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Record W4415963082 · doi:10.1186/s13048-025-01828-7

Unbiased combination screening on repurposed drugs reveals synergistic potential of copanlisib and cerivastatin against chemoresistant high-grade serous ovarian cancer

2025· article· en· W4415963082 on OpenAlexfundno aff
Yuling Sun, Yangyang Wang, Syed Umbreen, Benjamin Pepperrell, Niamh E. Buckley, Paul B. Mullan, Ahlam Ali, Fiona Furlong

Bibliographic record

VenueJournal of Ovarian Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
FundersChina Scholarship CouncilQueen's UniversityPublic Health AgencyHigher Education AuthorityQueen's University Belfast
KeywordsCombination therapyCerivastatinOvarian cancerSerous ovarian cancerDrug

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.334
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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