Abstract

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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Change history

  • 09 August 2018

    In the version of this article originally published, the P statistic described in Fig. 3d was incorrect. It was described as “P < 22 × 10–16”. It should have been “P < 2.2 × 10–16”. Also, the “CD8+ Treg” label in Fig. 4f was incorrect. It should have been “CD4+ Treg”. The errors have been corrected in the HTML and PDF versions of this article.

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Acknowledgements

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.

Author information

Author notes

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

Affiliations

  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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Contributions

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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DOI

https://doi.org/10.1038/s41591-018-0045-3

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