Carboplatin sensitivity in epithelial ovarian cancer cell lines: The impact of model systems
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy in North America, underscoring the need for the development of new therapeutic strategies for the management of this disease. Although many drugs are pre-clinically tested every year, only a few are selected to be evaluated in clinical trials, and only a small number of these are successfully incorporated into standard care. Inaccuracies with the initial in vitro drug testing may be responsible for some of these failures. Drug testing is often performed using 2D monolayer cultures or 3D spheroid models. Here, we investigate the impact that these different in vitro models have on the carboplatin response of four EOC cell lines, and in particular how different 3D models (polydimethylsiloxane-based microfluidic chips and ultra low attachment plates) influence drug sensitivity within the same cell line. Our results show that carboplatin responses were observed in both the 3D spheroid models tested using apoptosis/cell death markers by flow cytometry. Contrary to previously reported observations, these were not associated with a significant decrease in spheroid size. For the majority of the EOC cell lines (3 out of 4) a similar carboplatin response was observed when comparing both spheroid methods. Interestingly, two cell lines classified as resistant to carboplatin in 2D cultures became sensitive in the 3D models, and one sensitive cell line in 2D culture showed resistance in 3D spheroids. Our results highlight the challenges of choosing the appropriate pre-clinical models for drug testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".