TY - JOUR
T1 - Combination of host immune metabolic biomarkers for the PD-1 blockade cancer immunotherapy
AU - Hatae, Ryusuke
AU - Chamoto, Kenji
AU - Kim, Young Hak
AU - Sonomura, Kazuhiro
AU - Taneishi, Kei
AU - Kawaguchi, Shuji
AU - Yoshida, Hironori
AU - Ozasa, Hiroaki
AU - Sakamori, Yuichi
AU - Akrami, Maryam
AU - Fagarasan, Sidonia
AU - Masuda, Izuru
AU - Okuno, Yasushi
AU - Matsuda, Fumihiko
AU - Hirai, Toyohiro
AU - Honjo, Tasuku
N1 - Funding Information:
personal fees from Bristol- Myers Squibb, grants from Ono Pharmaceutical Company, grants from Sysmex Corporation, and grants from Shimazu Corporation and has a patent (2019-000181). KC has received personal fees from Bristol-Myers Squibb and grants from Sysmex and has a patent (2019-000181). FM has a patent (2019-000181).
Funding Information:
This work was supported by AMED under grant numbers 18cm0106302h0003 (T. Honjo), 18gm0710012h0105 (SF), and 18lk1403006h0002 (KC); the Tang Prize Foundation (T. Honjo); and JSPS KAKENHI grant numbers JP16H06149 (KC), 17K19593 (KC), and 19K17673 (RH). Analyses were supported by Cell Innovator Inc.
Funding Information:
FUNDING. AMED under grant numbers 18cm0106302h0003, 18gm0710012h0105, and 18lk1403006h0002; the Tang Prize Foundation; and JSPS KAKENHI grant numbers JP16H06149, 17K19593, and 19K17673.
Publisher Copyright:
© 2020, American Society for Clinical Investigation.
PY - 2020/1/30
Y1 - 2020/1/30
N2 - 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.
AB - 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.
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U2 - 10.1172/jci.insight.133501
DO - 10.1172/jci.insight.133501
M3 - Article
C2 - 31855576
AN - SCOPUS:85079522432
SN - 2379-3708
VL - 5
JO - JCI Insight
JF - JCI Insight
IS - 2
M1 - e133501
ER -