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...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-03-01
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| Series: | Molecular Therapy: Oncolytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2372770520301868 |
| _version_ | 1818460928638713856 |
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| author | Zihao Wang Xiaopeng Guo Lu Gao Yu Wang Yi Guo Bing Xing Wenbin Ma |
| author_facet | Zihao Wang Xiaopeng Guo Lu Gao Yu Wang Yi Guo Bing Xing Wenbin Ma |
| author_sort | Zihao Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T23:38:03Z |
| format | Article |
| id | doaj.art-af8a326e0d2f47c4a3913458120a50a7 |
| institution | Directory Open Access Journal |
| issn | 2372-7705 |
| language | English |
| last_indexed | 2024-12-14T23:38:03Z |
| publishDate | 2021-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Molecular Therapy: Oncolytics |
| spelling | doaj.art-af8a326e0d2f47c4a3913458120a50a72022-12-21T22:43:34ZengElsevierMolecular Therapy: Oncolytics2372-77052021-03-01203447Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategiesZihao Wang0Xiaopeng Guo1Lu Gao2Yu Wang3Yi Guo4Bing Xing5Wenbin Ma6Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, ChinaDepartment of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; Corresponding author: Bing Xing, Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China.Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; Corresponding author: Wenbin Ma, Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuaifuyuan, Dongcheng District, Beijing 100730, China.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.http://www.sciencedirect.com/science/article/pii/S2372770520301868pediatric gliomaimmune classificationoverall survivalimmunotherapy responsivenesstumor microenvironment |
| spellingShingle | Zihao Wang Xiaopeng Guo Lu Gao Yu Wang Yi Guo Bing Xing Wenbin Ma Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies Molecular Therapy: Oncolytics pediatric glioma immune classification overall survival immunotherapy responsiveness tumor microenvironment |
| 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 | pediatric glioma immune classification overall survival immunotherapy responsiveness tumor microenvironment |
| url | http://www.sciencedirect.com/science/article/pii/S2372770520301868 |
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