# Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection

## TL;DR

This paper introduces a Bayesian model to analyze DNA methylation epialleles across tumor regions, revealing tumor evolution, heterogeneity, and epigenetic disorder with improved statistical power and uncertainty quantification.

## Contribution

A novel Bayesian approach for inferring and analyzing epialleles across multiple tumor regions, enhancing understanding of tumor heterogeneity and evolution.

## Key findings

- Identified tumor-specific epialleles and their distribution.
- Reconstructed tumor phylogenetic history based on epiallele patterns.
- Quantified epigenetic disorder within tumor samples.

## Abstract

Motivation: Epigenetic 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. Results: We 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.