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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
          9502015
          8791
          Nat Med
          Nat. Med.
          Nature medicine
          1078-8956
          1546-170X
          23 April 2019
          20 August 2018
          October 2018
          29 April 2019
          : 24
          : 10
          : 1550-1558
          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 Statistics, Harvard University, Cambridge, MA, USA
          [8 ]Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
          [9 ]Present address: Department of Bioinformatics, UT Southwestern, Dallas, TX, USA
          [11 ]These authors jointly supervised: Kai W. Wucherpfennig, X. Shirley Liu
          Author notes

          Author 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.

          [* ] Correspondence and requests for materials should be addressed to K.W.W. or X.S.L. kai_wucherpfennig@ 123456dfci.harvard.edu ; xsliu@ 123456jimmy.harvard.edu
          Author information
          http://orcid.org/0000-0002-7828-5486
          Article
          PMC6487502 PMC6487502 6487502 nihpa1024718
          10.1038/s41591-018-0136-1
          6487502
          30127393
          785472b5-a3d7-4482-9aeb-01a8749fc3b4

          Reprints and permissions information is available at www.nature.com/reprints.

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