Pharmacologic inhibition of Akt in combination with chemotherapeutic agents effectively induces apoptosis in ovarian and endometrial cancer cell lines
Bibliographic record
Abstract
The PI3K/Akt signaling pathway, the most frequently altered signaling system in human cancer, is a crucial inducer of dysregulated proliferation and neoplastic processes; however, few therapeutic strategies using PI3K/Akt inhibitors singly have been shown to be effective. The purpose of this paper was to underline the potential benefit of pharmacological modulation of the PI3K/Akt pathway when combined with specific chemotherapeutic regimens. We have studied the ability of NVP-BEZ235 (PI3K/mTOR inhibitor) and AZD5363 (Akt inhibitor) in the sensitization of cancer cells to cisplatin and doxorubicin. Our results show that NVP-BEZ235 sensitizes cells preferentially to cisplatin while AZD5363 sensitizes cells to doxorubicin. At equal concentrations (5 μm), both inhibitors reduce ribosomal protein S6 phosphorylation, but AZD5363 is more effective in reducing GSK3β phosphorylation as well as S6 phosphorylation. Additionally, AZD5363 is capable of inducing FOXO1 and p53 nuclear localization and reduces BAD phosphorylation, which is generally increased by cisplatin and doxorubicin. Finally, the combination of AZD5363 and doxorubicin induces apoptosis in cells and robustly reduces cell ability to clonally replicate, which underlines a potential cooperative effect of the studied compounds.
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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".