PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data
PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data
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DOI：
10.1101/gr.234435.118
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发表时间：
2019-11-01
期刊：
影响因子：
7
通讯作者：
Sahinalp, S. Cenk
中科院分区：
文献类型：
--
作者：
Malikic, Salem;Mehrabadi, Farid Rashidi;Sahinalp, S. Cenk
通过单细胞测序（SCS）数据进行肿瘤发生推断的可用计算方法通常旨在鉴定满足无限位点假设（伊萨）的最可能的完美发生树。然而，SCS技术的局限性，包括频繁的等位基因缺失和可变的序列覆盖可能会阻止完美的同源性。此外，由于杂合性丢失、缺失和趋同进化，在肿瘤发生中通常观察到伊萨违反。为了解决这样的限制，我们引入了最优次完美测序问题，其要求通过最小化潜在假阴性（由于等位基因缺失或序列覆盖度的变化）、突变调用中的假阳性（由于读取错误）和违反伊萨的突变的数量（真实的或由于不正确的拷贝数估计）的线性组合来整合SCS数据与匹配的批量测序数据。然后，我们描述了一个组合配方来解决这个问题，确保了几个谱系的约束所施加的变异等位基因频率（VAF，来自批量序列数据）的使用得到满足。我们以整数线性规划（ILP）的形式表达我们的公式，并作为肿瘤系统发育重建中的第一个布尔约束满足问题（CSP），并通过利用最先进的ILP/CSP求解器来解决它们。由此产生的方法，我们命名为PhISCS，是第一个整合SCS和批量测序数据，同时考虑伊萨违反突变。与通常基于概率方法的替代方法相比，PhISCS在报告的解决方案中提供了最优性保证。使用模拟和真实的数据集，我们证明了PhISCS比所有可用的方法更普遍和准确。
Available computational methods for tumor phylogeny inference via single-cell sequencing (SCS) data typically aim to identify the most likely perfect phylogeny tree satisfying the infinite sites assumption (ISA). However, the limitations of SCS technologies including frequent allele dropout and variable sequence coverage may prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions, and convergent evolution. In order to address such limitations, we introduce the optimal subperfect phylogeny problem which asks to integrate SCS data with matching bulk sequencing data by minimizing a linear combination of potential false negatives (due to allele dropout or variance in sequence coverage), false positives (due to read errors) among mutation calls, and the number of mutations that violate ISA (real or because of incorrect copy number estimation). We then describe a combinatorial formulation to solve this problem which ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and-as a first in tumor phylogeny reconstruction-a Boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data while accounting for ISA violating mutations. In contrast to the alternative methods, typically based on probabilistic approaches, PhISCS provides a guarantee of optimality in reported solutions. Using simulated and real data sets, we demonstrate that PhISCS is more general and accurate than all available approaches.