(590e) Kinetic Analysis of CD3? and CD28 Chimeric Antigen Receptor T Cell Activation

As T cell immunotherapy applications have expanded, it has become increasingly important to better understand the signaling events that lead to T cell activation. For example, T cells bearing chimeric antigen receptors (CARs) containing the CD3ζ T cell activating domain and the CD28 co-stimulatory domain have emerged as promising cancer therapies; but, to expand this therapy to a wider patient population, the CAR proteins need to be better engineered to control T cell activation. The six tyrosine sites on CD3ζ and four on CD28 are phosphorylated by lymphocyte-specific protein tyrosine kinase (LCK), resulting in downstream signaling that leads to T cell activation; however, the specific mechanisms that control LCK CAR binding and catalytic activity are not well defined. These ten phosphorylation sites on the CAR contribute to different signaling events in the T cells, so understanding the mechanisms through which these sites are activated can inform the best strategies for engineering the next generations of CARs. In this study, we have used quantitative phospho-proteomic mass spectroscopy and computational modeling to quantify the site-specific kinetics of CD3ζ and CD28 phosphorylation.

We constructed the model using BioNetGen, a rule-based formalism that allows us to account for the many species that arise due to the complexity of the multiple phosphorylation sites on the CARs. The model is implemented as a set of ordinary differential equations in MATLAB and fit to experimental data generated using an in vitro reconstituted membrane system that mimics the two-dimensional interactions that occur in T cells [1]. We used phospho-proteomic mass spectrometry to measure the level of phosphorylation at each individual tyrosine residue in the system over time. We have also tested how mutations in the CAR and changes in the lipid microenvironment could affect the model kinetic rates. The correct model predictions were validated experimentally. By combining this work with our existing model of LCK autoregulation [2], we can predict how changing CAR structure and microenvironment will affect the rate of T cell activation. Thus, we are establishing a quantitative framework that can be used to design optimal CAR activation of T cells for immunotherapy.

Citations

[1] Hui, E. and R. Vale. Nat. Struct. Mol. Biol., 2014, 21(2), 133-142.

[2] Rohrs, J. A., Wang, P., and S. D. Finley. Cell Mol. Bioeng., 2016, 9(3), 351-367.