Synergistic apoptotic response between valproic acid and fludarabine in chronic lymphocytic leukaemia (CLL) cells involves the lysosomal protease cathepsin B
Bibliographic record
Abstract
Fludarabine, a nucleoside analogue, is commonly used in combination with other agents for the treatment of chronic lymphocytic leukaemia (CLL). In previous studies, valproic acid (VPA), an inhibitor of histone deacetylases, combined with fludarabine to synergistically increase apoptotic cell death in CLL cells. In the present study, we found that the combination of fludarabine and VPA decreases the level of the anti-apoptotic proteins Mcl-1 and XIAP in primary CLL cells. Treatment with fludarabine alone, or in combination with VPA, led to the loss of lysosome integrity, and chemical inhibition of the lysosomal protease cathepsin B, using CA074-Me, was sufficient to reduce apoptosis. VPA treatment increased cathepsin B levels and activities in primary CLL cells, thereby priming CLL cells for lysosome-mediated cell death. Six previously treated patients with relapsed CLL were treated with VPA, followed by VPA/fludarabine combination. The combined therapy resulted in reduced lymphocyte count in five out of six and reduced lymph node sizes in four out of six patients. In vivo VPA treatment increased histone-3 acetylation and cathepsin B expression levels. Thus, the synergistic apoptotic response with VPA and fludarabine in CLL is mediated by cathepsin B activation leading to a decrease in the anti-apoptotic proteins.
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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) | 0.001 | 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".