Evan O. Paull¹, Daniel E. Carlin¹, Mario Niepel², Peter K. Sorger², David Haussler^1,3, and Joshua M. Stuart¹

¹ Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA; ² HMS LINCS Center, Harvard Medical School, Boston, MA; ³ Howard Hughes Medical Institute, Santa Cruz, CA

Bioinformatics (2013) 29 (21), 2757-2764.
doi:10.1093/bioinformatics/btt471 / PMID:23986566 / PMCID:PMC3799471

Synopsis

Identifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation and 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.

Key findings

• TieDIE computes a subnetwork of protein–protein interactions, predicted transcription factor-to-target connections, and curated interactions from literature that connects genomic and transcriptomic perturbations.
• When TieDIE was applied to The Cancer Genome Atlas and a breast cancer cell line dataset, it identified key signaling pathways, with examples impinging on MYC activity.
• TieDIE finds interlinking genes that 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.
• Using TieDIE we also are able to infer networks that integrate multi-modal data from a single tumor sample with prior pathway knowledge. These networks may suggest mechanisms of drug resistance or sensitivity for a given patient.

Available data and software

Funding sources

NIH LINCS grant U54 HG006097 to PKS; National Cancer Institute TCGA grant 5U24CA143858 to JMS and DH; National Science Foundation CAREER Award DBI 0845783 to JMS.