PROJECT SUMMARY The survival of advanced or recurrent epithelial ovarian cancer (EOC) remains dismal due in part to the complex tumor–immune microenvironment (TIME). The presence of tumor-infiltrating T cells correlates with improved patient outcome in advanced EOC, yet checkpoint inhibitor immunotherapy has generally shown poor efficacy against EOC in clinical trials. A major challenge is the low number of T cells compared to EOC cells in the tumor that establish an immune-suppressive TIME. Tumor-targeted, cell-activatable photoimmunotherapy (taPIT, a near infrared phototherapy) may provide an alternative treatment approach that selectively destroys EOC cells expressing cell-surface epidermal growth factor receptor (EGFR) while enhancing the preservation of tumor infiltrating immune cells salient to an adaptive immune response (e.g., local cytotoxic T cells and dendritic cells). While photoimmunotherapy is not new, this is the first exploration of taPIT dose fractionation to selective eradicate EOC while stimulating immune cells at lower doses and preferentially sparing immune cells at higher doses. Based on our rich preliminary data, we propose that mathematical modeling-informed taPIT serves as a new paradigm for combined cytotoxic therapy and immunotherapy in metastatic EOC. Our overall goal is a novel experimental, simulation- and image-guided approach for utilizing the local selectivity of taPIT to prime “cold” TIME’s for immune checkpoint inhibition. This proposal contributes an innovative physical sciences approach to cancer therapy integrating mathematical modeling with a 3D culture model of the TIME and in vivo imaging experiments in immunocompetent mouse models of EOC, including in silico immuno-oncology modeling to optimize TIME composition-specific therapy; fractionated taPIT dosimetry to reduce the EOC cell burden relative to effector T cells and dendritic cells within the TIME; and, in vivo multiplexed fluorescence microendoscopy to interrogate the TIME of metastatic EOC within the peritoneal cavity. The unique ability to image micronodular disease will enable parameterizing a mathematical model with pre-treatment conditions and dynamic in vivo responses to therapy as a basis for the quantitative design of custom-tailored therapies. Our aims are (1) to train an in silico model by correlating clinical pre-treatment EOC TIME compositions with pathological anti-PD1 response; (2) to derive optimal fractionated taPIT protocols that shift “cold” to anti-PD1-sensitive “hot” TIME in vitro, and (3) to prime the TIME for anti-PD1 therapy in vivo. The concepts introduced here will ultimately enable taPIT–anti-PD1 therapy dosimetry that synergistically stimulates both local and distal immune-enhancing effects to impact disease sites missed by near infrared light, in combination with frontline surgical tumor debulking and systemic chemotherapy. The approaches developed here are translatable to other tumor sites that can be treated with taPIT, including head and neck cancers, skin cancers, and lung cancers.

PROJECT NARRATIVE Ovarian cancers have co-evolved with the patient’s immune system and build a complex tumor-immune microenvironment. As anti-PD1 immunotherapy has generally shown poor efficacy against ovarian cancers, there is a dire clinical need to complement checkpoint inhibition with treatments that stimulate infiltration or proliferation of tumor-specific TC’s. We propose an integrated experimental–mathematical oncology approach to decipher how to best harness immune sparing and immune stimulation of fractionated photoimmunotherapy to personalize treatments for advanced or recurrent epithelial ovarian cancer patients with presently dismal survival rates.