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Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

Nature Medicinevolume 24pages15501558 (2018) | Download Citation

Abstract

Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients respond to treatment. To predict ICB response, we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: the induction of T cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and the prevention of T cell infiltration in tumors with low CTL level. We identified signatures of T cell dysfunction from large tumor cohorts by testing how the expression of each gene in tumors interacts with the CTL infiltration level to influence patient survival. We also modeled factors that exclude T cell infiltration into tumors using expression signatures from immunosuppressive cells. Using this framework and pre-treatment RNA-Seq or NanoString tumor expression profiles, TIDE predicted the outcome of melanoma patients treated with first-line anti-PD1 or anti-CTLA4 more accurately than other biomarkers such as PD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such as SERPINB9, demonstrating utility for immunotherapy research.

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Acknowledgements

The research was supported by the Cancer Immunologic Data Commons (1U24CA224316-01) grant of the National Cancer Institute (NCI), the Pathway to Independence Award (1K99CA218900-01) grant of NCI (to P.J.), the Specialized Center (1P50CA206963-01) grant of NCI (to G.J.F.), and the Breast Cancer Research Foundation (to X.S.L.). D.P. is a Cancer Research Institute/Robertson Foundation Fellow.

Author information

Author notes

    • Bo Li

    Present address: Department of Bioinformatics, UT Southwestern, Dallas, TX, USA

  1. These authors contributed equally: Peng Jiang, Shengqing Gu, Deng Pan.

  2. These authors jointly supervised: Kai W. Wucherpfennig, X. Shirley Liu.

Affiliations

  1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Peng Jiang
    • , Avinash Sahu
    • , Xihao Hu
    • , Bo Li
    •  & X. Shirley Liu
  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • Peng Jiang
    • , Avinash Sahu
    • , Xihao Hu
    • , Bo Li
    •  & X. Shirley Liu
  3. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Shengqing Gu
    • , Nicole Traugh
    • , Xia Bu
    • , Gordon J. Freeman
    •  & Myles A. Brown
  4. Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA

    • Deng Pan
    •  & Kai W. Wucherpfennig
  5. Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA

    • Deng Pan
    •  & Kai W. Wucherpfennig
  6. School of Life Science and Technology, Tongji University, Shanghai, China

    • Jingxin Fu
    • , Ziyi Li
    •  & X. Shirley Liu
  7. Department of Statistics, Harvard University, Cambridge, MA, USA

    • Jun Liu
  8. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA

    • Myles A. Brown
    •  & X. Shirley Liu

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Contributions

P.J., K.W.W. and X.S.L. designed the study and wrote the manuscript. P.J. carried out the computational works. S.G., D.P., Z.L. and N.T. carried out the experimental validation. P.J. and J.F. developed the website. A.S., X.H., X.B., B.L, J.L., G.J.F. and M.A.B. participated in discussions.

Competing interests

X.S.L. is a cofounder and board member of GV20 Oncotherapy, a scientific advisor of 3DMedCare and a paid consultant for Genentech. K.W.W. is a member of the scientific advisory board for TCR2 and Nextech; he serves as a consultant for Novartis. The laboratory of K.W.W. received sponsored research funding from Astellas Pharma Inc.

Corresponding authors

Correspondence to Kai W. Wucherpfennig or X. Shirley Liu.

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https://doi.org/10.1038/s41591-018-0136-1

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