Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. (October 2018)

• Record Type: Journal Article
• Authors: Jiang, Peng; Gu, Shengqing; Pan, Deng; Fu, Jingxin; Sahu, Avinash; Hu, Xihao; Li, Ziyi; Traugh, Nicole; Bu, Xia; Li, Bo; Liu, Jun; Freeman, Gordon; Brown, Myles; Wucherpfennig, Kai; Liu, X.
• Is Part Of: Nature medicine. Volume 24:Number 10(2018)
• Page Start: 1550
• Page End: 1558
• Publication Date: 2018-10
• Subjects: Pathology, Molecular -- Periodicals; Molecular biology -- Periodicals; 610.724
• DOI: 10.1038/s41591-018-0136-1
• Languages: English
• ISSNs: 1078-8956

Abstract

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 asPD-L1 level and mutation load. TIDE also revealed new candidate ICB resistance regulators, such asSERPINB9, demonstrating utility for immunotherapy research. An algorithm-selected gene signature focused on tumor immune evasion and suppression predicts response to immune checkpoint blockade in melanoma, exceeding the accuracy of current clinical biomarkers.