Modeling High-Risk Pediatric Cancers in Zebrafish to Inform Precision Therapy
Bibliographic record
Abstract
Despite advances in precision medicine, 30% of high-risk pediatric cancers lack an actionable molecular target, hindering effective treatment and affecting survival outcomes. Although mouse patient-derived xenograft (PDX) models offer additional insights into clinical drug responses, delivering findings from these models within a clinically actionable time frame remains challenging. This international collaboration between two national precision medicine programs demonstrates proof-of-principle that individualized larval zebrafish PDXs can robustly and rapidly assess clinical responses in high-risk pediatric cancers. Retrospective zebrafish PDX testing was performed on tumor samples from 10 pediatric patients with high-risk cancers. Drug responses in zebrafish models were correlated with clinical responses for each patient and directly compared with responses in cognate mouse PDX models. Responses to conventional and targeted therapies, administered as single agents or in combinations, were assessed. Zebrafish PDXs were successfully established from all 10 patients and provided robust drug response data in every case, including from three patients whose tumor samples could not be engrafted in mice. Remarkably, zebrafish models accurately recapitulated patient responses for 11 of 12 treatment regimens. These findings highlight the potential of larval zebrafish PDX models to provide real-time, clinically relevant drug response data, supporting their potential use in prospective precision medicine studies. SIGNIFICANCE: This proof-of-principle study is the first to compare drug responses in larval zebrafish and mouse PDX models with patient outcomes in pediatric precision oncology, showing high concordance. Results highlight the potential of zebrafish PDX models to predict drug responses in high-risk cancers more accurately, rapidly, and cost-effectively in prospective studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".