The dynamics of DNA methylation covariation patterns in carcinogenesis

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 corre...

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Published inPLoS computational biology Vol. 10; no. 7; p. e1003709
Main Authors Teschendorff, Andrew E, Liu, Xiaoping, Caren, Helena, Pollard, Steve M, Beck, Stephan, Widschwendter, Martin, Chen, Luonan
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 01.07.2014
Public Library of Science (PLoS)
Subjects
Algorithms
Bioinformatics (Computational Biology)
Bioinformatik (beräkningsbiologi)
Biology and life sciences
Biomarkers, Tumor - genetics
Cancer
Cancer and Oncology
Cancer och onkologi
Carcinogenesis - classification
Carcinogenesis - genetics
Carcinogens
Cervical cancer
Computational Biology - methods
Deoxyribonucleic acid
Disease
Disease Progression
DNA
DNA methylation
DNA Methylation - genetics
DNA sequencing
Epigenetics
Female
Gene loci
Genetic aspects
Genetic research
Genomes
Human papillomavirus
Humans
Medical Biotechnology
Medicine and Health Sciences
Medicinsk bioteknologi
Metastasis
Methylation
Nucleotide sequencing
Oncology, Experimental
Papillomavirus Infections
Phase transitions
Physical Sciences
Standard deviation
Studies
Uterine Cervical Neoplasms
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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.
AbstractList 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.
  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.
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. DNA methylation is a covalent modification of DNA which can regulate how active genes are. DNA methylation is altered at many genomic loci in cancer cells, leading to widespread functional disruption. Importantly, DNA methylation alterations across the genome are seen even in early carcinogenesis. Although the pattern of DNA methylation change during carcinogenesis has been studied at individual genomic loci, no study has yet analysed how these patterns change at a systems-level, specifically how do DNA methylation patterns at pairs of genomic sites change during disease progression. Doing so can shed light on how the epigenetic diversity of cell populations changes during the carcinogenic process. This study performs a systems-level analysis of the dynamic changes in DNA methylation correlation pattern during cervical carcinogenesis, demonstrating that epigenetic diversity is maximal just prior to the onset of cancer. Importantly, this supports the view that the risk of cancer development is closely related to an increase in epigenetic diversity in apparently healthy cells. In addition, the study provides a computational algorithm which successfully identifies the altered genomic sites confering the risk of cervical cancer.
Audience Academic
Author Widschwendter, Martin
Teschendorff, Andrew E
Pollard, Steve M
Caren, Helena
Liu, Xiaoping
Beck, Stephan
Chen, Luonan
AuthorAffiliation 1 CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, Shanghai, China
2 Statistical Genomics Group, UCL Cancer Institute, University College London, London, United Kingdom
3 Key Laboratory of Systems Biology, SIBS-Nordisk Translational Research Centre for PreDiabetes, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
7 Department of Women's Cancer, UCL EGA Institute for Women's Health, University College London, London, United Kingdom
4 Sahlgrenska Cancer Center, Department of Pathology, University of Gothenburg, Gothenburg, Sweden
Ottawa University, Canada
6 Medical Genomics Group, UCL Cancer Institute, University College London, London, United Kingdom
5 Department of Cancer Biology, UCL Cancer Institute, University College London, London, United Kingdom
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– name: 3 Key Laboratory of Systems Biology, SIBS-Nordisk Translational Research Centre for PreDiabetes, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
– name: 6 Medical Genomics Group, UCL Cancer Institute, University College London, London, United Kingdom
– name: 2 Statistical Genomics Group, UCL Cancer Institute, University College London, London, United Kingdom
– name: 7 Department of Women's Cancer, UCL EGA Institute for Women's Health, University College London, London, United Kingdom
– name: 1 CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, Shanghai, China
– name: Ottawa University, Canada
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  surname: Teschendorff
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  organization: CAS-MPG Partner Institute for Computational Biology, Chinese Academy of Sciences, Shanghai Institute for Biological Sciences, Shanghai, China; Statistical Genomics Group, UCL Cancer Institute, University College London, London, United Kingdom
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  organization: Key Laboratory of Systems Biology, SIBS-Nordisk Translational Research Centre for PreDiabetes, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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ContentType Journal Article
Copyright COPYRIGHT 2014 Public Library of Science
2014 Teschendorff et al 2014 Teschendorff et al
2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Teschendorff AE, Liu X, Caren H, Pollard SM, Beck S, Widschwendter M, et al. (2014) The Dynamics of DNA Methylation Covariation Patterns in Carcinogenesis. PLoS Comput Biol 10(7): e1003709. doi:10.1371/journal.pcbi.1003709
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– notice: 2014 Teschendorff et al 2014 Teschendorff et al
– notice: 2014 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Teschendorff AE, Liu X, Caren H, Pollard SM, Beck S, Widschwendter M, et al. (2014) The Dynamics of DNA Methylation Covariation Patterns in Carcinogenesis. PLoS Comput Biol 10(7): e1003709. doi:10.1371/journal.pcbi.1003709
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Notes Conceived and designed the experiments: AET. Performed the experiments: AET XL HC SMP. Analyzed the data: AET XL. Contributed reagents/materials/analysis tools: LC MW SB HC SMP. Wrote the paper: AET.
The authors have declared that no competing interests exist.
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Snippet Recently it has been observed that cancer tissue is characterised by an increased variability in DNA methylation patterns. However, how the correlative...
  Recently it has been observed that cancer tissue is characterised by an increased variability in DNA methylation patterns. However, how the correlative...
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StartPage e1003709
SubjectTerms Algorithms
Bioinformatics (Computational Biology)
Bioinformatik (beräkningsbiologi)
Biology and life sciences
Biomarkers, Tumor - genetics
Cancer
Cancer and Oncology
Cancer och onkologi
Carcinogenesis - classification
Carcinogenesis - genetics
Carcinogens
Cervical cancer
Computational Biology - methods
Deoxyribonucleic acid
Disease
Disease Progression
DNA
DNA methylation
DNA Methylation - genetics
DNA sequencing
Epigenetics
Female
Gene loci
Genetic aspects
Genetic research
Genomes
Human papillomavirus
Humans
Medical Biotechnology
Medicine and Health Sciences
Medicinsk bioteknologi
Metastasis
Methylation
Nucleotide sequencing
Oncology, Experimental
Papillomavirus Infections
Phase transitions
Physical Sciences
Standard deviation
Studies
Uterine Cervical Neoplasms
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Title The dynamics of DNA methylation covariation patterns in carcinogenesis
URI https://www.ncbi.nlm.nih.gov/pubmed/25010556
https://pubmed.ncbi.nlm.nih.gov/PMC4091688
https://gup.ub.gu.se/publication/200892
https://doaj.org/article/9bf8292883f84c2aa825e8947f100590
http://dx.doi.org/10.1371/journal.pcbi.1003709
Volume 10
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