Therapeutic suppression of translation initiation modulates chemosensitivity in a mouse lymphoma model
Bibliographic record
Abstract
Disablement of cell death programs in cancer cells contributes to drug resistance and in some cases has been associated with altered translational control. As eukaryotic translation initiation factor 4E (eIF4E) cooperates with c-Myc during lymphomagenesis, induces drug resistance, and is a genetic modifier of the rapamycin response, we have investigated the effect of dysregulation of the ribosome recruitment phase of translation initiation on tumor progression and chemosensitivity. eIF4E is a subunit of eIF4F, a complex that stimulates ribosome recruitment during translation initiation by delivering the DEAD-box RNA helicase eIF4A to the 5' end of mRNAs. eIF4A is thought to prepare a ribosome landing pad on mRNA templates for incoming 40S ribosomes (and associated factors). Using small molecule screening, we found that cyclopenta[b]benzofuran flavaglines, a class of natural products, modulate eIF4A activity and inhibit translation initiation. One member of this class of compounds, silvestrol, was able to enhance chemosensitivity in a mouse lymphoma model in which carcinogenesis is driven by phosphatase and tensin homolog (PTEN) inactivation or elevated eIF4E levels. These results establish that targeting translation initiation can restore drug sensitivity in vivo and provide an approach to modulating chemosensitivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".