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 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".