Teschendorff, AE;
Jones, A;
Widschwendter, M;
(2016)
Stochastic epigenetic outliers can define field defects in cancer.
BMC Bioinformatics
, 17
, Article 178. 10.1186/s12859-016-1056-z.
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Abstract
BACKGROUND: There is growing evidence that DNA methylation alterations may contribute to carcinogenesis. Recent data also suggest that DNA methylation field defects in normal pre-neoplastic tissue represent infrequent stochastic “outlier” events. This presents a statistical challenge for standard feature selection algorithms, which assume frequent alterations in a disease phenotype. Although differential variability has emerged as a novel feature selection paradigm for the discovery of outliers, a growing concern is that these could result from technical confounders, in principle thus favouring algorithms which are robust to outliers. RESULTS: Here we evaluate five differential variability algorithms in over 700 DNA methylomes, including two of the largest cohorts profiling precursor cancer lesions, and demonstrate that most of the novel proposed algorithms lack the sensitivity to detect epigenetic field defects at genome-wide significance. In contrast, algorithms which recognise heterogeneous outlier DNA methylation patterns are able to identify many sites in pre-neoplastic lesions, which display progression in invasive cancer. Thus, we show that many DNA methylation outliers are not technical artefacts, but define epigenetic field defects which are selected for during cancer progression. CONCLUSIONS: Given that cancer studies aiming to find epigenetic field defects are likely to be limited by sample size, adopting the novel feature selection paradigm advocated here will be critical to increase assay sensitivity.
| Type: | Article |
|---|---|
| Title: | Stochastic epigenetic outliers can define field defects in cancer |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12859-016-1056-z |
| Publisher version: | http://dx.doi.org/10.1186/s12859-016-1056-z |
| Language: | English |
| Additional information: | This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| Keywords: | Science & Technology, Life Sciences & Biomedicine, Biochemical Research Methods, Biotechnology & Applied Microbiology, Mathematical & Computational Biology, Biochemistry & Molecular Biology, DNA methylation, Field defect, Cancer, EWAS, Differential variability, Differential methylation, Stochastic, DNA METHYLATION DATA, DIFFERENTIAL VARIABILITY, BREAST-CANCER, GENE-EXPRESSION, MICROARRAY, CELLS, SCALE, TAXONOMY, RISK |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Womens Cancer |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1490312 |
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