Regulation of apoptosis-inducing factor-mediated, cisplatin-induced apoptosis by Akt
Bibliographic record
Abstract
Cisplatin is a first-line chemotherapeutic for ovarian cancer, although chemoresistance limits treatment success. Apoptosis, an important determinant of cisplatin sensitivity, occurs via caspase-dependent and -independent mechanisms. Activation of the protein kinase Akt, commonly observed in ovarian tumours, confers resistance to ovarian cancer cells via inhibition of caspase-dependent apoptosis. However, the effect of Akt on cisplatin-induced, caspase-independent apoptosis remains unclear. We show that in chemosensitive ovarian cancer cells, cisplatin induces the mitochondrial release and nuclear translocation of apoptosis-inducing factor (AIF), a mediator of caspase-independent apoptosis, and AIF-dependent apoptosis. Cisplatin failed to induce these effects in the chemoresistant variant cells. Overexpression of AIF sensitised resistant cells to cisplatin-induced apoptosis. Finally, activation of Akt attenuated the cisplatin-induced mitochondrial release and nuclear accumulation of AIF and apoptosis in chemosensitive cells, whereas inhibition of Akt activity facilitated these effects and sensitised chemoresistant cells to AIF-dependent, cisplatin-induced apoptosis. These results suggest that cisplatin-induced apoptosis proceeds, in part, via a caspase-independent mechanism involving AIF, and that Akt activation confers resistance to cisplatin-induced apoptosis by blocking this pathway. These results provide insights into the molecular mechanism of chemoresistance, and suggest that inhibition of Akt activity may represent a novel therapeutic approach to the treatment of cisplatin-resistant ovarian cancer.
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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".