Data from Dormant SOX9-Positive Cells Facilitate MYC-Driven Recurrence of Medulloblastoma
Bibliographic record
Abstract
Abstract Relapse is the leading cause of death in patients with medulloblastoma, the most common malignant pediatric brain tumor. A better understanding of the mechanisms underlying recurrence could lead to more effective therapies for targeting tumor relapses. Here, we observed that SOX9, a transcription factor and stem cell/glial fate marker, is limited to rare, quiescent cells in high-risk medulloblastoma with MYC amplification. In paired primary-recurrent patient samples, SOX9-positive cells accumulated in medulloblastoma relapses. SOX9 expression anti-correlated with MYC expression in murine and human medulloblastoma cells. However, SOX9-positive cells were plastic and could give rise to a MYC high state. To follow relapse at the single-cell level, an inducible dual Tet model of medulloblastoma was developed, in which MYC expression was redirected in vivo from treatment-sensitive bulk cells to dormant SOX9-positive cells using doxycycline treatment. SOX9 was essential for relapse initiation and depended on suppression of MYC activity to promote therapy resistance, epithelial–mesenchymal transition, and immune escape. p53 and DNA repair pathways were downregulated in recurrent tumors, whereas MGMT was upregulated. Recurrent tumor cells were found to be sensitive to treatment with an MGMT inhibitor and doxorubicin. These findings suggest that recurrence-specific targeting coupled with DNA repair inhibition comprises a potential therapeutic strategy in patients affected by medulloblastoma relapse. Significance: SOX9 facilitates therapy escape and recurrence in medulloblastoma via temporal inhibition of MYC/MYCN genes, revealing a strategy to specifically target SOX9-positive cells to prevent tumor relapse.
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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".