Dasatinib sensitizes primary chronic lymphocytic leukaemia lymphocytes to chlorambucil and fludarabine <i>in vitro</i>
Bibliographic record
Abstract
The dual c-abl/Src kinase inhibitor, dasatinib, utilized to treat chronic myeloid leukaemia (CML) when used at clinically attainable sublethal concentrations, synergistically sensitized primary chronic lymphocytic leukaemia (CLL) lymphocytes to chlorambucil and fludarabine. In contrast, dasatinib alone demonstrated toxicity to CLL lymphocytes at concentrations that are generally not clinically attainable. Dasatinib resistance and poorer dasatinib-mediated sensitization to chlorambucil and fludarabine was associated with higher expression of c-abl protein levels. In contrast, chlorambucil and fludarabine resistance correlated with basal p53 protein levels. Moreover, Western blot analysis after in vitro treatment of primary CLL lymphocytes with dasatinib, chlorambucil and/or fludarabine, showed that dasatinib: (i) inhibited c-abl function (e.g. downregulation of c-abl protein levels and decreased the phosphorylation of a c-abl downstream target, Dok2), (ii) decreased chlorambucil/fludarabine induced accumulation of p53 protein levels, (iii) altered the response to chlorambucil/fludarabine induced DNA-damage as evidenced by an increase in chlorambucil/fludarabine-induced H2AX phosphorylation, and (iv) accentuated the c-abl downregulation induced by chlorambucil/fludarabine. Our results suggest that dasatinib in combination with chlorambucil or fludarabine may improve the therapy of CLL.
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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 | 0.000 |
| 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".