MEDB-39. Oncogenic mechanisms underlying GLI2-amplified medulloblastoma
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is the most common malignant brain tumor in children. There are several subtypes of MB, and among them, the subtype of GLI2-amplified SHH-MB associated with P53 mutations has the worst prognosis and a poor survival rate; the 5-year survival rate is <30%. Moreover, the GLI2-amplified MBs are non-responsive to the only targeted treatment option available for SHH-MB, the SMO inhibitors. This leaves an unmet critical treatment gap, and there is an urgent need to identify novel targets to develop effective therapeutics. However, a deeper understanding of the cellular and molecular mechanisms driving GLI2-amplified MB tumorigenesis is currently lacking. With a focused goal to resolve this particular type of MB tumorigenesis, we recently generated an engineered mouse model of GLI2-driven MB. Using this model, we demonstrated that GLI2 is the critical driver of tumorigenesis and identified granule cell progenitors (GCPs) as the cells of origin. Interestingly, we have also found that GLI2 drives only Math1+ embryonic GCPs but not neonatal GCPs to form SHH-MB. Correspondingly, our scRNA-seq analysis revealed that the MAPK pathway is specifically enriched in embryonic but not neonatal Math1+ GCPs. Moreover, the MAPK pathway is activated in mouse and human GLI2-driven MB tumors, and a MEK/ERK inhibitor significantly delayed the growth of GLI2-driven MB in vivo. Based on these exciting results, we hypothesize that GLI2-driven MB originates from a specific cell population of Math1+ GCPs and in a particular spatiotemporal window during cerebellar development, and targeting MAPK/MEK/ERK pathway may represent a novel effective approach to treating GLI2-amplified MB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".