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Record W4412817743 · doi:10.1158/1538-7445.fcs2024-p87

Abstract P87: Combined therapy with CRIF1/CDK2 interface inhibitor and Taxol against Triple-Negative Breast Cancer

2025· article· en· W4412817743 on OpenAlexaff
Xiaoye Sang, Nassira Belmessabih, Ruixuan Wang, Preyesh Stephen, Eva Lin

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer-related Molecular Pathways
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsBreast cancerCancerMedicineOncologyInternal medicineCancer research

Abstract

fetched live from OpenAlex

Abstract A chemotherapeutic agent, Paclitaxel (Taxol), remains the standard of care for the lethal triple-negative breast cancer (TNBC). However, over 50% of TNBC patients become resistant to chemotherapy typically after 6–10 months of treatment. To date, no solution is available. CR6-interacting factor 1 (CRIF1) is reported to act as a negative regulator of the cell cycle by interacting with cyclin-dependent kinase 2 (CDK2). In our study, two selective CRIF1–CDK2 interface inhibitors [4-(4-methylphenyl)-N-[3-(morpholin-4-yl)propyl]phthalazin-1-amine; bis(oxalic acid) and N-[2-(benzylsulfanyl)ethyl]-3-{1-[(3-nitrophenyl)methyl]-2,4-dioxo-1,2,3,4-tetrahydroquinazolin-3-yl}propanamide] were obtained by molecular modeling and docking and then used to investigate whether they could exert anti-proliferative activity on the TNBC cell lines. When combined with Taxol treatment, these two inhibitors are able to advance the cells from G0/G1 to S and G2/M phases, producing irreparable damage to the cells, which then undergo apoptosis. Moreover, they enhanced the reduction in cell proliferation induced by Taxol in TNBC cells, thereby improving sensitivity to Taxol in these cell lines. It is important that the inhibitors did not regulate the cell cycle in normal cells, indicating their high selectivity towards TNBC cells. The resistance to the anti-proliferative effects induced by Taxol can thus be significantly reduced by the combined treatment with selective CRIF1–CDK2 interface inhibitors, making a conceptual advance in the CDK-related cancer treatment. Animal assay will be carried out with improved interface inhibitors. Citation Format: Xiaoye Sang, Nassira Belmessabih, Ruixuan Wang, Preyesh Stephen & Sheng-Xiang Lin. Combined therapy with CRIF1/CDK2 interface inhibitor and Taxol against Triple-Negative Breast Cancer [abstract]. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85(15_Suppl):Abstract nr P87.

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 score0.004

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)0.0010.000

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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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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