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Increased methylation variation in epigenetic domains across cancer types

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      Summary

      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, suggesting that these cDMRs might be generalized across cancer types. Here we show stochastic methylation variation of the same cDMRs, distinguishing cancer from normal, in colon, lung, breast, thyroid, and Wilms tumors, with intermediate variation in adenomas. Whole genome bisulfite sequencing shows these variable cDMRs are related to loss of sharply delimited methylation boundaries at CpG islands. Furthermore, we find hypomethylation of discrete blocks encompassing half the genome, with extreme gene expression variability. Genes associated with the cDMRs and large blocks are involved in mitosis and matrix remodeling, respectively. These data suggest a model for cancer involving loss of epigenetic stability of well-defined genomic domains that underlies increased methylation variability in cancer and could contribute to tumor heterogeneity.

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      Most cited references 42

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      A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

      When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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        Human DNA methylomes at base resolution show widespread epigenomic differences.

        DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA-protein interaction for several key regulatory factors. Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes. Nearly one-quarter of all methylation identified in embryonic stem cells was in a non-CG context, suggesting that embryonic stem cells may use different methylation mechanisms to affect gene regulation. Methylation in non-CG contexts showed enrichment in gene bodies and depletion in protein binding sites and enhancers. Non-CG methylation disappeared upon induced differentiation of the embryonic stem cells, and was restored in induced pluripotent stem cells. We identified hundreds of differentially methylated regions proximal to genes involved in pluripotency and differentiation, and widespread reduced methylation levels in fibroblasts associated with lower transcriptional activity. These reference epigenomes provide a foundation for future studies exploring this key epigenetic modification in human disease and development.
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          Epigenetics in cancer.

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            Author and article information

            Affiliations
            [1 ] Dept. of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
            [2 ] Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
            [3 ] Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
            [4 ] Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
            [5 ] Center for Bioinformatics and Computational Biology, Department of Computer Science, University of Maryland, College Park, MD, USA
            [6 ] Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
            [7 ] Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
            [8 ] Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
            [9 ] Department of Bioengineering, Institute for Genomic Medicine and Institute of Engineering in Medicine, University of California at San Diego, San Diego, CA, USA
            Author notes
            []Correspondence to Rafael A. Irizarry and Andrew P. Feinberg: rafa@ 123456jhu.edu , afeinberg@ 123456jhu.edu
            [*]

            Equal contributions from these authors

            Journal
            9216904
            2419
            Nat Genet
            Nature genetics
            1061-4036
            1546-1718
            21 June 2011
            26 June 2011
            1 February 2012
            : 43
            : 8
            : 768-775
            3145050
            21706001
            10.1038/ng.865
            nihpa299446

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            Genetics

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