重建体细胞DNA变异的历史可以帮助理解肿瘤的演化过程并预测其对治疗的耐药性。单细胞DNA测序（scDNAseq）可以用于研究克隆异质性并推断系统发育关系。然而，现有的用于scDNAseq数据的大多数系统发育方法要么设计用于单核苷酸变异（SNVs），要么设计用于大的拷贝数变异（CNAs），或者不适用于靶向测序。在此，我们开发了一种名为COMPASS的计算方法，用于从靶向scDNAseq数据中推断SNVs和CNAs的联合系统发育。我们使用模拟数据评估了COMPASS，并将其应用于多个数据集，包括一个由123例急性髓系白血病患者组成的队列。COMPASS检测到了可以通过大规模数据进行正交验证的克隆性CNAs，除此之外还检测到了需要单细胞分辨率的次克隆性CNAs，其中一些指向了趋同进化现象。© 2023年 Springer Nature有限公司。

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.© 2023. Springer Nature Limited.