PhyClone: accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing

TL;DR

PhyClone is a new method that accurately reconstructs the evolutionary history of cancer clones from bulk sequencing data.

Contribution

PhyClone introduces a probabilistic model that improves accuracy and scalability in inferring clonal phylogenies from bulk sequencing.

Findings

PhyClone outperforms existing methods in reconstructing clonal phylogenies from bulk sequencing data.

The method demonstrates strong performance on both simulated and real-world datasets.

PhyClone is scalable and can handle larger sample sizes effectively.

Abstract

Cancer is driven by somatic mutations that result in the expansion of genomically distinct sub-populations of cells called clones. Identifying the clonal composition of tumours and understanding the evolutionary relationships between clones is a crucial task in cancer genomics. Bulk DNA sequencing is commonly used for studying the clonal composition of tumours, but it is challenging to infer the genetic relationship between different clones due to the mixture of different cell populations.

In this work, we introduce a new probabilistic model called PhyClone that can infer clonal phylogenies from bulk-sequencing data. We demonstrate the performance of PhyClone on simulated and real-world datasets and show that it outperforms previous methods in terms of accuracy and sample scalability.

Source code is available on Github at: https://github.com/Roth-Lab/PhyClone under the GPL v3.0 license.

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