Advances in the molecular classification of pediatric brain tumors: a guide to the galaxy

Abstract

Central nervous system (CNS) tumors are the most common solid tumor in pediatrics, accounting for approximately 25% of all childhood cancers, and the second most common pediatric malignancy after leukemia. CNS tumors can be associated with significant morbidity, even those classified as low grade. Mortality from CNS tumors is disproportionately high compared to other childhood malignancies, although surgery, radiation, and chemotherapy have improved outcomes in these patients over the last few decades. Current therapeutic strategies lead to a high risk of side effects, especially in young children. Pediatric brain tumor survivors have unique sequelae compared to age‐matched patients who survived other malignancies. They are at greater risk of significant impairment in cognitive, neurological, endocrine, social, and emotional domains, depending on the location and type of the CNS tumor. Next‐generation genomics have shed light on the broad molecular heterogeneity of pediatric brain tumors and have identified important genes and signaling pathways that serve to drive tumor proliferation. This insight has impacted the research field by providing potential therapeutic targets for these diseases. In this review, we highlight recent progress in understanding the molecular basis of common pediatric brain tumors, specifically low‐grade glioma, high‐grade glioma, ependymoma, embryonal tumors, and atypical teratoid/rhabdoid tumor (ATRT). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Endothelial, pericyte and tumor cell expression in glioblastoma identifies fibroblast activation protein (FAP) as an excellent target for immunotherapy

“…All analyses conducted so far have focussed on glioblastoma in adults, but glioblastoma also occurs in the paediatric population. In addition, children are affected by several other forms of glioma which are rare in the adult population 47 . Analysis of two published microarray datasets (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50161 48 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50021 49 ) revealed that FAP gene expression was above normal brain levels in a variable proportion of tumor tissues for five distinct paediatric glioma types (Figure 7).…”

“…In addition, children are affected by several other forms of glioma which are rare in the adult population. 47 Analysis of two published microarray datasets (GSE50161 48 and GSE50021 49 ) revealed that FAP gene expression was above normal brain levels in a variable proportion of tumor tissues for five distinct paediatric glioma types (Figure 7). For paediatric glioblastoma, the proportion of FAPexpressing tumors (32.4%) was comparable to the adult disease, while pilocytic astrocytoma, a lower grade glioma, was characterised by a lower frequency of FAP-expressing tumors (13.3%), again similar to adult low-grade glioma.…”

Immune Checkpoint-Associated Locations of Diffuse Gliomas Comparing Pediatric With Adult Patients Based on Voxel-Wise Analysis

“…The incidence of CNS tumors in children and adolescents in the United States is 6.06 per 100,000 according to the latest CBTRUS statistical report (1). High childhood cancerrelated mortality is observed in pediatric patients with CNS tumors, which is the second most malignancy after leukemia (2). Pediatric gliomas are the most common type therein, and the low-grade gliomas (WHO grades I and II) constitute a majority of pediatric gliomas, such as pilocytic astrocytoma and subependymal giant cell astrocytoma (3).…”

Prognostic implications of immune-related eight-gene signature in pediatric brain tumors

“…Brain tumors are the most common solid tumor in pediatrics, accounting for 23.7% of new cancer diagnoses in children ( 1 ), and the second most common pediatric malignancy after leukemia ( 2 , 3 ). The life-saving treatments these children receive may result in impaired brain structure and function, leading to long-term major cognitive deficits ( 1 , 4 – 8 ).…”