Nilotinib treatment in mouse models of P190 Bcr/Abl lymphoblastic leukemia
Bibliographic record
Abstract
BACKGROUND: Ph-positive leukemias are caused by the aberrant fusion of the BCR and ABL genes. Nilotinib is a selective Bcr/Abl tyrosine kinase inhibitor related to imatinib, which is widely used to treat chronic myelogenous leukemia. Because Ph-positive acute lymphoblastic leukemia only responds transiently to imatinib therapy, we have used mouse models to test the efficacy of nilotinib against lymphoblastic leukemia caused by the P190 form of Bcr/Abl. RESULTS: After transplant of 10,000 highly malignant leukemic cells into compatible recipients, untreated mice succumbed to leukemia within 21 days, whereas mice treated with 75 mg/kg nilotinib survived significantly longer. We examined cells from mice that developed leukemia while under treatment for Bcr/Abl kinase domain point mutations but these were not detected. In addition, culture of such cells ex vivo showed that they were as sensitive as the parental cell line to nilotinib but that the presence of stromal support allowed resistant cells to grow out. Nilotinib also exhibited impressive anti-leukemia activity in P190 Bcr/Abl transgenic mice that had developed overt leukemia/lymphoma masses and that otherwise would have been expected to die within 7 days. Visible lymphoma masses disappeared within six days of treatment and leukemic cell numbers in peripheral blood were significantly reduced. Treated mice survived more than 30 days. CONCLUSION: These results show that nilotinib has very impressive anti-leukemia activity but that lymphoblastic leukemia cells can become unresponsive to it both in vitro and in vivo through mechanisms that appear to be Bcr/Abl independent.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".