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.

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.

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.

This dataset contains genome-wide DNA methylation data generated from 142 pediatric acute myeloid leukemia (AML) samples originating from bone marrow or peripheral blood samples taken at AML diagnosis (N=123) or relapse (N=19). Further details regarding the samples are available in Supplementary Table S1 from Krali and Palle et. al., 2021 (https://doi.org/10.3390/genes12060895).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 200ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The Methylprep Python library was used to generate and normalize the beta-value matrix (https://pypi.org/project/methylprep/1.3.3/).The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation, and signal intensities matrix will be made available upon request. Limited phenotype information is available in the Supplemental Table 1 of the manuscript. All scripts that give a walk-through from data preprocessing from the raw idat files until the modelling process with Machine Learning can be found on the following GitHub repository: https://github.com/Molmed/Krali-Palle_2021.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.

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

Aging and cancer are two interrelated biological processes, with aging being one of the most important risk factors for the development of cancer. Parallel epigenetic alterations have been described for both, although differences, especially within the DNA hypomethylation scenario, have also been identified in recent literature. While many of these observations arise from the use of mouse models, there is a lack of systematic and single-base resolution comparisons of human and mouse epigenetic patterns in the context of disease. However, such comparisons are especially significant with respect to the DNA methylation alterations found independently in the two species as they allow to establish the extent to which some of the observed similarities or differences arise from pre-existing species-specific epigenetic traits. Here, we have used reduced representation bisulfite sequencing to profile the brain methylomes of young and old, tumoral and non-tumoral brain samples from human and mouse. We first characterized the baseline epigenomic patterns of the species and subsequently focused on the DNA methylation alterations associated with cancer and aging. Next, we described the functional genomic and epigenomic context associated with the alterations, and finally we integrated our data in order to study interspecies DNA methylation levels at specific CpG sites. Globally, we found robust evidence for the conservation of cancer and aging-associated epigenomic patterns in both species, and our observations point towards the preservation of the functional consequences of these alterations at multiple levels of genomic regulation.

This dataset contains information related to dataframes, databases, scripts and supplementary material mentioned in the original manuscript.

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)

Tumor heterogeneity is a major barrier to effective cancer diagnosis and treatment. We recently identified cancer-specific differentially DNA-methylated regions (cDMRs) in colon cancer, which also distinguish normal tissue types from each other. We therefore hypothesized that these colon cDMRs might also be cDMRs for other cancer types. For colon, lung, breast, thyroid, and Wilms tumors, we show stochastic variation in methylation within each tumor type, but involving the same loci across tumor types, with intermediate values of variation for premalignant adenomas. Seeking to increase the precision of DNA methylation measurements over our previous tiling array-based approach, termed CHARM (Comprehensive High-throughput Array for Relative Methylation), we designed a custom nucleotide-specific bead array on the Illumina GoldenGate platform to analyze 151 colon cDMRs from Irizarry et al. (PMID 19151715). The resulting 384 probes covered 139 regions, with 1-7 probes per region. We studied 290 samples, including cancers from colon (10), lung (24), breast (27), thyroid (36), and kidney (Wilms) (25), with matched normal tissues to 111 of these 122 cancers, along with 30 colon premalignant adenomas, 18 normal colon, and 9 normal breast samples.

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

Accumulating evidence has suggested the requirement for further stratification of patients in the same tumor stage according to molecular factors. We evaluate the combination of cancer stage and DNA methylation status as an indicator of the risk of recurrence and mortality among patients with colorectal cancer (CRC). A cohort study of 215 patients with CRC (mean age 64.32 years; 50.5% of men) from Tri-Service General Hospital in Taiwan examined the association between cancer stage and risk of CRC recurrence and mortality. A Cox proportional hazard model was used to analyze patient methylation status and clinical information at study entry, and their associations with CRC recurrence and mortality during follow-up. The advanced stage patients with p16, hMLH1, and MGMT methylation were associated with higher risk of CRC recurrence compared with the local stage patients with unmethylation status in tumor tissues, with adjusted hazard ratios (HRs) (95% confidence interval [CI]) of 9.64 (2.92–31.81), 8.29 (3.40–20.22), and 11.83 (3.49–40.12), respectively. When analyzing normal tissues, we observed similar risk of CRC recurrence with adjusted HRs (95% CI) of 10.85 (4.06–28.96), 9.04 (3.79–21.54), and 12.61 (4.90–32.44), respectively. For combined analyses, the risk of recurrence in the patients in advanced stage with DNA methylation in both normal and tumor tissues, compared with local stage with unmethylation, was increased with adjusted HR (95% CI) of 9.37 (3.36–26.09). In the advanced stage patients, methylation status and tissue subtype were associated with increased risk of 5-year cumulative CRC recurrence (p < 0.001). This study demonstrates that clustering DNA methylation status according to cancer stage and tissue subtype is critical for the assessment of risk of recurrence in CRC patients and also indicated an underlying mechanism.

