Zebrafish Xenotransplantation and Focused Chemical Genomics: A Preclinical Therapeutic Model For T-Cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Abstract T-cell acute lymphoblastic leukemia (T-ALL) is a high-risk subset of ALL, for which there is a need for new therapeutic strategies and efficient preclinical screening methods. We have pioneered an innovative zebrafish human cancer xenotransplantation (XT) model to examine drug-tumor interactions in vivo. T-ALL cell lines and primary patient T-ALL samples were microinjected into 48-hour zebrafish embryos, a stage at which the adaptive immune system has not yet developed. Fluorescent labelling of tumor cells prior to injection and use of casper pigment mutant fish facilitates evaluation of drug response both by direct observation in transparent fish and enumeration of human cells following embryo dissociation. Proliferation rates are rapidly determined by directly counting fluorescent cells using in silico-based programs and/or utilizing immunohistochemical approaches to distinguish human cancer cells from host cell populations. T-ALL cell lines harboring defined mutations in the NOTCH1, phosphoinositide 3-kinase (PI3K)/AKT and mTOR pathways differentially responded to targeted inhibition using the γ-secretase inhibitor Compound E, triciribine, and rapamycin, when xenografted into embryos, consistent with responses in vitro. Primary patient-derived T-ALL bone marrow samples similarly engrafted and proliferated in zebrafish embryos. Using this in vivo chemical genomic approach, a targetable mutation sensitive to γ-secretase inhibition was identified from the diagnostic bone marrow sample of a child with T-ALL, which was confirmed by exome Sanger sequencing, and validated as a gain-of-function mutation in the NOTCH1 gene by luciferase assay and Western blot. Focused chemical genomics using the zebrafish T-ALL XT model provides a means of tailoring therapy using a real time in vivo assay that more accurately recapitulates the tumor microenvironment than in vitro methods and more rapidly than mouse xenografts. Moreover, the efficiency and cost-effectiveness of this innovative platform provides a novel intermediary for the prioritization of much-needed drug candidates in the preclinical pipeline. Disclosures: No relevant conflicts of interest to declare.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".