Acquired resistance to TRAIL-induced apoptosis in human ovarian cancer cells is conferred by increased turnover of mature caspase-3
Bibliographic record
Abstract
Little is known on how cancer cells can acquire resistance to tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). In this study, we established TRAIL-resistant cells from the TRAIL-sensitive human ovarian carcinoma cell line OVCAR3 to evaluate the potential mechanisms of acquired resistance to TRAIL. The selected resistant cells were cross-resistant to Fas ligand but remained sensitive to drug-induced apoptosis. Expression of TRAIL receptors was not altered in TRAIL-resistant OVCAR3 cells. Cleavage of caspase-8 and caspase-3 occurred in both TRAIL-resistant and TRAIL-sensitive cells. However, mature caspase-3 fragments were not detected by immunoblot in TRAIL-resistant cells and caspase-3 activity was significantly inhibited in these cells. The addition of proteasome inhibitors significantly increased TRAIL-induced apoptosis in resistant cells and enhanced the accumulation of mature caspase-3 fragments. Pretreatment with cycloheximide showed that active caspase-3 fragments have a high turnover rate in OVCAR3 R350 cells. X-linked inhibitor of apoptosis down-regulation by RNA interference also increased the accumulation of cleaved caspase-3 intermediates and resensitized TRAIL-resistant cells. Our findings show that altered turnover of mature caspase-3 may lead to acquired TRAIL resistance in ovarian cancer cells. Proteasome and X-linked inhibitor of apoptosis inhibitors could have a role in clinical situations to potentiate the cytotoxic effects of TRAIL in resistant tumor cells.
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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 | 0.000 | 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) | 0.000 | 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".