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• W3017086493 abstract "Abstract Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumour microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analysing RNA sequencing data from a combined discovery cohort ( n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN- γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort ( n = 55) and three published datasets of metastatic melanoma treated with anti-PD-1 ( n = 48) and anti-CTLA-4 ( n = 42) as well as in patients with gastric cancer treated with antiPD-1 ( n = 45), demonstrating the potential utility of IMS as a predictive/prognostic biomarker that complements existing GEP signatures for immunotherapy." @default.
• W3017086493 created "2020-04-24" @default.
• W3017086493 creator A5003060362 @default.
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• W3017086493 creator A5068270780 @default.
• W3017086493 creator A5069813415 @default.
• W3017086493 date "2020-04-18" @default.
• W3017086493 modified "2023-10-12" @default.
• W3017086493 title "Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma" @default.
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