TRAIL Triggers Apoptosis in Human Malignant Glioma Cells Through Extrinsic and Intrinsic Pathways
Bibliographic record
Abstract
Many malignant glioma cells express death receptors for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), yet some of these cells are resistant to TRAIL. Here, we examined signaling events in TRAIL-induced apoptosis and searched for therapeutic agents that could overcome TRAIL resistance in glioma cells. TRAIL induced apoptosis through death receptor 5 (DR5) and was mediated by caspase-8-initiated extrinsic and intrinsic mitochondrial pathways in sensitive glioma cell lines. TRAIL also triggered apoptosis in resistant glioma cell lines through the same pathways, but only if the cells were pretreated with chemotherapeutic agents, cisplatin, camptothecin and etoposide. Previous studies suggested that this was due to an increase in DR5 expression in wild-type TP53 cells, but this mechanism did not account for cells with mutant TP53. Here, we show that a more general effect of these agents is to downregulate caspase-8 inhibitor c-FLIP(S) (the short form of cellular Fas-associated death domain-fike interleukin-1-converting enzyme-inhibitory protein) and up-regulate Bak, a pro-apoptotic Bcl-2 family member, independently of cell's TP53 status. Furthermore, we showed that TRAIL alone or in combination with chemotherapeutic agents, induced apoptosis in primary tumor cultures from patients with malignant gliomas, reinforcing the potential of TRAIL as an effective therapeutic agent for malignant gliomas.
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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".