Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures
Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures
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DOI：
10.1016/j.cels.2016.07.004
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发表时间：
2016-07-27
期刊：
影响因子：
9.3
通讯作者：
Raphael, Benjamin J.
中科院分区：
文献类型：
--
作者：
El-Kebir, Mohammed;Satas, Gryte;Raphael, Benjamin J.
系统发育技术越来越多地应用于从 DNA 测序数据推断肿瘤的体细胞突变历史。然而，标准的系统发育树重建技术没有考虑到批量测序数据测量细胞群中突变的事实。我们制定并解决了在多状态完美系统发育或无限等位基因模型下，在给定叶子混合的情况下重建系统发育树的多状态完美系统发育混合反卷积问题。我们使用组合枚举（SPRUCE）算法的体细胞系统发育重建使用该模型从单核苷酸变异（SNV）和拷贝数畸变（CNA）联合构建系统发育树。我们证明 SPRUCE 解决了 SNV 和 CNA 同步分析的复杂性。特别是，通常有许多可能的系统发育树与数据一致，但随着样本数量的增加，歧义性大大降低。这些发现对肿瘤测序策略具有影响，建议谨慎基于单树重建得出强有力的结论，并解释将现有系统发育技术应用于肿瘤测序数据所面临的困难。
Phylogenetic techniques are increasingly applied to infer the somatic mutational history of a tumor from DNA sequencing data. However, standard phylogenetic tree reconstruction techniques do not account for the fact that bulk sequencing data measures mutations in a population of cells. We formulate and solve the multi-state perfect phylogeny mixture deconvolution problem of reconstructing a phylogenetic tree given mixtures of its leaves, under the multi-state perfect phylogeny, or infinite alleles model. Our somatic phylogeny reconstruction using combinatorial enumeration (SPRUCE) algorithm uses thismodel to construct phylogenetic trees jointly from single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). We show that SPRUCE addresses complexities in simultaneous analysis of SNVs and CNAs. In particular, there are often many possible phylogenetic trees consistent with the data, but the ambiguity decreases considerably with an increasing number of samples. These findings have implications for tumor sequencing strategies, suggest caution in drawing strong conclusions based on a single tree reconstruction, and explain difficulties faced by applying existing phylogenetic techniques to tumor sequencing data.