Glycogen Synthase Kinase-3 Inhibition Sensitizes Pancreatic Cancer Cells to TRAIL-Induced Apoptosis
Bibliographic record
Abstract
Tumor necrosis factor-related apoptosis inducing ligand (TRAIL) induces apoptosis in a variety of cancer cell lines with little or no effect on normal cells. However, its effect is limited as some cancers including pancreatic cancer show de novo resistance to TRAIL induced apoptosis. In this study we report that GSK-3 inhibition using the pharmacologic agent AR-18, enhanced TRAIL sensitivity in a range of pancreatic and prostate cancer cell lines. This sensitization was found to be caspase-dependent, and both pharmacological and genetic knock-down of GSK-3 isoforms resulted in apoptotic features as shown by cleavage of PARP and caspase-3. Elevated levels of reactive oxygen intermediates and disturbance of mitochondrial membrane potential point to a mitochondrial amplification loop for TRAIL-induced apoptosis after GSK-3 inhibition. Consistent with this, overexpression of anti-apoptotic mitochondrial targets such as Bcl-XL, Mcl-1, and Bcl-2 rescued PANC-1 and PPC-1 cells from TRAIL sensitization. However, overexpression of the caspase-8 inhibitor CrmA also inhibited the sensitizing effects of GSK-3 inhibitor, suggesting an additional role for GSK-3 that inhibits death receptor signaling. Acute treatment of mice bearing PANC-1 xenografts with a combination of AR-18 and TRAIL also resulted in a significant increase in apoptosis, as measured by caspase-3 cleavage. Sensitization to TRAIL occurred despite an increase in β-catenin due to GSK-3 inhibition, suggesting that the approach might be effective even in cancers with dysregulated β-catenin. These results suggest that GSK-3 inhibitors might be effectively combined with TRAIL for the treatment of pancreatic cancer.
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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 | 0.001 |
| 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".