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  • Perspective
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Cellular neighborhoods in cancer

Abstract

The concept of cellular neighborhoods, defined as recurring structures within the tissue with characteristic cell compositions and interactions, has transformed our understanding of the complexity and dynamics of tumor ecosystems. Recent advances in spatial omics and computational modeling have enabled high-resolution mapping of these neighborhoods, providing unprecedented insights into their roles in shaping tumor heterogeneity, evolution and therapeutic responses. Despite these advances, a unified framework for interpreting cellular neighborhoods remains lacking. This Perspective synthesizes emerging concepts and insights, focusing on the definition and classification of cellular neighborhoods in cancer, computational methods for identifying and comparing them, and their clinical relevance.

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Fig. 1: Conceptual framework for defining and characterizing cellular neighborhoods in tissues.
Fig. 2: Classification of cellular neighborhoods in cancer.

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References

  1. Wilson, A. & Trumpp, A. Bone-marrow haematopoietic-stem-cell niches. Nat. Rev. Immunol. 6, 93–106 (2006).

    Article  CAS  PubMed  Google Scholar 

  2. Brittan, M. & Wright, N. A. Gastrointestinal stem cells. J. Pathol. 197, 492–509 (2002).

    Article  PubMed  Google Scholar 

  3. Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science 381, eabq4964 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lewis, S. M. et al. Spatial omics and multiplexed imaging to explore cancer biology. Nat. Methods 18, 997–1012 (2021).

    Article  CAS  PubMed  Google Scholar 

  5. Park, J. et al. Spatial omics technologies at multimodal and single cell/subcellular level. Genome Biol. 23, 256 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kang, L., Zhang, Q., Qian, F., Liang, J. & Wu, X. Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data. Nucleic Acids Res. 53, gkaf303 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Chen, C., Kim, H. J. & Yang, P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol. 25, 18 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Keren, L. et al. A structured tumor–immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Shiao, S. L. et al. Single-cell and spatial profiling identify three response trajectories to pembrolizumab and radiation therapy in triple negative breast cancer. Cancer Cell 42, 70–84 (2024).

    Article  CAS  PubMed  Google Scholar 

  11. Pentimalli, T. M. et al. High-resolution molecular atlas of a lung tumor in 3D. Preprint at bioRxiv https://doi.org/10.1101/2023.05.10.539644 (2023).

  12. Bandyopadhyay, S. et al. Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging. Cell 187, 3120–3140 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Barkley, D. et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat. Genet. 54, 1192–1201 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618, 598–606 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Liu, M. et al. Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer. Preprint at bioRxiv https://doi.org/10.1101/2025.03.07.642107 (2025).

  17. Yeh, C. Y. et al. Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer. Nat. Immunol. 25, 1943–1958 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Ren, Y. et al. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nat. Commun. 14, 1028 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor–host interdependence in glioblastoma. Cancer Cell 40, 639–655 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Schumacher, T. N. & Thommen, D. S. Tertiary lymphoid structures in cancer. Science 375, eabf9419 (2022).

    Article  CAS  PubMed  Google Scholar 

  22. Sautès-Fridman, C., Petitprez, F., Calderaro, J. & Fridman, W. H. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19, 307–325 (2019).

    Article  PubMed  Google Scholar 

  23. Gaglia, G. et al. Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma. Cancer Cell 41, 871–886 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Sorin, M. et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 614, 548–554 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Cheng, S. et al. A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells. Cell 184, 792–809 (2021).

    Article  CAS  PubMed  Google Scholar 

  26. Ma, R.-Y., Black, A. & Qian, B.-Z. Macrophage diversity in cancer revisited in the era of single-cell omics. Trends Immunol. 43, 546–563 (2022).

    Article  CAS  PubMed  Google Scholar 

  27. Karimi, E. et al. Single-cell spatial immune landscapes of primary and metastatic brain tumours. Nature 614, 555–563 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wattenberg, M. M. et al. Intratumoral cell neighborhoods coordinate outcomes in pancreatic ductal adenocarcinoma. Gastroenterology 166, 1114–1129 (2024).

    Article  PubMed  Google Scholar 

  29. Ruf, B. et al. Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin. Cell 186, 3686–3705 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Xue, R. et al. Liver tumour immune microenvironment subtypes and neutrophil heterogeneity. Nature 612, 141–147 (2022).

    Article  CAS  PubMed  Google Scholar 

  31. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lodyga, M. & Hinz, B. TGF-β1—a truly transforming growth factor in fibrosis and immunity. Semin. Cell Dev. Biol. 101, 123–139 (2020).

