<i>Sleeping Beauty</i> Insertional Mutagenesis Reveals Important Genetic Drivers of Central Nervous System Embryonal Tumors
Bibliographic record
Abstract
Abstract Medulloblastoma and central nervous system primitive neuroectodermal tumors (CNS-PNET) are aggressive, poorly differentiated brain tumors with limited effective therapies. Using Sleeping Beauty (SB) transposon mutagenesis, we identified novel genetic drivers of medulloblastoma and CNS-PNET. Cross-species gene expression analyses classified SB-driven tumors into distinct medulloblastoma and CNS-PNET subgroups, indicating they resemble human Sonic hedgehog and group 3 and 4 medulloblastoma and CNS neuroblastoma with FOXR2 activation. This represents the first genetically induced mouse model of CNS-PNET and a rare model of group 3 and 4 medulloblastoma. We identified several putative proto-oncogenes including Arhgap36, Megf10, and Foxr2. Genetic manipulation of these genes demonstrated a robust impact on tumorigenesis in vitro and in vivo. We also determined that FOXR2 interacts with N-MYC, increases C-MYC protein stability, and activates FAK/SRC signaling. Altogether, our study identified several promising therapeutic targets in medulloblastoma and CNS-PNET. Significance: A transposon-induced mouse model identifies several novel genetic drivers and potential therapeutic targets in medulloblastoma and CNS-PNET.
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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) | 0.001 | 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".