Focused chemical genomics using zebrafish xenotransplantation as a pre-clinical therapeutic platform for T-cell acute lymphoblastic leukemia
Bibliographic record
Abstract
Cancer therapeutics is evolving to precision medicine, with the goal of matching targeted compounds with molecular aberrations underlying a patient's cancer. While murine models offer a pre-clinical tool, associated costs and time are not compatible with actionable patient-directed interventions. Using the paradigm of T-cell acute lymphoblastic leukemia, a high-risk disease with defined molecular underpinnings, we developed a zebrafish human cancer xenotransplantation model to inform therapeutic decisions. Using a focused chemical genomic approach, we demonstrate that xenografted cell lines harboring mutations in the NOTCH1 and PI3K/AKT pathways respond concordantly to their targeted therapies, patient-derived T-cell acute lymphoblastic leukemia can be successfully engrafted in zebrafish and specific drug responses can be quantitatively determined. Using this approach, we identified a mutation sensitive to γ-secretase inhibition in a xenograft from a child with T-cell acute lymphoblastic leukemia, confirmed by Sanger sequencing and validated as a gain-of-function NOTCH1 mutation. The zebrafish xenotransplantation platform provides a novel cost-effective means of tailoring leukemia therapy in real time.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".