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Reconstructing Mutational History in Multiply Sampled Tumors Using Perfect Phylogeny Mixtures

机译:使用完善的系统发育混合物重建多样本肿瘤的突变史

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High-throughput sequencing of cancer genomes have motivated the problem of inferring the ancestral history of somatic mutations that accumulate in cells during cancer progression. While the somatic mutation process in cancer cells meets the requirements of the classic Perfect Phylogeny problem, nearly all cancer sequencing studies do not sequence single cancerous cells, but rather thousands-millions of cells in a tumor sample. In this paper, we formulate the Perfect Phylogeny Mixture problem of inferring a perfect phylogeny given somatic mutation data from multiple tumor samples, each of which is a superposition of cells, or "species." We prove that the Perfect Phylogeny Mixture problem is NP-hard, using a reduction from the graph coloring problem. Finally, we derive an algorithm to solve the problem.
机译:癌症基因组的高通量测序引发了一个问题,即推断在癌症进展过程中细胞中积累的体细胞突变的祖传历史。尽管癌细胞中的体细胞突变过程符合经典的“完美系统发育”问题的要求,但几乎所有的癌症测序研究都不对单个癌细胞进行测序,而是对肿瘤样本中成千上万的细胞进行测序。在本文中,我们制定了完善的系统发育混合物问题,可以根据来自多个肿瘤样本的体细胞突变数据推断出完美的系统发育,每个样本都是细胞或“物种”的叠加。我们通过使用图形着色问题的减少,证明了完美的系统发育混合物问题是NP难的。最后,我们推导了解决该问题的算法。

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