Phosphoproteomics and mass-action modeling to predict optimal drug combination in signaling networks

Abstract

Abstract Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combination must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulinlike growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line using reverse phase protein arrays to quantify the dynamic crosstalk between the phosphoinositide 3-kinase (PI3K), the mitogen-activated protein kinase (MAPK) and the mammalian target of rapamycin (mTOR) pathways. We developed a computational procedure that integrated mass-action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combination that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combination inhibited cell signaling and proliferation, while non-optimal combination of inhibitors increased phosphorylation of non-targeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combination and discover novel pharmacologic targets for cancer therapy.