The mTORC2 Component Rictor Contributes to Cisplatin Resistance in Human Ovarian Cancer Cells
Bibliographic record
Abstract
Resistance to cisplatin-based therapy is a major cause of treatment failure in human ovarian cancer. A better understanding of the mechanisms of cisplatin resistance will offer new insights for novel therapeutic strategies for this deadly disease. Akt and p53 are determinants of cisplatin sensitivity. Rictor is a component of mTOR protein kinase complex 2, which is required for Akt phosphorylation (Ser473) and full activation. However, the precise role of rictor and the relationship between rictor and p53 in cisplatin resistance remains poorly understood. Here, using sensitive wild-type p53 (OV2008 and A2780s), resistant wild-type p53 (C13* and OVCAR433), and p53 compromised (A2780cp, OCC1, and SKOV-3) ovarian cancer cells, we have demonstrated that (i) rictor is a determinant of cisplatin resistance in chemosensitive human ovarian cancer cells; (ii) cisplatin down-regulates rictor content by caspase-3 cleavage and proteasomal degradation; (iii) rictor down-regulation sensitizes chemo-resistant ovarian cancer cells to cisplatin-induced apoptosis in a p53-dependent manner; (iv) rictor suppresses cisplatin-induced apoptosis and confers resistance by activating and stabilizing Akt. These findings extend current knowledge on the molecular and cellular basis of cisplatin resistance and provide a rationale basis for rictor as a potential therapeutic target for chemoresistant 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".