Patient-derived tumor organoid co-culture systems as translational platforms for cancer immunotherapy and precision oncology

Abstract The tumor microenvironment (TME) is a dynamic and immunosuppressive niche that critically governs cancer progression, immune evasion, and therapeutic resistance. Accurately modeling tumor–immune interactions in vitro is therefore essential for advancing our understanding of cancer biology and accelerating the development of effective immunotherapies. Co-culture systems have emerged as powerful platforms for this purpose, enabling the controlled study of cellular crosstalk between tumor cells and immune, stromal, or endothelial components under defined experimental conditions. Among available co-culture approaches, patient-derived tumor organoid–peripheral blood mononuclear cell (PDTO–PBMC) co-culture systems represent the most physiologically faithful platform currently available, preserving the histological, genetic, and phenotypic heterogeneity of the original tumor while enabling reconstitution of autologous immune responses. These systems allow the expansion of tumor-reactive T cells in an antigen–agnostic manner, providing a clinically relevant platform for evaluating immune checkpoint inhibitors (ICI), adoptive cell therapies (ACT), chimeric antigen receptor (CAR) T cell constructs, and combination immunotherapy regimens across multiple cancer types. This review comprehensively examines the spectrum of tumor–immune co-culture systems, from conventional 2D monolayer models to 3D spheroid and organoid-based platforms, critically comparing their biological fidelity, technical requirements, and translational utility. Particular emphasis is placed on the PDTO–PBMC co-culture model, its applications across diverse immune cell populations, including T cells, NK cells, macrophages, dendritic cells, and CAR T cells, and its potential as a functional companion diagnostic tool for precision immunotherapy. Current limitations, standardization challenges, and future directions including microfluidics, spatial transcriptomics, and artificial intelligence integration are also discussed.