In vitro evaluation of the effects of gefitinib on the cytotoxic activity of selected anticancer agents in a panel of human endometrial cancer cell lines
Bibliographic record
Abstract
PURPOSE: This study was conducted to determine the in vitro optimal combination of selected anticancer agents with gefitinib and evaluate its effect on the expression of correlative biological targets in the cell-signaling pathway. In addition, the effect of gefitinib on the expression of ATP-binding cassette (ABC) transport proteins was evaluated. METHODS: Growth inhibition assays were conducted in six human endometrial cancer cell lines to evaluate the activity of selected anticancer agents with gefitinib compared to each alone. Enzyme linked immunosorbant assay (ELISA) assessed the presence of pEGFR in treated and untreated cells. Evaluation of the suppression of correlative biological targets in the cell-signaling pathway was completed by immunoblotting. RT-PCR was used to characterize the expression of MRP and ABC transport proteins. RESULTS: This in vitro study gefitinib did not observe cytotoxic activity as a single agent. However, the activity of gefitinib as EGFR inhibitor was confirmed. The combination of gefitinib with paclitaxel and docetaxel exhibited improved in vitro cytotoxic activity compared to each antineoplastic agent alone. Suppression of pAKT and p27 in the human endometrial cancer cells treated with selected combinations of chemotherapeutic drugs and gefitinib was observed. CONCLUSION: These data suggest that EGFRinhibitors, such as gefitinib, have the potential to modulate common mechanisms of drug resistance and may have a role in optimizing antineoplastic regimens for the treatment of recurrent endometrial cancer. This may represent a promising option for this class of agents in the treatment of endometrial 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".