Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response

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.

Immune checkpoint inhibitors: The linchpins of modern immunotherapy

“…The cancer Immunogram incorporates multiple microenvironmental signatures, including general immune status through lymphocyte count, soluble inhibitors such as interleukin 6 and C‐reactive protein, as well as genetic complexity, immune cell infiltration, and expression of checkpoint proteins . Multiparameter genetic expression signatures, such as Immunoscore, Tumor Inflammation Signature, and tumor immune dysfunction and exclusion (TIDE) scores offer an efficient snapshot of tumor cell, immune cell, and stromal contribution into anti‐tumor immunity, but have not been well‐explored in all cancers.…”

Homogeneous, Low‐volume, Efficient, and Sensitive Quantitation of Circulating Exosomal PD‐L1 for Cancer Diagnosis and Immunotherapy Response Prediction

“…[2] This poor response rate is the result of lacking effective methods for immunotherapy prediction. Although few exciting methods for immunotherapy response prediction have been developed, such as by analyzing high tumor mutational burden, [3] unique gene expression features of tumors analysis, [4] and highdimensional single-cell analysis. [5] Such sequencing-based prediction methods face bottlenecks,s uch as high technical requirements,a nd they are time-consuming and expensive.…”

CAR T cells for solid tumors: genome-wide dysfunction signature confirms value of c-Jun overexpression, but signals heterogeneity

“…However, in solid tumor transcriptomics, single-gene signatures are now not sufficient. They are complemented with genome-wide signatures, which, for example, better predict response to immune checkpoint inhibitors than single-gene signatures like PDL1 [4].…”

“…Among all genome-wide signatures, Tumor Immune Dysfunction and Exclusion, or TIDE [4], is particularly relevant, because it includes a distinctive T cell dysfunction signature, TID (Tumor Immune Dysfunction). In this paper, we apply TID signature to CAR T single-cell RNA-seq data from [5].…”