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.

Aberrant DNA methylation is frequently observed in breast cancer. However, the relationship between methylation patterns and the heterogeneity of breast cancer has not been comprehensively characterized. Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 188 human breast tumors. Unsupervised bootstrap consensus clustering was performed to identify DNA methylation epigenetic subgroups (epitypes). The Cancer Genome Atlas data, incluing methylation profiles of 669 human breast tumors, was utilized for validation. The identified epitypes were characterized by integration with publicly available genome-wide data, including gene expression levels, DNA copy numbers, whole-exome sequencing data, and chromatin states. We identified seven breast cancer epitypes. One epitype was distinctly associated with basal-like tumors and with BRCA1 mutations, one epitype contained a subset of ERBB2-amplified tumors characterized by multiple additional amplifications and the most complex genomes, and one epitype displayed a methylation profile similar to normal epithelial cells. Luminal tumors were stratified into the remaining four epitypes, with differences in promoter hypermethylation, global hypomethylation, proliferative rates and genomic instability. We observed two dominant patterns of aberrant methylation in breast cancer. One pattern, constitutively methylated in both basal-like and luminal breast cancer, was linked to genes with promoters in a Polycomb-repressed state in normal epithelial cells and displayed no correlation to gene expression levels. The second pattern correlated with gene expression levels and was associated with methylation in luminal tumors and genes with active promoters in normal epithelial cells. Our results suggest that hypermethylation patterns in basal-like breast cancer may have limited influence on tumor progression and instead reflects the repressed chromatin state of the tissue of origin. On the contrary, hypermethylation patterns specific to luminal breast cancer influence gene expression, may contribute to tumor progression, and may present an actionable epigenetic alteration in some luminal breast cancers. Genome-wide DNA methylation analysis of 188 breast cancers using Illumina Human Methylation 450K Beadchips.

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.

Aberrant hypermethylation of CpG dinucleotides located in CpG islands within the promoters of key cancer genes is an epigenetic abnormality associated with heritable transcriptional gene silencing and inactivation in cancer. The genes involved include important tumor suppressors affecting key pathways for tumor initiation and progression. These methylated sequences can serve as potentially valuable markers for cancer risk assessment, diagnosis, prognosis, and prediction of therapeutic responses. In addition, many key cancer genes may be targeted by both epigenetic and genetic alterations and, thus epigenetic analysis can help focus the search for mutations, and vice versa. Studies of major cancer types suggest that any individual patient’s tumor may harbor at least 300 or more DNA hypermethylated genes. In TCGA, a pilot project is underway to begin defining these genes for GBM via genomic approaches. The approach in the epigenetic pilot is a two-tiered one which, first, involves pharmacological treatment of both well established human GBM cell lines, and a cell line grown as a neurosphere to enrich for tumor propagating cells, with a DNA methylation inhibitor (5-aza-2’-deoxycytidine, DAC) or a histone deacetylation inhibitor (trichostatin A) followed by an expression transcriptome analysis as previously described (Schuebel et. al.). This has resulted in identification of more than 3,700 total candidate genes. In the second tier, the top candidates are then analyzed on a custom Illumina GoldenGate array with the capacity to monitor methylation at a single CpG dinucleotide in the CpG islands of 1,498 gene promoters for the high throughput analysis of TCGA GBM samples. Keywords: Microarray, Hypermethylome, DNA-hypermethylation, DAC, TSA, Epigenetic, TCGA, The Cancer Genome Atlas, GBM, Glioblastoma, Glioblastoma multiforme, Brain

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).

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.

Aberrant DNA methylation changes are known to occur during prostate cancer progression beginning with precursor lesions. Utilizing fifty nanograms of genomic DNA in Methylplex-Next Generation Sequencing (M-NGS) we mapped the global DNA methylation patterns in prostate tissues (n=17) and cells (n=2). Peaks were located from mapped reads obtained in each sequencing run using a Hidden Markov Model (HMM)-based algorithm previously used for Chip-Seq data analysis(http://www.sph.umich.edu/csg/qin/HPeak). The total methylation events in intergenic/intronic regions between benign adjacent and cancer tissues were comparable. Promoter CGI methylation gradually increased from -12.6% in benign samples to 19.3% and 21.8% in localized and metastatic cancer tissues and approximately 20% of all CpG islands (CGIs) (68,508) were methylated in tissues. We observed 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. Overall Design: 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). 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 sequenced twice on Illumina next generation sequencing platform to access overall repeatability of M-NGS.

Background: Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is an actual driver of cancer or is a mere consequence of the carcinogenic process remains to be elucidated.

Results: In this work we performed an integrative multi -omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a custom arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of such DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact in the reactivation of gene expression and in the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence.

Conclusions: These results highlight the potential role of DNA methylation as a driver mechanism of CRC and open up the venue for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.

