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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Does Relaxing the Infinite Sites Assumption Give Better Tumor Phylogenies? An ILP-Based Comparative Approach
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Does Relaxing the Infinite Sites Assumption Give Better Tumor Phylogenies? An ILP-Based Comparative Approach

机译：放宽无限的位点假设会带来更好的肿瘤系统发育吗？

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摘要

Most of the evolutionary history reconstruction approaches are based on the infinite sites assumption, which states that mutations appear once in the evolutionary history. The Perfect Phylogeny model is the result of the infinite sites assumption and has been widely used to infer cancer evolution. Nonetheless, recent results show that recurrent and back mutations are present in the evolutionary history of tumors, hence the Perfect Phylogeny model might be too restrictive. We propose an approach that allows losing previously acquired mutations and multiple acquisitions of a character. Moreover, we provide an ILP formulation for the evolutionary tree reconstruction problem. Our formulation allows us to tackle both the Incomplete Directed Phylogeny problem and the Clonal Reconstruction problem when general evolutionary models are considered. The latter problem is fundamental in cancer genomics, the goal is to study the evolutionary history of a tumor considering as input data the fraction of cells having a certain mutation in a set of cancer samples. For the Clonal Reconstruction problem, an experimental analysis shows the advantage of allowing mutation losses. Namely, by analyzing real and simulated datasets, our ILP approach provides a better interpretation of the evolutionary history than a Perfect Phylogeny. The software is at https://github.com/AlgoLab/gppf.
机译：大多数进化历史重建方法都是​​基于无限位点假设，该假设指出突变在进化历史中会出现一次。完善的系统发育模型是无限位点假设的结果，已被广泛用于推断癌症的发展。尽管如此，最近的结果表明，在肿瘤的进化史中存在反复出现的突变和反向突变，因此“完美系统发育”模型可能过于严格。我们提出了一种允许丢失先前获得的突变和角色的多次获得的方法。此外，我们为进化树重建问题提供了ILP公式。当考虑到一般的进化模型时，我们的公式使我们能够解决不完全定向系统发育问题和克隆重建问题。后一个问题是癌症基因组学的基础，目标是研究肿瘤的进化史，以一组癌症样本中具有一定突变的细胞比例作为输入数据。对于克隆重建问题，实验分析显示了允许突变丢失的优势。也就是说，通过分析真实和模拟的数据集，我们的ILP方法比“完美系统进化论”能够更好地解释进化历史。该软件位于https://github.com/AlgoLab/gppf。

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