e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.
e-MutPath: computational modeling reveals the functional landscape of genetic mutations rewiring interactome networks.
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DOI：
10.1093/nar/gkaa1015
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发表时间：
2021-01-11
影响因子：
14.9
通讯作者：
Yi SS
中科院分区：
文献类型：
--
作者：
Li Y;Burgman B;Khatri IS;Pentaparthi SR;Su Z;McGrail DJ;Li Y;Wu E;Eckhardt SG;Sahni N;Yi SS
了解癌症体细胞突变的功能影响是实施精确肿瘤学的关键知识差距。越来越多的人认识到，由基因组突变介导的相互作用谱提供了基因型和表型之间的基本联系。然而，大多数突变对生物信号网络的具体影响在很大程度上是未知的实验方法。为了解决这一挑战，我们开发了e-MutPath（边缘突变介导的通路扰动），这是一种基于网络的计算方法，用于识别干扰功能通路的候选“边缘”突变。e-MutPath确定了可用于区分疾病风险因素和中性元素的信息路径，并对具有临床相关性的疾病亚型进行分层。预测的靶点富含癌症易感基因，已知的药物靶点，但与副作用相关的蛋白质耗尽，证明了基于网络的策略在研究基因组突变的功能影响和干扰谱方面的能力。总之，e-MutPath代表了一个强大的计算工具，可以系统地为基因突变分配功能，特别是在其特定途径扰动效应的背景下。
Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.
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影响因子：
64.5
作者：
Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu;Cancer Genome Atlas Research Network
通讯作者：
Cancer Genome Atlas Research Network
影响因子：
12.3
作者：
Cho A;Shim JE;Kim E;Supek F;Lehner B;Lee I
通讯作者：
Lee I
影响因子：
4.3
作者：
Jia P;Zhao Z
通讯作者：
Zhao Z
DOI：
10.1073/pnas.0308531101
发表时间：
2004-03-23
影响因子：
11.1
作者：
Brunet, JP;Tamayo, P;Mesirov, JP
通讯作者：
Mesirov, JP
影响因子：
48
作者：
Lever, Jake;Zhao, Eric Y.;Jones, Steven J. M.
通讯作者：
Jones, Steven J. M.