You are currently viewing the abstract.View Full Text
Translating genomic mutations into drug treatments
A holy grail of systems biology is having sufficient information to use mathematical models to predict therapeutic strategies for patients. Halasz et al. developed a computational approach that used published information and newly acquired experimental data to construct signaling network models that could be tested to discover new potential therapeutic strategies for cancer. They applied this approach to colorectal cancer cell lines and identified a specific network feedback event present in a subset of the cells that was predicted to cause resistance to drugs that target the growth factor receptor EGFR. Testing this prediction in a zebrafish tumor migration model was consistent with the predictions. Thus, modeling patient cell signaling data may eventually aid in identifying the best personalized treatment.
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.