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An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC

Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with...

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Autores principales: Zheng, Kun, Gao, Lianchong, Hao, Jie, Zou, Xin, Hu, Xiaoyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452905/
https://www.ncbi.nlm.nih.gov/pubmed/36091022
http://dx.doi.org/10.3389/fimmu.2022.972227
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author Zheng, Kun
Gao, Lianchong
Hao, Jie
Zou, Xin
Hu, Xiaoyong
author_facet Zheng, Kun
Gao, Lianchong
Hao, Jie
Zou, Xin
Hu, Xiaoyong
author_sort Zheng, Kun
collection PubMed
description Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with good predictive performance for ICB outcomes in the ccRCC context, we reanalyzed two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients receiving ICB treatment. A subtype of proliferative CD4(+) T cells and regulatory T cells and a subtype of antigen-presenting monocytes that have good predictive capability and are correlated with ICB outcomes were identified. These findings were corroborated in independent ccRCC ICB pretreatment bulk RNA-seq datasets. By incorporating the cluster-specific marker genes of these three immunocyte subtypes, we developed a prediction model, which reached an AUC of 93% for the CheckMate cohort (172 samples). Our study shows that the ICB response prediction model can serve as a valuable clinical decision-making tool for guiding ICB treatment of ccRCC patients.
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spelling pubmed-94529052022-09-09 An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC Zheng, Kun Gao, Lianchong Hao, Jie Zou, Xin Hu, Xiaoyong Front Immunol Immunology Most patients with clear cell renal cell carcinoma (ccRCC) have an impaired response to immune checkpoint blockade (ICB) therapy. Few biomarkers can predict responsiveness, and there is insufficient evidence to extend them to ccRCC clinical use. To explore subtypes and signatures of immunocytes with good predictive performance for ICB outcomes in the ccRCC context, we reanalyzed two ccRCC single-cell RNA sequencing (scRNA-seq) datasets from patients receiving ICB treatment. A subtype of proliferative CD4(+) T cells and regulatory T cells and a subtype of antigen-presenting monocytes that have good predictive capability and are correlated with ICB outcomes were identified. These findings were corroborated in independent ccRCC ICB pretreatment bulk RNA-seq datasets. By incorporating the cluster-specific marker genes of these three immunocyte subtypes, we developed a prediction model, which reached an AUC of 93% for the CheckMate cohort (172 samples). Our study shows that the ICB response prediction model can serve as a valuable clinical decision-making tool for guiding ICB treatment of ccRCC patients. Frontiers Media S.A. 2022-08-25 /pmc/articles/PMC9452905/ /pubmed/36091022 http://dx.doi.org/10.3389/fimmu.2022.972227 Text en Copyright © 2022 Zheng, Gao, Hao, Zou and Hu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Zheng, Kun
Gao, Lianchong
Hao, Jie
Zou, Xin
Hu, Xiaoyong
An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title_full An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title_fullStr An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title_full_unstemmed An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title_short An immunotherapy response prediction model derived from proliferative CD4(+) T cells and antigen-presenting monocytes in ccRCC
title_sort immunotherapy response prediction model derived from proliferative cd4(+) t cells and antigen-presenting monocytes in ccrcc
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452905/
https://www.ncbi.nlm.nih.gov/pubmed/36091022
http://dx.doi.org/10.3389/fimmu.2022.972227
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