LAUSR

Despite success in treating several cancer types, immunotherapy only shows response in a subset of patients. Predicting immunotherapy response and understanding the resistance mechanisms are still open questions. We present a computational framework, Tumor Immune Dysfunction and Exclusion (TIDE), which utilizes the vast amount of public clinical datasets to identify biomarkers of immunotherapy response. TIDE utilized the interaction test in multivariate model to identify the molecular features of tumors where high CD8 T cell infiltration does not associate with survival benefits. The top scored genes are enriched with drivers of T cell dysfunction identified by shRNA screen in mouse models, markers of T cell exhaustion in irreversible state, and reversely correlated with transcriptome profiles of cell types that drives T cell exclusion in tumors. The TIDE signatures from clinical data without immunotherapies can reliably predict the response to both anti-PD1 and anti-CTLA4 therapies in melanoma using pretreatment tumor profiles, with higher accuracy than mutation load, PDL1 expression level, and other biomarkers. Citation Format: Peng Jiang. Digital signatures of T cell dysfunction predict immunotherapy response [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr B24.