Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy.
comparative analysis
immune checkpoint blockade (ICB)
immune response
immunotherapy
transcriptomic biomarkers
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
01 Apr 2021
01 Apr 2021
Historique:
received:
27
01
2021
revised:
13
03
2021
accepted:
26
03
2021
entrez:
30
4
2021
pubmed:
1
5
2021
medline:
1
5
2021
Statut:
epublish
Résumé
Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. Overall, "Immune-checkpoint molecule" biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the "Effector molecule" biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.
Sections du résumé
BACKGROUND
BACKGROUND
Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient.
METHODS
METHODS
We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers.
RESULTS
RESULTS
Overall, "Immune-checkpoint molecule" biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the "Effector molecule" biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy.
CONCLUSIONS
CONCLUSIONS
Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.
Identifiants
pubmed: 33915876
pii: cancers13071639
doi: 10.3390/cancers13071639
pmc: PMC8037221
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : the National Natural Science Foundation of China
ID : 81802926
Organisme : the National Natural Science Foundation of China
ID : 31871336
Organisme : the National Natural Science Foundation of China
ID : 31801116
Organisme : National Science Foundation of Heilongjiang Province
ID : YQ2019C012
Organisme : the China Postdoctoral Science Foundation
ID : 2017M621295
Organisme : the China Postdoctoral Science Foundation
ID : 2018M641842
Organisme : the Heilongjiang Postdoctoral Foundation
ID : LBH-Q18099
Organisme : the Heilongjiang Postdoctoral Foundation
ID : LBH-Z17160
Organisme : the Heilongjiang Postdoctoral Foundation
ID : LBH-Z17141
Organisme : the Health Department Science Foundation of Heilongjiang Province
ID : 2017-167
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