A zero-agnostic model for copy number evolution in cancer.
A zero-agnostic model for copy number evolution in cancer.
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DOI：
10.1371/journal.pcbi.1011590
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发表时间：
2023-11
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
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新的低覆盖率单细胞DNA测序技术能够测量肿瘤内数千个单个细胞的拷贝数谱。从这些数据中，人们可以通过模拟基因组的拷贝数畸变来推断肿瘤的进化史。拷贝数畸变改变了多个相邻的基因组位点，违反了位点独立进化的标准系统发育假设。因此，引入了专门的模型来推断拷贝数系统发育。一个广泛使用的模型是拷贝数转换（CNT）模型，其中基因组由整数向量表示，拷贝数畸变是增加或减少基因组连续片段拷贝数的事件。一对拷贝数配置文件之间的CNT距离是将一个配置文件转换为另一个配置文件所需的最小事件数。虽然这个距离可以有效地计算，但在碳纳米管模型下，还没有开发出有效的算法来找到最简洁的系统发育。我们引入了零不可知拷贝数转换（zero-agnostic copy number transformation， ZCNT）模型，这是CNT模型的一种简化，允许扩增或删除具有零拷贝的区域。我们推导了两个拷贝数轮廓之间的ZCNT距离的封闭形式表达式，并表明，与CNT距离不同，ZCNT距离形成了一个度量。我们利用ZCNT距离的封闭形式表达式和拷贝数轮廓的另一种表征来推导出拷贝数轮廓上小简约问题的两个自然松弛的多项式时间算法。虽然在ZCNT模型下允许的零拷贝数区域的改变在生物学上是不现实的，但我们在模拟和真实数据集上都表明，ZCNT距离与CNT距离非常接近。扩展了ZCNT小简约问题的多项式时间算法，我们开发了一种算法Lazac，用于解决拷贝数轮廓上的大简约问题。我们证明了Lazac在模拟和真实数据上都优于现有的推断拷贝数系统发育的方法。拷贝数畸变是大基因组区域的扩增或缺失，在癌症中经常发生。然而，从拷贝数畸变重建癌症进化是具有挑战性的，因为与单核苷酸突变不同，拷贝数畸变经常在基因组上重叠。在这里，我们引入零不可知论拷贝数转换（ZCNT）模型来描述基因组中拷贝数畸变的积累。ZCNT模型在一个能够对进化树进行可扩展推理的框架中解释了拷贝数进化的一些复杂性。我们通过有效地重建来自单个肿瘤的数千个单细胞的进化树，证明了ZCNT模型的实用性。我们预计ZCNT模型将被证明对未来单细胞分辨率下肿瘤进化的大规模研究有用。
New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data. Copy number aberrations are amplifications or deletions of large genomic regions, and occur frequently in cancer. However, reconstructing cancer evolution from copy number aberrations is challenging because unlike single-nucleotide mutations, copy number aberrations often overlap on the genome. Here, we introduce the zero agnostic copy number transformation (ZCNT) model to describe the accumulation of copy number aberrations in a genome. The ZCNT model accounts for some of the complexities of copy number evolution in a framework that enables scalable inference of evolutionary trees. We demonstrate the utility of the ZCNT model by efficiently reconstructing evolutionary trees for thousands of single cells from individual tumors. We anticipate that the ZCNT model will prove useful for future large-scale studies of tumor evolution at single-cell resolution.
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影响因子：
46.9
作者：
Gao, Teng;Soldatov, Ruslan;Kharchenko, Peter, V
通讯作者：
Kharchenko, Peter, V
DOI：
10.1007/978-1-4471-5298-9_3
发表时间：
2013-01-01
期刊：
MODELS AND ALGORITHMS FOR GENOME EVOLUTION
影响因子：
--
作者：
Csuroes, Miklos
通讯作者：
Csuroes, Miklos
DOI：
10.1093/bioinformatics/btac041
发表时间：
2022-03-28
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Hui S;Nielsen R
通讯作者：
Nielsen R
影响因子：
2.7
作者：
SANKOFF, D;ROUSSEAU, P
通讯作者：
ROUSSEAU, P
影响因子：
10.7
作者：
SAITOU, N;NEI, M
通讯作者：
NEI, M