(667c) Signal Transduction Networks In Cancer: Quantitative Parameters Influence Network Topology

The collective understanding of the flow of information within a cell is summarized by canonical cell signaling pathways. Analysis of expression profiling in cancer is typically done in the context of these canonical pathways. However, activation of non-canonical pathways provides a mechanism for a cell's acquisition of the hallmark characteristics of cancer. Translating basic experimental data to understand aberrant signaling networks also presents a significant challenge for the rational design of new therapies for cancer. The objective of this study was to assess the existence of non-canonical signaling pathways in breast cancer and to provide a computational framework for interpreting basic science data using concepts from reaction pathway analysis. A mathematical model of the early signaling events associated with the ErbB1 receptor was developed. The model was calibrated and validated against existing data in the literature and used as a platform to explore the activation of non-canonical signaling pathways in breast cancer cells. Protein-protein interaction data and patterns of protein expression among in vitro cell models for breast cancer were integrated within this computational framework. The cell models exhibited a broad diversity in patterns of protein expression. The phosphorylation of ErbB1 was predicted to activate Irs-1, a non-canonical interaction partner, to a similar extent as the primary canonical pathways that correspond to Shc and Grb2 interaction with ErbB1. Selecting an appropriate assay and a cellular context are shown to be critical factors for observing the protein-protein interactions responsible for non-canonical activation.