Zebrafish Cancer Predisposition Models
Bibliographic record
Abstract
Cancer predisposition syndromes are rare, typically monogenic disorders that result from germline mutations that increase the likelihood of developing cancer. Although these disorders are individually rare, resulting cancers collectively represent 5–10% of all malignancies. In addition to a greater incidence of cancer, affected individuals have an earlier tumor onset and are frequently subjected to long-term multi-modal cancer screening protocols for earlier detection and initiation of treatment. In vivo models are needed to better understand tumor-driving mechanisms, tailor patient screening approaches and develop targeted therapies to improve patient care and disease prognosis. The zebrafish (Danio rerio) has emerged as a robust model for cancer research due to its high fecundity, time- and cost-efficient genetic manipulation and real-time high-resolution imaging. Tumors developing in zebrafish cancer models are histologically and molecularly similar to their human counterparts, confirming the validity of these models. The zebrafish platform supports both large-scale random mutagenesis screens to identify potential candidate/modifier genes and recently optimized genome editing strategies. These techniques have greatly increased our ability to investigate the impact of certain mutations and how these lesions impact tumorigenesis and disease phenotype. These unique characteristics position the zebrafish as a powerful in vivo tool to model cancer predisposition syndromes and as such, several have already been created, including those recapitulating Li-Fraumeni syndrome, familial adenomatous polyposis, RASopathies, inherited bone marrow failure syndromes, and several other pathogenic mutations in cancer predisposition genes. In addition, the zebrafish platform supports medium- to high-throughput preclinical drug screening to identify compounds that may represent novel treatment paradigms or even prevent cancer evolution. This review will highlight and synthesize the findings from zebrafish cancer predisposition models created to date. We will discuss emerging trends in how these zebrafish cancer models can improve our understanding of the genetic mechanisms driving cancer predisposition and their potential to discover therapeutic and/or preventative compounds that change the natural history of disease for these vulnerable children, youth and adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".