Abstract 975: A multiscale computational model for spatio-temporal tumor immune response

Abstract

When the immune system responds to tumor development, patterns of immune infiltrates emerge, highlighted by expression of immune checkpoint-related molecules such as PD-L1 on cancer cells and its receptor PD-1 on cytotoxic T cells. Pre-treatment tumor spatial heterogeneity could bear information on intrinsic characteristics of the tumor lesion for individual patient, and thus has the potential to comprise biomarkers for anti-tumor therapeutics. We developed a systems biology computational multiscale agent-based model to capture the interactions between immune cells and cancer cells during tumor progression. Cytotoxic T cells and cancer cells are modeled as free-moving agents in a 3-dimensional grid, where each cell acts in response to its local microenvironment and carries out functions such as division, apoptosis, cytotoxic killing and switching between states with different PD-1 or PD-L1 expression levels. Subsequently, we analyzed the emergent behavior of tumor progression by looking at all these local interactions as a whole. Using this model, we are able to reproduce temporal dynamics of cytotoxic T cells and cancer cells during general tumor progression, as well as 3-dimensional spatial distributions of these cells over the time course of the simulation. By varying the characteristics of the neoantigen profile of individual patients, such as mutational burden and immunogenicity, a spectrum of pre-treatment spatial patterns of PD-1/PD-L1 expression is generated in our simulations, resembling immune-architectures obtained via immunohistochemistry from patient biopsies. We evaluate potential prognostic biomarkers by correlating these spatial characteristics with in silico treatment results with immune checkpoint inhibitors. Simulation results demonstrate that the percentage of PD-L1 positive cancer cells which are not in close proximity of the tumor boundary or vasculature is more indicative of successful anti-PD1/anti-PD-L1 treatment. Our findings suggest that tumor spatial heterogeneity, especially its immune-architecture, reflects the course of tumor progression as well as patient-specific properties, and is thus likely to carry important information about tumor susceptibility to treatment such as with immune checkpoint inhibitors. We demonstrated how prognostic biomarkers could be realistically simulated in a general cancer scenario. The model is further refined for use to predict treatment/biomarker combinations in specific cancer types.

Citation Format: Chang Gong, Oleg Milberg, Bing Wang, Paolo Vicini, Rajesh Narwal, Lorin Roskos, Aleksander S. Popel. A multiscale computational model for spatio-temporal tumor immune response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 975. doi:10.1158/1538-7445.AM2017-975