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Record W2058752290 · doi:10.1158/1538-7445.am10-3165

Abstract 3165: Anti-tumor activity of the eIF4E-targeted drug ribavirin in breast cancer cells

2010· article· en· W2058752290 on OpenAlexaff
Filippa Pettersson, Monica C. Dobocan, Hélène Retrouvay, Biljana Čuljković, Abdellatif Amri, Louis Gaboury, Katherine L. B. Borden, Wilson H. Miller

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsInstitute for Research in Immunology and CancerJewish General Hospital
Fundersnot available
KeywordsCancer researchEIF4EOncogeneBreast cancerCancerMedicineRibavirinInternal medicineBiologyImmunologyCell cycleTranslation (biology)Messenger RNABiochemistry

Abstract

fetched live from OpenAlex

Abstract In this study, we explored the potential of targeting the eukaryotic translation initiation factor (eIF4E) with ribavirin as a novel anti-tumor agent in breast cancer. eIF4E is an oncogene that facilitates nuclear export and translation of specific, growth-stimulatory mRNAs, including cyclins, c-myc, survivin, VEGF and others, thereby promoting cell survival. Overexpression of eIF4E also leads indirectly to activation of Akt, providing a positive feed-back loop for eIF4E activation and Akt signaling effects. Ribavirin is an antiviral drug that has been shown to inhibit oncogenic transformation mediated by eIF4E and reduce the clonogenic potential of cancer cells with high eIF4E levels. Ribavirin specifically inhibits translation and/or nuclear export of eIF4E targets in cells both in vitro and in patients, as shown in a recent phase I/II proof-of-principle trial in patients with AML. In this trial, dramatic clinical improvements were observed and reductions in eIF4E levels and activity correlated with clinical response. Importantly, ribavirin is largely non-toxic even at high doses, possibly due to an eIF4E oncogene addiction specific to tumor cells. eIF4E is overexpressed in more than 50% of breast cancers, and high levels are associated with increased angiogenesis, clinical progression and poor prognosis. Targeting eIF4E with ribavirin may therefore be an attractive therapeutic strategy for this malignancy. We studied the effects of ribavirin in a panel of breast cancer cells, representing luminal and basal-type tumors with various ER, PR and Her2 status. Western blot analysis showed that eIF4E was overexpressed compared to normal breast tissue and predominantly cytoplasmic in all of the cell lines. In addition, we examined eIF4E levels in metastatic skin lesions of three breast cancer patients and found highly elevated levels compared to normal skin. Ribavirin anti-proliferative activity was assessed using a cell viability assay and clonogenic assays were performed to examine changes in both anchorage dependent and -independent growth. At clinically relevant concentrations, the majority of the cell lines responded to ribavirin, with varying sensitivity. Inhibition of cell growth was associated with decreased protein levels of eIF4E targets such as cyclin D1 and survivin, and a reduction in phosphorylation of Akt as well as eIF4E binding protein 1 (4E-BP1) were observed. Cell cycle analysis showed that ribavirin caused a significant S-phase arrest in sensitive cells, while apoptosis was only observed at elevated concentrations of the drug. This data encourages further study of ribavirin as a breast cancer therapeutic and identification of potential combination regimens. A clinical trial of single agent ribavirin in patients with advanced metastatic breast cancer is planned in the near future. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3165.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.001

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 head0.031
GPT teacher head0.368
Teacher spread0.337 · 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
Published2010
Admission routes1
Has abstractyes

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