Abstract B72: A computational approach integrating proteomic, transcriptional, and interactome data for discovery of cellular signaling networks

Abstract

Abstract Cellular signaling and transcription are tightly integrated processes that have profound effects on every stage of tumor development and growth. Although there are now a variety of high-throughput methods for studying these processes, our knowledge of them remains fragmentary. Typically, the vast majority of hits identified by transcriptional, proteomic and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, as they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree algorithm, that uses previously reported protein-protein interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied to simultaneously to phosphoproteomic and transcriptional profiling data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways. We expect our current analysis of the experimental data from epidermal growth factor (EGF) signaling network in non-tumorigenic and tumorigenic cells will shed light on the regulation of cellular processes at the global level and the consequences of oncogenic mutations on signaling and transcription. Citation Information: Cancer Res 2009;69(23 Suppl):B72.