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Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies
Pediatric gliomas (PGs) are the most common brain tumors in children and the leading cause of childhood cancer-related death. The understanding of the immune microenvironment is essential for developing effective antitumor immunotherapies. Transcriptomic data from 495 PGs were analyzed in this study...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society of Gene & Cell Therapy
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851498/ https://www.ncbi.nlm.nih.gov/pubmed/33575469 http://dx.doi.org/10.1016/j.omto.2020.12.012 |
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author | Wang, Zihao Guo, Xiaopeng Gao, Lu Wang, Yu Guo, Yi Xing, Bing Ma, Wenbin |
author_facet | Wang, Zihao Guo, Xiaopeng Gao, Lu Wang, Yu Guo, Yi Xing, Bing Ma, Wenbin |
author_sort | Wang, Zihao |
collection | PubMed |
description | Pediatric gliomas (PGs) are the most common brain tumors in children and the leading cause of childhood cancer-related death. The understanding of the immune microenvironment is essential for developing effective antitumor immunotherapies. Transcriptomic data from 495 PGs were analyzed in this study, with 384 as a training cohort and 111 as a validation cohort. Macrophages were the most common immune infiltrates in the PG microenvironment, followed by T cells. PGs were classified into 3 immune subtypes (ISs) based on immunological profiling: “immune hot” (IS-I), “immune altered” (IS-II), and “immune cold” (IS-III). IS-I tumors, characterized by substantial immune infiltration and high immune checkpoint molecule (ICM) expression, had a favorable prognosis and were more likely to respond to anti-PD1 and anti-CTLA4 immunotherapies, whereas IS-III tumors, characterized by weak immune infiltration and low ICM expression, had a dismal prognosis and poor immunotherapy responsiveness. IS-II tumors represented a transitional stage. Immune classification was also correlated with somatic mutations, copy number alterations, and molecular pathways related to tumorigenesis, metabolism, and immune responses. Three predictive classifiers using eight representative genes were generated by machine learning methods for immune classification. This study established a reliable immunological profile-based classification system for PGs, providing implications for further immunotherapy strategies. |
format | Online Article Text |
id | pubmed-7851498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Society of Gene & Cell Therapy |
record_format | MEDLINE/PubMed |
spelling | pubmed-78514982021-02-10 Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies Wang, Zihao Guo, Xiaopeng Gao, Lu Wang, Yu Guo, Yi Xing, Bing Ma, Wenbin Mol Ther Oncolytics Original Article Pediatric gliomas (PGs) are the most common brain tumors in children and the leading cause of childhood cancer-related death. The understanding of the immune microenvironment is essential for developing effective antitumor immunotherapies. Transcriptomic data from 495 PGs were analyzed in this study, with 384 as a training cohort and 111 as a validation cohort. Macrophages were the most common immune infiltrates in the PG microenvironment, followed by T cells. PGs were classified into 3 immune subtypes (ISs) based on immunological profiling: “immune hot” (IS-I), “immune altered” (IS-II), and “immune cold” (IS-III). IS-I tumors, characterized by substantial immune infiltration and high immune checkpoint molecule (ICM) expression, had a favorable prognosis and were more likely to respond to anti-PD1 and anti-CTLA4 immunotherapies, whereas IS-III tumors, characterized by weak immune infiltration and low ICM expression, had a dismal prognosis and poor immunotherapy responsiveness. IS-II tumors represented a transitional stage. Immune classification was also correlated with somatic mutations, copy number alterations, and molecular pathways related to tumorigenesis, metabolism, and immune responses. Three predictive classifiers using eight representative genes were generated by machine learning methods for immune classification. This study established a reliable immunological profile-based classification system for PGs, providing implications for further immunotherapy strategies. American Society of Gene & Cell Therapy 2020-12-25 /pmc/articles/PMC7851498/ /pubmed/33575469 http://dx.doi.org/10.1016/j.omto.2020.12.012 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Article Wang, Zihao Guo, Xiaopeng Gao, Lu Wang, Yu Guo, Yi Xing, Bing Ma, Wenbin Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title | Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title_full | Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title_fullStr | Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title_full_unstemmed | Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title_short | Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
title_sort | classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7851498/ https://www.ncbi.nlm.nih.gov/pubmed/33575469 http://dx.doi.org/10.1016/j.omto.2020.12.012 |
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