Abstract 3763: How stochastic single-cell fate decisions drive population dynamics in oncogene-addicted cancer

Abstract

Abstract Cancer cells within a tumor environment exhibit a complex and adaptive nature whereby genetically and epigenetically distinct subpopulations compete for resources. The probabilistic nature of gene expression and intracellular molecular interactions confer a significant amount of stochasticity in cell fate decisions. This cellular heterogeneity is believed to underlie cases of cancer recurrence, acquired drug resistance, and so-called exceptional responders. From a population dynamics perspective, clonal heterogeneity and cell-fate stochasticity are distinct sources of noise, the former arising from genetic mutations and/or epigenetic transitions, extrinsic to the fate decision signaling pathways and the latter being intrinsic to biochemical reaction networks. Here, we present our results and ongoing work of a kinetic modeling study based on experimental time course data for EGFR-addicted non-small cell lung cancer (PC9) cells in both parental and isolated sublines. When PC9 cells are treated with erlotinib, an EGFR inhibitor, a complex array of division and death cell decisions arise within a given population in response to treatment. Although deterministic (ODE) simulations capture the ef-fects of clonal heterogeneity and describe the overall trends of experimentally treated tumor cell popu-lations, these are not capable of explaining the observed variability of drug response trajectories, in-cluding response magnitude and time to rebound. Our stochastic simulations, instead, capture the ef-fects of intrinsically noisy cell fate decisions that cause significant variability in cell population trajecto-ries. These findings indicate that stochastic simulations are necessary to distinguish the contribution of extrinsic (clonal heterogeneity) and intrinsic (cell fate decisions) noise to understand the variability of cancer-cell response treatment. Furthermore, they suggest that, whereas tumors with distinct clon-al structures are expected to behave differently in response to drug, two clonally identical tumors may also experience vastly different outcomes, such as exceptional response vs. rapid relapse, due to the intrinsic noise of cell fate decisions. The results of this study underscore the need to improve our un-derstanding of intracellular signaling networks that govern division vs. death decisions in cancer cells at the single-cell level. We will present our ongoing work to develop mechanistic models that link erlo-tinib-driven EGFR inhibition and intrinsic apoptotic execution. Our preliminary results give insight into what signaling interactions are required to generate the intrinsic apoptotic response to EGFR inhibi-tion with erlotinib. Citation Format: Erin M. Shockley, Leonard A. Harris, Carlos F. Lopez. How stochastic single-cell fate decisions drive population dynamics in oncogene-addicted cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3763. doi:10.1158/1538-7445.AM2015-3763