IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade.
J Clin Invest
; 127(8): 2930-2940, 2017 Aug 01.
Article
en En
| MEDLINE
| ID: mdl-28650338
ABSTRACT
Programmed death-1-directed (PD-1-directed) immune checkpoint blockade results in durable antitumor activity in many advanced malignancies. Recent studies suggest that IFN-γ is a critical driver of programmed death ligand-1 (PD-L1) expression in cancer and host cells, and baseline intratumoral T cell infiltration may improve response likelihood to anti-PD-1 therapies, including pembrolizumab. However, whether quantifying T cell-inflamed microenvironment is a useful pan-tumor determinant of PD-1-directed therapy response has not been rigorously evaluated. Here, we analyzed gene expression profiles (GEPs) using RNA from baseline tumor samples of pembrolizumab-treated patients. We identified immune-related signatures correlating with clinical benefit using a learn-and-confirm paradigm based on data from different clinical studies of pembrolizumab, starting with a small pilot of 19 melanoma patients and eventually defining a pan-tumor T cell-inflamed GEP in 220 patients with 9 cancers. Predictive value was independently confirmed and compared with that of PD-L1 immunohistochemistry in 96 patients with head and neck squamous cell carcinoma. The T cell-inflamed GEP contained IFN-γ-responsive genes related to antigen presentation, chemokine expression, cytotoxic activity, and adaptive immune resistance, and these features were necessary, but not always sufficient, for clinical benefit. The T cell-inflamed GEP has been developed into a clinical-grade assay that is currently being evaluated in ongoing pembrolizumab trials.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Gástricas
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Interferón gamma
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Perfilación de la Expresión Génica
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Anticuerpos Monoclonales Humanizados
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Receptor de Muerte Celular Programada 1
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Antineoplásicos
Tipo de estudio:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
J Clin Invest
Año:
2017
Tipo del documento:
Article