Abstract B007: High-risk pediatric cancer models in zebrafish, mouse and short-term culture predict individual patient responses to therapy
Bibliographic record
Abstract
Abstract Personalized medicine is revolutionizing cancer detection, characterization, and treatment, however, 30% of children with high-risk cancers lack an actionable molecular target and need alternate approaches to identify personalized treatment recommendations. While mouse patient-derived xenografts (PDXs) are widely regarded as the gold standard for preclinical drug response prediction, they remain highly resource-intensive, challenging to establish, and often have establishment times outside clinically relevant timeframes. The larval zebrafish model has gained prominence as a promising tool for personalized medicine, however, drug treatment responses in zebrafish and mouse PDX models, and in their cognate patients, have not previously been compared. Here, we present data from an international collaboration between two national child cancer precision medicine programs: Canada’s PRecision Oncology For Young peopLE (PROFYLE) and Australia’s Zero Childhood Cancer (ZERO), which, for the first time, directly compares single agent and combination treatment responses using individualized models in larval zebrafish, mouse, and short-term culture, with patient clinical responses. Samples were collected from ten high-risk childhood/adolescent cancer patients (aged 1.5-15 years) diagnosed with various tumor types and enrolled on ZERO for molecular profiling (whole genome and whole transcriptome sequencing analysis), and in vitro high throughput drug screening (HTS). Mouse PDX drug testing was guided by prior molecular sequencing findings, single agent HTS, and treatments received by each patient. Samples from these patients underwent retrospective zebrafish PDX testing. Labeled tumor cells were engrafted into the zebrafish larvae yolk sac at 48 hours, treated for 3 days from 72 hours by immersion, followed by ex vivo tumor cell quantification for drug response evaluation. Larval zebrafish models were successfully established for all ten patients, including three for whom a mouse PDX model was not able to be developed. Additionally, samples from three of the ten patients underwent a secondary in vitro screen of single drugs and drug combinations, for comparison with responses in larval zebrafish and mouse PDXs. Remarkably, a high degree of concordance was observed between evaluable patient responses and responses observed in preclinical models developed from the patient, with 10/11 zebrafish, 7/8 mouse and 3/3 short-term cultures recapitulating responses in patients. The larval zebrafish workflow was less than one week from engraftment to completion, comparable to direct HTS from patient samples, and far quicker than establishing mouse PDX models. These findings represent the first pediatric precision oncology study to demonstrate consistent and clinically informative drug responses across multiple modalities in successfully predicting drug responses in high-risk child cancer patients, and suggest the feasibility of the larval zebrafish PDX as an efficient preclinical tool for patient-specific therapeutic decision-making. Citation Format: Nadine Azzam, Jamie I. Fletcher, Nicole Melong, Loretta Lau, Emmy M. Dolman, Jie Mao, Gabor Tax, Roxanne Cadiz, Lissandra Tuzi, Alvin Kamili, Biljana Dumevska, Jinhan Xie, Jennifer A. Chan, Donna L. Senger, Stephanie A. Grover, David Malkin, Michelle Haber, Jason N. Berman. High-risk pediatric cancer models in zebrafish, mouse and short-term culture predict individual patient responses to therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B007.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".