PM-12 * USING A ZEBRAFISH PEDIATRIC BRAIN TUMOR MODEL FOR PRE-CLINICAL DRUG SCREENING
Bibliographic record
Abstract
Modeling cancer in zebrafish is a rapidly growing area of research due to unique imaging and drug screening attributes of the system, as well as cost. While over 20 different zebrafish cancer models have been established, no genetically engineered models of pediatric brain tumors exist. We are using genomic editing techniques with traditional transgenic approaches to model pediatric brain tumors in zebrafish for gene discovery and drug screening. As Primitive Neuroectodermal Tumors (PNETs) represent the largest group of malignant brain tumors in children, we used sequencing and genomics data to predict oncogenic drivers of different PNET subtypes and generated the first zebrafish PNET model. Specifically, we show that activation of NRAS signaling in embryonic oligodendrocyte precursor cells with p53-deficiency generate oligoneural PNETs along the entire CNS axis, with conserved histological and molecular features to human medulloblastoma and CNS-PNETs, including activated SHH signaling and loss of RB signaling. We have also developed new embryonic brain tumor transplantation assays for high-throughput drug screening on hundreds of animals/day and show MEK activity is essential for the oligoneural CNS-PNET tumor growth in vivo. Thus, MEK inhibitors may represent the first targeted therapy option for children with oligoneural CNS-PNETs. We will present these results and plans for expanding the zebrafish system to generate other classes of brain tumors and for growing human PDX's in zebrafish.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".