TMOD-31. RARE SOX9+ CELLS BEHIND MYC-DRIVEN MEDULLOBLASTOMA RECURRENCE
Bibliographic record
Abstract
Tumor recurrence is the leading cause of death among children with medulloblastoma, the most frequent type of malignant pediatric brain tumor. The mechanisms behind medulloblastoma recurrence are not fully understood. We found that the transcription factor SOX9 marks quiescent brain tumor stem cells and is suppressed by MYC overexpression in aggressive Group 3 tumors. By using an inducible Tet-OFF transgenic (GTML) mouse model for malignant MYCN-driven Group 3 tumors and human Group 3 MYC-driven PDX models we identified rare SOX9+, slow-cycling brain tumor cells that are more resistant to standard chemotherapy. Dox treatment normally cures GTML transgenic animals that developed aggressive medulloblastoma by turning MYCN off. However, when crossing the Tet-OFF GTML model with a Tet-ON rtTA-Sox9 model we can redirect MYCN expression to the SOX9 promoter ultimately driving brain tumor recurrence from rare SOX9+ cells with 100% penetrance. These recurrent tumors were actively disseminating from the hindbrain to the spinal cord and into the forebrain. Expression profiling comparing primary to recurrent tumors shows that recurring tumors maintain their molecular subgroup but have severely defective DNA repair system and present an increased inflammatory immune response. By overexpressing SOX9 into human Group 3 MB cells MYC was directly inhibited and decreased cell proliferation while promoting migration and metastasis. Paired primary and recurrent human Group 3 and Group 4 tumor biopsies further showed significantly higher levels of SOX9 at recurrence. Finally, PDX models of Group 3 tumors showed increased levels of SOX9 positivity in metastatic compartments. To summarize, our data clarify important and complex mechanisms by which dormant medulloblastoma cells fail to respond to standard therapy and generate relapses.
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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) | 0.003 | 0.001 |
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".