100). Bisulfite modified DNA was profiled for DNA methylation genome-wide using the Infinium 450K DNA methylation array. We tested the relation of known breast cancer risk factors such as age, BMI, parity, and family history of disease with DNA methylation adjusted for variation in cell type proportions using a novel cellular mixture deconvolution algorithm. We identified 787 CpGs that exhibited significant (FDR adjusted, q-value < 0.01) differential DNA methylation associated with subject age, but not with other breast cancer risk factors. We observed an enrichment among the risk factor-related CpGs for Polycomb group target genes (Fisher’s Exact test, P = 1.74E-06), and breast myoepithelial cell enhancer regions (H3K4me1; Fisher’s Exact test, P = 7.1E-20). We validated our risk factor-related findings in two independent populations of normal breast tissue (n=18 and n=97). In addition, age-related CpGs were further deregulated in both pre-invasive (DCIS, n=40) and invasive breast cancers (TCGA, n=731). Overall, our results suggest that the breast cancer risk factor age contributes to epigenetic dysregulation in normal breast tissue that exhibit progressive changes in cancer.