Division of Clinical Genetics, Department of Laboratory Medicine, Lund University, Lund, Sweden.
Department of Pediatrics, Skåne University Hospital, Lund, Sweden.
Commun Biol. 2021 Sep 20;4(1):1103. doi: 10.1038/s42003-021-02637-6.
Phylogenetic reconstruction of cancer cell populations remains challenging. There is a particular lack of tools that deconvolve clones based on copy number aberration analyses of multiple tumor biopsies separated in time and space from the same patient. This has hampered investigations of tumors rich in aneuploidy but few point mutations, as in many childhood cancers and high-risk adult cancer. Here, we present DEVOLUTION, an algorithm for subclonal deconvolution followed by phylogenetic reconstruction from bulk genotyping data. It integrates copy number and sequencing information across multiple tumor regions throughout the inference process, provided that the mutated clone fraction for each mutation is known. We validate DEVOLUTION on data from 56 pediatric tumors comprising 253 tumor biopsies and show a robust performance on simulations of bulk genotyping data. We also benchmark DEVOLUTION to similar bioinformatic tools using an external dataset. DEVOLUTION holds the potential to facilitate insights into the development, progression, and response to treatment, particularly in tumors with high burden of chromosomal copy number alterations.
癌症细胞群体的系统发生重建仍然具有挑战性。目前特别缺乏能够根据同一患者不同时间和空间采集的多个肿瘤活检的拷贝数异常分析来推断克隆的工具。这阻碍了对存在大量非整倍体但点突变较少的肿瘤的研究，如许多儿童癌症和高危成人癌症。在这里，我们提出了 DEVOLUTION，这是一种从批量基因分型数据进行亚克隆推断和系统发生重建的算法。它在整个推断过程中整合了多个肿瘤区域的拷贝数和测序信息，前提是每个突变的突变克隆分数是已知的。我们在包含 253 个肿瘤活检的 56 个儿科肿瘤数据上验证了 DEVOLUTION，并对批量基因分型数据的模拟表现出了稳健的性能。我们还使用外部数据集将 DEVOLUTION 与类似的生物信息学工具进行了基准测试。DEVOLUTION 有可能促进对肿瘤发展、进展和治疗反应的深入了解，特别是在具有高染色体拷贝数改变负担的肿瘤中。