    Article  CAS  PubMed  Google Scholar 

  33. Feng, Y. et al. Spatially organized tumor–stroma boundary determines the efficacy of immunotherapy in colorectal cancer patients. Nat. Commun. 15, 10259 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cremasco, V. et al. FAP delineates heterogeneous and functionally divergent stromal cells in immune-excluded breast tumors. Cancer Immunol. Res. 6, 1472–1485 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lavie, D., Ben-Shmuel, A., Erez, N. & Scherz-Shouval, R. Cancer-associated fibroblasts in the single-cell era. Nat. Cancer 3, 793–807 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Liu, Y. et al. Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics. Cancer Cell 43, 905–924 (2025).

    Article  CAS  PubMed  Google Scholar 

  37. Xie, Y. et al. FGF/FGFR signaling in health and disease. Signal Transduct. Target. Ther. 5, 181 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Calon, A., Tauriello, D. V. F. & Batlle, E. TGF-β in CAF-mediated tumor growth and metastasis. Semin. Cancer Biol. 25, 15–22 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  40. Sussman, J. H. et al. Multiplexed imaging mass cytometry analysis characterizes the vascular niche in pancreatic cancer. Cancer Res. 84, 2364–2376 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Gaglia, G. et al. Temporal and spatial topography of cell proliferation in cancer. Nat. Cell Biol. 24, 316–326 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Majmundar, A. J., Wong, W. J. & Simon, M. C. Hypoxia-inducible factors and the response to hypoxic stress. Mol. Cell 40, 294–309 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Qiu, G.-Z. et al. Reprogramming of the tumor in the hypoxic niche: the emerging concept and associated therapeutic strategies. Trends Pharmacol. Sci. 38, 669–686 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Smith, E. A. & Hodges, H. C. The spatial and genomic hierarchy of tumor ecosystems revealed by single-cell technologies. Trends Cancer 5, 411–425 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kathagen, A. et al. Hypoxia and oxygenation induce a metabolic switch between pentose phosphate pathway and glycolysis in glioma stem-like cells. Acta Neuropathol. 126, 763–780 (2013).

    Article  CAS  PubMed  Google Scholar 

  46. Seim, J. et al. Hypoxia-induced irreversible S-phase arrest involves down-regulation of cyclin A. Cell Prolif. 36, 321–332 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Nirmal, A. J. et al. The spatial landscape of progression and immunoediting in primary melanoma at single-cell resolution. Cancer Discov. 12, 1518–1541 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Zhao, Y. et al. Stromal cells in the tumor microenvironment: accomplices of tumor progression? Cell Death Dis. 14, 587 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ma, L., Li, C. C. & Wang, X. W. Roles of cellular neighborhoods in hepatocellular carcinoma pathogenesis. Annu. Rev. Pathol. 20, 169–192 (2025).

    Article  CAS  PubMed  Google Scholar 

  50. Wu, L. et al. An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte–tumor cell crosstalk, local immunosuppression and tumor progression. Cell Res. 33, 585–603 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lemaitre, L. et al. Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease. Nat. Cancer 5, 1534–1556 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Plaks, V., Kong, N. & Werb, Z. The cancer stem cell niche: how essential is the niche in regulating stemness of tumor cells? Cell Stem Cell 16, 225–238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Batlle, E. & Clevers, H. Cancer stem cells revisited. Nat. Med. 23, 1124–1134 (2017).

    Article  CAS  PubMed  Google Scholar 

  54. Prager, B. C., Xie, Q., Bao, S. & Rich, J. N. Cancer stem cells: the architects of the tumor ecosystem. Cell Stem Cell 24, 41–53 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, X. et al. Reciprocal signaling between glioblastoma stem cells and differentiated tumor cells promotes malignant progression. Cell Stem Cell 22, 514–528 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Su, S. et al. CD10+GPR77+ cancer-associated fibroblasts promote cancer formation and chemoresistance by sustaining cancer stemness. Cell 172, 841–856 (2018).

    Article  CAS  PubMed  Google Scholar 

  57. Wan, S. et al. Tumor-associated macrophages produce interleukin 6 and signal via STAT3 to promote expansion of human hepatocellular carcinoma stem cells. Gastroenterology 147, 1393–1404 (2014).

