TMOD-06. LOSS OF DICER COOPERATES WITH TUMOR SUPPRESSORS TO INITIATE METASTATIC MEDULLOBLASTOMA
Bibliographic record
Abstract
Abstract To determine the role of microRNA regulation in brain tumor development, we incorporated a conditional allele of the microRNA processing enzyme Dicer to a previously characterized glioma mouse model based on inactivation of the tumor suppressors Nf1, Trp53, and Pten using the Nestin-creERT2 transgene. Loss of Dicer and tumor suppressors at adult ages led to glioma development; however, mutant mice tamoxifen induced at early postnatal ages developed medulloblastoma instead of glioma. The switch in tumor spectrum occurred with 100% penetrance and tumors were histologically indistinguishable from human medulloblastoma (MB). The minimum genetic mutations required for MB formation were Dicer and Trp53. Nf1 was dispensable, while additional loss of Pten produced more invasive tumors and leptomeningeal metastases. The time window for initiation of tumorigenesis was until the 2nd postnatal week, coinciding with the disappearance of the external granule layer (EGL), where cerebellar granule neuron precursors (CGNPs) undergo proliferation. Analysis of pre-symptomatic mutant mice showed proliferative defects and retained cells in the EGL, suggesting that the tumors may arise from CGNPs. However, targeting a subset of CGNPs using Math1-creERT2 did not lead to MB development, suggesting that an earlier EGL precursor may be required for tumorigenesis. Analysis of tumor transcriptome and MB subtype-specific genes and markers show that Dicer tumors most resemble extremely high risk p53-mutated SHH MB. Small RNA and mRNA sequencing analyses showed downregulation of microRNAs and dysregulation of its targets such as N-Myc. These studies demonstrate a role for microRNAs in MB development and show a fully penetrant genetic mouse model of highly metastatic MB.
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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) | 0.001 | 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".