Prior candidate gene studies have shown tumor suppressor DNA methylation in breast milk related with history of breast biopsy, an established risk factor for breast cancer. To further establish the utility of breast milk as a tissue-specific biospecimen for investigations of breast carcinogenesis we measured genome-wide DNA methylation in breast milk from women with and without a diagnosis of breast cancer in two independent cohorts.

DNA methylation was assessed using Illumina HumanMethylation450k in 87 breast milk samples. Through an Epigenome Wide Association Study we explored CpG sites associated with a breast cancer diagnosis in the prospectively collected milk samples from the breast that would develop cancer compared with women without a diagnosis of breast cancer using linear mixed effects models adjusted for history of breast biopsy, age, RefFreeCellMix cell estimates, time of delivery, array chip, and subject as random effect.

The full analyses results are deposited here.

This dataset contains genome-wide DNA methylation data generated from 384 pediatric acute lymphoblastic leukemia (ALL) samples originating from bone marrow or peripheral blood samples taken at ALL diagnosis (n = 384). Further details regarding the samples are available in Supplementary Table S2 from Krali et al., 2023 (https://doi.org/10.1038/s41698-023-00479-5).Genome-wide DNA methylation was analyzed at the SNP&SEQ Technology Platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. 250 ng of bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human Methylation450k Beadchip using the standard protocol from Illumina (iScan SQ instrument).This metadata record contains information about the raw idat files generated from the Infinium DNA methylation arrays. The raw idat files were processed with Methylation Module (1.8.5) software in Genome Studio (V2010.3). Peak-based correction was used to normalize the beta-value matrix.The raw idat files along with a samplesheet, processed beta-value matrix, annotation file for CpG annotation will be made available upon request. Limited phenotype information is available in the Supplemental Table S2 of the manuscript. All scripts that give a walk-through to our project, including the modelling process with Machine Learning can be found in our GitHub repository.Terms for accessThe DNA methylation dataset is only to be used for research that is seeking to advance the understanding of the influence of epigenetic factors on leukemia etiology and biology.The data should not be used for other purposes, i.e. investigating the epigenetic signatures that may lead to identification of a person.For retrieving the data used for the scope of this publication, please contact datacentre@scilifelab.se.

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.

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

Breast cancer is the most common cancer in the world, and DNA methylation plays a key role in the occurrence and development of breast cancer. However, the effect of DNA methylation in different gene functional regions on gene expression and the effect of gene expression on breast cancer is not completely clear. In our study, we computed and analyzed DNA methylation, gene expression, and clinical data in the TCGA database. Firstly, we calculated the distribution of abnormal DNA methylated probes in 12 regions, found the abnormal DNA methylated probes in down-regulated genes were highly enriched, and the number of hypermethylated probes in the promoter region was 6.5 times than that of hypomethylated probes. Secondly, the correlation coefficients between abnormal DNA methylated values in each functional region of differentially expressed genes and gene expression values were calculated. Then, co-expression analysis of differentially expressed genes was performed, 34 hub genes in cancer-related pathways were obtained, of which 11 genes were regulated by abnormal DNA methylation. Finally, a multivariate Cox regression analysis was performed on 27 probes of 11 genes. Three DNA methylation probes (cg13569051 and cg14399183 of GSN, and cg25274503 of CAV2) related to survival were used to construct a prognostic model, which has a good prognostic ability. Furthermore, we found that the cg25274503 hypermethylation in the promoter region inhibited the expression of the CAV2, and the hypermethylation of cg13569051 and cg14399183 in the 5′UTR region inhibited the expression of GSN. These results may provide possible molecular targets for breast cancer.

