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

Abstract

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.

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

“…A notable absence from the Challenge was the use of mathematical, boolean or logic based mechanistic pathway modelling approaches [25][26][27][28][29] , likely due to the intensity of model creation. The dynamic nature of mechanistic models may offer an advantage by enabling consideration of the heterogeneity that exists across even apparently 'clonal' cell line populations 21 .…”

Logic Modeling in Quantitative Systems Pharmacology

“…In particular, a number of recent studies have successfully studied logic models to investigate signaling pathways and suggest effective drug combinations which were then validated in vitro and/or in vivo 18, 19, 20, 45. Mathematical models calibrated using cell lines have also been proved effective in predicting clinical patient outcomes 82…”

“…These and other examples19, 44 illustrate the value of logic modeling to enhance our understanding of the systemic effect of therapies. The models provide a formal tool to quickly evaluate in silico the effect of targeting one specific component of the model or explore the effects of possible drug combinations 20, 45, 46, 47. It is also possible to assess how changes in the network (e.g., missing or inactive receptor, etc.)…”

Looking beyond the cancer cell for effective drug combinations

“…In particular, logic modeling (also known as logical modeling) has been applied in diverse contexts that have relevance for cancer therapies, from the main apoptotic and mitogenic pathways in tumor cells to the cell cycle and cell–cell communication [88, 89]. In a logic model both molecular and phenomenological relationships can be encoded in the same formalism, enabling the inclusion of different layers, such that signaling pathways can be connected to downstream phenotypes to study drug synergy in cancer [80, 81, 90] and to predict combinations of treatments to halt pro-angiogenesis activity of monocytes in breast cancer [91], for example. Due to this versatility and simplicity, logic models are promising tools to use for studying complex and heterogenous combination therapies.…”