Introduction/ Background: The epigenome represents and adds another facet of complexity to cancer that needs to be understood to categorize patients into those at risk of disease, recurrence, or treatment failure. Our goal is to compare DNA methylation status between controls vs HGSOC, and relate it with clinical outcomes.Methodology: A retrospective review of our institution’s advanced/ recurrent HGSOC patients yielded 99 cases with good quality DNA and RNA. 12 normal fallopian tubes were also collected and used as controls. Infinium Illumina methylationEPIC was used to characterize DNA methylation status and RNA seq to evaluate gene expression. T- tests were used to compare methylation patterns between controls vs HGSOC, primary vs recurrent, and optimal vs suboptimal surgical outcomes. Spearman’s rank correlation was used to evaluate association between degree of methylation and expression of identified genes. Validation of our findings used the cancer genome atlas (TCGA) database followed by C-statistics to assess degree of agreement.Results: Out of 66,069 methylation probes that interrogate known genes, 5,852 probes were significantly different between normal and HGSOC. This includes genes that are enriched in several pathways such as the RAS signaling pathway. A similar analysis of the TCGA database showed 2,075 differentially methylated probes. 1,891 probes were significant in both datasets giving a 70.1% degree of agreement. When comparing differential DNA methylation between primary and recurrent disease, there were 57 probes which represent 17 genes that were significantly different. When comparing optimal vs suboptimal surgical outcomes, 99 probes were significantly different wherein 29 genes show expected inverse correlation between methylation status and gene expression.Conclusions: There are significant differences in methylation patterns between HGSOC and fallopian tubes, primary vs recurrent tissues, and optimal vs sub-optimal surgical outcomes. Cataloging these phenomena in a well characterized clinical population will aid in determining groupings for precision medicine treatment cohorts in the future.

Global loss of DNA methylation in mammalian genomes occurs cumulatively as a mitotic process during aging and cancer, primarily in Partially Methylated Domains (PMDs). It has been shown that local sequence context (100bp) has a strong effect on the rate of demethylation of individual CpG dinucleotides within PMDs. Here, we train a deep learning model to characterize this sequence dependence further, finding that methylation loss can be predicted from a CpG’s 150bp sequence context alone with an AUC of 0.95. We use re-methylation rates of newly synthesized DNA to show that CpGs with fast-loss sequence context are inefficiently re-methylated. Interestingly, we find that the 10% of CpGs predicted to have the “slowest” rate of loss lose almost no DNA methylation in healthy cell types. These same slow-loss CpGs lose a significant amount of DNA methylation in cancer, suggesting that they could be responsible for deregulation of genes and transposable elements that are associated with DNA hypomethylation in cancer.

This directory contains the Sep. 20, 2020 version of the human (hg19) CpG hypomethylation Neural network scores in one gzip-compressed tsv file per chromosome.

The Sep. 2020 Neural network score provides a prediction of the probability of each sequence to be a fast hypomethylation CpG, which was produced by a neural network model that used two independent input training datasets.

Files included in this directory:

- chr*. tsv.gz: Neural network score of each CpG in each chromosome, using hg19 coordinates. chrX and chrY are omitted.

Each row is a CG which provides (1) chromosome, (2) the corresponding C coordinate on the forward (watson) strand of the reference genome in one-based coordinates, (3) Neural network score, (4) number of CpGs within the 150bp sequence centered on this CpG, including the center CpG, (5) CpG is within a CpG island (0, no; 1, yes), CpG is within ENCODE blacklist (0, no; 1, yes)

Here the CpG islands are the union set of Irizarry (Irizarry et al. 2009, Nat Genet), Takai-Jones (Takai et al. 2002, PNAS), Gardner-Gardin CGIs (Gardner-Gardin et al. 1987, J Mol Biol.). The blacklist was downloaded from https://github.com/Boyle-Lab/Blacklist/tree/master/lists.

We obtained a comprehensive DNA methylation profile of 15 breast cancer discordant twins, using the high resolution Infinium HumanMethylation450 BeadChip platform (450K, Illumina), previously established to reliably detect methylation changes of more than 450,000 CpG sites. To provide insight into the temporal and causal relationships and predictive potential, samples from breast cancer patients before (7) and after diagnosis (8) were also analyzed. Using whole blood from 15 twin pairs discordant for breast cancer and high-resolution (450k) DNA methylation analysis we identified 403 differentially methylated CpG sites including known and novel potential breast cancer genes. 30 Samples

