Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are essential, they are expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous “virtual cells” created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic contexts indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies. ABMACT, an agent-based model of adoptive cell therapy, recapitulated cellular dynamics in two cancer preclinical models and showed that enhancing immune cell proliferation, cytotoxicity, and serial killing capacity is critical for optimal efficacy.