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Targeting the Oncogene eIF4E with Ribavirin: A Novel Therapeutic Avenue in Acute Myeloid Leukemia.

2009· article· en· W2572245593 on OpenAlexaff
Sarit Assouline, Eftihia Cocolakis, Caroline Rousseau, Biljana Čuljković, Nathalie Beslu, Abdellatif Amri, Stephen Caplan, Brian Leber, Denis‐Claude Roy, Katherine L. B. Borden, Wilson H. Miller

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsMcGill UniversityHôpital Maisonneuve-RosemontUniversité de MontréalMcMaster University Medical CentreInstitute for Research in Immunology and CancerJewish General Hospital
Fundersnot available
KeywordsRibavirinMyeloid leukemiaEIF4EMedicineImmunologyLeukemiaInternal medicineOncologyCytarabineCancer researchVirologyTranslation (biology)BiologyVirusHepatitis C virusMessenger RNA

Abstract

fetched live from OpenAlex

Abstract Abstract 2085 Poster Board II-62 Over the past 10 years, the incidence of acute myeloid leukemia (AML) has increased significantly with approximately 15 000 new cases annually. Standard induction chemotherapy consisting of cytarabine (Ara-C) and an anthracycline induces remission rates between 50% and 85%. Unfortunately, the majority of patients who achieve remission will relapse and die from their disease within 2 years, highlighting the need for novel therapeutic targets. The eukaryotic translation factor (eIF4E) is overexpressed in many human malignancies, including AML, and is associated with poor prognosis as well as clinical progression. Ribavirin, an anti-viral molecule, is classically used in the treatment of hepatitis C (with interferon), SARS, RSV, Lassa fever and influenza. Its structure physically mimics the m(7)G cap of mRNA, thus inhibiting eIF4E-induced export and translation of sensitive transcripts. We are carrying out the first clinical trial targeting eIF4E with ribavirin in AML patients. Clinical and molecular efficacy has been evaluated in 13 patients to date. The treatment was well tolerated by all patients with no marked toxicity observed. Importantly, no patients developed hemolytic anemia. We demonstrated that ribavirin effectively induces the relocalization of nuclear eIF4E to the cytoplasm and the reduction of eIF4E as well as its target proteins, including suppression of Akt activation. This led to dramatic clinical improvement, including one complete remission, two partial remissions, two blast responses and four patients with stable disease. Final response data will be presented along with translational correlates. Notably, lack of response or relapse after remission was associated with lack of molecular response in leukemic blasts. Despite the encouraging responses of patient on ribavirin, all patients acquired resistance to therapy and eventually relapsed. Hence, we sought novel therapies to combine with ribavirin in order to overcome resistance and maintain remissions. Using a cell line that overexpresses eIF4E, we screened a library of 5000 known drugs and searched for compounds that synergize with ribavirin to suppress tumor growth. We identified nearly 50 lead compounds, many of which are structurally related, with similar biological activity, and are currently used medically for indications other than cancer. Early clinical observations suggest that combinations of cytotoxic agents lead to substantially better clinical outcomes relative to monotherapies. Furthermore, various drugs that suppress the PI3/Akt pathway were found to sensitize leukemia cells to Ara-C. Thus, we combined Ara-C with ribavirin in vitro, and observed an improved reduction in colony growth of AML specimens. Combination therapy with ribavirin and Ara-C in patients with acute myelocytic leukemia is currently ongoing. Preliminary results will be presented. Disclosures: Borden: Translational Therapeutics: Equity Ownership.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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