VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.
VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data.
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DOI：
10.1371/journal.pcbi.1003460
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发表时间：
2014-02
影响因子：
4.3
通讯作者：
Zhao Z
中科院分区：
文献类型：
--
作者：
Jia P;Zhao Z
在解释由下一代测序（NGS）确定的大量突变数据时，一个主要挑战是区分驱动突变和中性乘客突变，以促进靶基因和新药的鉴定。目前的方法主要基于单个基因的突变频率，缺乏检测不频繁突变的驱动基因的能力，忽略了癌症基因之间的功能互联和调控。我们提出了一种新的突变网络方法，VarWalker，在大规模的癌症突变数据中优先考虑驱动基因。VarWalker根据样本特异性突变谱拟合每个样本的广义加性模型，并建立在两个突变基因及其密切相互作用者的联合频率上。在蛋白质-蛋白质相互作用网络中，使用带重启的随机行走算法对这些相互作用体进行选择和优化。我们将该方法应用于两个大规模NGS基准数据集（183个肺腺癌样本和121个黑色素瘤样本）中的bbbb300个肿瘤基因组。在每一种癌症中，我们都得到了一个共识突变子网络，其中包含显著丰富的共识癌症基因和癌症相关的功能途径。然后使用每种癌症的独立数据集验证这些癌症特异性突变网络。重要的是，VarWalker优先考虑已知的、不经常突变的基因，这些基因被证明与高度反复突变的基因相互作用，但却被传统的基于单基因的方法所忽视。利用VarWalker，我们证明了网络辅助方法可以有效地适应于促进NGS数据中癌症驱动基因的检测。癌症基因组通常包含驱动突变和乘客突变，前者有助于肿瘤发生，后者往往是中性的，随机发生。由于遗传和环境因素，癌症基因组差异很大。在利用下一代测序（NGS）解释癌症基因组中发现的大量突变数据时，一个主要挑战是区分驱动突变和中性乘客突变。我们提出了一种新的突变网络方法，VarWalker，在大规模的癌症突变数据中优先考虑驱动基因。将我们的方法应用于肺腺癌样本和黑色素瘤样本的大型队列中，我们得出了每个癌症的共识突变子网络，其中包含显著丰富的癌症基因和癌症相关的功能途径。我们的研究结果表明，驱动基因在广泛的频率范围内发生，相互作用，并在几个在肿瘤发生中起关键作用的关键途径中收敛。
A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data. A cancer genome typically harbors both driver mutations, which contribute to tumorigenesis, and passenger mutations, which tend to be neutral and occur randomly. Cancer genomes differ dramatically due to genetic and environmental factors. A major challenge in interpreting the large volume of mutation data identified in cancer genomes using next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. Applying our approach in a large cohort of lung adenocarcinoma samples and melanoma samples, we derived a consensus mutation subnetwork for each cancer containing significantly enriched cancer genes and cancer-related functional pathways. Our results indicated that driver genes occur within a broad spectrum of frequency, interact with each other, and converge in several key pathways that play critical roles in tumorigenesis.
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影响因子：
14.9
作者：
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14.9
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通讯作者：
Cesareni G