TMOD-25. LATENT SOX9-POSITIVE CELLS BEHIND MYC-DRIVEN MEDULLOBLASTOMA RELAPSE
Bibliographic record
Abstract
Abstract Tumor recurrence developing from therapy resistance, immune escape and metastasis is the leading cause of death in medulloblastoma, the most frequent malignant pediatric brain tumor. Amplification of MYC genes is the most common genetic alteration in Group 3 and Group 4 subgroups that constitute two thirds of medulloblastoma. SOX9 is a transcription factor present in stem cells in the normal brain but is limited to rare, quiescent cells in medulloblastoma patients with MYC gene amplifications. By studying paired primary-recurrent patient samples and patient-derived xenografts we here identified significant accumulation of SOX9-positive cells in Group 3 and Group 4 relapses. To follow relapse at the single cell level we developed an inducible dual Tet model of MYC-driven MB, where MYC was re-directed from the treatment-sensitive bulk cells to resistant, dormant SOX9-positive cells by doxycycline. In this model, distant recurrent tumors and spinal metastases developed. SOX9 promoted immune escape, DNA repair suppression and was essential for recurrence. Tumor cell dormancy was non-hierarchical, migratory and depended on MYC suppression by SOX9 to promote relapse. By using computational modeling and treatment we also showed how doxorubicin and MGMT inhibitors were specifically targeting recurrent cells that could be of potential use in future treatments for patients affected by these fatal 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 | 0.000 |
| 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".