Interactions between Bortezomib and Romidepsin and Belinostat in Chronic Lymphocytic Leukemia Cells
Bibliographic record
Abstract
PURPOSE: The goal of this study was to characterize interactions between the proteasome inhibitor bortezomib and the histone deacetylase (HDAC) inhibitors (HDACI) romidepsin or belinostat in chronic lymphocytic leukemia (CLL) cells. EXPERIMENTAL DESIGN: Primary and cultured (JVM-3 and MEC-2) CLL cells were exposed to agents alone or in combination, after which cell death was determined by 7-aminoactinomycin D staining/flow cytometry. Acetylation of target proteins, activation of caspase cascades, and expression of apoptosis-regulatory proteins were monitored by Western blot analysis. Nuclear factor-kappaB (NF-kappaB) activity was determined by luciferase reporter assay. Cells were transiently transfected with wild-type and acetylation site-mutated (inactive) RelA(p65) (e.g., K221R, K310R, or K281/221/310R) and assessed for HDACI sensitivity. RESULTS: Combined exposure to very low concentrations of romidepsin or belinostat (i.e., low nanomolar and submicromolar, respectively) in combination with low nanomolar concentrations of bortezomib synergistically induced cell death in primary and cultured CLL cells. These events were likely associated with prevention of HDACI-mediated RelA acetylation and NF-kappaB activation by bortezomib, down-regulation of antiapoptotic proteins (i.e., Bcl-xL, Mcl-1, and XIAP), as well as up-regulation of the proapoptotic protein Bim, resulting in activation of caspase cascade. Finally, CLL cells transfected with inactive RelA displayed a significant increase in HDACI lethality. CONCLUSIONS: Coadministration of the clinically relevant HDACIs romidepsin or belinostat with bortezomib synergistically induces cell death in CLL cells, likely through mechanisms involving, among other factors, NF-kappaB inactivation and perturbation in the expression of proapoptotic and antiapoptotic proteins. A strategy combining HDAC with proteasome inhibition warrants further attention in CLL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".