Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response
A l'aide d'une méthode utilisant des données génomiques et des données cliniques, cette étude identifie deux signatures génétiques, associées au dysfonctionnement des lymphocytes T intratumoraux ou à la faible infiltration des tumeurs par les lymphocytes T, permettant de prédire la réponse à un traitement de première ligne ciblant la protéine PD1 ou CTLA4 chez les patients atteints d'un mélanome
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.
Nature Medicine , résumé, 2018