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Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment

Abstract

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-sequencing analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance and metastasis.

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Fig. 1: Catalog of recurrent cancer gene modules.
Fig. 2: Expression of gene modules underlies cancer cell states.
Fig. 3: Spatial organization of the tumor microenvironment.
Fig. 4: Mapping cancer cell states and their interactions with the TME.
Fig. 5: Cancer cell states in perturbed tumor microenvironments.

Data availability

Data from this manuscript has been submitted to GEO with accession number GSE203612.

Code availability

Code from this manuscript is available at https://github.com/yanailab, and also through Zenodo (https://doi.org/10.5281/zenodo.6611786)99.

References

  1. Easwaran, H., Tsai, H.-C. & Baylin, S. B. Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol. Cell 54, 716–727 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).

    CAS  PubMed  Google Scholar 

  3. Marusyk, A. & Polyak, K. Tumor heterogeneity: causes and consequences. Biochim. Biophys. Acta 1805, 105–117 (2010).

    CAS  PubMed  Google Scholar 

  4. Heppner, G. H. & Miller, B. E. Tumor heterogeneity: biological implications and therapeutic consequences. Cancer Metastasis Rev. 2, 5–23 (1983).

    CAS  PubMed  Google Scholar 

  5. Alizadeh, A. A. et al. Toward understanding and exploiting tumor heterogeneity. Nat. Med. 21, 846–853 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Baron, M. et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst. 11, 536–546.e7 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Moncada, R. et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38, 333–342 (2020).

    CAS  PubMed  Google Scholar 

  10. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893.e13 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Izar, B. et al. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 26, 1271–1279 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).

    PubMed  PubMed Central  Google Scholar 

  14. Reitman, Z. J. et al. Mitogenic and progenitor gene programmes in single pilocytic astrocytoma cells. Nat. Commun. 10, 3731 (2019).

    PubMed  PubMed Central  Google Scholar 

  15. Rambow, F. et al. Toward minimal residual disease-directed therapy in melanoma. Cell 174, 843–855.e19 (2018).

    CAS  PubMed  Google Scholar 

  16. Baron, M. et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst. 11, 536–546.e7 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Dirkse, A. et al. Stem cell-associated heterogeneity in glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment. Nat. Commun. 10, 1787 (2019).

    PubMed  PubMed Central  Google Scholar 

  18. Cazet, A. S. et al. Targeting stromal remodeling and cancer stem cell plasticity overcomes chemoresistance in triple negative breast cancer. Nat. Commun. 9, 2897 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lin, W. et al. Single-cell transcriptome analysis of tumor and stromal compartments of pancreatic ductal adenocarcinoma primary tumors and metastatic lesions. Genome Med. 12, 80 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Sharma, A. et al. Onco-fetal reprogramming of endothelial cells drives immunosuppressive macrophages in hepatocellular carcinoma. Cell 183, 377–394.e21 (2020).

    CAS  PubMed  Google Scholar 

  22. Zhang, M. et al. Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma. J. Hepatol. 73, 1118–1130 (2020).

    CAS  PubMed  Google Scholar 

  23. Kim, N. et al. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11, 2285 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous. Cell Carcinoma Cell 182, 1661–1662 (2020).

    CAS  PubMed  Google Scholar 

  25. Cantini, L. et al. Classification of gene signatures for their information value and functional redundancy. NPJ Syst. Biol. Appl. 4, 2 (2018).

    PubMed  Google Scholar 

  26. Hu, Z. et al. The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells. Cancer Cell 37, 226–242.e7 (2020).

    CAS  PubMed  Google Scholar 

  27. Nguyen, Q. H. et al. Profiling human breast epithelial cells using single cell RNA sequencing identifies cell diversity. Nat. Commun. 9, 2028 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

    PubMed  PubMed Central  Google Scholar 

  29. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Brady, S. W. et al. Combating subclonal evolution of resistant cancer phenotypes. Nat. Commun. 8, 1231 (2017).

    PubMed  PubMed Central  Google Scholar 

  31. Borden, E. C. Interferons α and β in cancer: therapeutic opportunities from new insights. Nat. Rev. Drug Discov. 18, 219–234 (2019).

