Inference of mutability landscapes of tumors from single cell sequencing data.
Inference of mutability landscapes of tumors from single cell sequencing data.
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DOI：
10.1371/journal.pcbi.1008454
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发表时间：
2020-11
影响因子：
4.3
通讯作者：
Skums P
中科院分区：
文献类型：
--
作者：
Tsyvina V;Zelikovsky A;Snir S;Skums P
癌症的特征之一是肿瘤细胞具有极高的易变性和遗传不稳定性。肿瘤内群体的内在异质性表现在克隆不稳定率的高变异性上。与适合度景观类似，无性系种群的不稳定率形成了它们的易变性景观。在这里，我们提出了MULAN (MUtability LANdscape inference)，这是一个最大似然计算框架，用于使用单细胞测序数据推断单个癌症亚克隆的突变率。它利用癌症突变树提供的关于突变事件顺序的部分信息，并通过推断肿瘤的完整进化历史和易变性景观来扩展它。在亚克隆而不是单个基因的水平上评估突变率可以捕捉基因组相互作用和上位性的影响。我们估计了我们的方法的准确性，并证明它可以用于研究遗传不稳定性的进化和从实验数据推断肿瘤的进化史。花木兰可在https://github.com/compbel/MULAN上购买。癌症是一个在肿瘤细胞群中展开的动态进化过程。这些细胞的基因组改变组合会影响它们的复制和存活。特别是，肿瘤内突变率和遗传不稳定性往往显著高于正常率。基因改变组合对癌细胞遗传不稳定性的影响可能是高度非线性的。在本文中，我们提出了一种称为MULAN的计算方法，可以估计由这种非线性遗传相互作用形成的异质肿瘤内群体的不稳定率。为了实现这一目标，我们利用单细胞测序，可以捕捉精确的癌症克隆，而不仅仅是单个突变。我们证明了我们的方法的准确性，并展示了如何将其应用于实验肿瘤数据，以研究遗传不稳定性的演变和推断进化史。提出的方法可以为癌症的进化动力学提供新的见解。
One of the hallmarks of cancer is the extremely high mutability and genetic instability of tumor cells. Inherent heterogeneity of intra-tumor populations manifests itself in high variability of clone instability rates. Analogously to fitness landscapes, the instability rates of clonal populations form their mutability landscapes. Here, we present MULAN (MUtability LANdscape inference), a maximum-likelihood computational framework for inference of mutation rates of individual cancer subclones using single-cell sequencing data. It utilizes the partial information about the orders of mutation events provided by cancer mutation trees and extends it by inferring full evolutionary history and mutability landscape of a tumor. Evaluation of mutation rates on the level of subclones rather than individual genes allows to capture the effects of genomic interactions and epistasis. We estimate the accuracy of our approach and demonstrate that it can be used to study the evolution of genetic instability and infer tumor evolutionary history from experimental data. MULAN is available at https://github.com/compbel/MULAN. Cancer is a dynamical evolutionary process that unfolds in populations of tumor cells. Combinations of genomic alterations of these cells affect their replication and survival. In particular, intra-tumor rates of mutation and genetic instability are often significantly higher than the normal rate. The impact of combinations of gene alterations on the genetic instability of cancer cells could be highly non-linear. In this paper, we present a computational approach called MULAN, that allows for estimation of instability rates inside heterogeneous intra-tumor populations shaped by such non-linear genetic interactions. To achieve this, we make use of single-cell sequencing, that allows to capture exact cancer clones rather than just individual mutations. We demonstrate the accuracy of our approach and show how it could be applied to experimental tumor data to study the evolution of genetic instability and infer evolutionary history. The proposed method can be used to provide new insight into the evolutionary dynamics of cancer.
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影响因子：
5.8
作者：
Aguse, Nuraini;Qi, Yuanyuan;El-Kebir, Mohammed
通讯作者：
El-Kebir, Mohammed
单细胞数据的树推断。
影响因子：
12.3
作者：
Jahn K;Kuipers J;Beerenwinkel N
通讯作者：
Beerenwinkel N
影响因子：
64.8
作者：
Greaves, Mel;Maley, Carlo C.
通讯作者：
Maley, Carlo C.
影响因子：
11.2
作者：
Bonavia R;Inda MM;Cavenee WK;Furnari FB
通讯作者：
Furnari FB
DOI：
10.1007/978-1-4939-0847-9_6
发表时间：
2014-01-01
期刊：
CLINICAL BIOINFORMATICS, 2ND EDITION
影响因子：
--
作者：
Doyle, Maria A.;Li, Jason;Wong, Stephen Q.
通讯作者：
Wong, Stephen Q.