Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data

doi:10.1093/bioinformatics/btw059

CSpace > 应用数学研究所

	Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data
	Chen, Jinyu; Zhang, Shihua
	2016-06-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	32 期号:11 页码:1724-1732
摘要	Motivation: The underlying relationship between genomic factors and the response of diverse cancer drugs still remains unclear. A number of studies showed that the heterogeneous responses to anticancer treatments of patients were partly associated with their specific changes in gene expression and somatic alterations. The emerging large-scale pharmacogenomic data provide us valuable opportunities to improve existing therapies or to guide early-phase clinical trials of compounds under development. However, how to identify the underlying combinatorial patterns among pharmacogenomics data are still a challenging issue. Results: In this study, we adopted a sparse network-regularized partial least square (SNPLS) method to identify joint modular patterns using large-scale pairwise gene-expression and drug-response data. We incorporated a molecular network to the (sparse) partial least square model to improve the module accuracy via a network-based penalty. We first demonstrated the effectiveness of SNPLS using a set of simulation data and compared it with two typical methods. Further, we applied it to gene expression profiles for 13 321 genes and pharmacological profiles for 98 anticancer drugs across 641 cancer cell lines consisting of diverse types of human cancers. We identified 20 gene-drug co-modules, each of which consists of 30 cell lines, 137 genes and 2 drugs on average. The majority of identified co-modules have significantly functional implications and coordinated gene-drug associations. The modular analysis here provided us new insights into the molecular mechanisms of how drugs act and suggested new drug targets for therapy of certain types of cancers.
DOI	10.1093/bioinformatics/btw059
语种	英语
资助项目	National Natural Science Foundation of China[61379092] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61171007] ; National Natural Science Foundation of China[11131009] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; Outstanding Young Scientist Program of CAS ; Key Laboratory of Random Complex Structures and Data Science, CAS
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000377428700016
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/22878
专题	应用数学研究所
通讯作者	Zhang, Shihua
作者单位	Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jinyu,Zhang, Shihua. Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data[J]. BIOINFORMATICS,2016,32(11):1724-1732.
APA	Chen, Jinyu,&Zhang, Shihua.(2016).Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data.BIOINFORMATICS,32(11),1724-1732.
MLA	Chen, Jinyu,et al."Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data".BIOINFORMATICS 32.11(2016):1724-1732.