Repurposing disulfiram, an alcohol-abuse drug, in neuroblastoma causes KAT2A downregulation and in vivo activity with a water/oil emulsion
Bibliographic record
Abstract
Neuroblastoma, the most common type of pediatric extracranial solid tumor, causes 10% of childhood cancer deaths. Despite intensive multimodal treatment, the outcomes of high-risk neuroblastoma remain poor. We urgently need to develop new therapies with safe long-term toxicity profiles for rapid testing in clinical trials. Drug repurposing is a promising approach to meet these needs. Here, we investigated disulfiram, a safe and successful chronic alcoholism treatment with known anticancer and epigenetic effects. Disulfiram efficiently induced cell cycle arrest and decreased the viability of six human neuroblastoma cell lines at half-maximal inhibitory concentrations up to 20 times lower than its peak clinical plasma level in patients treated for chronic alcoholism. Disulfiram shifted neuroblastoma transcriptome, decreasing MYCN levels and activating neuronal differentiation. Consistently, disulfiram significantly reduced the protein level of lysine acetyltransferase 2A (KAT2A), drastically reducing acetylation of its target residues on histone H3. To investigate disulfiram's anticancer effects in an in vivo model of high-risk neuroblastoma, we developed a disulfiram-loaded emulsion to deliver the highly liposoluble drug. Treatment with the emulsion significantly delayed neuroblastoma progression in mice. These results identify KAT2A as a novel target of disulfiram, which directly impacts neuroblastoma epigenetics and is a promising candidate for repurposing to treat pediatric neuroblastoma.
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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".