Molecular biomarkers in pediatric glial tumors
Bibliographic record
Abstract
PURPOSE OF REVIEW: Glial tumors of the central nervous system (CNS) are the leading cause of cancer-related death and morbidity in children. Their diagnosis/prognosis relies mainly on clinical and histopathological factors. However, pathological grading is particularly challenging as there is substantial molecular heterogeneity in pediatric CNS tumors, which results in variable biological behavior in tumors with potentially identical histological diagnoses or limited reliable measures of classification for given subgroups. Novel molecular markers/pathways identified by integrated genomic/transcriptomic/epigenomic studies of cohorts of pediatric gliomas are revolutionizing this field and are summarized herein. RECENT FINDINGS: Studies of pediatric gliomas have identified unexpected oncogenic pathways implicated in gliomagenesis. These range from a single pathway/molecule defect such as abnormalities of the mitogen-activated-protein-kinase pathway considered to be a hallmark of pilocytic astrocytomas, to alterations in epigenomic modulators in higher-grade tumors. Importantly, the type, timing, and spatial clustering of these molecular alterations provide a better understanding of the pathogenesis of gliomas and critical markers for therapy that will help refine pathological grading. SUMMARY: Reappraisal of glioma classification using these novel biomarkers will likely change practice toward molecular pathology and their integration into clinical trials will enable personalized therapies based on the molecular fingerprint of individual tumors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".