Vorinostat and Sorafenib Synergistically Kill Tumor Cells via FLIP Suppression and CD95 Activation
Bibliographic record
Abstract
PURPOSE AND DESIGN: Mechanism(s) by which the multikinase inhibitor sorafenib and the histone deacetylase inhibitor vorinostat interact to kill hepatic, renal, and pancreatic adenocarcinoma cells has been defined. RESULTS: Low doses of sorafenib and vorinostat interacted in vitro in a synergistic fashion to kill hepatic, renal, and pancreatic adenocarcinoma cells in multiple short-term viability (24-96 h) and in long-term colony formation assays. Cell killing was suppressed by inhibition of cathepsin proteases and caspase-8 and, to a lesser extent, by inhibition of caspase-9. Twenty-four hours after exposure, the activities of extracellular signal-regulated kinase 1/2, AKT, and nuclear factor-kappaB were only modestly modulated by sorafenib and vorinostat treatment. However, 24 h after exposure, sorafenib- and vorinostat-treated cells exhibited markedly diminished expression of c-FLIP-s, full-length BID, BCL-2, BCL-XL, MCL-1, XIAP, increased expression of BIM, and increased activation of BAX, BAK, and BAD. Expression of eIF2alpha S51A blocked sorafenib- and vorinostat-induced suppression of c-FLIP-s levels and overexpression of c-FLIP-s abolished lethality. Sorafenib and vorinostat treatment increased surface levels of CD95 and CD95 association with caspase-8. Knockdown of CD95 or FADD expression significantly reduced sorafenib/vorinostat-mediated lethality. CONCLUSIONS: These data show that combined exposure of epithelial tumor cell types to sorafenib and vorinostat diminishes expression of multiple antiapoptotic proteins and promotes activation of the CD95 extrinsic apoptotic and the lysosomal protease pathways, and that suppression of c-FLIP-s expression represents a critical event in transduction of the proapoptotic signals from CD95 to promote mitochondrial dysfunction and death.
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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".