Stochastic epigenetic outliers can define field defects in cancer. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Teschendorff, Andrew; Jones, Allison; Widschwendter, Martin
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 14
• Publication Date: 2016-12
• Subjects: DNA methylation -- Field defect -- Cancer -- EWAS -- Differential variability -- Differential methylation -- Stochastic; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1056-z
• Languages: English
• ISSNs: 1471-2105

Abstract

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.