Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data

doi:10.1186/1752-0509-7-S2-S4

CSpace > 应用数学研究所

	Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data
	Zhang,Junhua; Zhang,Shihua; Wang,Yong; Zhang,Xiang-Sun
	2013-10-14
发表期刊	BMC Systems Biology
ISSN	1752-0509
卷号	7 期号:Suppl 2
摘要	AbstractMotivationUnderstanding the molecular mechanisms underlying cancer is an important step for the effective diagnosis and treatment of cancer patients. With the huge volume of data from the large-scale cancer genomics projects, an open challenge is to distinguish driver mutations, pathways, and gene sets (or core modules) that contribute to cancer formation and progression from random passengers which accumulate in somatic cells but do not contribute to tumorigenesis. Due to mutational heterogeneity, current analyses are often restricted to known pathways and functional modules for enrichment of somatic mutations. Therefore, discovery of new pathways and functional modules is a pressing need.ResultsIn this study, we propose a novel method to i dentify M utated C ore M odules in C ancer (iMCMC) without any prior information other than cancer genomic data from patients with tumors. This is a network-based approach in which three kinds of data are integrated: somatic mutations, copy number variations (CNVs), and gene expressions. Firstly, the first two datasets are merged to obtain a mutation matrix, based on which a weighted mutation network is constructed where the vertex weight corresponds to gene coverage and the edge weight corresponds to the mutual exclusivity between gene pairs. Similarly, a weighted expression network is generated from the expression matrix where the vertex and edge weights correspond to the influence of a gene mutation on other genes and the Pearson correlation of gene mutation-correlated expressions, respectively. Then an integrative network is obtained by further combining these two networks, and the most coherent subnetworks are identified by using an optimization model. Finally, we obtained the core modules for tumors by filtering with significance and exclusivity tests. We applied iMCMC to the Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and ovarian carcinoma data, and identified several mutated core modules, some of which are involved in known pathways. Most of the implicated genes are oncogenes or tumor suppressors previously reported to be related to carcinogenesis. As a comparison, we also performed iMCMC on two of the three kinds of data, i.e., the datasets combining somatic mutations with CNVs and secondly the datasets combining somatic mutations with gene expressions. The results indicate that gene expressions or CNVs indeed provide extra useful information to the original data for the identification of core modules in cancer.ConclusionsThis study demonstrates the utility of our iMCMC by integrating multiple data sources to identify mutated core modules in cancer. In addition to presenting a generally applicable methodology, our findings provide several candidate pathways or core modules recurrently perturbed in GBM or ovarian carcinoma for further studies.
DOI	10.1186/1752-0509-7-S2-S4
语种	英语
WOS记录号	BMC:10.1186/1752-0509-7-S2-S4
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/270
专题	应用数学研究所
通讯作者	Zhang,Junhua; Zhang,Shihua
作者单位	Chinese Academy of Sciences; National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science
推荐引用方式 GB/T 7714	Zhang,Junhua,Zhang,Shihua,Wang,Yong,et al. Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data[J]. BMC Systems Biology,2013,7(Suppl 2).
APA	Zhang,Junhua,Zhang,Shihua,Wang,Yong,&Zhang,Xiang-Sun.(2013).Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data.BMC Systems Biology,7(Suppl 2).
MLA	Zhang,Junhua,et al."Identification of mutated core cancer modules by integrating somatic mutation, copy number variation, and gene expression data".BMC Systems Biology 7.Suppl 2(2013).