Pediatric high-grade astrocytomas: a distinct neuro-oncological paradigm
Bibliographic record
Abstract
Brain tumors are the leading cause of cancer-related death in children. High-grade astrocytomas (HGAs), in particular, are lethal in children across all ages. Integrative genome-wide analyses of the tumor's genome, transcriptome and epigenome, using next-generation sequencing technologies and genome-wide DNA methylation arrays, have provided valuable breakthroughs in our understanding of the pathogenesis of HGAs across all ages. Recent profiling studies have provided insight into the epigenetic nature of gliomas in young adults and HGAs in children, particularly with the identification of recurrent gain-of-function driver mutations in the isocitrate dehydrogenase 1 and 2 genes (IDH1/2) and the epigenetic influence of their oncometabolite 2-hydroxyglutarate, as well as mutations in the histone 3 variant 3 gene (H3F3A) and loss-of-function mutations in the histone 3 lysine 36 trimethyltransferase gene (SETD2). Mutations in H3F3A result in amino acid substitutions at residues thought to directly (K27M) or indirectly (G34R/V) affect histone post-translational modifications, suggesting they have the capacity to affect the epigenome in a profound manner. Here, we review recent genomic studies, and discuss evidence supporting the molecular characterization of pediatric HGAs to complement traditional approaches, such as histology of resected tumors. We also describe newly identified molecular mechanisms and discuss putative therapeutic approaches for HGAs specific to pediatrics, highlighting the necessity for the evolution of HGA disease management approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".