MEK Inhibitors Reverse Growth of Embryonal Brain Tumors Derived from Oligoneural Precursor Cells
Bibliographic record
Abstract
Malignant brain tumors are the leading cause of cancer-related deaths in children. Primitive neuroectodermal tumors of the CNS (CNS-PNETs) are particularly aggressive embryonal tumors of unknown cellular origin. Recent genomic studies have classified CNS-PNETs into molecularly distinct subgroups that promise to improve diagnosis and treatment; however, the lack of cell- or animal-based models for these subgroups prevents testing of rationally designed therapies. Here, we show that a subset of CNS-PNETs co-express oligoneural precursor cell (OPC) markers OLIG2 and SOX10 with coincident activation of the RAS/MAPK (mitogen-activated protein kinase) pathway. Modeling NRAS activation in embryonic OPCs generated malignant brain tumors in zebrafish that closely mimic the human oligoneural/NB -FOXR2 CNS-PNET subgroup by histology and comparative oncogenomics. The zebrafish CNS-PNET model was used to show that MEK inhibitors selectively eliminate Olig2 + /Sox10 + CNS-PNET tumors in vivo without impacting normal brain development. Thus, MEK inhibitors represent a promising rationally designed therapy for children afflicted with oligoneural/NB -FOXR2 CNS-PNETs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".