Ribavirin Treatment Effects on Breast Cancers Overexpressing eIF4E, a Biomarker with Prognostic Specificity for Luminal B-Type Breast Cancer
Bibliographic record
Abstract
PURPOSE: We have evaluated the eukaryotic translation initiation factor 4E (eIF4E) as a potential biomarker and therapeutic target in breast cancer. eIF4E facilitates nuclear export and translation of specific, growth-stimulatory mRNAs and is frequently overexpressed in cancer. EXPERIMENTAL DESIGN: Breast cancer cells were treated with ribavirin, an inhibitor of eIF4E, and effects on cell proliferation and on known mRNA targets of eIF4E were determined. eIF4E expression was assessed, at the mRNA and protein level, in breast cancer cell lines and in skin biopsies from patients with metastatic disease. Additionally, pooled microarray data from 621 adjuvant untreated, node-negative breast cancers were analyzed for eIF4E expression levels and correlation with distant metastasis-free survival (DMFS), overall and within each intrinsic breast cancer subtype. RESULTS: At clinically relevant concentrations, ribavirin reduced cell proliferation and suppressed clonogenic potential, correlating with reduced mRNA export and protein expression of important eIF4E targets. This effect was suppressed by knockdown of eIF4E. Although eIF4E expression is elevated in all breast cancer cell lines, variability in ribavirin responsiveness was observed, indicating that other factors contribute to an eIF4E-dependent phenotype. Assessment of the prognostic value of high eIF4E mRNA in patient tumors found that significant discrimination between good and poor outcome groups was observed only in luminal B cases, suggesting that a specific molecular profile may predict response to eIF4E-targeted therapy. CONCLUSIONS: Inhibition of eIF4E is a potential breast cancer therapeutic strategy that may be especially promising against specific molecular subtypes and in metastatic as well as primary tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".