Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer

Abstract

Immunotherapy for metastatic colorectal cancer is effective only for mismatch repair-deficient tumors with high microsatellite instability that demonstrate immune infiltration, suggesting that tumor cells can determine their immune microenvironment. To understand this cross-talk, we analyzed the transcriptome of 91,103 unsorted single cells from 23 Korean and 6 Belgian patients. Cancer cells displayed transcriptional features reminiscent of normal differentiation programs, and genetic alterations that apparently fostered immunosuppressive microenvironments directed by regulatory T cells, myofibroblasts and myeloid cells. Intercellular network reconstruction supported the association between cancer cell signatures and specific stromal or immune cell populations. Our collective view of the cellular landscape and intercellular interactions in colorectal cancer provide mechanistic information for the design of efficient immuno-oncology treatment strategies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Cell type identification in CRC using joint application of RCA and CCA.
Fig. 2: Transcriptome signatures and heterogeneity in normal and tumor epithelial cells.
Fig. 3: Stromal cell dynamics in normal mucosa and CRC tissue.
Fig. 4: Transcriptional reprogramming in tumor-associated macrophages.
Fig. 5: Adaptive immune cell profiles in normal mucosa and CRC tissue.
Fig. 6: Differential reprogramming of the cellular interaction network in CRC for tissue CMS classes.

Data availability

The raw scRNA-seq data of the SMC cohort are available in the European Genome-phenome Archive database (EGAS00001003779, EGAS00001003769). The raw scRNA-seq and bulk RNA-seq data of the KUL3 cohort are available in the ArrayExpress under the accession codes E-MTAB-8410 and E-MTAB-8412. Processed scRNA-seq and metadata for the SMC and KUL3 cohorts are available in the NCBI Gene Expression Omnibus (GEO) database under the accession codes GSE132465, GSE132257 and GSE144735. Clusters and gene expression data of the SMC cohort can be found on the User-friendly InteRface tool to Explore Cell Atlas (URECA) website (http://ureca-singlecell.kr). Other datasets referenced in the study are available from the GEO database under the accession codes GSE14028, GSE131907 and GSE81861.

Code availability

The code for the intercellular interaction map has been deposited with GitHub (https://github.com/SGI-CRC/scRNA-seq).

References

  1. 1.

    Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2.

    Dienstmann, R. et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer. Nat. Rev. Cancer 17, 79–92 (2017).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Ren, X., Kang, B. & Zhang, Z. Understanding tumor ecosystems by single-cell sequencing: promises and limitations. Genome Biol. 19, 211 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Li, H. et al. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49, 708–718 (2017).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Binnewies, M. et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat. Med. 24, 541–550 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    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).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    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  Article  Google Scholar 

  9. 9.

    Lambrechts, D. et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24, 1277–1289 (2018).

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Becht, E. et al. Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin. Cancer Res. 22, 4057–4066 (2016).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Parikh, K. et al. Colonic epithelial cell diversity in health and inflammatory bowel disease. Nature 567, 49–55 (2019).

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Gerbe, F. et al. Distinct ATOH1 and Neurog3 requirements define tuft cells as a new secretory cell type in the intestinal epithelium. J. Cell Biol. 192, 767–780 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Suvà, M. L. & Tirosh, I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol. Cell 75, 7–12 (2019).

    PubMed  Article  CAS  Google Scholar 

  16. 16.

    Calon, A. et al. Stromal gene expression defines poor-prognosis subtypes in colorectal cancer. Nat. Genet. 47, 320–329 (2015).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Isella, C. et al. Stromal contribution to the colorectal cancer transcriptome. Nat. Genet. 47, 312–319 (2015).

    CAS  PubMed  Article  Google Scholar 

  18. 18.

