Proteasome Inhibiters Up-Regulate TRAIL/Apo2L and Its Receptors Significantly Contributing to Proteasome Inhibitor-Induced Apoptosis in Primary Chronic Lymphocytic Leukemia (CLL) Cells.
Bibliographic record
Abstract
Abstract The proteasome inhibitor, bortezomib has recently emerged as a new therapeutic treatment for refractory multiple myeloma and is presently being evaluated for other hematological malignancies either alone or in combination with other antitumor agents. Proteasome inhibitors cause the accumulation of many proteins but the precise mechanism responsible for their antitumor effect is unclear. In the present study, we have determined that cytotoxic effect the proteasome inhibitor MG-132 in primary chronic lymphocytic leukemia (CLL) cells is through the activation of the TRAIL (tumor necrosis factor-related apoptosis inducing ligand) apoptotic pathway. MG-132 induced apoptosis in approximately 70% of primary CLL cells as measured by annexin V staining. Addition of DR4:Fc that prevents TRAIL ligation with its receptors decreased the amount of MG-132 induced apoptosis by approximately 40% suggesting MG-132 caused activation of the TRAIL apoptotic pathway. MG-132 also up-regulated both the mRNA and protein levels of TRAIL and protein levels of TRAIL receptors DR4 and DR5. This upregulation correlated with activation of caspase 8 and cleavage of pro-apoptotic Bcl-2 family member Bid. Moreover, MG-132 treatment also induced a substantial reduction in the FLICE-like inhibitory protein (c-FLIP) protein levels. In contrast to CLL cells, proteasome inhibitors failed to activate the TRAIL apoptotic pathway in normal B-cells. This indicates that proteasome inhibitors are inducing apoptosis in primary CLL cells through activation of the TRAIL apoptotic signaling pathway through up-regulation of TRAIL and its cognate receptors and reduced FLIP expression. Thus, proteasome inhibitors may have a therapeutic role in CLL, either when used alone or in combination with TRAIL or antibodies against DR4/DR5.
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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) | 0.002 | 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".