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Cancer Cells and Metastasis · Journal article
Cancer Immunology Immunotherapy · September 7, 2026
Early or partial results. Treat as a signal, not a conclusion.
This systematic review catalogues 75 preclinical in vitro and ex vivo immunotherapy models across six platform types and 23 cancers, predominantly lung, breast, and colorectal. It evaluates model characteristics (immune-cell type, immunotherapy regimen, transferability) and provides contextual comparison of culture setup and validation but does not report quantitative evidence that any model reliably predicts clinical immunotherapy response or outcome.
Systematic review adhering to PRISMA 2020. Published studies of in vitro and ex vivo cancer immunotherapy models (solid tumors); 75 studies met inclusion criteria across 23 cancer types.. Intervention: In vitro and ex vivo preclinical model platforms: patient-derived organoids, 3D spheroids, 2D cell lines, tumor tissue slices, tumor-on-a-chip, and other types.. n = 75.
75 studies spanning six main experimental model types: patient-derived organoids, 3D spheroids, 2D cell lines, tumor tissue slices, tumor-on-a-chip, and others Studies covered 23 cancer types with prevalence of lung, breast, and colorectal cancers Main immune cell types used were T lymphocytes (TILs and CAR-T cells) followed by NK cells
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This review identifies and organizes available preclinical platforms for immunotherapy testing and provides a resource for researchers to select appropriate models. However, it does not establish which models predict clinical immunotherapy response, so its immediate clinical utility is limited to guiding experimental model selection rather than informing treatment decisions.
This is a systematic review of preclinical models (in vitro and ex vivo platforms) without primary efficacy data, clinical outcomes, or comparative effectiveness evidence; it maps landscape and provides catalogue value but does not establish whether any model predicts clinical benefit.
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This review identifies and organizes available preclinical platforms for immunotherapy testing and provides a resource for researchers to select appropriate models. However, it does not establish which models predict clinical immunotherapy response, so its immediate clinical utility is limited to guiding experimental model selection rather than informing treatment decisions.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Abstract Background Cancer immunotherapy (CI) has shown remarkable clinical results in solid tumors, highlighting the need for robust in vitro and ex vivo models to improve efficacy and elucidate mechanisms of resistance. In vitro and ex vivo models represent a critical tool to advance CI. We conducted a systematic review to: (1) map the use of preclinical models across cancer types, immune contexts, and immunotherapies, evaluating their degree of transferability and (2) provide a comprehensive dataset of studies employing these platforms to guide researchers. Methods Adhering to PRISMA 2020, we searched PubMed (2000–2025) for studies in vitro or ex vivo models of solid tumors tested in an immuno-oncology context. The main extracted domains included model type, immune-cell representation, type of immunotherapy assessed, and relative transferability (with culture setup, validation, and species recorded for contextual comparison). Extracted data were summarized into structured tables reported separately. Results We included 75 studies spanning six main experimental model types: patient-derived organoids (PDOs), 3D spheroid, 2D cell lines, Tumor tissue slices, Tumor-on-a-Chip, and other types across 23 cancer types with a prevalence of lung, breast and colorectal cancers. The main immune cell type used were T lymphocytes (i.e. TILs and CAR-T cells) followed by NK cells. Models investigated different immunotherapy regimens such as Immune Checkpoints inhibitors, particularly anti-PD-1/PD-L1 antibodies, followed by ACT such as CAR-T and NK cell-based therapies. While 2D models remain useful for initial screening, 3D spheroids and PDOs provide greater spatial and patient-derived context; tissue slices preserve native tumor architecture over short culture windows; and tumor-on-a-chip systems are particularly suited to modeling dynamic processes such as perfusion, gradients, and immune-cell trafficking. Conclusions We have generated a curated catalogue of models for CI that provides researchers with a valuable resource to advance experimental immunotherapy and preclinical testing.
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