Dissecting cancer resistance to therapies with cell-type-specific dynamic logic models

Therapies targeting specific molecular processes, in particular kinases, are major strategies to treat cancer. Genomic features are commonly used as biomarkers for drug sensitivity, but our ability to stratify patients based on these features is still limited. As response to kinase inhibitors is a dynamic process affecting largely signal transduction, we investigated the association between cell-specific dynamic signaling pathways and drug sensitivity. We measured 14 phosphoproteins under 43 different perturbed conditions (combination of 5 stimuli and 7 inhibitors) for 14 colorectal cancer cell-lines, and built cell-line-specific dynamic logic models of the underlying signaling network. Model parameters, representing pathway dynamics, were used as features to predict sensitivity to a panel of 27 drugs. This analysis revealed associations between cell-specific signaling pathways and drug sensitivity for 14 of the drugs, 9 of which have no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by co-blockade of GSK3. These results underscore the value of perturbation-based studies to find biomarkers and combination therapies complementing those based on a static genomic characterization.