Targeting Synthetic Lethal Interactions between Myc and the eIF4F Complex Impedes Tumorigenesis
Bibliographic record
Abstract
The energetically demanding process of translation is linked to multiple signaling events through mTOR-mediated regulation of eukaryotic initiation factor (eIF)4F complex assembly. Disrupting mTOR constraints on eIF4F activity can be oncogenic and alter chemotherapy response, making eIF4F an attractive antineoplastic target. Here, we combine a newly developed inducible RNAi platform and pharmacological targeting of eIF4F activity to define a critical role for endogenous eIF4F in Myc-dependent tumor initiation. We find elevated Myc levels are associated with deregulated eIF4F activity in the prelymphomatous stage of the Eμ-Myc lymphoma model. Inhibition of eIF4F is synthetic lethal with elevated Myc in premalignant pre-B/B cells resulting in reduced numbers of cycling pre-B/B cells and delayed tumor onset. At the organismal level, eIF4F suppression affected a subset of normal regenerating cells, but this was well tolerated and rapidly and completely reversible. Therefore, eIF4F is a key Myc client that represents a tumor-specific vulnerability.
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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 | 0.001 |
| 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".