A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.

Abstract

Small cell lung cancer (SCLC) and tuberculosis (TB) are both deadly diseases that present with spatially complex lung lesions. These lesions share similarities, including spatial interactions between T cells and macrophages. Both SCLC and TB exhibit heterogeneous disease progression and responses to treatment; current experimental methods have few tools to investigate the spatiotemporal evolution of these lesions within human lungs. We have applied our computational agent-based model (ABM), GranSim, to extensively study heterogeneity of TB granuloma formation and treatment efficacy. We introduce TumorSim, an analogous ABM designed to understand heterogeneity of SCLC lung tumors. TumorSim mechanistically captures immune-tumor interactions, many of which are well-studied in isolation, including cytokine-based recruitment of immune cells and PD-1/PD-L1-based inhibition of cytotoxic T cells. Drawing from lung immunology literature, we define and explore wide parameter ranges to characterize TumorSim behavior using global sensitivity analysis. We compare factors that drive both tumor and granuloma outcomes. As model validation, sensitivity analysis captures several well-known correlates of improved SCLC outcomes including macrophage-mediated cytotoxic T-cell recruitment. Surprisingly, both models predict a two-phase formation process occurring with an abrupt change in tumor/granuloma dynamics upon arrival of adaptive immune cells into the lung. Simulations suggest that while C-C motif chemokine ligand 5 (CCL5) is associated with improved tumor control later during tumor growth, CCL5 plays a pro-tumor role early during tumor growth by recruiting regulatory T cells. We find that, like virtual granulomas, TumorSim tumors become enlarged when immunosuppressive mechanisms outweigh pro-inflammatory responses. This novel tumor model enables future studies of both immunotherapeutics and anti-cancer drugs.