Cancer immunotherapies have shown sustained clinical responses in treating non-small-cell lung cancer1,2,3, but efficacy varies and depends in part on the amount and properties of tumor infiltrating lymphocytes4,5,6. To depict the baseline landscape of the composition, lineage and functional states of tumor infiltrating lymphocytes, here we performed deep single-cell RNA sequencing for 12,346 T cells from 14 treatment-naïve non-small-cell lung cancer patients. Combined expression and T cell antigen receptor based lineage tracking revealed a significant proportion of inter-tissue effector T cells with a highly migratory nature. As well as tumor-infiltrating CD8+ T cells undergoing exhaustion, we observed two clusters of cells exhibiting states preceding exhaustion, and a high ratio of “pre-exhausted” to exhausted T cells was associated with better prognosis of lung adenocarcinoma. Additionally, we observed further heterogeneity within the tumor regulatory T cells (Tregs), characterized by the bimodal distribution of TNFRSF9, an activation marker for antigen-specific Tregs. The gene signature of those activated tumor Tregs, which included IL1R2, correlated with poor prognosis in lung adenocarcinoma. Our study provides a new approach for patient stratification and will help further understand the functional states and dynamics of T cells in lung cancer.

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  1. 1.

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

  2. 2.

    Brahmer, J. R. et al. Safety and activity of anti–PD-L1 antibody in patients with advanced cancer. N. Engl. J. Med. 366, 2455–2465 (2012).

  3. 3.

    Hellmann, M. D. et al. Nivolumab plus ipilimumab as first-line treatment for advanced non-small-cell lung cancer (CheckMate 012): results of an open-label, phase 1, multicohort study. Lancet Oncol. 18, 31–41 (2017).

  4. 4.

    Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

  5. 5.

    Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer. Science 348, 124–128 (2015).

  6. 6.

    Huang, A. C. et al. T-cell invigoration to tumour burden ratio associated with anti-PD-1 response. Nature 545, 60–65 (2017).

  7. 7.

    Herbst, R. S., Heymach, J. V. & Lippman, S. M. Lung cancer. N. Engl. J. Med. 359, 1367–1380 (2008).

  8. 8.

    The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

  9. 9.

    Topalian, S. L., Taube, J. M., Anders, R. A. & Pardoll, D. M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 16, 275–287 (2016).

  10. 10.

    van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

  11. 11.

    Rodriguez, A. & Laio, A. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

  12. 12.

    Hidalgo, L. G., Einecke, G., Allanach, K. & Halloran, P. F. The transcriptome of human cytotoxic T cells: similarities and disparities among allostimulated CD4+CTL, CD8+CTL and NK cells. Am. J. Transplant. 8, 627–636 (2008).

  13. 13.

    Wherry, E. J. & Kurachi, M. Molecular and cellular insights into T cell exhaustion. Nat. Rev. Immunol. 15, 486–499 (2015).

  14. 14.

    Spitzer, M. H. et al. Systemic immunity is required for effective cancer immunotherapy. Cell 168, 487–502.e15 (2017).

  15. 15.

    Gerlach, C. et al. The chemokine receptor CX3CR1 defines three antigen-experienced CD8 T cell subsets with distinct roles in immune surveillance and homeostasis. Immunity 45, 1270–1284 (2016).

  16. 16.

    Szabo, S. J. et al. A novel transcription factor, T-bet, directs Th1 lineage commitment. Cell 100, 655–669 (2000).

  17. 17.

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  18. 18.

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

  19. 19.

    Danaher, P. et al. Gene expression markers of tumor infiltrating leukocytes. bioRxiv https://doi.org/10.1101/068940 (2016).

  20. 20.

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

  21. 21.

    Crawford, A. et al. Molecular and transcriptional basis of CD4+ T cell dysfunction during chronic infection. Immunity 40, 289–302 (2014).

  22. 22.

    Ganesan, A.-P. et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18, 940–950 (2017).

  23. 23.

    Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

  24. 24.

    Plitas, G. et al. Regulatory T cells exhibit distinct features in human breast cancer. Immunity 45, 1122–1134 (2016).

  25. 25.

    De Simone, M. et al. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells. Immunity 45, 1135–1147 (2016).

  26. 26.

    Bacher, P. et al. Regulatory T cell specificity directs tolerance versus allergy against aeroantigens in humans. Cell 167, 1067–1078.e16 (2016).

  27. 27.

    Grinberg-Bleyer, Y. et al. NF-κB c-Rel is crucial for the regulatory T cell immune checkpoint in cancer. Cell 170, 1096–1108.e13 (2017).

  28. 28.

    Wei, S. C. et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170, 1120–1133.e17 (2017).

  29. 29.

    Pauken, K. E. et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science 354, 1160–1165 (2016).

  30. 30.

    Sen, D. R. et al. The epigenetic landscape of T cell exhaustion. Science 354, 1165–1169 (2016).

  31. 31.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

  32. 32.

    Ruitenberg, J. J., Mulder, C. B., Maino, V. C., Landay, A. L. & Ghanekar, S. A. VACUTAINER® CPTTM and Ficoll density gradient separation perform equivalently in maintaining the quality and function of PBMC from HIV seropositive blood samples. BMC Immunol. 7, 11 (2006).

  33. 33.

    Sallusto, F., Lenig, D., Förster, R., Lipp, M. & Lanzavecchia, A. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401, 708–712 (1999).

  34. 34.

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

  35. 35.

    Wu, T. D. & Nacu, S. Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 26, 873–881 (2010).

  36. 36.

    L. Lun, A. T., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).

  37. 37.

    Stubbington, M. J. T. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 (2016).

  38. 38.

    Reantragoon, R. et al. Antigen-loaded MR1 tetramers define T cell receptor heterogeneity in mucosal-associated invariant T cells. J. Exp. Med. 210, 2305–2320 (2013).

  39. 39.

    Brennan, P. J., Brigl, M. & Brenner, M. B. Invariant natural killer T cells: an innate activation scheme linked to diverse effector functions. Nat. Rev. Immunol. 13, 101–117 (2013).

  40. 40.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47–e47 (2015).

  41. 41.

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

  42. 42.

    Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?. J. Classif. 31, 274–295 (2014).

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We thank S. Geng for discussions and members of the BIOPIC high-throughput sequencing facility and the Computing Platform of the Centre for Life Science. We also thank the National Centre for Protein Sciences Beijing (Peking University) and F. Wang, X. Zhang, S. Wang and Z. Fu for assistance with FACS. This project was supported by grants from the Beijing Advanced Innovation Centre for Genomics at Peking University, Key Technologies R&D Program (2016YFC0900100), the National Natural Science Foundation of China (81573022, 31530036, 91742203) and Bayer AG (Germany). C. Zheng and L. Zhang were supported by the Postdoctoral Foundation of Centre for Life Sciences at Peking University–Tsinghua University.

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Author notes

  1. These authors contributed equally: Xinyi Guo, Yuanyuan Zhang, Liangtao Zheng, Chunhong Zheng, Jintao Song.


  1. BIOPIC, Beijing Advanced Innovation Centre for Genomics, and School of Life Sciences, Peking University, Beijing, China

    • Xinyi Guo
    • , Yuanyuan Zhang
    • , Chunhong Zheng
    • , Qiming Zhang
    • , Boxi Kang
    • , Zhouzerui Liu
    • , Ranran Gao
    • , Minghui Dong
    • , Xueda Hu
    • , Xianwen Ren
    •  & Zemin Zhang
  2. Peking-Tsinghua Centre for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    • Liangtao Zheng
    • , Lei Zhang
    •  & Zemin Zhang
  3. Department of Thoracic Surgery, Peking University Third Hospital, Beijing, China

    • Jintao Song
    • , Liang Jin
    •  & Tiansheng Yan
  4. Peking University Cancer Hospital & Institute, Beijing, China

    • Rui Xing
  5. Bayer AG, Berlin, Germany

    • Dennis Kirchhoff
    •  & Helge Gottfried Roider


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Z.Z. and X.G. designed the experiments. X.G., C.Z., Q.Z., Z.L., L.Z. and R.G. performed the experiments. X.G., Y.Z., L.Z., X.R., H.G.R., D.K., X.H. and M.D. analyzed the sequencing data. J.S., L.J., T.Y. and R.X. collected clinical samples. B.K. built the website. X.G., Y.Z., L.Z., and Z.Z. wrote the manuscript with all authors contributing to writing and providing feedback.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Tiansheng Yan or Zemin Zhang.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–10

  2. Reporting Summary

  3. Supplementary Table 1

    Patient information and sequencing statistics

  4. Supplementary Table 2

    TCR typing of single T cells

  5. Supplementary Table 3

    List of signature genes of each T cell cluster

  6. Supplementary Table 4

    List of genes specifically expressed in exhausted tumor T cells

  7. Supplementary Table 5

    Differentially expressed genes of suppressive tumor Treg cells in CD4C9CTLA4

  8. Supplementary Table 6

    Differentially expressed genes of activated tumor Treg cells in CD4C9CTLA4

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