A Stochastic, Molecular Model of Non-small Cell Lung Cancer Fate Decision

Abstract

Lung cancer remains the leading cause of all cancer-associated mortalities within the United States. A contributing factor to the high mortality rate is heterogeneous patient response to cancer treatment. This work investigates the effect of cancer cell microenvironment on chemotherapeutic response and connects those results with changes in key regulators of the mitotic and apoptotic processes within the cell. The growth rate of a cancer cell population depends on the timing and outcome of individual cell decisions such as whether to initiate the cell cycle or whether to undergo cell death. To better understand population-level dynamics, I developed a molecular model of tumor cell fate as a function of experimentally measurable inputs including: local drug concentration, cell type, and substrate stiffness. The model predictions were compared to in vitro growth of a non-small cell lung cancer (PC9) line in the presence and absence of targeted drug therapy. Experimentally, the fate of individual PC9 cells were tracked over time as a function of erlotinib concentration and environmental factors. I found that increased substrate stiffness results in decreased cell death, decreased quiescence, and increased division rates in the presence of targeted drug therapy. My developed molecular cell-fate decision model recapitulates cellular population dynamics in response to changes in substrate stiffness and drug treatment. Results suggest that changes in cell fate decision are the result of bistable signaling behavior at the G1/S transition within the cell cycle. Through increased understanding of how cell fate decisions are made at the molecular level, novel treatments can be developed that take patient-specific measurements into account, including specific protein mutations, protein expression levels, and protein activity quantity.