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A tumor-on-a-chip for in vitro study of CAR-T cell immunotherapy in solid tumors

Nature Biotechnology (2025)Cite this article

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Abstract

Our limited understanding of cancer–immune interactions remains a critical barrier to advancing chimeric antigen receptor (CAR)-T cell therapy for solid malignancies. Here, we present a microengineered system that enables vascularization of human tumor explants and their controlled perfusion with immune cells to model the activity of CAR-T cells in the tumor microenvironment. Using vascularized human lung adenocarcinoma tumors, we first demonstrate the ability of our tumor-on-a-chip system to simulate, visualize and interrogate CAR-T cell function. We then test a chemokine-directed CAR-T cell engineering strategy in a model of malignant pleural mesothelioma and validate our findings in a matching in vivo mouse model. Finally, we describe a potential therapeutic target that can be pharmacologically modulated to increase the efficacy of CAR-T cells in lung adenocarcinoma, for which we present biomarkers identified by global metabolomics analysis. Our microphysiological system provides promising in vitro technology to advance the development of adoptive cell therapies for cancer and other diseases.

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Fig. 1: Microengineered system for in vitro transplantation and prolonged maintenance of human solid tumors.
Fig. 2: In vitro modeling of CAR-T–tumor interactions.
Fig. 3: Flow cytometric analysis of CAR-T cell phenotype and scRNA-seq analysis of microengineered tumor model.
Fig. 4: In vitro and in vivo analysis of human meso-CAR-T cells armored with CCR2.
Fig. 5: Transplantation and CAR-T cell treatment of human mesothelioma explants.
Fig. 6: Identification of therapeutic targets through analysis of ligand-receptor interactions in CAR-T cell-treated tumor models.

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Data availability

The scRNA-seq datasets generated and analyzed during the current study are available at the NCBI Gene Expression Omnibus, under accession number GSE240121 (ref. 110). All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files. The raw images are available in Dryad111, a general public repository, under the https://doi.org/10.5061/dryad.mw6m9068f. Source data are provided with this paper.

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Acknowledgements

We thank M. Luo, A. Georgescu, J. M. Oh, N. Matisioudis, A. C. Huang, J. E. Wu, J.-C. Beltra, J. R. Giles, C. Alanio, D. Oldridge, E. Niemeyer, Y. C. Choi and C. R. Kagan for their intellectual input and technical assistance. We also thank R. Pellegrino Da Silva, F. Mafra, J. M. Smiler, J. P. Garifallou and M. V. Gonzalez from the Center for Applied Genomics at The Children’s Hospital of Philadelphia, and J. Schug and O. Smirnova from the Next-Generation Sequencing Core at the University of Pennsylvania for their contributions to scRNA-seq; T. Skinner and D. Martinez from the Pathology Core Laboratory at The Children’s Hospital of Philadelphia for their technical assistance in immunohistochemistry; and S. Singhal, M. Culligan and J. Friedberg from the Hospital of the University of Pennsylvania and the Temple University Hospital for their help with mesothelioma patient explants. This work was supported by the Cancer Research Institute (DDH), the National Institutes of Health (NIH) (1DP2HL127720-01 to D.D.H.; F32DK127843 to W.D.L.; R01CA163591 and DP1DK113643 to J.D.R.), the National Science Foundation (CMMI:15-48571) (D.D.H.), the Ministry of Trade, Industry & Energy of the Republic of Korea (D.D.H.), the GRDC Cooperative Hub through the National Research Foundation of Korea funded by the Ministry of Science and ICT (RS-2023-00259341) (T.K., D.D.H.), the Paul Allen Foundation (J.D.R.), Ludwig Cancer Research (J.D.R.) and the University of Pennsylvania. H.L. is a recipient of the Penn Institute for Regenerative Medicine Postdoctoral Fellowship and the Penn Center for Engineering MechanoBiology Pilot Grant Award.

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Authors and Affiliations

  1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA

    Haijiao Liu, Xuanqi Dong, Jeehan Chang, Sezin Aday Aydin, Anni Wang, Zirui Ou, Jungwook Paek, Ju Young Park, Aidi Liu, Haonan Hu, Dora Maria Racca, Se-jeong Kim & Dan Dongeun Huh

  2. NSF Science and Technology Center for Engineering Mechanobiology, University of Pennsylvania, Philadelphia, PA, USA

    Haijiao Liu, Jeehan Chang & Dan Dongeun Huh

  3. Pulmonary, Allergy and Critical Care Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Estela Noguera-Ortega, Xinyi Shi, Maria Liousia, Marina C. Martinez, Joshua J. Brotman, Soyeon Kim, Edmund K. Moon & Steven M. Albelda

  4. Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Won Dong Lee & Joshua D. Rabinowitz

  5. Department of Chemistry, Princeton University, Princeton, NJ, USA

    Won Dong Lee & Joshua D. Rabinowitz

  6. Department of Biochemistry, Yonsei University, Seoul, Republic of Korea

    Won Dong Lee

  7. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    Yumei Li & Wei Guo

  8. Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, Republic of Korea

    Yonghee Shin & Taewook Kang

  9. Department of Biologics, Gachon University, Incheon, Republic of Korea

    Yonghee Shin

  10. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Zeyu Chen, Ellen Puré & E. John Wherry

  11. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Zeyu Chen & E. John Wherry

  12. Department of Biomedical Sciences, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Zebin Xiao & Ellen Puré

  13. Division of Hematology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    G. Scott Worthen

  14. Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ellen Puré & Dan Dongeun Huh

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Contributions

H.L. designed the research, performed most of the experiments, analyzed the data, created the figures and wrote the manuscript. E.N.-O., X.S., M.L., M.C.M., J.J.B., S.K., E.K.M. and S.M.A. provided materials, performed flow cytometry experiments and analyzed the data. Z.C. and E.J.W. provided assistance in the design of research and the analysis of the scRNA-seq data. W.D.L. and J.D.R. performed the experiments and provided assistance in the analysis of the metabolomics data. X.D., J.C., Y.L., A.W., Z.O., J.P., J.Y.P., A.L. and H.H. provided assistance in the experiments. S.A.A., Y.S., D.M.R., S.K., G.S.W., W.G. and T.K. provided assistance in the experiments and analyzed the data. Z.X. and E.P. provided materials and analyzed the data. D.D.H. designed the research, analyzed the data and wrote the manuscript.

Corresponding author

Correspondence to Dan Dongeun Huh.

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Competing interests

D.D.H. is a co-founder of Vivodyne and holds equity in the company. D.D.H. and H.L. are inventors on a patent application for tumor-on-a-chip technology. E.J.W. holds equity and has other ownership interests in Arseal Bio, Danger Bio and Surface Oncology. E.J.W. has consulting or advisory role at Danger Bio, Jaenssen, Marengo Therapeutics, NewLimit, Pluto Immunotherapeutics, Related Sciences, Santa Ana Bio, Surface Oncology and Synthekine. S.M.A. is a scientific founder and holds equity in Capstan Therapeutics. S.M.A. is on the scientific advisory boards of Verismo and Bio4t2. E.P. is a scientific founder and holds equity in Capstan Therapeutics. E.P. is on the scientific advisory boards of Parthenon Therapeutics and POINT Biopharma. E.P. is an inventor (University of Pennsylvania) on a patent (10329355) and patent application for the 4G5 FAP CAR (Patent Applications 20210087294 and 20210087295). E.P. is an inventor (University of Pennsylvania) on a patent for the use of CAR-T therapy in heart disease (US Provisional Patent Application 62/563,323 filed 26 September 2017, WIPO Patent Application PCT/US2018/052605). J.D.R. is an advisor and stockholder in Colorado Research Partners, L.E.A.F. Pharmaceuticals, Bantam Pharmaceuticals, Barer Institute and Rafael Pharmaceuticals; a paid consultant of Pfizer and Third Rock Ventures; a founder, director, and stockholder of Farber Partners, Serien Therapeutics and Sofro Pharmaceuticals; a founder and stockholder in Empress Therapeutics; inventor of patents held by Princeton University; and a director of the Princeton University-PKU Shenzhen collaboration. The remaining authors declare no competing interests.

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Nature Biotechnology thanks Michael Birnbaum, Justin Eyquem, Gordana Vunjak-Novakovic and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Single-cell trajectory analysis of CAR-T and tumor cells in the meso-tumor model.

a, UMAP plot showing CAR-T cell subpopulations in the meso-tumor model re-clustered for trajectory analysis. b, Transition of CAR-T cell phenotype along the pseudotime trajectory. c-h, Pseudotime dynamics of key marker expression plotted by cell type (c,e,g) and location (d,f,h). c,d, Biphasic regulation of gene markers associated with T cell activation (for example, GZMB, CCL3, IL2RA, IFNG). These markers begin to increase with the emergence of early activated CAR-T cells in the stroma and peak when the cells exhibit the phenotype of proliferative effector T cells within the tumors, which is followed by a plateau or gradual decrease with the progression of the cytotoxic activities of the CAR-T cells while they are still in the tumors. e,f, The activated effector CAR-T cells in the tumors show monotonically increasing expression of stress markers, such as AGR2, IGFBP1, S100P, and SPP1, demonstrating exposure and responses to increasing cellular stress in the CAR-T cells as they gain effector function, enter the tumor environment, and engage cancer cells. g,h, Among transcription factors correlated with the phenotypic changes of CAR-T cells are KLF2 and TXNIP. These markers exhibit almost monotonically decreasing expression, suggesting that their downregulation may be required for antigenic activation of CAR-T cells due to their established roles in regulating T cell effector functions and glucose consumption112,113. Increased expression of CREM and DDIT3 is closely associated with the transition of CAR-T cells towards effector lineages, consistent with their reported role as negative regulators of effector functions114,115. i, UMAP plots of tumor cell clusters in the meso-tumor model (left) and pseudotime trajectories (right). j,k, Dynamic expression of select markers by tumor cells over pseudotime. At the beginning of the pseudotime trajectory, the cells show the highest expression of genes activated by IFN-É£ (for example, STAT1, WARS), verifying their phenotype as tumor cells stimulated by effector CAR-T cell-produced IFN-É£. These genes show a cycle of downregulation and subsequent recovery, which is reversed in SERPINA1, FOS, and other transcription factors associated with survival promotion. Another notable feature is the induction of genes implicated in tumor cell apoptosis or inhibition of cancer progression (for example, MTRNR2L8, NDRG1)116,117 and their continuous increase prior to entering the apoptotic states, illustrating persistent deleterious effects of tumor-infiltrating CAR-T cells. Transcriptomic signatures of tumor cells also include markers that promote tumor development and progression (for example, MUC5AC, NEAT1), which may be interpreted as a mechanism to protect/recover stressed and apoptotic tumor cells affected by CAR-T cells. Increased induction of these genes is indeed visible at the late stages of phenotypic transition as the tumor cells move towards the apoptotic state.

Extended Data Fig. 2 Analysis of ligand-receptor interactions in a model of human lung adenocarcinoma tumors infused with meso-CAR-T cells.

a, Chord diagrams showing an overview of intercellular communication and the number of identified interactions in the meso-tumor (left) and control (middle) groups. Each chord represents a bundle of paired and statistically significant ligand-receptor interactions between a particular pair of cell types (right). The width of the chord indicates the number of interacting ligand-receptor pairs. A random-permutation test was used in the CellPhoneDB method to calculate empirical p values. b-e, Chord diagrams of cell pair-specific ligand-receptor interactions with p-values < 0.05 and total mean expression > 0.35. A random-permutation test was used in the CellPhoneDB method to calculate empirical p values.

Extended Data Fig. 3 Pharmacological inhibition of CAR-T-endothelial interactions mediated by CD38-PECAM1 signaling.

a, Visualization of the interacting pairs of ITGAL-ICAM1 and CD38-PECAM1 in the meso-tumor model (left) and violin plots comparing the expression of interacting genes of interest mediating the crosstalk between CAR-T cells and endothelial cells (right). n = 2574 CAR-T cells and 306 endothelial cells in the Meso group, and 3832 CAR-T cells and 1278 endothelial cells in the Control group. Boxplots overlaid show minimum, 25th percentile, mean (black line), 75th percentile, and maximum. b, Experimental timeline for CAR-T cell infusion and drug treatment. c, Representative fluorescence micrographs of single meso-tumors infused with meso-CAR-T cells without drug treatment (Untreated), treated with Daratumumab (0.5 or 10 μg/ml), or IgG isotype control antibody (10 μg/ml IgG). Blood vessels are not shown in these images. Scale bars, 250 μm. d,e, Quantification of T cell area (d) and normalized tumor area (e) over time. Data are presented as mean ± s.e.m. In d, n = 5 independent engineered tissue constructs for the untreated-meso-CAR-T only group; n = 6 independent engineered tissue constructs for the other groups treated with Daratumumab or IgG isotype control. One-way ANOVA with Tukey’s multiple comparisons test was used for statistical analysis. In e, n = 3 independent engineered tissue constructs at all time points for all treatment groups. Two-way ANOVA with Tukey’s multiple comparisons test was used for statistical analysis. Dara, daratumumab. CAR-T cells derived from one healthy donor were tested. These cells were administered at 2.5×105 cells per device.

Source data

Extended Data Fig. 4 Pharmacological inhibition of CAR-T-endothelial interactions mediated by LTB-LTBR signaling.

a, Visualization of the interacting LTB-LTBR pair in the meso-tumor model (left) and violin plots comparing the expression of interacting genes of interest mediating the crosstalk between CAR-T cells and endothelial cells (right). n = 2574 CAR-T cells and 306 endothelial cells in the Meso group, and 3832 CAR-T cells and 1278 endothelial cells in the Control group. Boxplots overlaid show minimum, 25th percentile, mean (black line), 75th percentile, and maximum. b, Experimental timeline for CAR-T cell infusion and drug treatment. c, Representative fluorescence micrographs of single meso-tumors infused with meso-CAR-T cells without drug treatment (Untreated), treated with Baminercept (0.5 or 10 μg/ml), or IgG isotope control antibody (10 μg/ml IgG). Blood vessels are not shown in these images. Scale bars, 250 μm. d,e, Quantification of (d) T cell area and (e) normalized tumor area over time. Data are presented as mean ± s.e.m. In d, n = 5 independent engineered tissue constructs for the group without drug treatment (Untreated) and the group treated with low concentration of Baminercept; n = 4 independent engineered tissue constructs for the group treated with high concentration of Baminercept; n = 6 independent engineered tissue constructs for the group treated with IgG isotype control. One-way ANOVA with Tukey’s multiple comparisons test was used for statistical analysis. In e, n = 3 independent engineered tissue constructs at all time points for all treatment groups. Two-way ANOVA with Tukey’s multiple comparisons test was used for statistical analysis. Bami, baminercept. CAR-T cells derived from one healthy donor were tested. These cells were administered at 2.5×105 cells per device.

Source data

Extended Data Fig. 5 Discovery of therapy biomarkers through metabolomic analysis of tumor-on-a-chip.

a, Workflow of untargeted, global metabolomic analysis using vascular perfusate from our meso-CAR-T cell-infused meso-tumor model treated with LAF237. Created with Biorender.com. The goal of this analysis was to identify metabolic signatures that correlate with the therapeutic outcome of the DPP4 inhibition method. Specific conditions considered included media only, pre-infusion, and CAR-T cell infusion groups with no LAF237 treatment (ctrl), 50 nM LAF237 (low), and 1,000 nM LAF237 (high) – examined at days 2, 7, 11, and 16 post initial infusion (Supplementary Table 2). b, Plot of partial least squares discriminant analysis (PLS-DA) scores. N = 4 independent engineered tissue constructs for each group. Our analysis revealed 205 differentially regulated metabolites (Supplementary Fig. 29). PLS-DA was performed to identify a fraction of these metabolites (FDR < 0.05) that can distinguish the tested conditions at different time points (Supplementary Fig. 30). c, Heatmap of 96 metabolites identified by multivariate analysis whose levels of expression were significantly changed by LAF237 treatment. 65 of these were upregulated and the other 31 metabolites were downregulated in LAF237-treated groups, compared to CAR-T cell-only control. N = 4 independent engineered tissue constructs for each group. The source of metabolites is color-coded in their labels – blue, amino acid metabolism; red, carbohydrate metabolism; green, nucleotide metabolism. The color gradient of the scale bar indicates the relative abundance of metabolites, with red and blue indicating higher and lower concentrations, respectively. Two-way ANOVA was used for statistical analysis, with multiple testing correction using false discovery rate. p < 0.05 was considered significant. d, Plot of significantly changed metabolic pathways identified by pathway impact analysis of significantly upregulated metabolites in the high-dose LAF237 group (Supplementary Fig. 31). The upregulated metabolites had significant effects on beta-alanine metabolism, citrate cycle, and other key processes, suggesting substantial changes in central carbon metabolism. Hypergeometric test was used as the enrichment method, with false discovery rate adjustment for significance. e-g, Comparison of normalized concentrations of select metabolites across all conditions. One-way ANOVA with Fisher’s LSD post-hoc test was used for statistical analysis. Key findings include elevated levels of carbohydrate metabolism products, such as cis-aconitic acid in the TCA cycle, at day 16 with 1,000 nM LAF237. This high-dose treatment also correlated with increased amino acid and nucleic acid metabolism, shown by higher levels of xanthine, L-proline, fumaric acid, and beta-alanine. Pyruvic acid levels were negatively correlated with drug treatment. h, Receiver operating characteristic (ROC) curves for biomarker prediction models to identify metabolites that can differentiate more efficacious CAR-T cell treatment with high-dose LAF237 from control CAR-T cell treatment without LAF237 at day 16 post initial infusion (Supplementary Fig. 32). Different numbers of constituent metabolite features per model are indicated by different colors. i, List of top metabolites yielded by the 25-feature prediction model for day 16 shown in h and ranked according to their predictive accuracy. j, Comparison of normalized concentrations of metabolites selected from i. One-way ANOVA with Fisher’s LSD post-hoc test was used for statistical analysis. Higher CAR-T therapy efficacy with 1,000 nM LAF237 was predicted to significantly correlate with reduced production of 1,2,3-propanetricarboxylic acid, 2-hydroxybutyric acid, 3-acetyl-2,7-naphthyridine, L-cystine and increased production of 3-nitrotyrosine and 2-hydroxyethanesulfonate at day 16 post initial infusion (Supplementary Fig. 33). k, ROC curves for biomarker prediction models to identify metabolites that can differentiate more efficacious CAR-T cell treatment with high-dose LAF237 from control treatment at all time points post-infusion. l, List of top metabolites yielded by the 15-feature prediction model shown in k and ranked according to their predictive accuracy. The predictions shown in i and l shared metabolites (labeled blue) that were upregulated (5-methoxytryptophan, ibudilast, vanilpyruvic acid) or downregulated (2-hydroxybutyric acid, inosine, glutaminylglutamine, 2-aminoacrylic acid) due to administration of 1,000 nM LAF237 during CAR-T cell infusion (Supplementary Fig. 33). m, Comparison of normalized concentrations of metabolites selected from l. One-way ANOVA with Fisher’s LSD post-hoc test was used for statistical analysis. Boxplots show minimum, 25th percentile, mean (yellow closed diamond), 75th percentile, and maximum. N = 4 independent engineered tissue constructs for each group. Note that post-hoc pairwise comparison was calculated between the high-dose (high) and control (ctrl) groups at all time points with *p < 0.05.

Supplementary information

Supplementary Information

Supplementary Figures 1–33.

Reporting Summary

Supplementary Table

Supplementary Table 1: List of significant interactions in CellPhoneDB analysis. Supplementary Table 2: Peak intensities of metabolites. Supplementary Table 3: Summary of MSEA and pathway impact analysis.

Supplementary Video 1

3D reconstruction of blood vessels wrapping around a tumor transplant in the device.

Supplementary Video 2

3D reconstruction of blood vessels penetrating a tumor transplant in the device.

Supplementary Video 3

Visualization of the perfusability of vascularized human lung tumor explants using 75- kDa FITC-Dextran.

Supplementary Video 4

Visualization of the perfusability of vascularized human lung tumor explants using 1-µm fluorescent microbeads.

Supplementary Video 5

Visualization of the perfusability of vascularized lung tumor spheroids using 1-µm fluorescent microbeads.

Supplementary Video 6

Comparison of initial CAR-T cell infusion between the meso-tumor and control groups.

Supplementary Video 7

Extravasation and directional migration of CAR-T cells in the meso-tumor model.

Supplementary Video 8

Behavior of CAR-T cells in the control tumor group.

Supplementary Video 9

Time-lapse imaging of CAR-T cell-infused lung tumor growth without LAF237.

Supplementary Video 10

Time-lapse imaging of CAR T cell-infused lung tumor growth treated with 50 nM of LAF237.

Supplementary Video 11

Time-lapse imaging of CAR T cell-infused lung tumor growth treated with 1000 nM of LAF237.

Source data

Source Data Figures 1–6 and Extended Data Figures 3–4

Statistical source data for Fig. 1 f Branch length and 1 f Vessel diameter. Statistical source data for Figs. 2i, 2j, 2k, 2p, 2q IL-2, 2q IFN-gamma, 2r Caspase3, and 2r LDH. Statistical source data for Figs. 3b, 3c, 3 d percentage of effector memory CAR T cells, and 3 d numbers of effector memory CAR T cells. Statistical source data for Figs. 4e, 4 f, 4i, 4j, 4k, 4 l, and 4 m. Statistical source data for Fig. 5c Vessel area, 5c Branch length, 5c Number of junctions, 5c Vessel diameter, 5e, 5 f, 5 g number of Tem cells, 5 g number of Tcm cells, 5 h, 5i, 5j, and 5k. Statistical source data for Fig. 6i Tumor area, 6i T cell area, 6j CXCL10, 6j CXCL11, and 6k. Statistical source data for Extended Data Figs. 3 d and 3e. Statistical source data for Extended Data Figs. 4 d and 4e.

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Liu, H., Noguera-Ortega, E., Dong, X. et al. A tumor-on-a-chip for in vitro study of CAR-T cell immunotherapy in solid tumors. Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02845-z

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