    Article  CAS  PubMed  Google Scholar 

  58. Lu, H. et al. A breast cancer stem cell niche supported by juxtacrine signalling from monocytes and macrophages. Nat. Cell Biol. 16, 1105–1117 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Forte, D. et al. Bone marrow mesenchymal stem cells support acute myeloid leukemia bioenergetics and enhance antioxidant defense and escape from chemotherapy. Cell Metab. 32, 829–843 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Semenza, G. L. Tumor metabolism: cancer cells give and take lactate. J. Clin. Invest. 118, 3835–3837 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Lyssiotis, C. A. & Kimmelman, A. C. Metabolic interactions in the tumor microenvironment. Trends Cell Biol. 27, 863–875 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Kennedy, K. M. & Dewhirst, M. W. Tumor metabolism of lactate: the influence and therapeutic potential for MCT and CD147 regulation. Future Oncol. 6, 127–148 (2010).

    Article  CAS  PubMed  Google Scholar 

  63. Sun, C. et al. Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proc. Natl Acad. Sci. USA 116, 52–57 (2019).

    Article  CAS  PubMed  Google Scholar 

  64. Smith, B. et al. Addiction to coupling of the Warburg effect with glutamine catabolism in cancer cells. Cell Rep. 17, 821–836 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Khaliq, A. M. et al. Spatial transcriptomic analysis of primary and metastatic pancreatic cancers highlights tumor microenvironmental heterogeneity. Nat. Genet. 56, 2455–2465 (2024).

    Article  CAS  PubMed  Google Scholar 

  66. Wilde, L. et al. Metabolic coupling and the Reverse Warburg Effect in cancer: implications for novel biomarker and anticancer agent development. Semin. Oncol. 44, 198–203 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Wang, Y. et al. Single-cell analysis of pancreatic ductal adenocarcinoma identifies a novel fibroblast subtype associated with poor prognosis but better immunotherapy response. Cell Discov. 7, 36 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Kloosterman, D. J. et al. Macrophage-mediated myelin recycling fuels brain cancer malignancy. Cell 187, 5336–5356 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ikeda, H. et al. Immune evasion through mitochondrial transfer in the tumour microenvironment. Nature 638, 225–236 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Hioki, K. A. et al. The mosquito effect: regulatory and effector T cells acquire cytoplasmic material from tumor cells through intercellular transfer. Front. Immunol. 14, 1272918 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Mo, C.-K. et al. Tumour evolution and microenvironment interactions in 2D and 3D space. Nature 634, 1178–1186 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wu, R. et al. Comprehensive analysis of spatial architecture in primary liver cancer. Sci. Adv. 7, eabg3750 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Matusiak, M. et al. Spatially segregated macrophage populations predict distinct outcomes in colon cancer. Cancer Discov. 14, 1418–1439 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Shi, Q. et al. Cross-tissue multicellular coordination and its rewiring in cancer. Nature 643, 529–538 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Jahanban-Esfahlan, R., de la Guardia, M., Ahmadi, D. & Yousefi, B. Modulating tumor hypoxia by nanomedicine for effective cancer therapy. J. Cell. Physiol. 233, 2019–2031 (2018).

    Article  CAS  PubMed  Google Scholar 

  76. Liu, Y. et al. Identification of a tumour immune barrier in the HCC microenvironment that determines the efficacy of immunotherapy. J. Hepatol. 78, 770–782 (2023).

    Article  CAS  PubMed  Google Scholar 

  77. Sathe, A. et al. Colorectal cancer metastases in the liver establish immunosuppressive spatial networking between tumor-associated SPP1+ macrophages and fibroblasts. Clin. Cancer Res. 29, 244–260 (2023).

    Article  CAS  PubMed  Google Scholar 

  78. Christofori, G. New signals from the invasive front. Nature 441, 444–450 (2006).

    Article  CAS  PubMed  Google Scholar 

  79. Ding, G.-Y. et al. Distribution and density of tertiary lymphoid structures predict clinical outcome in intrahepatic cholangiocarcinoma. J. Hepatol. 76, 608–618 (2022).

    Article  CAS  PubMed  Google Scholar 

  80. Black, S. et al. CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc. 16, 3802–3835 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).

    Article  CAS  PubMed  Google Scholar 

  82. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Chen, H. et al. Integrative spatial analysis reveals tumor heterogeneity and immune colony niche related to clinical outcomes in small cell lung cancer. Cancer Cell 43, 519–536 (2025).

    Article  CAS  PubMed  Google Scholar 

  84. Kim, J. et al. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat. Methods 19, 1653–1661 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56, 431–441 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Ma, Y. & Zhou, X. Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics. Nat. Methods 21, 1231–1244 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. 43, 269–280 (2025).

    Article  CAS  PubMed  Google Scholar 

  88. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).

    Google Scholar 

  89. Chen, Z., Soifer, I., Hilton, H., Keren, L. & Jojic, V. Modeling multiplexed images with Spatial-LDA reveals novel tissue microenvironments. J. Comput. Biol. 27, 1204–1218 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Li, Z. & Zhou, X. BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies. Genome Biol. 23, 168 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).

    Article  PubMed  Google Scholar 

  93. Hu, Y. et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat. Methods 21, 267–278 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Varrone, M., Tavernari, D., Santamaria-Martínez, A., Walsh, L. A. & Ciriello, G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat. Genet. 56, 74–84 (2024).

    Article  CAS  PubMed  Google Scholar 

  96. Li, Y., Zhang, J., Gao, X. & Zhang, Q. C. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell–cell interaction-aware cell embedding. Cell Syst. 15, 578–592 (2024).

    Article  CAS  PubMed  Google Scholar 

  97. Chitra, U. et al. Mapping the topography of spatial gene expression with interpretable deep learning. Nat. Methods 22, 298–309 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Birk, S. et al. Quantitative characterization of cell niches in spatially resolved omics data. Nat. Genet. 57, 897–909 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Hickey, J. W. et al. T cell-mediated curation and restructuring of tumor tissue coordinates an effective immune response. Cell Rep. 42, 113494 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Samadi, Z., Hao, K. & Askary, A. SMORE: spatial motifs reveal patterns in cellular architecture of complex tissues. Genome Biol. 26, 3 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  101. Chen, J. H. et al. Human lung cancer harbors spatially organized stem-immunity hubs associated with response to immunotherapy. Nat. Immunol. 25, 644–658 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Salié, H. et al. Spatial single-cell profiling and neighbourhood analysis reveal the determinants of immune architecture connected to checkpoint inhibitor therapy outcome in hepatocellular carcinoma. Gut 74, 451–466 (2025).

    Article  PubMed  Google Scholar 

  103. Mascharak, S. et al. Desmoplastic stromal signatures predict patient outcomes in pancreatic ductal adenocarcinoma. Cell Rep. Med. 4, 101248 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Qi, J. et al. Single-cell and spatial analysis reveal interaction of FAP+ fibroblasts and SPP1+ macrophages in colorectal cancer. Nat. Commun. 13, 1742 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Maestri, E. et al. Spatial proximity of tumor–immune interactions predicts patient outcome in hepatocellular carcinoma. Hepatology 79, 768–779 (2024).

    Article  PubMed  Google Scholar 

  106. Peyraud, F. et al. Spatially resolved transcriptomics reveal the determinants of primary resistance to immunotherapy in NSCLC with mature tertiary lymphoid structures. Cell Rep. Med. 6, 101934 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Magen, A. et al. Intratumoral dendritic cell–CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma. Nat. Med. 29, 1389–1399 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Jia, G. et al. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature 640, 1031–1041 (2025).

    Article  CAS  PubMed  Google Scholar 

  110. Ali, H. R. et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat. Cancer 1, 163–175 (2020).

    Article  CAS  PubMed  Google Scholar 

  111. Chen, J., Larsson, L., Swarbrick, A. & Lundeberg, J. Spatial landscapes of cancers: insights and opportunities. Nat. Rev. Clin. Oncol. 21, 660–674 (2024).

    Article  PubMed  Google Scholar 

  112. Väyrynen, S. A. et al. Composition, spatial characteristics, and prognostic significance of myeloid cell infiltration in pancreatic cancer. Clin. Cancer Res. 27, 1069–1081 (2021).

    Article  PubMed  Google Scholar 

  113. Zheng, Y., Carrillo-Perez, F., Pizurica, M., Heiland, D. H. & Gevaert, O. Spatial cellular architecture predicts prognosis in glioblastoma. Nat. Commun. 14, 4122 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Phillips, D. et al. Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma. Nat. Commun. 12, 6726 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Niazi, M. K. K., Parwani, A. V. & Gurcan, M. N. Digital pathology and artificial intelligence. Lancet Oncol. 20, e253–e261 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Wang, X. et al. A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634, 970–978 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Huang, Z., Bianchi, F., Yuksekgonul, M., Montine, T. J. & Zou, J. A visual–language foundation model for pathology image analysis using medical twitter. Nat. Med. 29, 2307–2316 (2023).

    Article  CAS  PubMed  Google Scholar 

  118. Lu, M. Y. et al. A visual–language foundation model for computational pathology. Nat. Med. 30, 863–874 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Yang, Z. et al. A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images. Nat. Commun. 16, 2366 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Hua, S., Yan, F., Shen, T., Ma, L. & Zhang, X. PathoDuet: foundation models for pathological slide analysis of H&E and IHC stains. Med. Image Anal. 97, 103289 (2024).

    Article  PubMed  Google Scholar 

  121. Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).

    Article  CAS  PubMed  Google Scholar 

  122. Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481–1491 (2024).

    Article  CAS  PubMed  Google Scholar 

  123. Rood, J. E. et al. The Human Cell Atlas from a cell census to a unified foundation model. Nature 637, 1065–1071 (2025).

    Article  CAS  PubMed  Google Scholar 

  124. Boiarsky, R. et al. Deeper evaluation of a single-cell foundation model. Nat. Mach. Intell. 6, 1443–1446 (2024).

    Article  Google Scholar 

  125. Wang, H. et al. SpatialAgent: an autonomous AI agent for spatial biology. Preprint at bioRxiv https://doi.org/10.1101/2025.04.03.646459 (2025).

  126. Tejada-Lapuerta, A. et al. Nicheformer: a foundation model for single-cell and spatial omics. Nat. Methods 22, 2525–2538 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Marks, M. et al. CellSAM: a foundation model for cell segmentation. Nat. Methods 22, 2585–2593 (2025).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Wang, C. et al. scGPT-spatial: continual pretraining of single-cell foundation model for spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2025.02.05.636714 (2025).

  129. Xu, Z. et al. STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization. Nucleic Acids Res. 52, D1053–D1061 (2024).

    Article  CAS  PubMed  Google Scholar 

  130. Yuan, Z. et al. SODB facilitates comprehensive exploration of spatial omics data. Nat. Methods 20, 387–399 (2023).

    Article  CAS  PubMed  Google Scholar 

  131. Zheng, Y., Chen, Y., Ding, X., Wong, K. H. & Cheung, E. Aquila: a spatial omics database and analysis platform. Nucleic Acids Res. 51, D827–D834 (2023).

    Article  CAS  PubMed  Google Scholar 

  132. Dhainaut, M. et al. Spatial CRISPR genomics identifies regulators of the tumor microenvironment. Cell 185, 1223–1239 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Binan, L. et al. Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits. Preprint at bioRxiv https://doi.org/10.1101/2023.11.30.569494 (2023).

  134. Gu, J. et al. Mapping multimodal phenotypes to perturbations in cells and tissue with CRISPRmap. Nat. Biotechnol. 43, 1101–1115 (2025).

    Article  CAS  PubMed  Google Scholar 

  135. Chuprin, J. et al. Humanized mouse models for immuno-oncology research. Nat. Rev. Clin. Oncol. 20, 192–206 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Connolly, K. A., Fitzgerald, B., Damo, M. & Joshi, N. S. Novel mouse models for cancer immunology. Annu. Rev. Cancer Biol. 6, 269–291 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Ingber, D. E. Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat. Rev. Genet. 23, 467–491 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Leung, C. M. et al. A guide to the organ-on-a-chip. Nat. Rev. Methods Primers 2, 33 (2022).

    Article  CAS  Google Scholar 

  139. Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  PubMed  Google Scholar 

  140. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  141. Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14, 8353 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Merritt, C. R. et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat. Biotechnol. 38, 586–599 (2020).

    Article  CAS  PubMed  Google Scholar 

  143. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  144. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  145. Schott, M. et al. Open-ST: high-resolution spatial transcriptomics in 3D. Cell 187, 3953–3972 (2024).

    Article  CAS  PubMed  Google Scholar 

  146. He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40, 1794–1806 (2022).

    Article  CAS  PubMed  Google Scholar 

  147. Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Keren, L. et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci. Adv. 5, eaax5851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    Article  CAS  PubMed  Google Scholar 

  150. Niehaus, M., Soltwisch, J., Belov, M. E. & Dreisewerd, K. Transmission-mode MALDI-2 mass spectrometry imaging of cells and tissues at subcellular resolution. Nat. Methods 16, 925–931 (2019).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by grants from the National Institutes of Health (NIH) under award numbers U2C CA233285 and U54HL165442 (K.T.). L.M. was supported by grants (ZIA BC 012079 and ZIA BC 012083) from the Intramural Research Program of the Center for Cancer Research, US National Cancer Institute. B.X. was supported by NIH grant F30CA298606. K.T. holds the Richard and Sheila Sanford Endowed Chair at CHOP. This research was supported in part by the Intramural Research Program of the NIH. The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements and are considered works of the US government. However, the findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services.

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Ma, L., Xiong, B., Liu, M. et al. Cellular neighborhoods in cancer. Nat Cancer 7, 16–28 (2026). https://doi.org/10.1038/s43018-025-01107-w

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