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Record W4409690342 · doi:10.1158/1538-7445.am2025-364

Abstract 364: Oxaliplatin and 5-fluorouracil cause therapy-induced senescence in low-grade serous ovarian cancer cells

2025· article· en· W4409690342 on OpenAlexaff
Rewati Prakash, Alicia A. Goyeneche, Edith Zorychta, Shuk On Annie Leung, Lucy Gilbert, Carlos Telleria

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsOxaliplatinSerous fluidSerous ovarian cancerMedicineFluorouracilOvarian cancerOncologyInternal medicineCancerCancer researchColorectal cancer

Abstract

fetched live from OpenAlex

Abstract A critical concern with managing low-grade serous ovarian cancer (LGSOC) is the lack of response to standard carboplatin-paclitaxel chemotherapy; less than 30% of patients respond to such initial chemotherapy. Oxaliplatin (OXP) and 5-Fluorouracil (5-FU) combination treatment is a standard treatment regimen against colorectal cancer (CRC); however, it could be repurposed for use against LGSOC. p53 wildtype CRC tumours have been shown to respond better to OXP & 5-FU suggesting that LGSOC, which is well-characterized to also be p53 wildtype, would also respond effectively to this treatment. Consequently, in this study, we utilized three LGSOC cell lines: VOA6406, VOA7681, and VOA1056. We determined the cytotoxic effects of treatment with OXP & 5-FU; viability, proliferation, clonogenic capacity, and drug-withdrawal recovery were assessed. Results showed a dramatic decrease in cell proliferation in the short-and-long term, without a reduction in cell viability. This effect was confirmed further by measuring Ki67, a marker of cell proliferation; OXP & 5-FU-treated cells showed a significant reduction in Ki67 labelling. The persistence in viability alongside the reduced cell proliferation in cells treated with OXP & 5-FU led us to hypothesize that this drug combination induces senescence in LGSOC cells. A critical aspect of senescence is the arrest of the cell cycle. A propidium iodide-based cell cycle assay, along with western blotting to measure expression of p53, p21, and pRb, demonstrated that the LGSOC cells treated with OXP & 5-FU were arresting at the G1-S phase. Morphology alterations and lysosomal swelling were measured via expression of senescence-associated β-galactosidase (SA-β-gal); OXP & 5-FU-treated LGSOC cells demonstrated a marked increase in SA-β-gal as well as a distinct flattening of cell morphology. Consistent with hallmarks of senescence, anti-apoptotic proteins Bcl-2 and Mcl-1, were elevated upon treatment with OXP & 5-FU. A compelling increase in reactive oxygen species (ROS) production, as well as an elevated expression of caveolin-1, suggest a senescence-induction pathway dependent on oxidative stress. The use of a known ROS quencher, α-tocopherol, confirmed this pathway, as it partially rescued the senescence phenotype induced by OXP & 5-FU. The use of OXP & 5-FU to induce senescence in LGSOC shows promise as an alternate treatment option for this rare disease. We will further study if facilitating a “one-two punch theory” could be used against LGSOC, whereby OXP & 5-FU induces senescence, which could be subsequently “cleared” by a senolytic drug. Citation Format: Rewati Prakash, Alicia A. Goyeneche, Edith Zorychta, Shuk On Annie Leung, Lucy Gilbert, Carlos Telleria. Oxaliplatin and 5-fluorouracil cause therapy-induced senescence in low-grade serous ovarian cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 364.

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 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)0.0020.001

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 head0.107
GPT teacher head0.418
Teacher spread0.312 · 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
Published2025
Admission routes1
Has abstractyes

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