Abstract 4670: True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention
Bibliographic record
Abstract
Abstract There has been dramatic improvement in treatment outcomes for many pediatric cancers over the last three decades. However, for the 20% of young people with relapsed or refractory cancer, the prognosis remains grim. Canada’s PRecision Oncology For Young peopLE (PROFYLE) and Australia’s Zero Childhood Cancer (ZERO) programs, leverage nation-wide scientific and clinical oncology expertise to provide personalized precision medicine to children, adolescent and young adult (CAYA) cancer patients who lack treatment options. Beyond improving the lives of young cancer patients nationally, PROFYLE and ZERO have partnered to create an international pipeline for knowledge sharing and sample acquisition. A key component of both programs is the use of mice for patient-derived xenografts (PDXs). However, long lead times for mouse PDX generation make the timely return of preclinical drug response data challenging. PROFYLE has uniquely incorporated zebrafish larval xenografts, which have the potential to provide comparable information in a clinically actionable timeframe. In a pilot study, we compared retrospective matched ZERO patient and mouse PDX therapeutic response data with prospective zebrafish larval PDX data as a proof-of-principle that drug efficacy signals were maintained across model systems. Three ZERO avatars: high-risk neuroblastoma, Ewing’s sarcoma, and anaplastic large cell lymphoma were shipped from Australia to Canada and transplanted into 48h casper zebrafish. Following dose optimization, zebrafish PDXs were treated with targeted single and combination drug treatments by immersion therapy. Strikingly, in as little time as a week, cell proliferation rates and drug responses to single agents and combinatorial therapy in zebrafish PDXs recapitulated mouse and patient data. To expand on this pilot project, we tested additional patient samples, including multiple subtypes of sarcomas, T-cell acute lymphoblastic leukemia and an embryonal tumor with multilayered rosettes. Results further validated the practical utility of the zebrafish larval PDX model and in fact provided drug response data when mouse PDX data were unavailable. This study demonstrates the robustness and feasibility of the zebrafish larval PDX model as a preclinical tool for personalized precision therapeutic decision-making and highlights the value of international collaboration in improving outcomes of rare childhood cancers. Citation Format: Nadine Azzam, Nicole Melong, Lissandra Tuzi, Lisa Pinto, Jamie I. Fletcher, Alvin Kamili, Biljana Dumevska, Loretta Lau, Jennifer A. Chan, Donna L. Senger, Stephanie A. Grover, Michelle Haber, David Malkin, Jason N. Berman. True bench-to-bedside science: An international pilot study modeling hard-to-cure pediatric cancers to prioritize therapeutic intervention. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4670.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".