Combination of AKT inhibition with autophagy blockade effectively reduces ascites-derived ovarian cancer cell viability
Bibliographic record
Abstract
Recent genomics analysis of the high-grade serous subtype of epithelial ovarian cancer (EOC) show aberrations in the phosphatidylinositol 3-kinase (PI3K)/AKT pathway that result in upregulated signaling activity. Thus, the PI3K/AKT pathway represents a potential therapeutic target for aggressive high-grade EOC. We previously demonstrated that treatment of malignant ascites-derived primary human EOC cells and ovarian cancer cell lines with the allosteric AKT inhibitor Akti-1/2 induces a dormancy-like cytostatic response but does not reduce cell viability. In this report, we show that allosteric AKT inhibition in these cells induces cytoprotective autophagy. Inhibition of autophagy using chloroquine (CQ) alone or in combination with Akti-1/2 leads to a significant decrease in viable cell number. In fact, Akti-1/2 sensitizes EOC cells to CQ-induced cell death by exhibiting markedly reduced EC50 values in combination-treated cells compared with CQ alone. In addition, we evaluated the effects of the novel specific and potent autophagy inhibitor-1 (Spautin-1) and demonstrate that Spautin-1 inhibits autophagy in a Beclin-1-independent manner in primary EOC cells and cell lines. Multicellular EOC spheroids are highly sensitive to Akti-1/2 and CQ/Spautin-1 cotreatments, but resistant to each agent alone. Indeed, combination index analysis revealed strong synergy between Akti-1/2 and Spautin-1 when both agents were used to affect cell viability; Akti-1/2 and CQ cotreatment also displayed synergy in most samples. Taken together, we propose that combination AKT inhibition and autophagy blockade would prove efficacious to reduce residual EOC cells for supplying ovarian cancer recurrence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| 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".