Zebrafish screen identifies novel compound with selective toxicity against leukemia
Bibliographic record
Abstract
To detect targeted antileukemia agents we have designed a novel, high-content in vivo screen using genetically engineered, T-cell reporting zebrafish. We exploited the developmental similarities between normal and malignant T lymphoblasts to screen a small molecule library for activity against immature T cells with a simple visual readout in zebrafish larvae. After screening 26 400 molecules, we identified Lenaldekar (LDK), a compound that eliminates immature T cells in developing zebrafish without affecting the cell cycle in other cell types. LDK is well tolerated in vertebrates and induces long-term remission in adult zebrafish with cMYC-induced T-cell acute lymphoblastic leukemia (T-ALL). LDK causes dephosphorylation of members of the PI3 kinase/AKT/mTOR pathway and delays sensitive cells in late mitosis. Among human cancers, LDK selectively affects survival of hematopoietic malignancy lines and primary leukemias, including therapy-refractory B-ALL and chronic myelogenous leukemia samples, and inhibits growth of human T-ALL xenografts. This work demonstrates the utility of our method using zebrafish for antineoplastic candidate drug identification and suggests a new approach for targeted leukemia therapy. Although our efforts focused on leukemia therapy, this screening approach has broad implications as it can be translated to other cancer types involving malignant degeneration of developmentally arrested cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".