Abstract B2-33: Computational model of chimeric antigen receptor-engineered T cells

Abstract

Abstract Introduction. The purpose of this study is to create a predictive model that can be used to test hypotheses about chimeric antigen receptor (CAR) activation of T cells in order to optimize the development of these engineered proteins used for cancer immunotherapy. CARs are engineered proteins that contain an extracellular antibody-like region linked to a selection of intracellular signaling domains involved in T cell activation. CAR-engineered T cells have shown promise in treating B cell lymphoma, but they have not been successfully applied to other types of cancers. Additionally, there have been serious side effects when T cell activation goes awry. The specific mechanisms of the signaling and co-stimulatory domains on the CAR are not well understood, and therefore they are hard to control. Developing a computational model capable of comprehensively and quantitatively describing the biochemical reactions involved the CAR-mediated T cell signaling network will help guide the development and optimization of CAR-based cancer immunotherapy. Methods. We have constructed a model of T cell signaling mediated by CARs. Due to the complexity of the multiprotein interactions in this pathway, a rule-based model was written in BioNetGen and implemented in MatLab. The model consists of coupled, nonlinear differential equations, which can be solved to predict the concentrations of molecular species in CAR-mediated T cell signaling. In parallel, we have obtained signaling data using flow cytometry measurements to quantify the time required for ERK phosphorylation, indicating signaling through the MAPK pathway. These data are used to calibrate the model and estimate unknown parameter values. Results. Our computational model is comprised of 181 rules, encoding 1,989 reactions between 20 species. The initial parameter estimates are based on experimentally measured values and a model of T cell receptor signaling (Altan-Bonnet, et al., 2005). Importantly, our model expands upon previous works by modeling separate pathways for specific signaling domains that can be incorporated into the CAR and incorporating more detail into the large multi-protein complexes downstream of the CAR. Extending the model to these multi-protein complexes allows us to predict the levels of downstream proteins that correlate to specific responses of T cell activation, including cell proliferation and survival. We constructed two CAR proteins with different signaling domains (CD3ζ-only and CD3ζ+CD28), and stably expressed them in the Jurkat T cell line. We similarly expressed CD19 in the K562 cell line to be used as target cells. The CAR cells were stimulated with target cells and stained for intracellular flow cytometry analysis of doubly phosphorylated ERK. We performed a local sensitivity analysis to identify the effect of individual parameters on the model predictions. This analysis identified six parameters that most significantly influenced the model. These parameters were fit to ERK phosphorylation data, and we estimated the optimal parameter values required to match the experimental measurements using a nonlinear optimization procedure. Interestingly, the fitted model predicts that the time required for phosphorylation of ERK differs in CD3ζ-only and CD3ζ-CD28 CARs, matching our experimental measurements. The fitting procedure identified multiple parameter sets that fit the data equally well, indicating model robustness. We are currently collecting additional flow cytometry measurements to validate the model predictions. Conclusions. The model is able to accurately represent the effects of CAR signaling to provide insight into how different signaling domains control the strength and persistence of activated T cells. This understanding can be used to optimize CAR engineered T cells to target cancer cells in a manner that is both sensitive and specific. Citation Format: Jennifer Rohrs, Pin Wang, Stacey D. Finley. Computational model of chimeric antigen receptor-engineered T cells. [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 B2-33.