Targeting glioblastoma with a brain-penetrant drug that impairs brain tumor stem cells via NLE1-Notch1 complex
Bibliographic record
Abstract
Brain tumor stem cells (BTSCs) are a population of self-renewing malignant stem cells that play an important role in glioblastoma tumor hierarchy and contribute to tumor growth, therapeutic resistance, and tumor relapse. Thus, targeting of BTSCs within the bulk of tumors represents a crucial therapeutic strategy. Here, we report that edaravone is a potent drug that impairs BTSCs in glioblastoma. We show that edaravone inhibits the self-renewal and growth of BTSCs harboring a diverse range of oncogenic mutations without affecting non-oncogenic neural stem cells. Global gene expression analysis revealed that edaravone significantly alters BTSC transcriptome and attenuates the expression of a large panel of genes involved in cell cycle progression, stemness, and DNA repair mechanisms. Mechanistically, we discovered that edaravone directly targets Notchless homolog 1 (NLE1) and impairs Notch signaling pathway, alters the expression of stem cell markers, and sensitizes BTSC response to ionizing radiation (IR)-induced cell death. Importantly, we show that edaravone treatment in preclinical models delays glioblastoma tumorigenesis, sensitizes their response to IR, and prolongs the lifespan of animals. Our data suggest that repurposing of edaravone is a promising therapeutic strategy for patients with glioblastoma.
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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 | 0.000 |
| 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".