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Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy

SIMPLE SUMMARY: The aim of our study was to evaluate the predictive performance of transcriptomic biomarkers to immune response. The study collected 22 transcriptomic biomarkers and constructed multiple benchmark datasets to evaluate their predictive performance of immune checkpoint blockade (ICB) r...

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Autores principales: Sun, Shangqin, Xu, Liwen, Zhang, Xinxin, Pang, Lin, Long, Zhilin, Deng, Chunyu, Zhu, Jiali, Zhou, Shuting, Wan, Linyun, Pang, Bo, Xiao, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037221/
https://www.ncbi.nlm.nih.gov/pubmed/33915876
http://dx.doi.org/10.3390/cancers13071639
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author Sun, Shangqin
Xu, Liwen
Zhang, Xinxin
Pang, Lin
Long, Zhilin
Deng, Chunyu
Zhu, Jiali
Zhou, Shuting
Wan, Linyun
Pang, Bo
Xiao, Yun
author_facet Sun, Shangqin
Xu, Liwen
Zhang, Xinxin
Pang, Lin
Long, Zhilin
Deng, Chunyu
Zhu, Jiali
Zhou, Shuting
Wan, Linyun
Pang, Bo
Xiao, Yun
author_sort Sun, Shangqin
collection PubMed
description SIMPLE SUMMARY: The aim of our study was to evaluate the predictive performance of transcriptomic biomarkers to immune response. The study collected 22 transcriptomic biomarkers and constructed multiple benchmark datasets to evaluate their predictive performance of immune checkpoint blockade (ICB) response in pre-treatment patients with distinct ICB agents in diverse cancers. We found “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 and clear cell renal-cell cancer. Interestingly, for anti-PD-1 therapy and anti-CTLA-4 therapy, the top-performing response biomarkers were usually mutually exclusive even though in the same biomarker category and most of biomarkers with outstanding predictive power were observed in patients with combined anti-PD-1 and anti-CTLA-4 therapy. ABSTRACT: 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: 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: 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: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers.
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spelling pubmed-80372212021-04-12 Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy Sun, Shangqin Xu, Liwen Zhang, Xinxin Pang, Lin Long, Zhilin Deng, Chunyu Zhu, Jiali Zhou, Shuting Wan, Linyun Pang, Bo Xiao, Yun Cancers (Basel) Article SIMPLE SUMMARY: The aim of our study was to evaluate the predictive performance of transcriptomic biomarkers to immune response. The study collected 22 transcriptomic biomarkers and constructed multiple benchmark datasets to evaluate their predictive performance of immune checkpoint blockade (ICB) response in pre-treatment patients with distinct ICB agents in diverse cancers. We found “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 and clear cell renal-cell cancer. Interestingly, for anti-PD-1 therapy and anti-CTLA-4 therapy, the top-performing response biomarkers were usually mutually exclusive even though in the same biomarker category and most of biomarkers with outstanding predictive power were observed in patients with combined anti-PD-1 and anti-CTLA-4 therapy. ABSTRACT: 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: 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: 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: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers. MDPI 2021-04-01 /pmc/articles/PMC8037221/ /pubmed/33915876 http://dx.doi.org/10.3390/cancers13071639 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Shangqin
Xu, Liwen
Zhang, Xinxin
Pang, Lin
Long, Zhilin
Deng, Chunyu
Zhu, Jiali
Zhou, Shuting
Wan, Linyun
Pang, Bo
Xiao, Yun
Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title_full Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title_fullStr Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title_full_unstemmed Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title_short Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy
title_sort systematic assessment of transcriptomic biomarkers for immune checkpoint blockade response in cancer immunotherapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8037221/
https://www.ncbi.nlm.nih.gov/pubmed/33915876
http://dx.doi.org/10.3390/cancers13071639
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