Aberrant DNA methylation is a critical feature of cancer. Epigenetic therapy seeks to reverse these changes to restore normal gene expression. DNA demethylating agents, including 5-aza-2′-deoxycytidine (DAC), are currently used to treat certain leukemias, and can sensitize solid tumors to chemotherapy and immunotherapy. However, it has been difficult to pin the clinical efficacy of these agents to specific demethylation events, and the factors that contribute to the durability of response remain largely unknown. Here we examined the genome-wide kinetics of DAC-induced DNA demethylation and subsequent remethylation after drug withdrawal in breast cancer cells. We find that CpGs differ in both their susceptibility to demethylation and propensity for remethylation after drug removal. DAC-induced demethylation was most apparent at CpGs with higher initial methylation levels and further from CpG islands. Once demethylated, such sites exhibited varied remethylation potentials. The most rapidly remethylating CpGs regained >75% of their starting methylation within a month of drug withdrawal. These sites had higher pretreatment methylation levels, were enriched in gene bodies, marked by H3K36me3, and tended to be methylated in normal breast cells. In contrast, a more resistant class of CpG sites failed to regain even 20% of their initial methylation after 3 months. These sites had lower pretreatment methylation levels, were within or near CpG islands, marked by H3K79me2 or H3K4me2/3, and were overrepresented in sites that become aberrantly hypermethylated in breast cancers. Thus, whereas DAC-induced demethylation affects both endogenous and aberrantly methylated sites, tumor-specific hypermethylation is more slowly regained, even as normal methylation promptly recovers. Taken together, these data suggest that the durability of DAC response is linked to its selective ability to stably reset at least a portion of the cancer methylome.

BackgroundHundreds of genes with differential DNA methylation of promoters have been identified for various cancers. However, the reproducibility of differential DNA methylation discoveries for cancer and the relationship between DNA methylation and aberrant gene expression have not been systematically analysed. Methodology/Principal FindingsUsing array data for seven types of cancers, we first evaluated the effects of experimental batches on differential DNA methylation detection. Second, we compared the directions of DNA methylation changes detected from different datasets for the same cancer. Third, we evaluated the concordance between methylation and gene expression changes. Finally, we compared DNA methylation changes in different cancers. For a given cancer, the directions of methylation and expression changes detected from different datasets, excluding potential batch effects, were highly consistent. In different cancers, DNA hypermethylation was highly inversely correlated with the down-regulation of gene expression, whereas hypomethylation was only weakly correlated with the up-regulation of genes. Finally, we found that genes commonly hypomethylated in different cancers primarily performed functions associated with chronic inflammation, such as ‘keratinization’, ‘chemotaxis’ and ‘immune response’. ConclusionsBatch effects could greatly affect the discovery of DNA methylation biomarkers. For a particular cancer, both differential DNA methylation and gene expression can be reproducibly detected from different studies with no batch effects. While DNA hypermethylation is significantly linked to gene down-regulation, hypomethylation is only weakly correlated with gene up-regulation and is likely to be linked to chronic inflammation.

Breast cancer has various molecular subtypes and displays high heterogeneity. Aberrant DNA methylation is involved in tumor origin, development and progression. Moreover, distinct DNA methylation patterns are associated with specific breast cancer subtypes. We explored DNA methylation patterns in association with gene expression to assess their impact on the prognosis of breast cancer based on Infinium 450K arrays (training set) from The Cancer Genome Atlas (TCGA). The DNA methylation patterns of 12 featured genes that had a high correlation with gene expression were identified through univariate and multivariable Cox proportional hazards models and used to define the methylation risk score (MRS). An improved ability to distinguish the power of the DNA methylation pattern from the 12 featured genes (p = 0.00103) was observed compared with the average methylation levels (p = 0.956) or gene expression (p = 0.909). Furthermore, MRS provided a good prognostic value for breast cancers even when the patients had the same receptor status. We found that ER-, PR- or Her2- samples with high-MRS had the worst 5-year survival rate and overall survival time. An independent test set including 28 patients with death as an outcome was used to test the validity of the MRS of the 12 featured genes; this analysis obtained a prognostic value equivalent to the training set. The predict power was validated through two independent datasets from the GEO database. The DNA methylation pattern is a powerful predictor of breast cancer survival, and can predict outcomes of the same breast cancer molecular subtypes.