PURPOSE: Non-small cell lung cancers (NSCLC) comprise multiple distinct biological groups with different prognoses. For example, patients with epithelial-like (EL) tumors have a better prognosis and exhibit greater sensitivity to inhibitors of the epidermal growth factor receptor (EGFR) pathway than patients with mesenchymal-like (ML) tumors. Here we test the hypothesis that EL NSCLCs can be distinguished from ML NSCLCs on the basis of global DNA methylation patterns. EXPERIMENTAL DESIGN: To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. RESULTS: We show that patterns of methylation divide NSCLCs into EL and ML subsets as defined by gene expression and that these signatures are similarly correlated in NSCLC cell lines and tumors. We identify multiple DMRs, including ERBB2 and ZEB2, whose methylation status is strongly associated with an epithelial phenotype in NSCLC cell lines, surgically resected tumors, and formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy. CONCLUSIONS: Our data demonstrate that patterns of DNA methylation can divide NSCLCs into two phenotypically distinct subtypes of tumors and provide proof of principle that differences in DNA methylation can be used for predictive biomarker discovery and development. To determine whether phenotypic subsets of NSCLC can be defined based on their DNA methylation patterns, we combined microfluidics-based gene expression analysis and genome-wide methylation profiling. We derived robust classifiers for both gene expression and methylation in cell lines and tested these classifiers in surgically resected NSCLC tumors. We validate our approach using quantitative RT-PCR and methylation specific PCR in formalin-fixed biopsies from NSCLC patients who went on to fail front-line chemotherapy.

In our study we applied a genome-wide DNA methylation analysis approach, MethylCap-seq, to map the differentially methylated regions in 24 tumor and matched normal colon samples. In total, 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, which include potential biomarkers for CRC diagnosis. Hypermethylation in the tumor samples was enriched at CpG islands and gene promoters, while hypomethylation was distributed throughout the genome. Using epigenetic data from human embryonic stem cells, we show that frequent differentially methylated regions (DMRs) coincide with bivalent loci in human embryonic stem cells. DNA methylation is commonly thought to lead to cancer gene related silencing, however integration of publically available expression analysis shows that 75% of the frequently hypermethylation genes were most likely already lowly or not expressed in normal tissue. Collectively, our study provides genome-wide DNA methylation maps of colon cancer, comprehensive lists of DMRs, and gives further clues on the role of aberrant DNA methylation in CRC formation.

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.

Glioblastoma (GBM) is thought to be driven by a sub-population of cancer stem cells (CSCs) that self-renew and recapitulate tumor heterogeneity, yet remain poorly understood. Here we present a comparative epigenomic analysis of GBM CSCs that reveals widespread activation of genes normally held in check by Polycomb repressors. These activated targets include a large set of developmental transcription factors (TFs) whose coordinated activation is unique to the CSCs. We demonstrate that a critical factor in the set, ASCL1, activates Wnt signaling by repressing the negative regulator DKK1. We show that ASCL1 is essential for maintenance and in vivo tumorigenicity of GBM CSCs. Genomewide binding profiles for ASCL1 and the Wnt effector LEF1 provide mechanistic insight and suggest widespread interactions between the TF module and the signaling pathway. Our findings demonstrate regulatory connections between ASCL1, Wnt signaling and collaborating TFs that are essential for the maintenance and tumorigenicity of GBM CSCs. Epigenomic profiling of glioblastoma stem cells

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 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 Overall design: Bisulphite converted DNA from the 3 samples were hybridised to the Illumina Infinium 27k Human Methylation Beadchip v1.2

Ductal carcinoma in situ (DCIS) is a heterogeneous, pre-invasive lesion associated with an increased risk for future invasive ductal carcinoma. However, accurate risk stratification for development of invasive disease and appropriate treatment decisions remain clinical challenges. DNA methylation alterations are recognized to be early events in the progression of cancer and represent emerging molecular markers that may predict invasive recurrence more accurately than traditional measures of DCIS prognosis. We measured DNA methylation of DCIS (n=40) and adjacent normal (n=15) tissues using the Illumina HumanMethylation450 array. We identified locus-specific methylation differences between DCIS and matched adjacent-normal tissue (95,609 CpGs, Q < 0.05). Among 40 DCIS cases 13 later developed invasive disease and we identified 641 CpG sites that exhibited differential DNA methylation (P < 0.01 and medianúΔβú > 0.1) in these cases compared with age-matched subjects without invasive disease over a similar follow up period. The set of differentially methylated CpG loci associated with disease progression was enriched in homeobox-containing genes (P = 1.3E-9) and genes involved with limb morphogenesis (P = 1.0E-05). In an independent cohort, a subset of genes with progression-related differential methylation between DCIS and invasive breast cancer were confirmed. Further, the functional relevance of these genes’ regulation by methylation was demonstrated in early stage breast cancers from The Cancer Genome Atlas database. This work contributes to the understanding of epigenetic alterations that occur in DCIS and illustrates the potential of DNA methylation as markers of DCIS progression.

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.