PhyClone: Accurate Bayesian reconstruction of cancer phylogenies from bulk sequencing.

Bioinformatics (Oxford, England) Pub Date : 2025-06-13 DOI:10.1093/bioinformatics/btaf344

Emilia Hurtado, Alexandre Bouchard-Côté, Andrew Roth

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Abstract

Motivation: 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.

Results: 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.

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

Supplementary information: Supplementary data are available at Bioinformatics online.

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PhyClone：从大量测序中精确的贝叶斯重建癌症系统发育。

动机：癌症是由体细胞突变驱动的，这种突变导致基因组不同的细胞亚群（称为克隆）的扩张。确定肿瘤的克隆组成和了解克隆之间的进化关系是癌症基因组学的关键任务。大量DNA测序通常用于研究肿瘤的克隆组成，但由于不同细胞群的混合，推断不同克隆之间的遗传关系具有挑战性。结果：在这项工作中，我们引入了一个新的概率模型，称为PhyClone，可以从大量测序数据推断克隆系统发育。我们演示了PhyClone在模拟和真实数据集上的性能，并表明它在准确性和样本可扩展性方面优于以前的方法。可用性和实现：在GPL v3.0许可下，源代码可在Github上获得：https://github.com/Roth-Lab/PhyClone。补充信息：补充数据可在生物信息学在线获取。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Bioinformatics (Oxford, England)

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