期刊
MOLECULAR THERAPY-ONCOLYTICS
卷 20, 期 -, 页码 34-47出版社
CELL PRESS
DOI: 10.1016/j.omto.2020.12.012
关键词
-
资金
- Graduate Innovation Fund of the Chinese Academy of Medical Sciences
- Peking Union Medical College [2019-1002-73]
Pediatric gliomas were classified into three immune subtypes based on immunological profiling, with immune hot tumors having favorable prognosis and better response to immunotherapies, while immune cold tumors had dismal prognosis and poor immunotherapy responsiveness. Immune subtypes were also correlated with somatic mutations, copy number alterations, and molecular pathways related to tumorigenesis, metabolism, and immune responses.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据