Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection

BMC Bioinformatics

Methodology Article

Andrew Feber¹  Javier Herrero¹  Stephan Beck¹  James E. Barrett¹  Miljana Tanic¹  Gareth A. Wilson²  Charles Swanton³ 

[1] UCL Cancer Institute, University College London, London, UK;UCL Cancer Institute, University College London, London, UK;The Francis Crick Institute, London, UK;UCL Cancer Institute, University College London, London, UK;The Francis Crick Institute, London, UK;Cancer Research U.K. Lung Cancer Centre of Excellence, UCL Cancer Institute, London, UK;University College London Hospitals NHS Foundation Trust, London, UK;

关键词: Epigenetics;    Phylogenetics;    Heterogeneity;   

DOI  :  10.1186/s12859-017-1753-2

 received in 2017-02-28, accepted in 2017-07-05,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundEpigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome – so-called ‘epialleles’ – offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually.ResultsWe have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are contaminated with normal tissue can be estimated and corrected for. By tracing the distribution of epialleles throughout the tumour we can infer the phylogenetic history of the tumour, identify epialleles that differ between normal and cancer tissue, and define a measure of global epigenetic disorder.ConclusionsDetection and comparison of epialleles within multiple tumour regions enables phylogenetic analyses, identification of differentially expressed epialleles, and provides a measure of epigenetic heterogeneity. R code is available at github.com/james-e-barrett.

【 授权许可】

CC BY   
© The Author(s) 2017

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