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Comparing copy-number profiles under multi-copy amplifications and deletions

机译：比较多拷贝放大和删除下的副本数字配置文件

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During cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by 1 or ?1, even though these events can affect large portions of a chromosome. We propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor 2 approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called cnp2cnp. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by cnp2cnp and compare it against two other alternatives, namely the MEDICC distance and the Euclidean distance. The experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the MEDICC distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the MEDICC approach should preferred over the Euclidean distance.
机译：在癌症进展期间，恶性细胞积累了可导致遗传畸变的体细胞突变。特别地，类似于节段性重复或缺失的进化事件可以改变基因组中一组基因的拷贝数曲线（CNP）。我们的目的是计算只知道CNP的两个小区之间的进化距离。这要求对分段放大的最小数量和删除，以将一个CNP变为另一个CNP。这最近被形式化为一个模型，其中假设每个事件以1或？1更改副本号，即使这些事件可能会影响染色体的大量部分。我们提出了一般成本框架，其中一个事件可以通过较大的量来修改基因的拷贝数。我们表明，允许任意长度的分段删除的任何成本方案都使得计算距离强烈的NP-HARD。然后，当复制编号非零时，我们为问题设计了一个因子2近似算法并提供称为CNP2CNP的实现。我们通过从CNP2CNP推断的成对距离重建模拟癌症系统来评估我们的方法，并将其与另外两种替代方案进行比较，即Medicc距离和欧几里德距离。实验结果表明，如果给定的CNPS是无差错的话，我们的距离平均比这些替代更准确的系统发育，但是，Mymedc距离对数据中的错误稍微鲁棒。在所有情况下，我们的实验表明我们的方法或Medicc方法应该优于欧几里德距离。

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