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首页> 外文期刊>BMC Medical Genomics >Parameter, noise, and tree topology effects in tumor phylogeny inference
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Parameter, noise, and tree topology effects in tumor phylogeny inference

机译：参数，噪声和树形拓扑效应在肿瘤系统发生的推理中

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Accurate inference of the evolutionary history of a tumor has important implications for understanding and potentially treating the disease. While a number of methods have been proposed to reconstruct the evolutionary history of a tumor from DNA sequencing data, it is not clear how aspects of the sequencing data and tumor itself affect these reconstructions. We investigate when and how well these histories can be reconstructed from multi-sample bulk sequencing data when considering only single nucleotide variants (SNVs). Specifically, we examine the space of all possible tumor phylogenies under the infinite sites assumption (ISA) using several approaches for enumerating phylogenies consistent with the sequencing data. On noisy simulated data, we find that the ISA is often violated and that low coverage and high noise make it more difficult to identify phylogenies. Additionally, we find that evolutionary trees with branching topologies are easier to reconstruct accurately. We also apply our reconstruction methods to both chronic lymphocytic leukemia and clear cell renal cell carcinoma datasets and confirm that ISA violations are common in practice, especially in lower-coverage sequencing data. Nonetheless, we show that an ISA-based approach can be relaxed to produce high-quality phylogenies. Consideration of practical aspects of sequencing data such as coverage or the model of tumor evolution (branching, linear, etc.) is essential to effectively using the output of tumor phylogeny inference methods. Additionally, these factors should be considered in the development of new inference methods.
机译：准确推理肿瘤的进化历史具有重要意义对理解和潜在治疗疾病。虽然已经提出了许多方法来从DNA测序数据重建肿瘤的进化历史，但目前尚不清楚测序数据和肿瘤本身的方面如何影响这些重建。在考虑仅考虑单个核苷酸变体（SNV）时，我们可以调查这些历史可以从多样本批量测序数据重建。具体而言，我们使用几种方法检查无限位点下的所有可能肿瘤系统的空间，所述方法是使用几种方法来枚举与测序数据一致的文化。在嘈杂的模拟数据中，我们发现ISA经常违反，低覆盖率和高噪声使得识别系统发育更难。此外，我们发现具有分支拓扑的进化树更容易准确地重建。我们还将重建方法应用于慢性淋巴细胞白血病和透明细胞肾细胞癌数据集，并确认ISA违规在实践中是常见的，特别是在较低覆盖的测序数据中。尽管如此，我们表明可以放宽基于ISA的方法以产生高质量的系统发育。对诸如覆盖或肿瘤演化模型（分支，线性等）的测序数据的实际方面的考虑对于有效地使用肿瘤系统发育方法的输出至关重要。此外，应考虑在新推理方法的发展中考虑这些因素。

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