多态最佳系统发育混合解卷积及其在癌症测序中的应用
Multi-State Perfect Phylogeny Mixture Deconvolution and Applications to Cancer Sequencing
摘要：
从混合群体重建系统发育树在癌症演变的研究中变得重要，因为测序通常在含有混合细胞群体的大块肿瘤组织上进行。最近的工作表明，如何从包含两种状态字符的混合物的样本中重建一个完美的遗传树，其中每个字符/位点要么突变要么不突变。然而，大多数癌症含有更复杂的突变，如拷贝数畸变，表现出两种以上的状态。我们制定了多状态完美的系统发育混合反卷积问题重建一个多状态完美的系统发育树给定的混合物的树叶。我们将该问题的解刻画为由输入数据构造的图中的一类受限生成树，并证明了该问题是NP-完全的。我们推导出一个算法来枚举这样的树的分支特征的重要特殊情况下，每个字符的状态的顺序。我们将我们的算法应用于模拟数据和两个癌症数据集。在模拟数据上，我们发现，对于少量的样本，多状态完美系统发育混合反卷积问题往往有许多解决方案，但这种模糊性下降迅速样本数量的增加。在真实数据上，我们恢复了拷贝中性的杂合性丢失、单拷贝扩增和单拷贝缺失事件，以及它们与单核苷酸变体的相互作用。
英文摘要：
The reconstruction of phylogenetic trees from mixed populations has become important in the study of cancer evolution, as sequencing is often performed on bulk tumor tissue containing mixed populations of cells. Recent work has shown how to reconstruct a perfect phylogeny tree from samples that contain mixtures of two-state characters, where each character/locus is either mutated or not. However, most cancers contain more complex mutations, such as copy-number aberrations, that exhibit more than two states. We formulate the Multi-State Perfect Phylogeny Mixture Deconvolution Problem of reconstructing a multi-state perfect phylogeny tree given mixtures of the leaves of the tree. We characterize the solutions of this problem as a restricted class of spanning trees in a graph constructed from the input data, and prove that the problem is NP-complete. We derive an algorithm to enumerate such trees in the important special case of cladisitic characters, where the ordering of the states of each character is given. We apply our algorithm to simulated data and to two cancer datasets. On simulated data, we find that for a small number of samples, the Multi-State Perfect Phylogeny Mixture Deconvolution Problem often has many solutions, but that this ambiguity declines quickly as the number of samples increases. On real data, we recover copy-neutral loss of heterozygosity, single-copy amplification and single-copy deletion events, as well as their interactions with single-nucleotide variants.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
