Resource | Published:

Phenotype molding of stromal cells in the lung tumor microenvironment


Cancer cells are embedded in the tumor microenvironment (TME), a complex ecosystem of stromal cells. Here, we present a 52,698-cell catalog of the TME transcriptome in human lung tumors at single-cell resolution, validated in independent samples where 40,250 additional cells were sequenced. By comparing with matching non-malignant lung samples, we reveal a highly complex TME that profoundly molds stromal cells. We identify 52 stromal cell subtypes, including novel subpopulations in cell types hitherto considered to be homogeneous, as well as transcription factors underlying their heterogeneity. For instance, we discover fibroblasts expressing different collagen sets, endothelial cells downregulating immune cell homing and genes coregulated with established immune checkpoint transcripts and correlating with T-cell activity. By assessing marker genes for these cell subtypes in bulk RNA-sequencing data from 1,572 patients, we illustrate how these correlate with survival, while immunohistochemistry for selected markers validates them as separate cellular entities in an independent series of lung tumors. Hence, in providing a comprehensive catalog of stromal cells types and by characterizing their phenotype and co-optive behavior, this resource provides deeper insights into lung cancer biology that will be helpful in advancing lung cancer diagnosis and therapy.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

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

  2. 2.

    Albini, A. & Sporn, M. B. The tumour microenvironment as a target for chemoprevention. Nat. Rev. Cancer 7, 139 (2007).

  3. 3.

    Vaupel, P., Kallinowski, F. & Okunieff, P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: A review. Cancer Res. 49, 6449–6465 (1989).

  4. 4.

    Eberhard, A. et al. Heterogeneity of angiogenesis and blood vessel maturation in human tumors: Implications for antiangiogenic tumor therapies. Cancer Res. 60, 1388–1393 (2000).

  5. 5.

    Gordon, S. & Taylor, P. R. Monocyte and macrophage heterogeneity. Nat. Rev. Immunol. 5, 953 (2005).

  6. 6.

    Sugimoto, H., Mundel, T. M., Kieran, M. W. & Kalluri, R. Identification of fibroblast heterogeneity in the tumor microenvironment. Cancer Biol. Ther. 5, 1640–1646 (2006).

  7. 7.

    Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765.e717 (2017).

  8. 8.

    Rittmeyer, A. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): A phase 3, open-label, multicentre randomised controlled trial. Lancet 389, 255–265 (2017).

  9. 9.

    Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 2016, 1823–1833 (2016).

  10. 10.

    Reck, M. et al. Docetaxel plus nintedanib versus docetaxel plus placebo in patients with previously treated non-small-cell lung cancer (LUME-Lung 1): A phase 3, double-blind, randomised controlled trial. Lancet Oncol. 15, 143–155 (2014).

  11. 11.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  12. 12.

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

  13. 13.

    van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

  14. 14.

    Trapnell, C. Defining cell types and states with single-cell genomics. Genome Res. 25, 1491–1498 (2015).

  15. 15.

    Mazzone, M. et al. Heterozygous deficiency of PHD2 restores tumor oxygenation and inhibits metastasis via endothelial normalization. Cell 136, 839–851 (2009).

  16. 16.

    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).

  17. 17.

    Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).

  18. 18.

    Baudino, T. A. et al. c-Myc is essential for vasculogenesis and angiogenesis during development and tumor progression. Genes Dev. 16, 2530–2543 (2002).

  19. 19.

    Cantelmo, A. R. et al. Inhibition of the glycolytic activator PFKFB3 in endothelium induces tumor vessel normalization, impairs metastasis, and improves chemotherapy. Cancer Cell 30, 968–985 (2016).

  20. 20.

    Arany, Z. et al. HIF-independent regulation of VEGF and angiogenesis by the transcriptional coactivator PGC-1alpha. Nature 451, 1008–1012 (2008).

  21. 21.

    De Bock, K. et al. Role of PFKFB3-driven glycolysis in vessel sprouting. Cell 154, 651–663 (2013).

  22. 22.

    Kambayashi, T. & Laufer, T. M. Atypical MHC class II-expressing antigen-presenting cells: Can anything replace a dendritic cell? Nat. Rev. Immunol. 14, 719–730 (2014).

  23. 23.

    Tian, L. et al. Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming. Nature 544, 250–254 (2017).

  24. 24.

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

  25. 25.

    Wang, N. et al. Adenovirus-mediated overexpression of c-Jun and c-Fos induces intercellular adhesion molecule-1 and monocyte chemoattractant protein-1 in human endothelial cells. Arterioscler. Thromb. Vasc. Biol. 19, 2078–2084 (1999).

  26. 26.

    Kalluri, R. The biology and function of fibroblasts in cancer. Nat. Rev. Cancer 16, 582–598 (2016).

  27. 27.

    Gelse, K., Poschl, E. & Aigner, T. Collagens—Structure, function, and biosynthesis. Adv. Drug Deliv. Rev. 55, 1531–1546 (2003).

  28. 28.

    Lin, Q., Schwarz, J., Bucana, C. & Olson, E. N. Control of mouse cardiac morphogenesis and myogenesis by transcription factor MEF2C. Science 276, 1404–1407 (1997).

  29. 29.

    Lu, J., Webb, R., Richardson, J. A. & Olson, E. N. MyoR: A muscle-restricted basic helix-loop-helix transcription factor that antagonizes the actions of MyoD. Proc. Natl. Acad. Sci. USA 96, 552–557 (1999).

  30. 30.

    Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014).

  31. 31.

    Biswas, S. K. et al. A distinct and unique transcriptional program expressed by tumor-associated macrophages (defective NF-kappaB and enhanced IRF-3/STAT1 activation). Blood 107, 2112–2122 (2006).

  32. 32.

    Gunthner, R. & Anders, H. J. Interferon-regulatory factors determine macrophage phenotype polarization. Mediat. Inflamm. 2013, 731023 (2013).

  33. 33.

    Medzhitov, R. & Horng, T. Transcriptional control of the inflammatory response. Nat. Rev. Immunol. 9, 692 (2009).

  34. 34.

    Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252 (2012).

  35. 35.

    Zhang, Y. et al. Enhancing CD8+ T cell fatty acid catabolism within a metabolically challenging tumor microenvironment increases the efficacy of melanoma immunotherapy. Cancer Cell 32, 377–391.e39 (2017).

  36. 36.

    Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

  37. 37.

    Shaykhiev, R. et al. Smoking-induced CXCL14 expression in the human airway epithelium links chronic obstructive pulmonary disease to lung cancer. Am. J. Respir. Cell. Mol. Biol. 49, 418–425 (2013).

  38. 38.

    Buffa, F. M., Harris, A. L., West, C. M. & Miller, C. J. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br. J. Cancer 102, 428–435 (2010).

  39. 39.

    Ishibashi, M. et al. CD200-positive cancer associated fibroblasts augment the sensitivity of Epidermal Growth Factor Receptor mutation-positive lung adenocarcinomas to EGFR Tyrosine kinase inhibitors. Sci. Rep. 7, 46662 (2017).

  40. 40.

    Djureinovic, D. et al. Profiling cancer testis antigens in non-small-cell lung cancer. JCI Insight 1, e86837 (2016).

  41. 41.

    Director’s Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma. et al. Gene expression-based survival prediction in lung adenocarcinoma: A multi-site, blinded validation study. Nat. Med. 14, 822–827 (2008).

  42. 42.

    Clevers, H. et al. What is your conceptual definition of ‘cell type’ in the context of a mature organism? Cell Syst. 4, 255–259 (2017).

  43. 43.

    Zhang, Y. & Ertl, H. C. Starved and asphyxiated: How can CD8+ T cells within a tumor microenvironment prevent tumor progression. Front. Immunol. 7, 32 (2016).

  44. 44.

    Horn, J. L. A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185 (1965).

  45. 45.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

  46. 46.

    Thienpont, B. et al. Tumour hypoxia causes DNA hypermethylation by reducing TET activity. Nature 537, 63–68 (2016).

  47. 47.

    Whitlock, M. C. Combining probability from independent tests: The weighted Z‐method is superior to Fisher’s approach. J. Evol. Biol. 18, 1368–1373 (2005).

  48. 48.

    Gaude, E. & Frezza, C. Tissue-specific and convergent metabolic transformation of cancer correlates with metastatic potential and patient survival. Nat. Commun. 7, 13041 (2016).

  49. 49.

    Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14, 7 (2013).

  50. 50.

    Kiselev, V. Y. et al. SC3: Consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483 (2017).

  51. 51.

    Lin, P., Troup, M. & Ho, J. W. CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 18, 59 (2017).

  52. 52.

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

  53. 53.

    Wauters, E. et al. DNA methylation profiling of non-small cell lung cancer reveals a COPD-driven immune-related signature. Thorax 70, 1113–1122 (2015).

  54. 54.

    Kristofer, D. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell (2018).

Download references


We thank M. De Waegeneer, T. Van Brussel, G. Peuteman, E. Vanderheyden and B. Tembuyser for technical assistance. This work was supported by a VIB TechWatch Grant to D.L. and B.T., Foundation Against Cancer grants to S.Aerts (2016-070) and E.W., ERC Consolidator Grants to S.Aerts (724226_cis-CONTROL) and D.L. (CHAMELEON), Funds for Research - Flanders grants to H.D. (1701018N) and D.L. (G065615N), an Austrian Science Fund (FWF) grant to A.P. (J3730-B26) and KU Leuven grants to D.L. and S.Aerts (PFV/10/016 SymBioSys), and to B.T. (BOFZAP).

Author information

D.L. and B.T. designed and supervised the study and wrote the manuscript. E.W. supervised sample collection and clinical annotation, with important help from H.D., A.P., K.V.d.E., B.W., E.V., P.D.L. and J.V. B.T. performed data analysis, with significant contributions from B.B., S.Ai., S.Ae. and A.B. D.N. and B.T. performed immunohistofluorescence analyses. O.B., A.L., P.C. and S.Ae. contributed critical data interpretation. All of the authors have read or provided comments on the manuscript.

Correspondence to Diether Lambrechts or Bernard Thienpont.

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 Text and Figures

Supplementary Figures 1–20 and Supplementary Tables 1–3

Reporting Summary

Supplementary Table 4

Gene expression data for all 52 clusters

Supplementary Table 5

Gene expression data for tumor-derived and non-malignant lung-tissue-derived cells, per cluster having >100 cells from both sources

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading

Fig. 1: Overview of the 52,698 single cells from lung tumors and distal non-malignant lung samples.
Fig. 2: Endothelial cell clusters.
Fig. 3: Fibroblast clusters in lungs and lung tumors.
Fig. 4: B-cell and myeloid-like cell clusters in lungs and lung tumors.
Fig. 5: T-cell clusters in lungs and lung tumors.
Fig. 6: Distribution of stromal cells in tumor samples and their role as markers of patient survival.