Understanding and Targeting the Eukaryotic Translation Initiation Factor eIF4E in Head and Neck Cancer
Bibliographic record
Abstract
The eukaryotic translation initiation factor eIF4E is elevated in about 30% of human malignancies including HNSCC where its levels correlate with poor prognosis. Here, we discuss the biochemical and molecular underpinnings of the oncogenic potential of eIF4E. Studies in human leukemia specimens, and later in a mouse model of prostate cancer, strongly suggest that cells with elevated eIF4E develop an oncogene dependency to it, making them more sensitive to targeting eIF4E than normal cells. We describe several strategies that have been suggested for eIF4E targeting in the clinic: the use of a small molecule antagonist of eIF4E (ribavirin), siRNA or antisense oligonucleotide strategies, suicide gene therapy, and the use of a tissue-targeting 4EBP fusion peptide. The first clinical trial targeting eIF4E indicates that ribavirin effectively targets eIF4E in poor prognosis leukemia patients and more importantly leads to striking clinical responses including complete and partial remissions. Finally, we discuss the relevance of these findings to HNSCC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".