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Journal Article

A DNA Methylation Fingerprint of 1628 Human Samples

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Assenov,  Yassen
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Bock,  Christoph
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Citation

Fernandez, A. F., Assenov, Y., Martin-Subero, J. I., Balint, B., Siebert, R., Taniguchi, H., et al. (2012). A DNA Methylation Fingerprint of 1628 Human Samples. Genome Research, 22(2), 407-419. doi:10.1101/gr.119867.110.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-C9A2-D
Abstract
Most of the studies characterizing DNA methylation patterns have been restricted to particular genomic loci in a limited number of human samples and pathological conditions. Herein, we present a compromise between an extremely comprehensive study of a human sample population with an intermediate level of resolution of CpGs at the genomic level. We obtained a DNA methylation fingerprint of 1628 human samples in which we interrogated 1505 CpG sites. The DNA methylation patterns revealed show this epigenetic mark to be critical in tissue-type definition and stemness, particularly around transcription start sites that are not within a CpG island. For disease, the generated DNA methylation fingerprints show that, during tumorigenesis, human cancer cells underwent a progressive gain of promoter CpG-island hypermethylation and a loss of CpG methylation in non-CpG-island promoters. Although transformed cells are those in which DNA methylation disruption is more obvious, we observed that other common human diseases, such as neurological and autoimmune disorders, had their own distinct DNA methylation profiles. Most importantly, we provide proof of principle that the DNA methylation fingerprints obtained might be useful for translational purposes by showing that we are able to identify the tumor type origin of cancers of unknown primary origin (CUPs). Thus, the DNA methylation patterns identified across the largest spectrum of samples, tissues, and diseases reported to date constitute a baseline for developing higher-resolution DNA methylation maps and provide important clues concerning the contribution of CpG methylation to tissue identity and its changes in the most prevalent human diseases.