Cancer treatment by immune checkpoint blockade (ICB) can bring long-lasting clinical benefits, but only a fraction of patients responds to treatment. To predict ICB response we developed TIDE, a computational method to model two primary mechanisms of tumor immune evasion: inducing T-cell dysfunction in tumors with high infiltration of cytotoxic T lymphocytes (CTL) and preventing 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. TIDE source code and a web application are available at http://tide.dfci.harvard.edu.
Citation Format: Peng Jiang, Shengqing Gu, Deng Pan, Jingxin Fu, Avinash Sahu, Xihao Hu, Ziyi Li, Nicole Traugh, Xia Bu, Bo Li, Jun Liu, Gordon J Freeman, Myles A Brown, Kai W. Wucherpfennig, Xiaole Shirley Liu. Signatures of T-cell dysfunction and exclusion predict cancer immunotherapy response [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B077.