Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors
Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors
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DOI：
10.1186/s13015-019-0155-6
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发表时间：
2019-09-03
影响因子：
1
通讯作者：
El-Kebir, Mohammed
中科院分区：
文献类型：
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作者：
Qi, Yuanyuan;Pradhan, Dikshant;El-Kebir, Mohammed
背景肿瘤在肿瘤内表现出广泛的异质性，即具有不同体细胞突变集合的细胞群体的存在。这种异质性是进化过程的结果，用系统发育树来描述。除了使临床医生能够设计针对患者的治疗计划外，肿瘤的系统发育树还使研究人员能够破译肿瘤发生和转移的机制。然而，在给定大量肿瘤测序数据的情况下，重建系统发育树T的问题比经典的系统发育推断问题要复杂得多。我们不是直接观察T的叶子，而是给出突变频率，这些频率是T的叶子混合的结果。目前的大多数肿瘤系统发育推断方法采用了完美的系统发育进化模型。潜在的完美系统发育混合体(PPM)组合问题通常有多个解。结果我们证明了确定PPM问题的精确解的个数是#P-完全的，并且很难在一个常数因子内逼近。此外，我们还证明了均匀随机抽样解也是困难的。在积极的一面，我们提供了一个多项式时间的可计算的解的数量的上限，并介绍了一个简单的基于拒绝抽样的方案，该方案在小实例中工作得很好。使用模拟和真实数据，我们确定了导致和抵消解的非唯一性的因素。此外，我们还研究了现有方法的抽样性能，识别出显著的偏差。结论认识到PPM问题解决方案的非唯一性是在基于肿瘤系统发生的下游分析中得出准确结论的关键。这项工作为从大量DNA样本推断肿瘤系统发育的解的非唯一性提供了理论基础。
Background Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. In addition to enabling clinicians to devise patient-specific treatment plans, phylogenetic trees of tumors enable researchers to decipher the mechanisms of tumorigenesis and metastasis. However, the problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. The underlying Perfect Phylogeny Mixture (PPM) combinatorial problem typically has multiple solutions. Results We prove that determining the exact number of solutions to the PPM problem is #P-complete and hard to approximate within a constant factor. Moreover, we show that sampling solutions uniformly at random is hard as well. On the positive side, we provide a polynomial-time computable upper bound on the number of solutions and introduce a simple rejection-sampling based scheme that works well for small instances. Using simulated and real data, we identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases. Conclusions Awareness of non-uniqueness of solutions to the PPM problem is key to drawing accurate conclusions in downstream analyses based on tumor phylogenies. This work provides the theoretical foundations for non-uniqueness of solutions in tumor phylogeny inference from bulk DNA samples.