Using epigenetic outliers to diagnose cancer

This 2016 Chinese/UK human cancer cell study tested five algorithms and found:

“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.

Many DNA methylation outliers are not technical artefacts, but define epigenetic field defects which are selected for during cancer progression.”

The usual method of epigenetic studies involves:

“Identify genomic sites where the mean level of DNAm [DNA methylation] differs as much as possible between the two phenotypes. As we have seen however, such an approach is seriously underpowered in cancer studies where tissue availability is a major obstacle.

In addition to allelic frequency, we also need to take the magnitude of the alteration into consideration. As shown here, infrequent but bigger changes in DNAm (thus defining outliers) are more likely to define cancer field defects, than more frequent yet smaller DNAm changes.”

A similar point was made in Genetic statistics don’t necessarily predict the effects of an individual’s genes:

“Epigenomic analyses are limited by averaging of population-wide dynamics and do not inform behavior of single cells.”

One of the five tested algorithms was made freely available by the researchers. The limitations on its use were discussed, and included:

“Studies conducted in a surrogate tissue such as blood are scenarios where DNAm outliers are probably not of direct biological relevance to cancer development.” “Stochastic epigenetic outliers can define field defects in cancer”


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