类器官-T细胞共培养：一个用于双特异性抗体免疫疗法开发的稳健平台

Recent advances in cancer immunotherapy have significantly improved patient survival across multiple indications. As new treatment strategies and druggable targets emerge, the number of patients eligible for immunotherapy continues to grow. However, translating promising preclinical findings into clinical success remains challenging, as conventional 2D cancer models offer limited predictive value. We developed an innovative alternative based on the ability of adult stem cells to form three-dimensional organotypic structures within an extracellular matrix. Patient-derived organoids, generated from normal and malignant tissues and stored in high-quality biobanks, yield highly reproducible results. HUB Organoids faithfully recapitulate the complexity of parental tissue, including molecular heterogeneity and morphological and functional traits. Over the last decade, bispecific antibodies (bsAbs) have gained significant attention in cancer treatment, leading to several approved therapies. Bispecific T-cell engagers (BiTEs), a subclass of bsAbs, bring T cells into close proximity with cancer cells and promote immunologic memory, enabling the immune system to more effectively recognize and eliminate tumor cells. To de-risk drug development and accelerate clinical translation, we developed an assay in which labeled tumor organoids are co-cultured with labeled PBMCs and exposed to BiTEs to assess immune cell-mediated cytotoxicity. Fluorescent dyes enable real-time evaluation of cytotoxic activity via high-resolution microscopy, complemented by cytokine secretion analysis to confirm immune cell activation. HUB's organoid co-culture system is robust and reproducible, providing a dynamic window to evaluate the activity of multiple BiTEs. Organoid-based cytotoxicity assays accurately reflect BiTE-mediated T cell killing, demonstrating that our model captures the expected mechanism of action. This platform offers a powerful tool for developing and validating cancer immunotherapies, helping to de-risk novel strategies through patient-derived models.