Exploiting a metabolic vulnerability in brain tumour stem cells using a brain-penetrant drug with safe profile
Bibliographic record
Abstract
Abstract Glioblastoma (GB) remains one of the most treatment refractory and fatal tumour in humans. GB contains a population of self-renewing stem cells, the brain tumour stem cells (BTSC) that are highly resistant to therapy and are at the origin of tumour relapse. Here, we report, for the first time, that mubritinib potently impairs stemness and growth of patient-derived BTSCs harboring different oncogenic mutations. Mechanistically, by employing bioenergetic assays and rescue experiments, we provide compelling evidence that mubritinib acts on complex I of the electron transport chain to impair BTSC stemness pathways, self-renewal and proliferation. Global gene expression profiling revealed that mubritinib alters the proliferative, neural-progenitor-like, and the cell-cycling state signatures. We employed in vivo pharmacokinetic assays to establish that mubritinib crosses the blood-brain barrier. Using preclinical models of patient-derived and syngeneic murine orthotopic xenografts, we demonstrated that mubritinib delays GB tumourigenesis, and expands lifespan of animals. Interestingly, its combination with radiotherapy offers survival advantage to animals. Strikingly, thorough toxicological and behavioral studies in mice revealed that mubritinib does not induce any damage to normal cells and has a well-tolerated and safe profile. Our work warrants further exploration of this drug in in-human clinical trials for better management of GB tumours.
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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) | 0.002 | 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".