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

August Discovery of Drug Synergies in Gastric Cancer Cells Predicted by Logical Modeling Åsmund Flobak 0 1 Anaïs Baudot 0 1 Elisabeth Remy 0 1 Liv Thommesen 0 1 Denis Thieffry 0 1 Martin Kuiper 0 1 Astrid Laegreid 0 1 0 1 Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU) , Trondheim , Norway , 2 Aix Marseille Université , CNRS, Centrale Marseille, I2M, UMR 7373, Marseille , France , 3 Faculty of Technology, Sør-Trøndelag University College , Trondheim , Norway , 4 Institut de Biologie de l'Ecole Normale Supérieure (IBENS) , Paris, France, 5 CNRS UMR 8197, Paris , France , 6 INSERM U1024, Paris, France, 7 Department of Biology, Norwegian University of Science and Technology (NTNU) , Trondheim , Norway 1 Editor: Ioannis Xenarios, Swiss Institute of Bioinformatics , UNITED STATES 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 wellcharacterized 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. - Competing Interests: The authors have declared that no competing interests exist. 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. It has long been envisaged that future anticancer treatment will adopt combinatorial approaches, in which several specific anti-cancer drugs together target multiple robustness features or weaknesses of a specific tumor [1–3]. The effectiveness of combinatorial anti-cancer treatments can be further maximized by exploiting synergistic drug actions, meaning that different drugs administered together exhibit a potentiated effect compared to the individual drugs. Drug synergy is attractive because it allows for a significant reduction in the dosage of the individual drugs, while retaining the desired effect. Synergies therefore hold the potential to increase treatment efficacy without pushing single drug doses to levels where they lead to adverse reactions. Hence, synergies identified in preclinical studies represent interesting candidates for further characterization in cancer models and clinical trials. Current efforts to identify beneficial combinatorial anti-cancer therapies typically rely on large-scale experimental perturbation data, either for deciding on specific patient treatment [4], or for pre-clinical pipelines to suggest new drug combinations [5–8]. This work, however, faces challenges posed by the large search space that needs to be supported by experimental data, making systematic searches for efficient combinations challenging. Moreover, the number of conditions for testing dramatically increases when considering higher-order combinations, multiple drug dosages, t (...truncated)