Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer
Bibliographic record
Abstract
INTRODUCTION: High-grade serous ovarian cancers (HGSOCs) are challenging to treat and often resistant to therapy. Despite ongoing therapeutic progress, relapse and poor outcomes remain common among both newly diagnosed and recurrent cases. Systematic high-throughput screening of clinically approved compounds holds significant promise for uncovering novel therapeutic responses and developing new treatment strategies for this disease. METHODS: A panel of drugs were screened for cytotoxicity in five HGSOC cell lines, with drug efficacy quantified using the drug sensitivity score (DSS). All pairwise combinations of 384 low-cytotoxic drugs were screened by grouping 10 compounds in each well. The potent 10-compound combinations were deconvoluted into 2-drug pairings for secondary screening and ranked by the Bliss independent model and the Loewe additive model. Promising drug responses were further characterised in 3D spheroid cultures and patient ascites-derived cells (PADCs). The mechanism of action of the drugs was investigated by Western blot analysis. RESULTS: The DSS profile of drug responses provided a more robust clustering of 5 HGSOC cell lines according to their chemosensitivity responses compared to gene expression analysis of chemoresistance markers. Furthermore, chemoresistant HGSOC cell lines exhibited limited efficacy to single-agent treatments and generally demonstrated resistance to most anti-cancer agents. However, combination screens identified several novel drug pairings that were cytotoxic to chemoresistant HGSOC cells. Drug combinations involving traditional anticancer agents showed superior synergy and toxicity in chemosensitive cell lines, while all cell lines demonstrated good sensitivity to PI3K and HMG-CoA reductase inhibitors at sub-maximal clinically relevant concentrations, with the greatest sensitivity observed in chemoresistant cells. The combination of PI3K and HMG-CoA reductase inhibition significantly reduced the viability and growth of HGSOC spheroids. PADCs exhibited intrinsic sensitivity to HMG-CoA inhibition, while the combination with PI3K inhibition facilitated further dose reductions. Mechanistic studies revealed that the HMG-CoA inhibitor increased phospho-Akt levels in chemoresistant cell lines, sensitising them to PI3K inhibition. CONCLUSION: This study demonstrates the application of multiplex drug combination screening to identify effective synergistic therapies. Co-targeting PI3-kinase and HMG-CoA reductase could be repurposed as a potent combination to treat chemoresistant HGSOC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".