Abstract B2-52: Long range personalized cancer treatment strategies incorporating evolutionary dynamics

Abstract

Abstract Introduction: Current personalized cancer medicine tailors therapy to heterogeneity between cancers of the same organ type occurring within different individuals. However, it does not yet address heterogeneity at the single cell level within individual cancers or the dynamics of cancer, due to heritable genetic and epigenetic change, as well as transient functional changes. We established computational methods for evaluating personalized medicine strategies, comparing the current personalized medicine strategy to alternatives. Current personalized medicine matches therapy to a tumor molecular profile at diagnosis and at tumor relapse or progression. This strategy focuses on the average, static, and current properties of the sample. Non-standard strategies also consider minor sub-clones, dynamics, and predicted future tumor states. Previous simulation results in a system with two non-cross resistant agents using non-standard strategies, optimized in single 45 day blocks, demonstrated significantly improved outcomes (Beckman, Schemmann, and Yeang, 109: 14586-91, 2012). The current work explores the effect of long range planning with a time horizon of up to five years on the effectiveness of the strategies, as well as generalizing the model to three non-cross resistant agents.. Methods: We developed a mathematical model of cancer therapy with two non-cross resistant agents incorporating genetic evolutionary dynamics and single cell heterogeneity, and examined simulated clinical outcomes (cell numbers of clones and sub-clones, projected survival). Previously we compared the current personalized medicine strategy to 5 alternative personalized strategies. The latter strategies explicitly considered sub-clones, evolutionary dynamics, and likely future sub-clones in addition to the current predominant clone. Particular emphasis was given to the prevention of incurable, multiply resistant sub-clones. The optimization was performed in single 45 day blocks. In the current work, we extended the work to three drug systems. We also extended the previous single step heuristic strategies to encompass multistep heuristics of up to five 45 day steps, and global optimization in 45 day blocks over a 5 year time horizon, with strategic updates every 45 days. Branch and bound methods were used for pruning decision trees. Parallel processing (23 servers) facilitated computations. Results: Previously we had carried out a computerized virtual clinical trial of over 3 million evaluable cancer “patients”, comparing current personalized medicine and 5 non-standard strategies. The 3 million virtual patients represented a comprehensive survey of likely population structures, growth rates, phenotypic transition rates (by heritable genetic or epigenetic mechanisms), and drug sensitivities. All alternatives tested resulted in an approximate doubling in mean and median survival compared to current personalized medicine and an increase in the apparent cure rate from 0.7% for current personalized medicine to 17-20% for alternatives. In no case was the current personalized medicine strategy superior. In the current work, we found in large simulations that planning ahead led to further increases in cure rates and analyzed several examples where highly complex treatment sequences led to cures which were not possible with single step 45 day optimization. Further, previous and current conclusions applied equally for three non-cross resistant agents. Conclusions: Explicit consideration of intratumoral heterogeneity and evolutionary dynamics, with probabilistic consideration of future outcomes with a long strategy horizon, can potentially lead to markedly improved patient outcomes, including cure rates. Application of knowledge from growing molecular and clinical oncology databases may allow more informative therapeutic simulations than previously possible. Citation Format: Robert A. Beckman, Chen-Hsiang Yeang. Long range personalized cancer treatment strategies incorporating evolutionary dynamics. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-52.