Automated network analysis identifies core pathways in glioblastoma.

Automated network analysis identifies core pathways in glioblastoma.
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DOI:
10.1371/journal.pone.0008918
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发表时间:
2010-02-12
期刊:
影响因子:
3.7
通讯作者:
Sander C
Sander C
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Cerami E;Demir E;Schultz N;Taylor BS;Sander C

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多形性胶质母细胞瘤(GBM)是人类最常见和最具侵袭性的脑肿瘤类型,也是第一种由癌症基因组图谱(TCGA)计划绘制的具有全面基因组图谱的癌症。大规模基因组计划(如TCGA GBM计划)的核心挑战是区分致癌“驱动”突变与被动选择的“乘客”突变的能力。与纯粹基于频率的方法来识别癌症中的驱动突变相比,我们提出了一种基于自动网络的方法来识别候选致癌过程和驱动基因。该方法基于细胞网络包含功能模块的假设,并且肿瘤靶向对其生长至关重要的特定模块。该方法的关键要素包括序列突变和DNA拷贝数改变的组合分析;使用由蛋白质-蛋白质相互作用和信号传导途径组成的统一分子相互作用网络;以及网络模块的识别和统计评估,即感兴趣的基因的内聚组,其组内的相互作用密度高于组间的相互作用密度。我们确认并扩展了GBM改变倾向于发生在特定功能模块内的观察结果,尽管患者之间存在相当大的差异,并且两个最大的模块涉及通过p53,Rb,PI 3 K和受体蛋白激酶的信号传导。我们还确定了GBM中新的候选驱动程序,包括AGAP 2/CENTG 1,一种假定的致癌基因和PI 3 K通路的激活剂;以及三个额外的显著改变的模块,包括一个参与微管组织的模块。为了促进我们基于网络的方法在其他癌症类型中的应用,我们将该方法作为NetBox软件工具的一部分免费提供。
Glioblastoma multiforme (GBM) is the most common and aggressive type of brain tumor in humans and the first cancer with comprehensive genomic profiles mapped by The Cancer Genome Atlas (TCGA) project. A central challenge in large-scale genome projects, such as the TCGA GBM project, is the ability to distinguish cancer-causing “driver” mutations from passively selected “passenger” mutations. In contrast to a purely frequency based approach to identifying driver mutations in cancer, we propose an automated network-based approach for identifying candidate oncogenic processes and driver genes. The approach is based on the hypothesis that cellular networks contain functional modules, and that tumors target specific modules critical to their growth. Key elements in the approach include combined analysis of sequence mutations and DNA copy number alterations; use of a unified molecular interaction network consisting of both protein-protein interactions and signaling pathways; and identification and statistical assessment of network modules, i.e. cohesive groups of genes of interest with a higher density of interactions within groups than between groups. We confirm and extend the observation that GBM alterations tend to occur within specific functional modules, in spite of considerable patient-to-patient variation, and that two of the largest modules involve signaling via p53, Rb, PI3K and receptor protein kinases. We also identify new candidate drivers in GBM, including AGAP2/CENTG1, a putative oncogene and an activator of the PI3K pathway; and, three additional significantly altered modules, including one involved in microtubule organization. To facilitate the application of our network-based approach to additional cancer types, we make the method freely available as part of a software tool called NetBox.
DOI: 10.1002/cfg.91
发表时间: 2001
影响因子: --
作者:
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DOI: 10.1038/35011540
发表时间: 1999-12-02
期刊: NATURE
影响因子: 64.8
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DOI: 10.1111/j.2517-6161.1995.tb02031.x
发表时间: 1995-01-01
影响因子: 5.8
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通讯作者: HOCHBERG, Y
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