Dissecting eIF4E action in tumorigenesis
Bibliographic record
Abstract
Genetically engineered mouse models are powerful tools for studying cancer genes and validating targets for cancer therapy. We previously used a mouse lymphoma model to demonstrate that the translation initiation factor eIF4E is a potent oncogene in vivo. Using the same model, we now show that the oncogenic activity of eIF4E correlates with its ability to activate translation and become phosphorylated on Ser 209. Furthermore, constitutively activated MNK1, an eIF4E Ser 209 kinase, promotes tumorigenesis in a manner similar to eIF4E, and a dominant-negative MNK mutant inhibits the in vivo proliferation of tumor cells driven by mutations that deregulate translation. Phosphorylated eIF4E promotes tumorigenesis primarily by suppressing apoptosis and, accordingly, the anti-apoptotic protein Mcl-1 is one target of both phospho-eIF4E and MNK1 that contributes to tumor formation. Our results provide insight into how eIF4E contributes to tumorigenesis and pinpoint a level of translational control that may be suitable for therapeutic intervention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".