Abstract LB-372: Computational modeling of evolution in tumor cell populations shows differences in the symmetry of resistance after treatment with synergistic or antagonistic combination drugs
Authors: Elysia C. Saputra, Vivek Murali, Lisa Tucker-Kellogg
Journal: Cancer Research (2020)

Abstract Introduction: Evolution is easy to observe in individual cases but difficult to generalize. Computational simulations of evolution offer the opportunity to articulate general trends and to compare relative probabilities. In this work, we use simplified mathematical models of cancer evolution during treatment with combination drugs to make predictions about how the synergism (the non-additivity) between two drugs can bias the probability of whether the most likely drug-resistance to evolve in the future will be against one drug at a time, or against both at once. Methods: Suppose drug-A and drug-B are given simultaneously to a population of tumor cells. If there is any subsequent expansion or treatment failure, we can define the “symmetry of resistance” as the extent to which the post-relapse cells are equally resistant against both drugs (e.g., 44% resistant to drug-A and 44% resistant to drug-B) versus not equally resistant (e.g., 88% resistant to one drug and fully sensitive to the other). We studied the symmetry of resistance during simulations of cancer evolution using a computational model that tracks the number of cells and the degree of drug-resistance of each cell over time. The model includes a simplified process of single-cell phenotype change (e.g., mutation or plasticity) and processes of proliferation and death. Results and Discussion: Virtual populations of tumor cells evolved under selective pressure from two-drug combinations that were additive, synergistic (more efficacious than additive) or antagonistic (less efficacious than additive). When resistance arose against treatment with synergistic combinations, the form of resistance was on average more asymmetric (stronger resistance to one drug than then other), whereas when cells escaped antagonistic combinations, the resistance was more likely symmetric (e.g., moderately resistant to both drugs). This suggests that at the time of relapse/resistance after a synergistic two-drug treatment, the tumor would be more likely to retain sensitivity to one of the drugs, compared with tumors that develop equally severe relapse after a non-synergistic treatment. Additional simulations considered asymmetric drug efficacy in 3-drug cocktails, as well as asymmetry of resistance. Conclusion: When two or more drugs have non-additive efficacy (synergism or antagonism), then the process of evolving drug-resistance has non-obvious winners and losers, measured as the relative speed of evolving drug-resistant subpopulations. Although the progression of any specific tumor would be dominated by individual circumstances, our high-level generalizations offer novel testable predictions, and a novel lens for viewing the challenges of chasing an evolving population using non-additive combinations of drugs. Citation Format: Elysia C. Saputra, Vivek Murali, Lisa Tucker-Kellogg. Computational modeling of evolution in tumor cell populations shows differences in the symmetry of resistance after treatment with synergistic or antagonistic combination drugs [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-372.