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The dynamics of DNA methylation covariation patterns in carcinogenesis

Journal article
Authors Teschendorff A
X Liu
Helena Carén
SM Pollard
S Beck
M Widschwendter
L Chen
Published in PLoS Computational Biology
Volume 10
Issue 7
ISSN 1553-734X
Publication year 2014
Published at Sahlgrenska Cancer Center
Institute of Biomedicine, Department of Pathology
Language en
Links dx.doi.org/10.1371/journal.pcbi.100...
Subject categories Medical Biotechnology, Cancer and Oncology, Biostatistics, Bioinformatics (Computational Biology)

Abstract

Recently it has been observed that cancer tissue is characterised by an increased variability in DNA methylation patterns. However, how the correlative patterns in genome-wide DNA methylation change during the carcinogenic progress has not yet been explored. Here we study genome-wide inter-CpG correlations in DNA methylation, in addition to single site variability, during cervical carcinogenesis. We demonstrate how the study of changes in DNA methylation covariation patterns across normal, intra-epithelial neoplasia and invasive cancer allows the identification of CpG sites that indicate the risk of neoplastic transformation in stages prior to neoplasia. Importantly, we show that the covariation in DNA methylation at these risk CpG loci is maximal immediately prior to the onset of cancer, supporting the view that high epigenetic diversity in normal cells increases the risk of cancer. Consistent with this, we observe that invasive cancers exhibit increased covariation in DNA methylation at the risk CpG sites relative to normal tissue, but lower levels relative to pre-cancerous lesions. We further show that the identified risk CpG sites undergo preferential DNA methylation changes in relation to human papilloma virus infection and age. Results are validated in independent data including prospectively collected samples prior to neoplastic transformation. Our data are consistent with a phase transition model of carcinogenesis, in which epigenetic diversity is maximal prior to the onset of cancer. The model and algorithm proposed here may allow, in future, network biomarkers predicting the risk of neoplastic transformation to be identified.

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