Department of Cell and Developmental Biology, University College London, London, UK.
UCL Genetics Institute, University College London, London, UK.
Genome Biol. 2023 Jun 20;24(1):144. doi: 10.1186/s13059-023-02983-0.
Phylogenetic trees based on copy number profiles from multiple samples of a patient are helpful to understand cancer evolution. Here, we develop a new maximum likelihood method, CNETML, to infer phylogenies from such data. CNETML is the first program to jointly infer the tree topology, node ages, and mutation rates from total copy numbers of longitudinal samples. Our extensive simulations suggest CNETML performs well on copy numbers relative to ploidy and under slight violation of model assumptions. The application of CNETML to real data generates results consistent with previous discoveries and provides novel early copy number events for further investigation.
基于患者多个样本的拷贝数谱构建的系统进化树有助于理解癌症的进化。在这里，我们开发了一种新的最大似然法 CNETML，以从这些数据中推断系统进化树。CNETML 是第一个从纵向样本的总拷贝数联合推断树拓扑、节点年龄和突变率的程序。我们的广泛模拟表明，CNETML 在与ploidy 相关的拷贝数上表现良好，并且在轻微违反模型假设的情况下也能很好地工作。CNETML 在真实数据上的应用产生的结果与先前的发现一致，并为进一步研究提供了新的早期拷贝数事件。
