Cell-intrinsic platinum response and associated genetic and gene expression signatures in ovarian cancer
Bibliographic record
Abstract
Abstract Ovarian cancers are still largely treated with platinum-based chemotherapy as the standard of care, yet few biomarkers of clinical response have had an impact on clinical decision making. Previous work has relied on poor models of the most common subtypes of epithelial ovarian cancers and necessitates a careful examination of the most suitable in vitro models. We performed extensive drug dose response assays and gene expression profiling on 36 ovarian cancer cell lines across over seven subtypes. This is the largest quantitative database of quantitative cisplatin and carboplatin response in ovarian cancer cell lines. Our results demonstrate that cell lines largely fall either well above or below the clinical maximally achievable dose (Cmax) of each compound. We performed differential expression analysis for high-grade serous ovarian carcinoma cell lines. Further, we generated two platinum-resistant derivatives each for OVCAR3 and OVCAR4. Combined with clinically resistant PEO1/PEO4/PEO6 and PEA1/PEA2 isogenic models, we performed differential expression analysis for seven platinum-resistant isogenic pairs. Common themes in differential expression were innate immunity/STAT activation, epithelial-to-mesenchymal transition (EMT) and stemness, and platinum influx/efflux regulators. We also performed copy number signature analysis and orthogonal measures of homologous recombination deficiency (HRD) scar scores and copy number burden, which is the first report to our knowledge applying field-standard copy number signatures to ovarian cancer cell lines. We also examined markers and functional readouts of stemness that revealed that cell lines are poor models for examination of stemness contributions to platinum resistance, suggesting that this is a transient state. Overall, this study serves as a resource to determine the best cell lines to utilize for ovarian cancer research on certain subtypes and platinum response studies, as well as sparks new hypotheses for future study in ovarian 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 | 0.000 | 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".