HDAC and MAPK/ERK Inhibitors Cooperate to Reduce Viability and Stemness in Medulloblastoma
Bibliographic record
Abstract
Abstract Medulloblastoma (MB), which originates from embryonic neural stem cells (NSCs) or neural precursors in the developing cerebellum, is the most common malignant brain tumor of childhood. Recurrent and metastatic disease is the principal cause of death and may be related to resistance within cancer stem cells (CSCs). Chromatin state is involved in maintaining signaling pathways related to stemness, and inhibition of histone deacetylase enzymes (HDAC) has emerged as an experimental therapeutic strategy to target this cell population. Here, we observed antitumor actions and changes in stemness induced by HDAC inhibition in MB. Analyses of tumor samples from patients with MB showed that the stemness markers BMI1 and CD133 are expressed in all molecular subgroups of MB. The HDAC inhibitor (HDACi) NaB reduced cell viability and expression of BMI1 and CD133 and increased acetylation in human MB cells. Enrichment analysis of genes associated with CD133 or BMI1 expression showed mitogen-activated protein kinase (MAPK)/ERK signaling as the most enriched processes in MB tumors. MAPK/ERK inhibition reduced expression of the stemness markers, hindered MB neurosphere formation, and its antiproliferative effect was enhanced by combination with NaB. These results suggest that combining HDAC and MAPK/ERK inhibitors may be a novel and more effective approach in reducing MB proliferation when compared to single-drug treatments, through modulation of the stemness phenotype of MB cells.
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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".