CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features

doi:10.3389/fgene.2019.00121

	CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features
	Kartha, Vinay K.1,2; Sebastiani, Paola 1,3; Kern, Joseph G.4; Zhang, Liye5 ; Varelas, Xaralabos 4; Monti, Stefano 1,2,3
	2019-02-19
发表期刊	FRONTIERS IN GENETICS (IF:2.8[JCR-2023],3.3[5-Year])
ISSN	1664-8021
卷号	10
发表状态	已发表
DOI	10.3389/fgene.2019.00121
摘要	The identification of genetic alteration combinations as drivers of a given phenotypic outcome, such as drug sensitivity, gene or protein expression, and pathway activity, is a challenging task that is essential to gaining new biological insights and to discovering therapeutic targets. Existing methods designed to predict complementary drivers of such outcomes lack analytical flexibility, including the support for joint analyses of multiple genomic alteration types, such as somatic mutations and copy number alterations, multiple scoring functions, and rigorous significance and reproducibility testing procedures. To address these limitations, we developed Candidate Driver Analysis or CaDrA, an integrative framework that implements a step-wise heuristic search approach to identify functionally relevant subsets of genomic features that, together, are maximally associated with a specific outcome of interest. We show CaDrA's overall high sensitivity and specificity for typically sized multi-omic datasets using simulated data, and demonstrate CaDrA's ability to identify known mutations linked with sensitivity of cancer cells to drug treatment using data from the Cancer Cell Line Encyclopedia (CCLE). We further apply CaDrA to identify novel regulators of oncogenic activity mediated by Hippo signaling pathway effectors YAP and TAZ in primary breast cancer tumors using data from The Cancer Genome Atlas (TCGA), which we functionally validate in vitro. Finally, we use pan-cancer TCGA protein expression data to show the high reproducibility of CaDrA's search procedure. Collectively, this work demonstrates the utility of our framework for supporting the fast querying of large, publicly available multi-omics datasets, including but not limited to TCGA and CCLE, for potential drivers of a given target profile of interest.
关键词	oncogenic driver analysis stepwise search TCGA CCLE R package
收录类别	SCI ; SCIE
语种	英语
资助项目	Clinical and Translational Science Institute (Clinical and Translational Research Award CTSA) at Boston University School of Medicine[UL1-TR001430]
WOS研究方向	Genetics & Heredity
WOS类目	Genetics & Heredity
WOS记录号	WOS:000459168100001
出版者	FRONTIERS MEDIA SA
WOS关键词	B-CELL LYMPHOMA ; MOLECULAR SIGNATURE ; PATHOLOGICAL ROLES ; SIGNALING PATHWAYS ; NETWORK ANALYSIS ; BREAST-CANCER ; HIPPO PATHWAY ; TUMOR-GROWTH ; EXPRESSION ; YAP/TAZ
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/30411
专题	生命科学与技术学院_PI研究组_张力烨组
通讯作者	Monti, Stefano
作者单位	1.Boston Univ, Bioinformat Program, Boston, MA 02215 USA 2.Boston Univ, Sch Med, Sect Computat Biomed, Boston, MA 02118 USA 3.Boston Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02215 USA 4.Boston Univ, Sch Med, Dept Biochem, Boston, MA 02118 USA 5.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Kartha, Vinay K.,Sebastiani, Paola,Kern, Joseph G.,et al. CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features[J]. FRONTIERS IN GENETICS,2019,10.
APA	Kartha, Vinay K.,Sebastiani, Paola,Kern, Joseph G.,Zhang, Liye,Varelas, Xaralabos,&Monti, Stefano.(2019).CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features.FRONTIERS IN GENETICS,10.
MLA	Kartha, Vinay K.,et al."CaDrA: A Computational Framework for Performing Candidate Driver Analyses Using Genomic Features".FRONTIERS IN GENETICS 10(2019).