ArticleMolecular therapy oncolytics2021
Classification of pediatric gliomas based on immunological profiling: implications for immunotherapy strategies.
Article in Molecular therapy oncolytics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Should checkpoint inhibitors be reserved for biomarker-selected pediatric brain tumors?Journal of neuro-oncology · 2026Pooled it
- Advances in the Repurposing and Blood-Brain Barrier Penetrance of Drugs in Pediatric Brain Tumors.Cancers · 2025Review
- Multiparametric MRI along with machine learning predicts prognosis and treatment response in pediatric low-grade glioma.Nature communications · 2025Article
- Pediatric brain tumor patients display altered immune activation and reduced lymphopoiesis at the onset of disease.NPJ precision oncology · 2024Article
- Immunotherapy for pediatric low-grade gliomas.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2024Review
- Pan-cancer investigation of RFX family and associated genes identifies RFX8 as a therapeutic target in leukemia.Heliyon · 2024Article
- Review
- Review
- H3K27M mutant glioma: Disease definition and biological underpinnings.Neuro-oncology · 2024Review
- Transcriptomics-based characterization of the immuno-stromal microenvironment in pediatric low-grade glioma.Oncoimmunology · 2024Article
- Myeloid cells as potential targets for immunotherapy in pediatric gliomas.Frontiers in pediatrics · 2024Review
- Radiomics for characterization of the glioma immune microenvironment.NPJ precision oncology · 2023Review
- Pediatric Brain Tumours: Lessons from the Immune Microenvironment.Current oncology (Toronto, Ont.) · 2023Review
- Immune-related matrisomes are potential biomarkers to predict the prognosis and immune microenvironment of glioma patients.FEBS open bio · 2023Article
- The tumor micro-environment in pediatric glioma: friend or foe?Frontiers in immunology · 2023Review
- Investigation of white blood cell characteristics in cerebrospinal fluid samples at pediatric brain tumor diagnosis.Journal of neuro-oncology · 2022Article
- Glioma Stem Cells in Pediatric High-Grade Gliomas: From Current Knowledge to Future Perspectives.Cancers · 2022Review
- Identification of four immune subtypes in locally advanced rectal cancer treated with neoadjuvant chemotherapy for predicting the efficacy of subsequent immune checkpoint blockade.Frontiers in immunology · 2022Article
- Multi-Omics Investigations Revealed Underlying Molecular Mechanisms Associated With Tumor Stiffness and Identified Sunitinib as a Potential Therapy for Reducing Stiffness in Pituitary Adenomas.Frontiers in cell and developmental biology · 2022Article
- Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.