Distinct genetic alterations occur in ovarian tumor cells selected for combined resistance to carboplatin and docetaxel
Bibliographic record
Abstract
BACKGROUND: Current protocols for the treatment of ovarian cancer include combination chemotherapy with a platinating agent and a taxane. However, many patients experience relapse of their cancer and the development of drug resistance is not uncommon, making successful second line therapy difficult to achieve. The objective of this study was to develop and characterize a cell line resistant to both carboplatin and docetaxel (dual drug resistant ovarian cell line) and to compare this cell line to cells resistant to either carboplatin or docetaxel. METHODS: The A2780 epithelial endometrioid ovarian cancer cell line was used to select for isogenic carboplatin, docetaxel and dual drug resistant cell lines. A selection method of gradually increasing drug doses was implemented to avoid clonal selection. Resistance was confirmed using a clonogenic assay. Changes in gene expression associated with the development of drug resistance were determined by microarray analysis. Changes in the expression of selected genes were validated by Quantitative Real-Time Polymerase Chain Reaction (QPCR) and immunoblotting. RESULTS: Three isogenic cell lines were developed and resistance to each drug or the combination of drugs was confirmed. Development of resistance was accompanied by a reduced growth rate. The microarray and QPCR analyses showed that unique changes in gene expression occurred in the dual drug resistant cell line and that genes known to be involved in resistance could be identified in all cell lines. CONCLUSIONS: Ovarian tumor cells can acquire resistance to both carboplatin and docetaxel when selected in the presence of both agents. Distinct changes in gene expression occur in the dual resistant cell line indicating that dual resistance is not a simple combination of the changes observed in cell lines exhibiting single agent resistance.
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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".