Predicting the success of combination cancer immunotherapy is a major challenge due to the multiscale complex- ity of the immune response to the cancer and the therapy. For example, the pharmaceutical activation of the T cell coreceptors OX40 (CD134) and 4-1BB (CD137) has been shown to synergistically decrease T cell apoptosis rates and increase their rate of cytokine secretion, creating 'supereffector' T cells that can potently kill tumors in vivo, but it is not currently known whether the synergy occurs at the intracellular level or at the level of cell-cell interactions. Therefore, development of optimized clinical treatment protocols for use of these coreceptor agonists for cancer immunotherapy can only be done on a trial-and-error basis. In recent years, mathematical and computational modeling has emerged as a tool to organize current knowledge of the immune response into a dynamic system that can aide in predicting tumor-immune development and identify emergent properties of complex, multiscale interactions between different cell types. Additionally, the emergence of high-throughput methods to assay cel- lular and organismal response to therapy offers promise to elucidate molecular response mechanisms, but the data must still be incorporated into a predictive framework. The global research objective of the proposed project is to develop intracellular and multiscale mathematical and computational models of T cell coreceptor activation that use information from high-throughput technologies and can serve to explain how dual coreceptor activation can generate supereffector T cells. The central hypothesis is that mathematical and computational modeling will be effective in deciphering the critical mechanisms and scales underlying synergistic behavior of T cell agonists. The models will be built by pursuing the following two specific aims: (1) Develop dynamic mathematical models of OX40 and 4-1BB network-level activation for CD8, CD4, and Treg cells, and (2) Develop a multiscale agent- based model of T cell activation by OX40 and 4-1BB. In the first aim, the intracellular models will be built within a discrete dynamic framework, using data from the existing literature and high-throughput methods such as ChIP- seq. In the second aim, the population-level model will be developed using an agent-based approach by coupling intracellular-level dynamics of individual cells and cell types with intercellular interactions rules of heterogeneous T cell types. The proposed project is innovative, in the applicant's opinion, as it will generate a novel mathematical and computational platform to examine multiscale drug synergism. The models will make a significant contribution to cancer immunology as they will allow for both a better understanding of how dual costimulated T cells contribute to the tumor-immune response, and provide a platform to optimize therapy development.
The proposed research is relevant to public health because development of a family of mathematical and com- putational models of T cell activation by multiple immunotherapeutic agents will provide a predictive framework for optimizing combination therapy treatments. Moreover, the models will have the capacity to uncover novel immunotherapy targets via identification of critical molecular components of pharmaceutical T cell activation.
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