Inhibition of PI3K-AKT-mTOR pathway sensitizes endometrial cancer cell lines to PARP inhibitors
Bibliographic record
Abstract
BACKGROUND: Phosphatase and Tensin homolog (PTEN) is a tumor suppressor gene. Loss of its function is the most frequent genetic alteration in endometrioid endometrial cancers (70-80%) and high grade tumors (90%). We assessed the sensitivity of endometrial cancer cell lines to PARP inhibitors (olaparib and BMN-673) and a PI3K inhibitor (BKM-120), alone or in combination, in the context of their PTEN mutation status. We also highlighted a direct pathway linking PTEN to DNA repair. METHODS: Using endometrial cancer cellular models with known PTEN status, we evaluated their homologous recombination (HR) functionality by RAD51 foci formation assay. The 50% Inhibitory concentration (IC50) of PI3K and PARP inhibitors in these cells was assessed, and western blotting was performed to determine the expression of proteins involved in the PI3K/mTOR pathway. Moreover, we explored the interaction between RAD51 and PI3K/mTOR by immunofluorescence. Next, the combination effect of PI3K and PARP inhibitors on cell proliferation was evaluated by a clonogenic assay. RESULTS: Cells with mutated PTEN showed over-activation of the PI3K/mTOR pathway. These cells were more sensitive to PARP inhibition compared to PTEN wild-type cells. In addition, PI3K inhibitor treatment reduced RAD51 foci formation in PTEN mutated cells, and sensitized these cells to PARP inhibitor. CONCLUSION: Targeting both PARP and PI3K might lead to improved personalized therapeutic approaches in endometrial cancer patients with PTEN mutations. Understanding the complex interaction of PTEN mutations with DNA repair in endometrial cancer will help to better select patients that are likely to respond to some of the new and costly targeted therapies.
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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 | 0.001 |
| 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".