Dexamethasone-Induced p57-Mediated Quiescence Drives Chemotherapy Resistance in Sonic Hedgehog Medulloblastoma
Bibliographic record
Abstract
ABSTRACT Medulloblastoma (MB), the most common malignant pediatric brain tumor, remains difficult to cure upon relapse, with only 12.4% of patients surviving five years post-recurrence. While specific quiescent tumor cell populations are known to contribute to treatment-resistance, the molecular mechanisms that maintain quiescence remain poorly defined. Here, we identify the cell cycle inhibitor p57 as a regulator of quiescence and chemotherapy resistance in Sonic Hedgehog (SHH) MB. Nuclear p57 was enriched in Sox2+ and Nestin+ stem-like MB cells compared to proliferative Atoh1+ cells. Inducing p57 expression in SHH MB cells led to a six-fold increase in G 0 -phase cells and conferred resistance to the frontline chemotherapeutic vincristine. Clinically, dexamethasone is a glucocorticoid given to nearly all MB patients to manage cerebral edema and is administered with wide variability in timing and dosing. We show that dexamethasone significantly increased nuclear p57 levels and expanded the G 0 population in both Ptch1 +/− and Ptch1 +/− ; Trp53 −/− SHH MB mouse models. Pre-treatment with dexamethasone reduced vincristine sensitivity in SHH MB cells. Together, our findings reveal a clinically relevant and previously unrecognized mechanism of treatment resistance, whereby dexamethasone, despite its benefits in managing edema, may inadvertently contribute to tumor persistence or recurrence by driving a quiescent, drug-resistant state. Addressing the lack of standardization in steroid use or targeting p57 may improve treatment response and reduce recurrence in patients diagnosed with SHH MB.
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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 | 0.000 |
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".