    CAS  PubMed  Google Scholar 

  32. Dunn, G. P. et al. A critical function for type I interferons in cancer immunoediting. Nat. Immunol. 6, 722–729 (2005).

    CAS  PubMed  Google Scholar 

  33. Parker, B. S., Rautela, J. & Hertzog, P. J. Antitumour actions of interferons: implications for cancer therapy. Nat. Rev. Cancer 16, 131–144 (2016).

    PubMed  Google Scholar 

  34. Vilgelm, A. E. & Richmond, A. Chemokines modulate immune surveillance in tumorigenesis, metastasis, and response to immunotherapy. Front. Immunol. 10, 333 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Wan, S. et al. Chemotherapeutics and radiation stimulate MHC class I expression through elevated interferon-beta signaling in breast cancer cells. PLoS One 7, e32542 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Dunn, G. P., Koebel, C. M. & Schreiber, R. D. Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 6, 836–848 (2006).

    CAS  PubMed  Google Scholar 

  37. Park, I. A. et al. Expression of the MHC class II in triple-negative breast cancer is associated with tumor-infiltrating lymphocytes and interferon signaling. PLoS One 12, e0182786 (2017).

    PubMed  PubMed Central  Google Scholar 

  38. Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019).

    CAS  PubMed  Google Scholar 

  39. Cherian, M. G., Jayasurya, A. & Bay, B.-H. Metallothioneins in human tumors and potential roles in carcinogenesis. Mutat. Res. 533, 201–209 (2003).

    CAS  PubMed  Google Scholar 

  40. Jin, R. et al. Metallothionein 2A expression is associated with cell proliferation in breast cancer. Carcinogenesis 23, 81–86 (2002).

    CAS  PubMed  Google Scholar 

  41. Pereira, H. et al. Metallothionein expression in human breast cancer. Breast 1, 159–160 (1992).

    Google Scholar 

  42. Pedersen, M. Ø., Larsen, A., Stoltenberg, M. & Penkowa, M. The role of metallothionein in oncogenesis and cancer prognosis. Prog. Histochem. Cytochem. 44, 29–64 (2009).

    CAS  PubMed  Google Scholar 

  43. Laughney, A. M. et al. Regenerative lineages and immune-mediated pruning in lung cancer metastasis. Nat. Med. 26, 259–269 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Marjanovic, N. D. et al. Emergence of a high-plasticity cell state during lung cancer evolution. Cancer Cell 38, 229–246.e13 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Maynard, A. et al. Therapy-induced evolution of human lung cancer revealed by single-cell RNA sequencing. Cell 182, 1232–1251.e22 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Hao, D. et al. Integrated analysis reveals tubal- and ovarian-originated serous ovarian cancer and predicts differential therapeutic responses. Clin. Cancer Res. 23, 7400–7411 (2017).

    CAS  PubMed  Google Scholar 

  47. Zhang, S. et al. Both fallopian tube and ovarian surface epithelium are cells-of-origin for high-grade serous ovarian carcinoma. Nat. Commun. 10, 5367 (2019).

    PubMed  PubMed Central  Google Scholar 

  48. Fischer, K. R. et al. Epithelial-to-mesenchymal transition is not required for lung metastasis but contributes to chemoresistance. Nature 527, 472–476 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Aiello, N. M. et al. EMT subtype influences epithelial plasticity and mode of cell migration. Dev. Cell 45, 681–695.e4 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Cook, D. P. & Vanderhyden, B. C. Transcriptional census of epithelial-mesenchymal plasticity in cancer. Sci. Adv. 8, eabi7640 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Kalluri, R. & Weinberg, R. A. The basics of epithelial-mesenchymal transition. J. Clin. Invest. 119, 1420–1428 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Hayashi, A. et al. A unifying paradigm for transcriptional heterogeneity and squamous features in pancreatic ductal adenocarcinoma. Nat. Cancer 1, 59–74 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Baylor, S. M. & Berg, J. W. Cross-classification and survival characteristics of 5,000 cases of cancer of the pancreas. J. Surg. Oncol. 5, 335–358 (1973).

    CAS  PubMed  Google Scholar 

  56. Al-Shehri, A., Silverman, S. & King, K. M. Squamous cell carcinoma of the pancreas. Curr. Oncol. 15, 293–297 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  58. Weis, S. M. & Cheresh, D. A. Tumor angiogenesis: molecular pathways and therapeutic targets. Nat. Med. 17, 1359–1370 (2011).

