Novel MYC-driven medulloblastoma models from multiple embryonic cerebellar cells
Bibliographic record
Abstract
Group3 medulloblastoma (MB G3 ) that predominantly occur in young children are usually associated with MYC amplification and/or overexpression, frequent metastasis and a dismal prognosis. Physiologically relevant MB G3 models are currently lacking, making inferences related to their cellular origin thus far limited. Using in utero electroporation, we here report that MB G3 mouse models can be developed in situ from different multipotent embryonic cerebellar progenitor cells via conditional expression of Myc and loss of Trp53 function in several Cre driver mouse lines. The Blbp-Cre driver that targets embryonic neural progenitors induced tumors exhibiting a large-cell/anaplastic histopathology adjacent to the fourth ventricle, recapitulating human MB G3 . Enforced co-expression of luciferase together with Myc and a dominant-negative form of Trp53 revealed that GABAergic neuronal progenitors as well as cerebellar granule cells give rise to MB G3 with their distinct growth kinetics. Cross-species gene expression analysis revealed that these novel MB G3 models shared molecular characteristics with human MB G3 , irrespective of their cellular origin. We here developed MB G3 mouse models in their physiological environment and we show that oncogenic insults drive this MB subgroup in different cerebellar lineages rather than in a specific cell of origin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".