Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling

Discovery of efficient anti-cancer drug combinations is a major challenge, since experimental testing of all possible combinations is clearly impossible. Recent efforts to computationally predict drug combination responses retain this experimental search space, as model definitions typically rely on extensive drug perturbation data. We developed a dynamical model representing a cell fate decision network in the AGS gastric cancer cell line, relying on background knowledge extracted from literature and databases. We defined a set of logical equations recapitulating AGS data observed in cells in their baseline proliferative state. Using the modeling software GINsim, model reduction and simulation compression techniques were applied to cope with the vast state space of large logical models and enable simulations of pairwise applications of specific signaling inhibitory chemical substances. Our simulations predicted synergistic growth inhibitory action of five combinations from a total of 21 possible pairs. Four of the predicted synergies were confirmed in AGS cell growth real-time assays, including known effects of combined MEK-AKT or MEK-PI3K inhibitions, along with novel synergistic effects of combined TAK1-AKT or TAK1-PI3K inhibitions. Our strategy reduces the dependence on a priori drug perturbation experimentation for well-characterized signaling networks, by demonstrating that a model predictive of combinatorial drug effects can be inferred from background knowledge on unperturbed and proliferating cancer cells. Our modeling approach can thus contribute to preclinical discovery of efficient anticancer drug combinations, and thereby to development of strategies to tailor treatment to individual cancer patients.Author Summary: Fighting cancer with combinations of drugs increases success of treatment. However, due to the large number of drugs and tumor variants, it remains a tremendous challenge to identify efficient combinations. To illustrate this, a set of 150 drugs corresponds to more than 10.000 possible pairwise drug combinations. Experimental testing of all possibilities is clearly impossible. We have developed a computational model that allows us to identify presumably effective combinations, and that simultaneously suggests combinations likely to be without effect. The model is based on specific cancer cell biomarkers obtained from unperturbed cancerous cells, and is then used to perform extensive automated logical reasoning. Laboratory testing of drug response predictions confirmed results for 20 of 21 drug combinations, including four of five drug pairs predicted to synergistically inhibit growth. Our approach is relevant to preclinical discovery of efficient anticancer drug combinations, and thus for the development of strategies to tailor treatment to individual cancer patients.