|Title:||Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy|
Kim, Young Hak
|Author's alias:||波多江, 龍亮|
|Publisher:||American Society for Clinical Investigation|
|Journal title:||JCI Insight|
|Abstract:||BACKGROUND. Current clinical biomarkers for the programmed cell death 1 (PD-1) blockade therapy are insufficient because they rely only on the tumor properties, such as programmed cell death ligand 1 expression frequency and tumor mutation burden. Identifying reliable, responsive biomarkers based on the host immunity is necessary to improve the predictive values. METHODS. We investigated levels of plasma metabolites and T cell properties, including energy metabolism markers, in the blood of patients with non-small cell lung cancer before and after treatment with nivolumab (n = 55). Predictive values of combination markers statistically selected were evaluated by cross-validation and linear discriminant analysis on discovery and validation cohorts, respectively. Correlation between plasma metabolites and T cell markers was investigated. RESULTS. The 4 metabolites derived from the microbiome (hippuric acid), fatty acid oxidation (butyrylcarnitine), and redox (cystine and glutathione disulfide) provided high response probability (AUC = 0.91). Similarly, a combination of 4 T cell markers, those related to mitochondrial activation (PPARγ coactivator 1 expression and ROS), and the frequencies of CD8+PD-1hi and CD4+ T cells demonstrated even higher prediction value (AUC = 0.96). Among the pool of selected markers, the 4 T cell markers were exclusively selected as the highest predictive combination, probably because of their linkage to the abovementioned metabolite markers. In a prospective validation set (n = 24), these 4 cellular markers showed a high accuracy rate for clinical responses of patients (AUC = 0.92). CONCLUSION. Combination of biomarkers reflecting host immune activity is quite valuable for responder prediction.|
|Description:||PD-1抗体がん免疫治療の有効性を判別するバイオマーカーを同定 --血液検査のみで有効性の診断が可能に--. 京都大学プレスリリース. 2020-01-31.|
|Rights:||© 2020, American Society for Clinical Investigation|
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|Appears in Collections:||Journal Articles |
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