Integrating network reconstruction with mechanistic modeling to predict cancer therapies

Signal transduction networks are often rewired in cancer cells. Identifying these alterations will enable more effective cancer treatment. We developed a computational framework that can identify, reconstruct, and mechanistically model these rewired networks from noisy and incomplete perturbation response data and then predict potential targets for intervention. As a proof of principle, we analyzed a perturbation data set targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor 1 receptor (IGF1R) pathways in a panel of colorectal cancer cells. Our computational approach predicted cell line-specific network rewiring. In particular, feedback inhibition of insulin receptor substrate 1 (IRS1) by the kinase p70S6K was predicted to confer resistance to EGFR inhibition, suggesting that disrupting this feedback may restore sensitivity to EGFR inhibitors in colorectal cancer cells. We experimentally validated this prediction with colorectal cancer cell lines in culture and in a zebrafish (Danio rerio) xenograft model.