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

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          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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          Author and article information

          Journal
          Nat Med
          Nature medicine
          Springer Science and Business Media LLC
          1546-170X
          1078-8956
          October 2018
          : 24
          : 10
          Affiliations
          [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.
          Article
          10.1038/s41591-018-0136-1 NIHMS1024718
          10.1038/s41591-018-0136-1
          6487502
          30127393
          785472b5-a3d7-4482-9aeb-01a8749fc3b4
          History

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