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Abstract B007: High-risk pediatric cancer models in zebrafish, mouse and short-term culture predict individual patient responses to therapy

2024· article· en· W4402267843 on OpenAlexaffabout
Nadine Azzam, Jamie I. Fletcher, Nicole Melong, Loretta M. S. Lau, M. Emmy M. Dolman, Jie Mao, Gábor 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

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicZebrafish Biomedical Research Applications
Canadian institutionsSickKids FoundationUniversity of TorontoHospital for Sick ChildrenMcGill UniversityChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsZebrafishMedicineCancerTerm (time)Pediatric cancerOncologyCancer therapyInternal medicineBiologyGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.050
GPT teacher head0.389
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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