Abstract
Tumor clearance by T cells is impaired by insufficient tumor antigen recognition, insufficient tumor infiltration, and the immunosuppressive tumor microenvironment (TME). Although targeted T cell therapy circumvents failures in tumor antigen recognition, suppression by the TME and failure to infiltrate the tumor can hinder tumor clearance. Checkpoint inhibitors (CPI) promise to reverse T cell suppression and can be combined with bispecific antibody armed T cell (BATs) therapy to improve clinical outcomes. We hypothesize that adoptively transferred T cell function may be improved by the addition of CPI if the inhibitory pathway is functionally active. This study develops a kinetic-dynamic model of killing of hormone receptor-positive (HR+) breast cancer cells mediated by BATs using single-cell transcriptomic and temporal protein data to identify T cell phenotypes and quantify inhibitory receptor expression. LAG3, PD-1, and TIGIT were identified as inhibitory receptors expressed by cytotoxic effector CD8 BATs upon exposure to HR+ breast cancer cell lines. These data were combined with real-time tumor cytotoxicity data in a multivariate statistical analysis framework to predict the relevant contributions of T cells expressing each receptor to tumor reduction. A mechanistic kinetic-dynamic mathematical model was developed and parametrized using protein expression and cytotoxicity data for in silico validation of the findings of the multivariate statistical analysis. The model corroborated the predictions of the multivariate statistical analysis which identified LAG3+ BATs as the primary effectors, while TIGIT expression dampened cytotoxic function. These results inform CPI selection for BATs combination therapy and provide a framework to maximize BATs anti-tumor function.