Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)

Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting E...

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Bibliographic Details
Published in:	Bioinformatics Vol. 29; no. 21; pp. 2757 - 2764
Main Authors:	Paull, Evan O, Carlin, Daniel E, Niepel, Mario, Sorger, Peter K, Haussler, David, Stuart, Joshua M
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 01-11-2013
Subjects:	Breast Neoplasms - classification Breast Neoplasms - genetics Breast Neoplasms - metabolism Cell Line, Tumor Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Genomics Humans Neoplasms - genetics Original Papers Protein Interaction Mapping Signal Transduction Software Transcription Factors - metabolism
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Summary:	Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations. Application of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets. Software is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie. jstuart@ucsc.edu. Supplementary data are available at Bioinformatics online.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Associate Editor: Martin Bishop
ISSN:	1367-4803 1367-4811 1460-2059
DOI:	10.1093/bioinformatics/btt471