    CAS  PubMed  Google Scholar 

  59. Viallard, C. & Larrivée, B. Tumor angiogenesis and vascular normalization: alternative therapeutic targets. Angiogenesis 20, 409–426 (2017).

    CAS  PubMed  Google Scholar 

  60. Solinas, G., Germano, G., Mantovani, A. & Allavena, P. Tumor-associated macrophages (TAM) as major players of the cancer-related inflammation. J. Leukoc. Biol. 86, 1065–1073 (2009).

    CAS  PubMed  Google Scholar 

  61. Zhang, M. et al. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. J. Ovarian Res. 7, 19 (2014).

    PubMed  PubMed Central  Google Scholar 

  62. Yuan, A. et al. Opposite effects of M1 and M2 macrophage subtypes on lung cancer progression. Sci. Rep. 5, 14273 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Diamond, M. S. et al. Type I interferon is selectively required by dendritic cells for immune rejection of tumors. J. Exp. Med. 208, 1989–2003 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Deng, L. et al. STING-dependent cytosolic DNA sensing promotes radiation-induced type I interferon-dependent antitumor immunity in immunogenic tumors. Immunity 41, 843–852 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Ng, K. W., Marshall, E. A., Bell, J. C. & Lam, W. L. cGAS-STING and cancer: dichotomous roles in tumor immunity and development. Trends Immunol. 39, 44–54 (2018).

    CAS  PubMed  Google Scholar 

  67. Williams, J. B. et al. Tumor heterogeneity and clonal cooperation influence the immune selection of IFN-γ-signaling mutant cancer cells. Nat. Commun. 11, 602 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Kinker, G. S. et al. Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity. Nat. Genet. 52, 1208–1218 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Weinberg, R. A. The Biology of Cancer (Garland Publishing, 2007).

  70. Zaidi, M. R. & Merlino, G. The two faces of interferon-γ in cancer. Clin. Cancer Res. 17, 6118–6124 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Sottoriva, A. et al. A Big Bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  74. Archetti, M. & Pienta, K. J. Cooperation among cancer cells: applying game theory to cancer. Nat. Rev. Cancer 19, 110–117 (2019).

    CAS  PubMed  Google Scholar 

  75. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360.e4 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Pu, W. et al. Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma. Nat. Commun. 12, 6058 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Mabbott, N. A., Baillie, J. K., Brown, H., Freeman, T. C. & Hume, D. A. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14, 632 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Galgano, M. T., Hampton, G. M. & Frierson, H. F. Comprehensive analysis of HE4 expression in normal and malignant human tissues. Mod. Pathol. 19, 847–853 (2006).

    CAS  PubMed  Google Scholar 

  82. Chen, L., O’Bryan, J. P., Smith, H. S. & Liu, E. Overexpression of matrix Gla protein mRNA in malignant human breast cells: isolation by differential cDNA hybridization. Oncogene 5, 1391–1395 (1990).

    CAS  PubMed  Google Scholar 

  83. Kosanam, H. et al. Laminin, gamma 2 (LAMC2): a promising new putative pancreatic cancer biomarker identified by proteomic analysis of pancreatic adenocarcinoma tissues. Mol. Cell. Proteom. 12, 2820–2832 (2013).

    CAS  Google Scholar 

  84. Zheng, B. et al. TM4SF1 as a prognostic marker of pancreatic ductal adenocarcinoma is involved in migration and invasion of cancer cells. Int. J. Oncol. 47, 490–498 (2015).

    CAS  PubMed  Google Scholar 

  85. Jothy, S., Yuan, S. Y. & Shirota, K. Transcription of carcinoembryonic antigen in normal colon and colon carcinoma. In situ hybridization study and implication for a new in vivo functional model. Am. J. Pathol. 143, 250–257 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Jing, X., Piao, Y.-F., Liu, Y. & Gao, P.-J. Beta2-GPI: a novel factor in the development of hepatocellular carcinoma. J. Cancer Res. Clin. Oncol. 136, 1671–1680 (2010).

    CAS  PubMed  Google Scholar 

  87. Landers, K. A. et al. Identification of claudin-4 as a marker highly overexpressed in both primary and metastatic prostate cancer. Br. J. Cancer 99, 491–501 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Liao, S. Y., Aurelio, O. N., Jan, K., Zavada, J. & Stanbridge, E. J. Identification of the MN/CA9 protein as a reliable diagnostic biomarker of clear cell carcinoma of the kidney. Cancer Res. 57, 2827–2831 (1997).

    CAS  PubMed  Google Scholar 

  89. Allander, S. V. et al. Gastrointestinal stromal tumors with KIT mutations exhibit a remarkably homogeneous gene expression profile. Cancer Res. 61, 8624–8628 (2001).

    CAS  PubMed  Google Scholar 

  90. West, R. B. et al. The novel marker, DOG1, is expressed ubiquitously in gastrointestinal stromal tumors irrespective of KIT or PDGFRA mutation status. Am. J. Pathol. 165, 107–113 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinf. 11, 367 (2010).

    Google Scholar 

  92. Carmona-Saez, P., Pascual-Marqui, R. D., Tirado, F., Carazo, J. M. & Pascual-Montano, A. Biclustering of gene expression data by non-smooth non-negative matrix factorization. BMC Bioinf. 7, 78 (2006).

    Google Scholar 

  93. Csardi, G., Nepusz, T. The igraph software package for complex network research. InterJournal 1695 (2006).

  94. Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford) 2019, baz046 (2019).

    Google Scholar 

  97. Mullen, K. M., van Stokkum, I. H. M. & Mullen, M. K. nnls: The Lawson-Hanson algorithm for non-negative least squares (NNLS). R package version 1.4 https://CRAN.R-project.org/package=nnls (2015).

  98. Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    CAS  PubMed  Google Scholar 

  99. Barkley, D. Code for the analyses described in Barkley et al. Nature Genetics. Zenodo https://doi.org/10.5281/zenodo.6611786 (2022).

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Acknowledgements

We thank R. White, F. Kuperwaser, R. Satija and B. Neel for critical readings and helpful suggestions. We thank E. Hernando for helpful discussions and for providing the PDX samples. This work was supported by the following NIH grants: P50 CA225450 (to I.O. and I.Y.), R01 LM013522 (to I.Y.), R21 CA264361 (to I.Y.), U01CA260432 (to I.Y.), GM126573 and F30 CA257400 (to D.B.). This work was also supported by a DOD Team grant ME200052 (to A.W.L. and I.Y.) and The Leon Lowenstein Foundation (to I.Y.); The Mary Kay Foundation (to I.Y.).

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Contributions

D.B., R.M., G.W., W.W., M.B., A.R. and B.X. performed transcriptomic experiments. D.B. performed the transcriptomic data analysis with contributions from I.Y., A.R., G.S.F. A.W. and I.O. collected patient samples. D.F.D. and C.H. annotated patient samples. M.P., D.A.L., I.D. and A.W.L. performed CODEX experiments and analysis. D.B. and I.Y. designed the experiments and wrote the manuscript.

Corresponding author

Correspondence to Itai Yanai.

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The authors declare no competing interests.

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Nature Genetics thanks Alexander Swarbrick 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 Quality control and cell type annotation in the single-cell RNA-Seq data.

a. Violin plots of the number of UMIs per cell in each tumor sample. b. Violin plots of the number of genes per cell in each tumor sample. c. Heatmap of average scaled gene expression per cell type per sample. Top bar represents cell type (colored as indicated) and sample (colored as in Fig. 1a).

Extended Data Fig. 2 Control analysis for purity of single-cell RNA-Seq data.

a. UMAP embeddings of cells annotated as malignant per cancer type or organ system, colored by sample. b. Control analysis for annotations of cells as malignant, using the method described by Kim et al.1. Briefly, inferred CNV profiles (from the scRNA-Seq data) were scored as the sum of the squared values (shown as the x-axis). The cells with the top 10 scores are assumed to be malignant and each cell is then correlated with the average profile of the top 10 cells (y-axis). In tumors with CNVs these two measures are consistent. Color indicates the annotation as malignant or normal cells, per sample.

Extended Data Fig. 3 Additional analyses for the derivation of the cancer gene modules catalog.

a-e. Heatmap of the significance of overlap (hypergeometric test) of the consensus modules across (a) the indicated Gene Ontology terms, (b) cell type markers, (c) signatures derived by Neftel et al.2, (d) signatures derived from Puram et al.3, and (e) signatures derived from Ji et al.4. f. Network of genes belonging to the consensus modules, colored as in Fig. 1f. Lines connect genes that are found together in at least 2 individual tumor modules (see Methods). g. Heatmap of the significance of the overlap between consensus modules and individual tumor modules (hypergeometric test). The bottom bar indicates the significance of the overlap with consensus modules (hypergeometric test). The top bar indicates the identity of the tumor samples, colored as in Fig. 1a. h. Heatmap of the Jaccard similarity (intersect/union) between consensus modules and SCENIC regulons obtained for individual tumors. The bar indicates the identity of the tumor samples, colored as in Fig. 1a. To test whether the catalog of 16 modules can also be detected using an independent approach, we used SCENIC5, a method that identifies genes that are both correlated in their expression and regulated by the same transcription factor. We found that each module of our catalog had significant overlap with several SCENIC regulons (Supplementary Table 4, see Methods). For instance, the interferon response module overlapped with several SCENIC regulons annotated with the transcription factors STAT1 and IRF1.

Extended Data Fig. 4 Pattern of presence and absence of the catalog gene modules across malignant and normal epithelial cells.

a-c. Heatmap of the significance of the presence of each module (see Methods) in the malignant cells of each tumor sample (a), in epithelial cells of normal samples6,7,8 (b), and for malignant and epithelial cells from paired normal and tumor samples (c). Gray indicates a complete lack of gene expression of the module. FTE: Fallopian tube epithelium. BRE: Breast epithelium. LE: Liver epithelium. d. Volcano plots of differential gene expression between malignant and normal epithelial cells from paired LUAD samples (Kim et al.1 T20 vs. N20). Each panel highlights genes from the indicated module.

Extended Data Fig. 5 Cell states across tumors.

a-c. Module score TSNE embedding of the cancer cells of all 19 tumors, colored by the most highly expressed module (a), by module score as in Fig. 1f(b) and by entropy of tumor of origin (c). d. Gene expression UMAP embedding of the cancer cells of OVCA NYU3 and BRCA NYU1, colored by module score for the ciliated and cycle module respectively. Unlike other modules, the cycle and cilium module were expressed by cells forming discrete clusters. These clusters are also identified when examining tumors individually in gene expression-based dimensionality reductions, and are therefore not artifacts of the module score dimensionality reduction (Extended Data Fig. 6d). e. Heatmap of the frequency of expression of each module in the malignant cells of each sample. f. Module expression frequencies in normal vs malignant epithelia. Points represent individual samples, bars indicate mean + /- standard error calculated across individual tumors of the same cancer type.

Extended Data Fig. 6 Spatial organization of the tumor microenvironment.

a. OVCA NYU1 H&E image with spots colored by annotation (scale bar represents 1 mm). b-c. Joint dimensionality reduction after mutual nearest neighbor integration (MNN) of single-cell and ST spots for the OVCA NYU1 sample, with (b) single-cell transcriptomes in gray and ST spots colored according to their annotation and (c) spots in gray and single-cell transcriptomes colored by their annotated cell type. The single cells form clusters at the periphery, indicating distinct cell types. The ST spots are either mixed with individual single-cell clusters, indicating a pure population, or bridge multiple clusters, indicating a combination of cell types. Specifically, ‘Malignant’ spots are mixed with the malignant cell cluster, ‘Normal’ spots are in the region of nonmalignant cell types, and ‘Both’ spots span both malignant and nonmalignant single-cell clusters. d. LIHC NYU1 H&E image with spots colored by annotation as in a. (scale bar represents 1 mm). e-g. Joint dimensionality reduction of single-cell and ST spots for the LIHC NYU1 sample, with (e) single-cell transcriptomes in gray and ST spots colored according to their annotation, (f) spots in gray and single-cell transcriptomes colored by their annotated cell type and (g) spots colored according to their coordinate along the x-axis. This sample has two spatially distinct tumor nodules, with the left having substantial mixing between malignant and nonmalignant cells and the right consisting almost exclusively of malignant cells. The joint dimensionality reduction analysis reflects the two corresponding malignant clusters, which were not distinct when considering the single-cell dimensionality reduction alone.

Extended Data Fig. 7 Validation of spot annotation and module presence using PDX samples.

a. UMAP of single-cell RNA-Seq data for sample 1, colored by the number of UMIs corresponding to human (left) or mouse (right) genes. b. Heatmap of module overdispersion in malignant cells of sample 1 (see Extended Data Fig. 4a-c and Methods). Since human malignant cells can unambiguously be distinguished from mouse TME cells in this system, we used the single-cell data to confirm that the modules are differentially expressed by malignant cells themselves and rule out the possibility of an artifact stemming from TME contamination. For example, the pEMT module includes genes normally expressed by fibroblasts, but we detected its overdispersion in malignant cells. c-d. Spatial transcriptomic spots colored by the number of UMIs corresponding to human (left) or mouse (right) genes for sample 1 (c) and sample 2 (d). Scale bar represents 1 mm. e-f. Spatial transcriptomic spots colored by annotation as ‘Malignant’, ‘Both’ or ‘Normal’ using the NNLS method on the full transcriptome (left) or on human orthologs (right) in sample 1 (e) and sample 2 (f). To test the accuracy of the NNLS method to annotate spots, we performed paired scRNA-Seq and ST on two patient-derived melanoma xenografts (PDX). In this setting, only malignant cells are of human origin and therefore express human genes, enabling us to reliably identify malignant cells or spots. Using the NNLS method on the full mouse and human transcriptomes, we first established a ‘ground truth’ for spot identities. We then simulated the patient samples by converting mouse genes to their human orthologs, thereby removing the species information. This resulted in 99% (sample 1) and 89% (sample 2) specificity ‘Malignant’ spots. g-h. ‘Malignant’ spatial transcriptomic spots colored by expression score for the cycle, stress, hypoxia and pEMT modules for sample 1 (g) and sample 2 (h).

Extended Data Fig. 8 Relationship between pEMT and cancer spot depth.

a. Sample UCEC NYU3, ‘Malignant’ only spots colored by their depth: the distance to the nearest spot containing nonmalignant cells. b. Sample UCEC NYU3, ‘Malignant’ only spots colored by pEMT module score. c. Boxplots of correlation scores (±log10(p-value)) between module scores and depth of malignant spots across 10 samples, colored as in Fig. 1a. For each boxplot, the line indicates the median, the box indicates the 1st and 3rd quartile, the whiskers indicate the minimum and maximum values. Positive scores correspond to positive correlations. Dashed lines indicate p-value=0.05. Plots of the relationship between the pEMT module score and depth in the 10 ST samples, colored as in Fig. 1a. Lines are drawn for correlations with p-value<0.05.

Extended Data Fig. 9 CODEX analysis of samples from four cancer types supporting a proximity of interferon response-expressing malignant cells to macrophages and T cells.

a. Cell populations and marker expression in a region of OVCA NYU1. Top row displays an entire tile, bottom row displays an enlargement. Top and bottom left: Colored by populations as defined in Extended Data Fig. 15. Top right and bottom center: Colored by expression of markers used to define cell types, as indicated. Bottom right: Colored by expression of PanCK and of HLA-DRA, used to define interferon response positive and negative malignant cells. Scale bar represents 50 µm. b. For the tile shown in a., histogram showing the distance between malignant cells and the nearest macrophage, for interferon response positive (light green) and negative (dark green) malignant cells. Lines indicate the mean distance for each population, used to calculate the log2(proximity ratio). c-d. Boxplots of the distribution of log2(proximity ratio) (c) and log2(neighborhood ratio) (d) of macrophages, T cells and malignant cells across tiles of each sample (*, p-value<0.05; ***, p-value<0.001; two-sided t-test). For each boxplot, the line indicates the median, the box indicates the 1st and 3rd quartile, the whiskers indicate the minimum and maximum values.

Extended Data Fig. 10 Additional experiments relating to the orthotopic and heterotopic mouse experiments.

a. UMAP embedding of cells from 16 orthotopic pancreatic tumors across the 3 experiments, colored by annotation as malignant or nonmalignant cells. b. Same as a, colored by sample. c. Violin plots of module expression scores in individual tumors across the 3 experiments. d. Barplots of the average expression of the interferon response module genes in cancer cells in the WT and Rag1-/- tumors according to their interferon response expression.

Supplementary information

Supplementary Information

Supplementary Figs. 1–4, captions for Data 1–3 and captions for Tables 1–7.

Reporting Summary

Supplementary Data 1

Differential gene expression in scRNA-seq data. For each of the tumors collected for scRNA-Seq, the heatmap shows the scaled gene expression across cell types. Top bar represents cell type annotation. For each cell type the top ten differentially expressed genes are selected (Methods).

Supplementary Data 2

Differential gene expression in ST data. For each of the ten tumors studied by spatial transcriptomics, the heatmap shows the scaled gene expression across spots (columns) annotated as ‘Malignant’, ‘Both’ and ‘Normal’ for genes (rows) identified as differentially expressed across cell types in the paired single-cell RNA-seq data.

Supplementary Data 3

Key markers from CODEX used for proximity analysis of interferon response-expressing malignant cells to macrophages and T cells. a, OVCA NYU1. b, UCEC NYU3. c, LIHC NYU1. d, GIST NYU1. White dotted lines signify areas excluded from analysis due to obvious folding or bubbles. 1. Tiles; scale bar, 500 µm. 2. Single stain, DAPI (white); scale bar, 500 µm. 3. Single stain, DAPI (white), inset from b; scale bar, 50 µm. 4. Single stain, DAPI (white) with segmentation, inset from c; scale bar, 25 µm. Single stain, CD3 (magenta); scale bar, 500 µm. 5. Single stain, DAPI (white) with segmentation, inset from c; scale bar, 25 µm. Single stain, CD3 (magenta); scale bar, 500 µm. 6. Single stain, CD68 (red), scale bar, 500 µm. 7. Single stain, Pan-Cytokeratin (green) in ac or podoplanin (blue) in d; scale bar, 500 µm. 8. Single stain, HLA-DR (white); scale bar, 500 µm. 9. Single stain, CD3 (magenta), inset from e; scale bar, 50 µm. 10. Single stain, CD68 (red), inset from f; scale bar, 50 µm. 11. Single stain, pan-cytokeratin (green) in ac or podoplanin (blue) in d; inset from g; scale bar, 50 µm. 12. Single stain, HLA-DR (white), inset from h; scale bar, 50 µm. 13. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in ac or podoplanin (blue) in d and HLA-DR (white); scale bar, 500 µm. 14. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in ac or podoplanin (blue) in d and HLA-DR (white), inset from m; scale bar, 50 µm. 15. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in ac or podoplanin (blue) in d and HLA-DR (white) with populations as colored in Extended Data Fig. 15a: vascular (yellow), macrophages (red), T cells (magenta), B cells (orange), DC (purple), epithelial malignant cells that are interferon response positive (light green), epithelial malignant cells that are interferon response negative (dark green), stroma in epithelial cancers (blue), stromal malignant cells that are interferon response positive (cyan), stromal malignant cells that are interferon response negative (dark blue) and smooth muscle (light gray); scale bar, 500 µm. 16. Composite image of CD3 (magenta), CD68 (red), Pan-Cytokeratin (green) in ac or podoplanin (blue) in d, and HLA-DR (white) with populations as colored in Extended Data Figure 15a: vascular (yellow), macrophages (red), T cells (magenta), B cells (orange), DC (purple), epithelial malignant cells that are interferon response positive (light green), epithelial malignant cells that are interferon response negative (dark green), stroma in epithelial cancers (blue), stromal malignant cells that are interferon response positive (cyan), stromal malignant cells that are interferon response negative (dark blue) and smooth muscle (light gray), inset from o; scale bar, 50 µm.

Supplementary Tables

Table 1. Pathology annotation of patient samples. Table 2. Annotation of cells from 19 patient samples. Table 3. Composition of recurrent gene modules. Table 4. Transcription factors identified using SCENIC. Table 5. Composition of mouse pancreatic cancer gener modules. Table 6. Differentially expressed genes between M1 and M2 macrophages. Table 7. Antibodies used for CODEX workflow.

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Barkley, D., Moncada, R., Pour, M. et al. Cancer cell states recur across tumor types and form specific interactions with the tumor microenvironment. Nat Genet 54, 1192–1201 (2022). https://doi.org/10.1038/s41588-022-01141-9

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