    Perez-Villamil, B. et al. Colon cancer molecular subtypes identified by expression profiling and associated to stroma, mucinous type and different clinical behavior. BMC Cancer 12, 260 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Thanki, K. et al. Consensus molecular subtypes of colorectal cancer and their clinical implications. Int. Biol. Biomed. J. 3, 105–111 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Berger, M., Bergers, G., Arnold, B., Hämmerling, G. J. & Ganss, R. Regulator of G-protein signaling-5 induction in pericytes coincides with active vessel remodeling during neovascularization. Blood 105, 1094–1101 (2005).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Rensen, S. S. M., Doevendans, P. A. F. M. & van Eys, G. J. J. M. Regulation and characteristics of vascular smooth muscle cell phenotypic diversity. Neth. Heart J. 15, 100–108 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Rao, M. et al. Enteric glia express proteolipid protein 1 and are a transcriptionally unique population of glia in the mammalian nervous system. Glia 63, 2040–2057 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Kinchen, J. et al. Structural remodeling of the human colonic mesenchyme in inflammatory bowel disease. Cell 175, 372–386.e17 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Xie, T. et al. Single-cell deconvolution of fibroblast heterogeneity in mouse pulmonary fibrosis. Cell Rep. 22, 3625–3640 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Green, J., Endale, M., Auer, H. & Perl, A.-K. T. Diversity of interstitial lung fibroblasts is regulated by platelet-derived growth factor receptor α kinase activity. Am. J. Respir. Cell Mol. Biol. 54, 532–545 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Nabhan, A. N., Brownfield, D. G., Harbury, P. B., Krasnow, M. A. & Desai, T. J. Single-cell Wnt signaling niches maintain stemness of alveolar type 2 cells. Science 359, 1118–1123 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Vanuytsel, T., Senger, S., Fasano, A. & Shea-Donohue, T. Major signaling pathways in intestinal stem cells. Biochim. Biophys. Acta 1830, 2410–2426 (2013).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Otranto, M. et al. The role of the myofibroblast in tumor stroma remodeling. Cell Adh. Migr. 6, 203–219 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Vermeulen, L. et al. Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat. Cell Biol. 12, 468–476 (2010).

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Kumar, A. et al. Specification and diversification of pericytes and smooth muscle cells from mesenchymoangioblasts. Cell Rep. 19, 1902–1916 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Zhao, Q. et al. Single-cell transcriptome analyses reveal endothelial cell heterogeneity in tumors and changes following antiangiogenic treatment. Cancer Res. 78, 2370–2382 (2018).

    CAS  PubMed  Article  Google Scholar 

  32. 32.

    Lamorte, S. et al. Syndecan-1 promotes the angiogenic phenotype of multiple myeloma endothelial cells. Leukemia 26, 1081–1090 (2012).

    CAS  PubMed  Article  Google Scholar 

  33. 33.

    O’Connor, D. S. et al. Control of apoptosis during angiogenesis by survivin expression in endothelial cells. Am. J. Pathol. 156, 393–398 (2000).

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Griffioen, A. W., Damen, C. A., Blijham, G. H. & Groenewegen, G. Tumor angiogenesis is accompanied by a decreased inflammatory response of tumor-associated endothelium. Blood 88, 667–673 (1996).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Baitsch, D. et al. Apolipoprotein E induces antiinflammatory phenotype in macrophages. Arterioscler. Thromb. Vasc. Biol. 31, 1160–1168 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Benoit, M. E., Clarke, E. V., Morgado, P., Fraser, D. A. & Tenner, A. J. Complement protein C1q directs macrophage polarization and limits inflammasome activity during the uptake of apoptotic cells. J. Immunol. 188, 5682–5693 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Bronte, V. et al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat. Commun. 7, 12150 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Villani, A.-C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Guo, H., Cai, C. Q., Schroeder, R. A. & Kuo, P. C. Osteopontin is a negative feedback regulator of nitric oxide synthesis in murine macrophages. J. Immunol. 166, 1079–1086 (2001).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Castello, L. M. et al. Osteopontin at the crossroads of inflammation and tumor progression. Mediators Inflamm. 2017, 4049098 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  41. 41.

    Wang, K. X. & Denhardt, D. T. Osteopontin: role in immune regulation and stress responses. Cytokine Growth Factor Rev. 19, 333–345 (2008).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    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).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Zhang, W. et al. Characterization of the B cell receptor repertoire in the intestinal mucosa and of tumor-infiltrating lymphocytes in colorectal adenoma and carcinoma. J. Immunol. 198, 3719–3728 (2017).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Ramilowski, J. A. et al. A draft network of ligand–receptor-mediated multicellular signalling in human. Nat. Commun. 6, 7866 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal–fetal interface in humans. Nature 563, 347–353 (2018).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Efremova, M., Vento-Tormo, M., Teichman, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit receptor–ligand complexes. Nat. Protoc. 15, 1484–1506 (2020).

    CAS  PubMed  Article  Google Scholar 

  47. 47.

    Calon, A. et al. Dependency of colorectal cancer on a TGF-β-driven program in stromal cells for metastasis initiation. Cancer Cell 22, 571–584 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Klement, J. D. et al. An osteopontin/CD44 immune checkpoint controls CD8+ T cell activation and tumor immune evasion. J. Clin. Invest. 128, 5549–5560 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Tauriello, D. V. F. et al. TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis. Nature 554, 538–543 (2018).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Gutzeit, C., Magri, G. & Cerutti, A. Intestinal IgA production and its role in host-microbe interaction. Immunol. Rev. 260, 76–85 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Nielsen, M. M., Witherden, D. A. & Havran, W. L. γδ T cells in homeostasis and host defence of epithelial barrier tissues. Nat. Rev. Immunol. 17, 733–745 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  53. 53.

    Kitajima, S., Thummalapalli, R. & Barbie, D. A. Inflammation as a driver and vulnerability of KRAS mediated oncogenesis. Semin. Cell Dev. Biol. 58, 127–135 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Trinh, A. et al. Tumour budding is associated with the mesenchymal colon cancer subtype and RAS/RAF mutations: a study of 1320 colorectal cancers with Consensus Molecular Subgroup (CMS) data. Br. J. Cancer 119, 1244–1251 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Fessler, E. et al. TGFβ signaling directs serrated adenomas to the mesenchymal colorectal cancer subtype. EMBO Mol. Med. 8, 745–760 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. 56.

    Levy, L. & Hill, C. S. Smad4 dependency defines two classes of transforming growth factor β (TGF-β) target genes and distinguishes TGF-β-induced epithelial-mesenchymal transition from its antiproliferative and migratory responses. Mol. Cell. Biol. 25, 8108–8125 (2005).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. 58.

    DePristo, M. A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. 59.

    Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).

    CAS  PubMed  Article  Google Scholar 

  60. 60.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65.

    Zhang, Q. et al. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179, 829–845.e20 (2019).

    CAS  PubMed  Article  Google Scholar 

  66. 66.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. 67.

    Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 19, 619–625 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69.

    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  Article  Google Scholar 

  70. 70.

    Mi, H. & Thomas, P. PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. Methods Mol. Biol. 563, 123–140 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).

    CAS  PubMed  Article  Google Scholar 

  72. 72.

    De Sousa e Melo, F. et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions. Nat. Med. 19, 614–618 (2013).

    CAS  PubMed  Article  Google Scholar 

  73. 73.

    Kang, H. C. et al. Identification of genes with differential expression in acquired drug-resistant gastric cancer cells using high-density oligonucleotide microarrays. Clin. Cancer Res. 10, 272–284 (2004).

    CAS  PubMed  Article  Google Scholar 

  74. 74.

    Liu, Z. et al. Reconstructing cell cycle pseudo time-series via single-cell transcriptome data. Nat. Commun. 8, 22 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the Bio & Medical Technology Development Program of the National Research Foundation funded by the Ministry of Science & ICT (grant no. NRF-2017M3A9A7050803), by the Belgian Federation against Cancer grant nos. 2018-127 and 2016-133 and by a grant from Fondation Roi-Baudouin. S.T. and S.V. are respectively supported by a Senior Clinical Investigator award and a postdoctoral fellowship of the Research Foundation—Flanders.

Author information

Affiliations

Authors

Contributions

H.-O.L., Y.H., H.E.E. and Y.B.C. analyzed and interpreted the data. V.P., B.V.B., J.V. and H.H. processed the tumors. S.V., J.-W.M., N.K., H.H.E., J.Q., B.B., D.L., P.T., T.L., M.A. and P.W. provided bioinformatics support. M.-H.J., G.D.H., W.C., H.-T.S. and J.-G.J. set up the server and analyzed the bulk data. Y.H. and G.K. constructed the visualization website. S.H.K. provided pathological examination. H.C.K., S.H.Y., W.Y.L., T.-Y.K., J.K.C. and Y.-J.K. interpreted the clinical data. I.B.H.T., B.R. and S.P. provided critical bioinformatics guidance. S.T. and W.-Y.P. conceived and supervised the study. H.-O.L., Y.H., H.E.E., Y.B.C., S.T. and W.-Y.P. wrote the manuscript with contributions and approval from all authors.

Corresponding authors

Correspondence to Sabine Tejpar or Woong-Yang Park.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–13

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lee, HO., Hong, Y., Etlioglu, H.E. et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. Nat Genet 52, 594–603 (2020). https://doi.org/10.1038/s41588-020-0636-z

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing