Modeling human brain tumors in flies, worms, and zebrafish: From proof of principle to novel therapeutic targets
Bibliographic record
Abstract
For decades, cell biologists and cancer researchers have taken advantage of non-murine species to increase our understanding of the molecular processes that drive normal cell and tissue development, and when perturbed, cause cancer. The advent of whole-genome sequencing has revealed the high genetic homology of these organisms to humans. Seminal studies in non-murine organisms such as Drosophila melanogaster, Caenorhabditis elegans, and Danio rerio identified many of the signaling pathways involved in cancer. Studies in these organisms offer distinct advantages over mammalian cell or murine systems. Compared to murine models, these three species have shorter lifespans, are less resource intense, and are amenable to high-throughput drug and RNA interference screening to test a myriad of promising drugs against novel targets. In this review, we introduce species-specific breeding strategies, highlight the advantages of modeling brain tumors in each non-mammalian species, and underscore the successes attributed to scientific investigation using these models. We conclude with an optimistic proposal that discoveries in the fields of cancer research, and in particular neuro-oncology, may be expedited using these powerful screening tools and strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".