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Abstract A013: Auranofin induces lethality driven by reactive oxygen species in high-grade serous ovarian cancer cells

2024· article· en· W4392375451 on OpenAlexaff
Farah H. Abdalbari, Elvis M. Jaramillo, Benjamin N. Forgie, Estelle Tran, Alicia A. Goyeneche, Carlos M. Telleria

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRedox biology and oxidative stress
Canadian institutionsMcGill University
Fundersnot available
KeywordsAuranofinReactive oxygen speciesSerous fluidOvarian cancerSerous ovarian cancerCancer researchCancerMedicineChemistryBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Abstract High-grade serous ovarian cancer (HGSOC) accounts for 70% of ovarian cancer cases in the clinical setting; the survival rate for this disease remains remarkably low due to the lack of a long-term consolidation therapy following standard platinum-based chemotherapy. There is minimal improvement in the survival rate of this disease due to the development of resistance to platinum drugs as the disease recurs. The purpose of this study was to develop a novel treatment modality against HGSOC using the gold complex auranofin (AF). AF primarily functions as a pro-oxidant agent through the inhibition of thioredoxin reductase (TrxR), an antioxidant enzyme that is overexpressed in ovarian cancer. We explored the effect of AF on TrxR activity and various mechanisms of cytotoxicity using HGSOC cells that are clinically sensitive (PEO1) or resistant (PEO4) to platinum. We treated PEO1 cells and PEO4 cells with AF for 72 hours and analyzed its short-term toxicity by assessing cell viability and DNA content by flow cytometry, and the long-term anti-proliferative capacity using colony formation assays. We also assessed the mechanisms of cell death induced by AF including the activation of mechanisms of apoptosis, activation of executor caspases -3 and -7, cleavage of poly-(ADP-ribose) polymerase (PARP), changes in the mitochondrial membrane potential (MMP), and DNA damage. In addition, we studied the interaction between AF and another pro-oxidant, L-buthionine sulfoximine (L-BSO), an anti-glutathione (GSH) agent. We demonstrate that AF potently inhibits the activity of TrxR at non-cytotoxic concentrations while reducing the viability of HGSOC cells regardless of their sensitivities to platinum. We showed that AF induces accumulation of reactive oxygen species (ROS), triggers the depolarization of the mitochondrial membrane, and kills HGSOC cells by inducing apoptosis. AF-induced cell death and morphological effects were abrogated by the ROS scavenger, N-acetyl cysteine (NAC). In addition, the lethality of AF in HGSOC was associated with the activation of caspase-3/7, cleavage of PARP, and the generation of DNA damage, effects that were prevented by the ROS scavenger NAC. Finally, when AF and L-BSO were combined, we observed a synergistic lethality against HGSOC cells, which was mediated by a further increase in ROS with a decrease in the antioxidant, GSH. In summary, our results support the concept that AF can be used alone to kill HGSOC cells by causing ROS-mediated apoptosis. Furthermore, we demonstrate that a large depletion of antioxidants caused by combining AF and L-BSO is highly efficient in synergistically killing HGSOC cells. These findings suggest that the combination of AF and L-BSO is a potential option to develop a consolidation therapy against HGSOC, which can overcome the problem of platinum resistance during recurrent stages of the disease. Citation Format: Farah H. Abdalbari, Elvis M. Jaramillo, Benjamin N. Forgie, Estelle Tran, Alicia A. Goyeneche, Carlos M. Telleria. Auranofin induces lethality driven by reactive oxygen species in high-grade serous ovarian cancer cells [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A013.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.054
GPT teacher head0.382
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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