Summary Bladder cancer is the fourth most common cancer in men and a leading cause of cancer mortality among men and women with the rates of bladder cancer incidents and bladder cancer-related deaths that have not decreased over the last 40 years. Therefore, novel more precise treatment strategies are desired. Bladder cancer has long been recognized as a malignancy that is responsive to immune-based therapy. One of the novel immunotherapy approaches utilizes the adoptive cell therapy (ACT), where autologous tumor-infiltrating T lymphocytes (TIL) are expanded and activated ex vivo and then reinfused into the cancer patient. However, typical systemic administration of ACT-TIL requires prior lymphodepletion and thus TIL are injected infrequently and in large numbers. Bladder is a specific organ which allows for intravesical delivery by direct administration of drugs and T cells through a catheter making it amenable to multiple and more frequent infusions of TIL and other therapeutics, potentially with smaller doses. This gives an unprecedented opportunity to use mathematical modeling integrated with pre-clinical mouse models to design, optimize and validate ACT-TIL combination therapies that will maximize tumor response and minimize toxicity. Our methods include microscopic- and macroscopic-level in silico models supported by in vitro, in vivo and ex vivo experimental data, defined T cells and a murine models of bladder cancer that mimic clinically-relevant procedures. In Aim 1, we will develop in silico models to enhance T cell infiltration into the bladder tumors. In Aim 2, we will optimize T cell properties ex vivo for the most effective T cell-cancer cell interactions. In Aim 3, we will apply our integrated methodology to design most effective combination therapy protocols to increase the effectiveness of reinfused TILs. The deliverables of this study will improve our understanding of how the bladder tumor microenvironment impacts T cell infiltration and T cell-tumor cell interactions and will lead to strategies that improve bladder cancer treatment by mathematically optimizing treatment schedules.