Distance measures for tumor evolutionary trees
Distance measures for tumor evolutionary trees
复制标题
DOI：
10.1093/bioinformatics/btz869
复制
发表时间：
2020-04-01
期刊：
影响因子：
5.8
通讯作者：
Oesper, Layla
中科院分区：
文献类型：
--
作者：
DiNardo, Zach;Tomlinson, Kiran;Oesper, Layla
动机：最近，人们对使用算法方法来推断肿瘤发展历史的进化树越来越感兴趣。比较这些树的定量测量对于许多不同的应用至关重要，包括基准树推理方法和评估患者之间的常见遗传模式。然而，很少有合适的距离测量存在，和那些有低分辨率区分树或不完全考虑树拓扑和标记拓扑的突变的继承之间的复杂关系。结果：在这里，我们提出了两个新的距离度量，共同祖先集距离（CASet）和独特继承集比较距离（DISC），它们是专门设计来解释肿瘤进化树的亚克隆突变遗传模式的。我们将CASet和DISC应用于多个模拟数据集和两个乳腺癌数据集，并表明我们的距离测量比现有的距离测量允许肿瘤进化树之间更细致入微和准确的描绘。
Motivation: There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology.Results: Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures.