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Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies

Nature Reviews Immunology volume 24, pages 18–32 (2024)Cite this article

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Abstract

In vitro 3D models are advanced biological tools that have been established to overcome the shortcomings of oversimplified 2D cultures and mouse models. Various in vitro 3D immuno-oncology models have been developed to mimic and recapitulate the cancer–immunity cycle, evaluate immunotherapy regimens, and explore options for optimizing current immunotherapies, including for individual patient tumours. Here, we review recent developments in this field. We focus, first, on the limitations of existing immunotherapies for solid tumours, secondly, on how in vitro 3D immuno-oncology models are established using various technologies — including scaffolds, organoids, microfluidics and 3D bioprinting — and thirdly, on the applications of these 3D models for comprehending the cancer–immunity cycle as well as for assessing and improving immunotherapies for solid tumours.

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Fig. 1: Schematics of scaffold-based 3D models for immuno-oncology research.
Fig. 2: Schematics of tumour organoid models for immuno-oncology research.
Fig. 3: Schematics of microfluidic 3D models for immuno-oncology research.
Fig. 4: Schematics of bioprinting-based 3D models for immuno-oncology research.

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References

  1. Ribas, A. & Wolchok, J. D. Cancer immunotherapy using checkpoint blockade. Science 359, 1350–1355 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Porter, D. L., Levine, B. L., Kalos, M., Bagg, A. & June, C. H. Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia. N. Engl. J. Med. 365, 725–733 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Melero, I. et al. Therapeutic vaccines for cancer: an overview of clinical trials. Nat. Rev. Clin. Oncol. 11, 509–524 (2014).

    Article  CAS  PubMed  Google Scholar 

  6. O’Donnell, J. S., Teng, M. W. L. & Smyth, M. J. Cancer immunoediting and resistance to T cell-based immunotherapy. Nat. Rev. Clin. Oncol. 16, 151–167 (2019).

    Article  PubMed  Google Scholar 

  7. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Keskin, D. B. et al. Neoantigen vaccine generates intratumoral T cell responses in phase Ib glioblastoma trial. Nature 565, 234–239 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Postow, M. A., Sidlow, R. & Hellmann, M. D. Immune-related adverse events associated with immune checkpoint blockade. N. Engl. J. Med. 378, 158–168 (2018).

    Article  CAS  PubMed  Google Scholar 

  10. Hegde, P. S. & Chen, D. S. Top 10 challenges in cancer immunotherapy. Immunity 52, 17–35 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).

    Article  PubMed  Google Scholar 

  12. Fridman, W. H., Pagès, F., Saut̀s-Fridman, C. & Galon, J. The immune contexture in human tumours: impact on clinical outcome. Nat. Rev. Cancer 12, 298–306 (2012).

    Article  CAS  PubMed  Google Scholar 

  13. Hegde, P. S., Karanikas, V. & Evers, S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin. Cancer Res. 22, 1865–1874 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Joyce, A. J. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    Article  CAS  PubMed  Google Scholar 

  15. Schreiber, R. D., Old, L. J. & Smyth, M. J. Cancer immunoediting: integrating immunity’s roles in cancer suppression and promotion. Science 331, 1565–1570 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Klemm, F. et al. Interrogation of the microenvironmental landscape in brain tumors reveals disease-specific alterations of immune cells. Cell 181, 1643–1660.e17 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mlecnik, B. et al. Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J. Clin. Oncol. 29, 610–618 (2011).

    Article  PubMed  Google Scholar 

  18. Melero, I., Rouzaut, A., Motz, G. T. & Coukos, G. T-cell and NK-cell infiltration into solid tumors: a key limiting factor for efficacious cancer immunotherapy. Cancer Discov. 4, 522–526 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. He, B. et al. Remodeling of metastatic vasculature reduces lung colonization and sensitizes overt metastases to immunotherapy. Cell Rep. 30, 714–724.e5 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Vong, S. & Kalluri, R. The role of stromal myofibroblast and extracellular matrix in tumor angiogenesis. Genes. Cancer 2, 1139–1145 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Allen, B. M. et al. Systemic dysfunction and plasticity of the immune macroenvironment in cancer models. Nat. Med. 26, 1125–1134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Krishnamurty, A. T. & Turley, S. J. Lymph node stromal cells: cartographers of the immune system. Nat. Immunol. 21, 369–380 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Cabrita, R. et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577, 561–565 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Stock, K. et al. Capturing tumor complexity in vitro: comparative analysis of 2D and 3D tumor models for drug discovery. Sci. Rep. 6, 28951 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Zitvogel, L., Pitt, J. M., Daillère, R., Smyth, M. J. & Kroemer, G. Mouse models in oncoimmunology. Nat. Rev. Cancer 16, 759–773 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Meraz, I. M. et al. An improved patient-derived xenograft humanized mouse model for evaluation of lung cancer immune responses. Cancer Immunol. Res. 7, 1267–1279 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ringquist, R., Ghoshal, D., Jain, R. & Roy, K. Understanding and improving cellular immunotherapies against cancer: from cell-manufacturing to tumor-immune models. Adv. Drug. Deliv. Rev. 179, 114003 (2021).

    Article  CAS  PubMed  Google Scholar 

  28. Riley, R. S., June, C. H., Langer, R. & Mitchell, M. J. Delivery technologies for cancer immunotherapy. Nat. Rev. Drug. Discov. 18, 175–196 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Francis, D. M. & Thomas, S. N. Progress and opportunities for enhancing the delivery and efficacy of checkpoint inhibitors for cancer immunotherapy. Adv. Drug. Deliv. Rev. 114, 33–42 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Carter, E. P., Roozitalab, R., Gibson, S. V. & Grose, R. P. Tumour microenvironment 3D-modeling: simplicity to complexity and back again. Trends Cancer 7, 1033–1046 (2021).

    Article  CAS  PubMed  Google Scholar 

  31. Hammel, J. H., Zatorski, J. M., Cook, S. R., Pompano, R. R. & Munson, J. M. Engineering in vitro immune-competent tissue models for testing and evaluation of therapeutics. Adv. Drug. Deliv. Rev. 182, 114111 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Hirt, C. et al. ‘In vitro’ 3D models of tumor-immune system interaction. Adv. Drug. Deliv. Rev. 79, 145–154 (2014).

    Article  PubMed  Google Scholar 

  33. Shelton, S. E., Nguyen, H. T., Barbie, D. A. & Kamm, R. D. Engineering approaches for studying immune-tumor cell interactions and immunotherapy. iScience 24, 101985 (2021).

    Article  CAS  PubMed  Google Scholar 

  34. Adu-Berchie, K. & Mooney, D. J. Biomaterials as local niches for immunomodulation. Acc. Chem. Res. 53, 1749–1760 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. MP, M. & SN, T. Lymphatic immunomodulation using engineered drug delivery systems for cancer immunotherapy. Adv. Drug. Deliv. Rev. 160, 19–35 (2020).

    Article  Google Scholar 

  36. Francis, D. M. et al. Blockade of immune checkpoints in lymph nodes through locoregional delivery augments cancer immunotherapy. Sci. Transl. Med. 12, eaay3575 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Durante, M. A. et al. Single-cell analysis reveals new evolutionary complexity in uveal melanoma. Nat. Commun. 11, 496 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    Article  CAS  PubMed  Google Scholar 

  40. Tahmasebi, S., Elahi, R. & Esmaeilzadeh, A. Solid tumors challenges and new insights of CAR T cell engineering. Stem Cell Rev. Rep. 15, 619–636 (2019).

    Article  PubMed  Google Scholar 

  41. Hong, M., Clubb, J. D. & Chen, Y. Y. Engineering CAR-T cells for next-generation cancer therapy. Cancer Cell 38, 473–488 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Eppler, H. B. & Jewell, C. M. Biomaterials as tools to decode immunity. Adv. Mater. 32, e1903367 (2020).

    Article  PubMed  Google Scholar 

  43. AJ, N. & DJ, M. Cell and tissue engineering in lymph nodes for cancer immunotherapy. Adv. Drug. Deliv. Rev. 161, 42–62 (2020).

    Google Scholar 

  44. Tabdanov, E. D. et al. Bimodal sensing of guidance cues in mechanically distinct microenvironments. Nat. Commun. 9, 4891 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tabdanov, E. D. et al. Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments. Nat. Commun. 12, 2815 (2021). The authors designed a nanotextured elastic platform to define how the balance between contractility localization-dependent T cell phenotypes influences migration in response to mechanical and structural cues that mimic tumour growth.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Scheetz, L. et al. Engineering patient-specific cancer immunotherapies. Nat. Biomed. Eng. 3, 768–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ferber, S., Gonzalez, R. J., Cryer, A. M., von Andrian, U. H. & Artzi, N. Immunology-guided biomaterial design for mucosal cancer vaccines. Adv. Mater. 32, e1903847 (2020).

    Article  PubMed  Google Scholar 

  48. Cheung, A. S. & Mooney, D. J. Engineered materials for cancer immunotherapy. Nano Today 10, 511–531 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Kim, J. et al. Injectable, spontaneously assembling, inorganic scaffolds modulate immune cells in vivo and increase vaccine efficacy. Nat. Biotechnol. 33, 64–72 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Zhang, Y. et al. 3D printing scaffold vaccine for antitumor immunity. Adv. Mater. 33, e2106768 (2021).

    Article  PubMed  Google Scholar 

  51. Abou-el-Enein, M. et al. Scalable manufacturing of CAR T cells for cancer immunotherapy. Blood Cancer Discov. 2, 408–422 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Olweus, J. Manufacture of CAR-T cells in the body. Nat. Biotechnol. 35, 520–521 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Kaiser, A. D. et al. Towards a commercial process for the manufacture of genetically modified T cells for therapy. Cancer Gene Ther. 22, 72–78 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hickey, J. W. et al. Adaptive nanoparticle platforms for high throughput expansion and detection of antigen-specific T cells. Nano Lett. 20, 6289–6298 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Cheung, A. S., Zhang, D. K. Y., Koshy, S. T. & Mooney, D. J. Scaffolds that mimic antigen-presenting cells enable ex vivo expansion of primary T cells. Nat. Biotechnol. 36, 160–169 (2018). These authors outline a micro-rod system that enables antigen-specific expansion of cytotoxic T cell subpopulations at a greater magnitude than is seen with the use of autologous monocyte-derived dendritic cells.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Delalat, B. et al. 3D printed lattices as an activation and expansion platform for T cell therapy. Biomaterials 140, 58–68 (2017).

    Article  CAS  PubMed  Google Scholar 

  57. Majedi, F. S. et al. Cytokine secreting microparticles engineer the fate and the effector functions of T-cells. Adv. Mater. 30, 1703178 (2018).

    Article  Google Scholar 

  58. Lin, H. et al. Automated expansion of primary human T cells in scalable and cell-friendly hydrogel microtubes for adoptive immunotherapy. Adv. Healthc. Mater. 7, e1701297 (2018).

    Article  PubMed  Google Scholar 

  59. Hickey, J. W. et al. Engineering an artificial T-cell stimulating matrix for immunotherapy. Adv. Mater. 31, e1807359 (2019). These authors show how the ECM affects the cellular therapeutic outcome and offer a case study on how to design ECM-imitating materials for therapeutic immune stimulation.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mellman, I. et al. De-risking immunotherapy: report of a consensus workshop of the cancer immunotherapy consortium of the cancer research institute. Cancer Immunol. Res. 4, 279–288 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Stephan, S. B. et al. Biopolymer implants enhance the efficacy of adoptive T-cell therapy. Nat. Biotechnol. 33, 97–101 (2015).

    Article  CAS  PubMed  Google Scholar 

  62. Phuengkham, H., Song, C. & Lim, Y. T. A designer scaffold with immune nanoconverters for reverting immunosuppression and enhancing immune checkpoint blockade therapy. Adv. Mater. 31, e1903242 (2019).

    Article  PubMed  Google Scholar 

  63. Wang, H. et al. Biomaterial-based scaffold for in situ chemo-immunotherapy to treat poorly immunogenic tumors. Nat. Commun. 11, 5696 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Smith, T. T. et al. Biopolymers codelivering engineered T cells and STING agonists can eliminate heterogeneous tumors. J. Clin. Invest. 127, 2176–2191 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Wallstabe, L. et al. ROR1-CAR T cells are effective against lung and breast cancer in advanced microphysiologic 3D tumor models. JCI Insight 4, e126345 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Wolf, M. T. et al. A biologic scaffold-associated type 2 immune microenvironment inhibits tumor formation and synergizes with checkpoint immunotherapy. Sci. Transl. Med. 11, eaat7973 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Anderson, A. E. et al. An immunologically active, adipose-derived extracellular matrix biomaterial for soft tissue reconstruction: concept to clinical trial. npj Regen. Med. 7, 6 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. O’Melia, M. J. et al. Synthetic matrix scaffolds engineer the in vivo tumor immune microenvironment for immunotherapy screening. Adv. Mater. 34, e2108084 (2022). These authors created biomaterials to use as scaffolding to reduce the variability in immunotherapeutic testing and enable more accurate modelling of tumour immune microenvironments.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Bian, S. et al. Genetically engineered cerebral organoids model brain tumor formation. Nat. Methods 15, 631–639 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Zhao, Y. et al. Single-cell transcriptome analysis uncovers intratumoral heterogeneity and underlying mechanisms for drug resistance in hepatobiliary tumor organoids. Adv. Sci. 8, e2003897 (2021).

    Article  Google Scholar 

  71. de Witte, C. J. et al. Patient-derived ovarian cancer organoids mimic clinical response and exhibit heterogeneous inter- and intrapatient drug responses. Cell Rep. 31, 107762 (2020).

    Article  PubMed  Google Scholar 

  72. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Guillen, K. P. et al. A human breast cancer-derived xenograft and organoid platform for drug discovery and precision oncology. Nat. Cancer 3, 232–250 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Yuki, K., Cheng, N., Nakano, M. & Kuo, C. J. Organoid models of tumor immunology. Trends Immunol. 41, 652–664 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Neal, J. T. et al. Organoid modeling of the tumor immune microenvironment. Cell 175, 1972–1988.e16 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Ootani, A. et al. Sustained in vitro intestinal epithelial culture within a Wnt-dependent stem cell niche. Nat. Med. 15, 701–706 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Kim, S. et al. Tissue extracellular matrix hydrogels as alternatives to Matrigel for culturing gastrointestinal organoids. Nat. Commun. 13, 1692 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Below, C. R. et al. A microenvironment-inspired synthetic three-dimensional model for pancreatic ductal adenocarcinoma organoids. Nat. Mater. 21, 110–119 (2022).

    Article  CAS  PubMed  Google Scholar 

  81. Hernandez-Gordillo, V. et al. Fully synthetic matrices for in vitro culture of primary human intestinal enteroids and endometrial organoids. Biomaterials 254, 120125 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Broutier, L. et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 23, 1424–1435 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Tsai, S. et al. Development of primary human pancreatic cancer organoids, matched stromal and immune cells and 3D tumor microenvironment models. BMC Cancer 18, 335 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Jacob, F., Ming, G. L. & Song, H. Generation and biobanking of patient-derived glioblastoma organoids and their application in CAR T cell testing. Nat. Protoc. 15, 4000–4033 (2020).

    Article  CAS  PubMed  Google Scholar 

  85. Chan, I. S. et al. Cancer cells educate natural killer cells to a metastasis-promoting cell state. J. Cell Biol. 219, e202001134 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Dijkstra, K. K. et al. Generation of tumor-reactive T cells by co-culture of peripheral blood lymphocytes and tumor organoids. Cell 174, 1586–1598.e12 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Zhou, Z. et al. An organoid-based screen for epigenetic inhibitors that stimulate antigen presentation and potentiate T-cell-mediated cytotoxicity. Nat. Biomed. Eng. 5, 1320–1335 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Dekkers, J. F. et al. Uncovering the mode of action of engineered T cells in patient cancer organoids. Nat. Biotechnol. 41, 60–69 (2023). This group created the ‘BEHAV3D’ system to investigate the dynamic interactions between immune cells and patient-derived cancer organoids. The system can define the behavioural phenotypic heterogeneity of cellular immunotherapies in solid tumours.

    Article  CAS  PubMed  Google Scholar 

  89. Neal, J. T. & Kuo, C. J. Organoids as models for neoplastic transformation. Annu. Rev. Pathol. Mech. Dis. 11, 199–220 (2016).

    Article  CAS  Google Scholar 

  90. Hu, Y. et al. Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week. Nat. Commun. 12, 2581 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386.e10 (2018).

    Article  CAS  PubMed  Google Scholar 

  92. Van De Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Fang, G. et al. Mammary tumor organoid culture in non-adhesive alginate for luminal mechanics and high-throughput drug screening. Adv. Sci. 8, e2102418 (2021).

    Article  Google Scholar 

  94. Gong, Z. et al. Acoustic droplet printing tumor organoids for modeling bladder tumor immune microenvironment within a week. Adv. Healthc. Mater. 10, 1–12 (2021).

    Article  Google Scholar 

  95. Ao, Z. et al. Rapid profiling of tumor-immune interaction using acoustically assembled patient-derived cell clusters. Adv. Sci. 9, e2201478 (2022).

    Article  Google Scholar 

  96. Jiang, X. et al. Cancer-on-a-chip for modeling immune checkpoint inhibitor and tumor interactions. Small 17, e2004282 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  97. LeSavage, B. L., Suhar, R. A., Broguiere, N., Lutolf, M. P. & Heilshorn, S. C. Next-generation cancer organoids. Nat. Mater. 21, 143–159 (2022).

    Article  CAS  PubMed  Google Scholar 

  98. Bandaru, P. et al. A microfabricated sandwiching assay for nanoliter and high-throughput biomarker screening. Small 15, e1900300 (2019).

    Article  PubMed  Google Scholar 

  99. Cornelius, S. L. et al. Generating and imaging mouse and human epithelial organoids from normal and tumor mammary tissue without passaging. Cancer Res. 189, 10.3791/e64626 (2022).

    Google Scholar 

  100. Schuster, B. et al. Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids. Nat. Commun. 11, 5271 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Brandenberg, N. et al. High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays. Nat. Biomed. Eng. 4, 863–874 (2020).

    Article  CAS  PubMed  Google Scholar 

  102. Jiang, S. et al. An automated organoid platform with inter-organoid homogeneity and inter-patient heterogeneity. Cell Rep. Med. 1, 100161 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Font-Clos, F., Zapperi, S. & La Porta, C. A. M. Blood flow contributions to cancer metastasis. iScience 23, 101073 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Buchanan, C. F., Verbridge, S. S., Vlachos, P. P. & Rylander, M. N. Flow shear stress regulates endothelial barrier function and expression of angiogenic factors in a 3D microfluidic tumor vascular model. Cell Adh. Migr. 8, 517–524 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Silvestri, V. L. et al. A tissue-engineered 3D microvessel model reveals the dynamics of mosaic vessel formation in breast cancer. Cancer Res. 80, 4288–4301 (2020). The authors created a tissue-engineered model that replicates the tumour-vascular milieu in solid tumours and enables real-time imaging of the cellular mechanisms of mosaic vessel formation and vascular defect generation.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Wong, A. D. & Searson, P. C. Live-cell imaging of invasion and intravasation in an artificial microvessel platform. Cancer Res. 74, 4937–4945 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Rajasekar, S. et al. IFlowPlate—a customized 384-well plate for the culture of perfusable vascularized colon organoids. Adv. Mater. 32, e2002974 (2020).

    Article  PubMed  Google Scholar 

  108. Palikuqi, B. et al. Adaptable haemodynamic endothelial cells for organogenesis and tumorigenesis. Nature 585, 426–432 (2020). This group created an ‘Organ-On-VascularNet’ model that enables investigation and screening in the areas of metabolism, immunology and physiochemistry to define the interactions between organotypic endothelial cells and parenchymal cells.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Sun, X. Y. et al. Generation of vascularized brain organoids to study neurovascular interactions. eLife 11, e76707 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Bhatia, S. N. & Ingber, D. E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014).

    Article  CAS  PubMed  Google Scholar 

  111. Cui, X. et al. Hacking macrophage-associated immunosuppression for regulating glioblastoma angiogenesis. Biomaterials 161, 164–178 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Kim, H. et al. Macrophages-triggered sequential remodeling of endothelium-interstitial matrix to form pre-metastatic niche in microfluidic tumor microenvironment. Adv. Sci. 6, 1900195 (2019).

    Article  Google Scholar 

  113. Aung, A., Kumar, V., Theprungsirikul, J., Davey, S. K. & Varghese, S. An engineered tumor-on-a-chip device with breast cancer-immune cell interactions for assessing T-cell recruitment. Cancer Res. 80, 263–275 (2020).

    Article  CAS  PubMed  Google Scholar 

  114. Ando, Y. et al. Evaluating CAR-T cell therapy in a hypoxic 3D tumor model. Adv. Healthc. Mater. 8, e1900001 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Cui, X. et al. Dissecting the immunosuppressive tumor microenvironments in glioblastoma-on-a-chip for optimized PD-1 immunotherapy. eLife 9, e52253 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Jenkins, R. W. et al. Ex vivo profiling of PD-1 blockade using organotypic tumor spheroids. Cancer Discov. 8, 196–215 (2018).

    Article  CAS  PubMed  Google Scholar 

  117. Lee, S. W. L. et al. Characterizing the role of monocytes in T cell cancer immunotherapy using a 3D microfluidic model. Front. Immunol. 9, 416 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  118. Pavesi, A. et al. A 3D microfluidic model for preclinical evaluation of TCR-engineered T cells against solid tumors. JCI Insight 2, e89762 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  119. McAleer, C. W. et al. Multi-organ system for the evaluation of efficacy and off-target toxicity of anticancer therapeutics. Sci. Transl. Med. 11, eaav1386 (2019). The authors created an in vitro multi-organ cell-based system for effective preclinical drug testing and identifying drug metabolite effects that manifest themselves at the organ level.

    Article  CAS  PubMed  Google Scholar 

  120. Edington, C. D. et al. Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci. Rep. 8, 4530 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Matuła, K., Rivello, F. & Huck, W. T. S. Single-cell analysis using droplet microfluidics. Adv. Biosyst. 4, e1900188 (2020).

    Article  PubMed  Google Scholar 

  123. Heath, J. R., Ribas, A. & Mischel, P. S. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug. Discov. 15, 204–216 (2016).

    Article  CAS  PubMed  Google Scholar 

  124. Tu, H. et al. Profiling of immune–cancer interactions at the single-cell level using a microfluidic well array. Analyst 145, 4138–4147 (2020).

    Article  CAS  PubMed  Google Scholar 

  125. Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Krishna, C. et al. Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy. Cancer Cell 39, 662–677.e6 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Zhang, S. Q. et al. High-throughput determination of the antigen specificities of T cell receptors in single cells. Nat. Biotechnol. 36, 1156–1159 (2018).

    Article  CAS  Google Scholar 

  128. Guo, X. et al. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24, 978–985 (2018).

    Article  CAS  PubMed  Google Scholar 

  129. Bounab, Y. et al. Dynamic single-cell phenotyping of immune cells using the microfluidic platform DropMap. Nat. Protoc. 15, 2920–2955 (2020).

    Article  CAS  PubMed  Google Scholar 

  130. Segaliny, A. I. et al. Functional TCR T cell screening using single-cell droplet microfluidics. Lab. Chip 18, 3733–3749 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Ding, S. et al. Patient-derived micro-organospheres enable clinical precision oncology. Cell Stem Cell 29, 905–917.e6 (2022). This group developed a method to rapidly create hundreds of micro-organospheres using droplet emulsion microfluidics; the method can be used in a clinical assay to evaluate immuno-oncology treatments.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Zeming, K. K. et al. Label-free biophysical markers from whole blood microfluidic immune profiling reveal severe immune response signatures. Small 17, e2006123 (2021).

    Article  PubMed  Google Scholar 

  133. Wang, Z. et al. Efficient recovery of potent tumour-infiltrating lymphocytes through quantitative immunomagnetic cell sorting. Nat. Biomed. Eng. 6, 108–117 (2022). These authors created a reconfigurable microfluidic system that effectively recovers potent TILs from solid tumours, which is crucial for adoptive cell therapies to be effective in the long run.

    Article  CAS  PubMed  Google Scholar 

  134. Dura, B. et al. Profiling lymphocyte interactions at the single-cell level by microfluidic cell pairing. Nat. Commun. 6, 5940 (2015).

    Article  CAS  PubMed  Google Scholar 

  135. Paijens, S. T., Vledder, A., de Bruyn, M. & Nijman, H. W. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell. Mol. Immunol. 18, 842–859 (2021).

    Article  CAS  PubMed  Google Scholar 

  136. Simoni, Y. et al. Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557, 575–579 (2018).

    Article  CAS  PubMed  Google Scholar 

  137. Scheper, W. et al. Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers. Nat. Med. 25, 89–94 (2019).

    Article  CAS  PubMed  Google Scholar 

  138. Knowlton, S., Onal, S., Yu, C. H., Zhao, J. J. & Tasoglu, S. Bioprinting for cancer research. Trends Biotechnol. 33, 504–513 (2015).

    Article  CAS  PubMed  Google Scholar 

  139. Liu, T. K., Pang, Y., Zhou, Z. Z., Yao, R. & Sun, W. An integrated cell printing system for the construction of heterogeneous tissue models. Acta Biomater. 95, 245–257 (2019).

    Article  CAS  PubMed  Google Scholar 

  140. Lawlor, K. T. et al. Cellular extrusion bioprinting improves kidney organoid reproducibility and conformation. Nat. Mater. 20, 260–271 (2021).

    Article  CAS  PubMed  Google Scholar 

  141. Heinrich, M. A. et al. 3D-Bioprinted mini-brain: a glioblastoma model to study cellular interactions and therapeutics. Adv. Mater. 31, 1–9 (2019).

    Google Scholar 

  142. Murphy, S. V. & Atala, A. 3D bioprinting of tissues and organs. Nat. Biotechnol. 32, 773–785 (2014).

    Article  CAS  PubMed  Google Scholar 

  143. Ayan, B. et al. Aspiration-assisted bioprinting for precise positioning of biologics. Sci. Adv. 6, eaaw5111 (2020). These authors developed ‘aspiration-assisted bioprinting’, which enables various biofabrication schemes, such as scaffold-based or scaffold-free bioprinting, at an unprecedented placement accuracy.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Xie, F. et al. Three-dimensional bio-printing of primary human hepatocellular carcinoma for personalized medicine. Biomaterials 265, 120416 (2021).

    Article  CAS  PubMed  Google Scholar 

  145. Tang, M. et al. Three-dimensional bioprinted glioblastoma microenvironments model cellular dependencies and immune interactions. Cell Res. 30, 833–853 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Grolman, J. M., Zhang, D., Smith, A. M., Moore, J. S. & Kilian, K. A. Rapid 3D extrusion of synthetic tumor microenvironments. Adv. Mater. 27, 5512–5517 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Neufeld, L. et al. Microengineered perfusable 3D-bioprinted glioblastoma model for in vivo mimicry of tumor microenvironment. Sci. Adv. 7, eabi9119 (2021). These authors created a 3D-bioprinted model that accurately represents the heterogeneous TME. It serves as a strong platform for quick, repeatable target discovery, tailored therapeutic screening and drug development.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Burdis, R. & Kelly, D. J. Biofabrication and bioprinting using cellular aggregates, microtissues and organoids for the engineering of musculoskeletal tissues. Acta Biomater. 126, 1–14 (2021).

    Article  CAS  PubMed  Google Scholar 

  149. Kim, E. et al. Creation of bladder assembloids mimicking tissue regeneration and cancer. Nature 588, 664–669 (2020). This group reconstructed tumour organoids with surrounding stromal components to produce tumour ‘assembloids’, which better reflect the in vivo pathophysiological characteristics of urothelial carcinoma.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2018YFA0703004), Tsinghua University Initiative Scientific Research Program (20213080030, 2022ZLB004), the Beijing Nova Program (20220484075), National Natural Science Foundation of China (52175273, 82072837, 52211540006), Beijing Natural Science Foundation (3212007) and the 111 Project (B17026).

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Authors and Affiliations

  1. Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China

    Zhenzhen Zhou, Yuan Pang, Jingyuan Ji, Jianyu He, Tiankun Liu, Liliang Ouyang & Wei Sun

  2. Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China

    Zhenzhen Zhou, Yuan Pang, Jingyuan Ji, Jianyu He, Tiankun Liu, Liliang Ouyang & Wei Sun

  3. Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China

    Zhenzhen Zhou, Yuan Pang, Jingyuan Ji, Jianyu He, Tiankun Liu, Liliang Ouyang & Wei Sun

  4. Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China

    Wen Zhang

  5. State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xue-Li Zhang & Zhi-Gang Zhang

  6. State Key Laboratory of Molecular Oncology, Department of Aetiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China

    Kaitai Zhang

  7. Department of Mechanical Engineering, Drexel University, Philadelphia, PA, USA

    Wei Sun

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  1. Zhenzhen Zhou
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Contributions

Z.Z., Y.P. and W.S. conceived the work. Z.Z. contributed to the initial version of the manuscript. All authors contributed to the continuing revisions and approved the submitted manuscript. Y.P. and W.S. provided administrative support and funding support to this work.

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Correspondence to Yuan Pang or Wei Sun.

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Supplementary information

Glossary

Adoptive T cell therapies

(ATCTs). Cell therapies that involve the following processes: extraction of in vivo T cells, selection for or introduction of tumour reactive cells (such as chimeric antigen receptor- or T cell receptor-engineered T cells and tumour-infiltrating lymphocytes), in vitro expansion and delivery of the T cell product back to the patient.

Alginate

A natural anionic polysaccharide that is extensively used as an instant gel for tissue engineering.

Cancer–immunity cycle

(CIC). A seven-step framework used to explain how the immune system detects and destroys cancer cells. Key steps in the CIC include the production of cancer antigens and their presentation by dendritic cells, T cell priming and activation, the movement of T cells into tumours, and the detection and destruction of tumour cells by T cells.

Cancer vaccines

Means to stimulate antigen (nucleic acids, proteins, peptides or patient-derived cells)-specific immune responses, especially CD8+ T cell response, to clear cancer cells.

Collagen

The main structural component of tissues and an ideal candidate for engineering scaffolds.

Decellularized extracellular matrix

(DECM). A native ECM obtained by removing immunogenic substances, such as residual cells, from human or animal tissues through a decellularization process.

Drug delivery system

(DDS). A technical system that fully controls the dosage, timing and distribution of medications within an organism. Study objects include drugs, materials and tools used to deliver the drug, and methods for physiochemically modifying the drug or carrier.

Fibrin

A self-assembling biopolymer derived from fibrinogen and thrombin, which is widely used in tissue regeneration.

Hyaluronic acid

A glycosaminoglycan component of the extracellular matrix in many connective tissues.

Immune checkpoint inhibitors

(ICIs). Monoclonal antibodies that block inhibitory checkpoint proteins and enable immune cells to detect and eradicate cancer.

Immune-related adverse events

(IRAEs). Toxic side effects caused by the use of immunotherapy to kill tumours. IRAEs mainly cause skin, endocrine, gastrointestinal, liver, lung and skeletal muscle toxicity, as well as infusion reactions.

Matrigel

A tissue-derived extracellular matrix used as a matrix biomaterial.

Tertiary lymphoid structures

(TLSs). Immune cell aggregates (lymphoid structures) located in non-lymphoid tissues, which develop during an inflammatory pathological state and usually occur at the infiltrative margins of the tumour and/or interstitium.

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Zhou, Z., Pang, Y., Ji, J. et al. Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies. Nat Rev Immunol 24, 18–32 (2024). https://doi.org/10.1038/s41577-023-00896-4

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