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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32CA214030-01
Application #
9258954
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Mcguirl, Michele
Project Start
2017-02-03
Project End
2020-02-02
Budget Start
2017-02-03
Budget End
2018-02-02
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Connecticut
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
022254226
City
Farmington
State
CT
Country
United States
Zip Code
06032
Blanchette-Farra, Nicole; Kita, Daniel; Konstorum, Anna et al. (2018) Contribution of three-dimensional architecture and tumor-associated fibroblasts to hepcidin regulation in breast cancer. Oncogene 37:4013-4032
Konstorum, Anna; Lynch, Miranda L; Torti, Suzy V et al. (2018) A Systems Biology Approach to Understanding the Pathophysiology of High-Grade Serous Ovarian Cancer: Focus on Iron and Fatty Acid Metabolism. OMICS 22:502-513
Konstorum, Anna; Vella, Anthony T; Adler, Adam J et al. (2017) Addressing current challenges in cancer immunotherapy with mathematical and computational modelling. J R Soc Interface 14: