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Nat Med. 2018 Oct;24(10):1550-1558. doi: 10.1038/s41591-018-0136-1. Epub 2018 Aug 20.

Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.

Jiang P1,2, Gu S3, Pan D4,5, Fu J6, Sahu A1,2, Hu X1,2, Li Z6, Traugh N3, Bu X3, Li B1,2,7, Liu J8, Freeman GJ3, Brown MA3,9, Wucherpfennig KW10,11, Liu XS12,13,14,15.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
2
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
3
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
4
Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA.
5
Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA.
6
School of Life Science and Technology, Tongji University, Shanghai, China.
7
Department of Bioinformatics, UT Southwestern, Dallas, TX, USA.
8
Department of Statistics, Harvard University, Cambridge, MA, USA.
9
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.
10
Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA. kai_wucherpfennig@dfci.harvard.edu.
11
Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, USA. kai_wucherpfennig@dfci.harvard.edu.
12
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA. xsliu@jimmy.harvard.edu.
13
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. xsliu@jimmy.harvard.edu.
14
School of Life Science and Technology, Tongji University, Shanghai, China. xsliu@jimmy.harvard.edu.
15
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. xsliu@jimmy.harvard.edu.

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.

PMID:
30127393
DOI:
10.1038/s41591-018-0136-1

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