Project Summary The principal goal of this proposal is to combine multiscale mathematical modeling with novel computational model-driven quantitative experimental platforms to develop a comprehensive and predictive 3D computational framework. Bladder cancer is one of the 10 most common cancers in the United States and in its advanced stages the 5-year survival rates are below 35%. Given the poor outcomes with chemotherapy in advanced cases, immunotherapy has emerged as an exciting domain for exploration. Monoclonal antibodies targeting the PD- 1/PD-L1 “immune checkpoint” pathway have resulted in favorable outcomes in advanced bladder cancer, and 5 drugs targeting this pathway have been approved in the past two years. Unfortunately, the objective response rates of current FDA approved immunotherapy drugs remain less than 25%. An alternative treatment strategy for bladder cancer is small molecule inhibitors (SMIs) of fibroblast growth factor receptor (FGFR3), and early clinical studies using these molecular-targeted agents have shown promise. Recently published data supporting the co-acting combination of potent immune checkpoint inhibitors and specific FGFR3 inhibitors potentially offer an advance in targeted therapeutics for cancer. A powerful and practical way to optimize novel drug combinations for clinical cancer treatment is to use sophisticated, data-driven computational models. Our proposed agent- based model platform will both aid in the characterization of tumor-immune dynamics and also suggest the best strategies for administering therapeutic combinations of immune-checkpoint and receptor kinase inhibitors. The model will be parameterized at the molecular and cellular scales by an innovative high-throughput image quantification pipeline that allows T-cell or cancer cell behaviors and interactions to be observed, tracked, and quantified. Importantly, this model system pipeline can measure the antigen burden on tumor cells and the proportion of the two types of T-cell cytotoxicity (Fas-ligand vs. granule-based). Our experimentally-driven multiscale approach is posed to (1) significantly enhance the current understanding of the impact of differential cell-kill mechanisms on tumor-immune outcomes; (2) optimize the administration of combination therapy and maximize tumor response; and (3) to improve the ability to select the most promising drugs for the clinical trials. While based on tumors of the bladder, the platform that we are developing is easily adaptable for the study of any therapy targeted to immune checkpoint proteins and receptor kinases in any tumor type. The true significance of our work lies in its translational value: our experimental and theoretical studies will be able to test clinically relevant hypotheses regarding the prospect of receptor tyrosine kinase inhibitors and immune checkpoint inhibitors to impact the mechanism of tumor cell kill by immune cells in distinct ways. Cancer is one of the leading causes of death for Americans and at present the overall effectiveness of therapeutic treatments is only approximately 50%. The development treatment optimization tools could have enormous and immediate impact on the lives of millions of people diagnosed with cancer.

Project Narrative We propose to combine novel cellular quantification experimental technologies with multiscale mathematical approaches in order to gain a deeper, more robust understanding of tumor-immune dynamics and to harness this knowledge for human benefit. Using bladder cancer as our platform, we will develop a comprehensive and predictive 3D computational framework that will be used to optimize combination immunotherapy and receptor kinase targeted therapy. Our experimentally- driven multiscale approach is poised to significantly enhance the current understanding of differential cell-kill mechanisms on tumor-immune outcomes and to improve the ability to combine promising drugs for clinical trials.