Modifying chemotherapy response by targeted inhibition of eukaryotic initiation factor 4A
Bibliographic record
Abstract
Translation is regulated predominantly at the initiation phase by several signal transduction pathways that are often usurped in human cancers, including the PI3K/Akt/mTOR axis. mTOR exerts unique administration over translation by regulating assembly of eukaryotic initiation factor (eIF) 4F, a heterotrimeric complex responsible for recruiting 40S ribosomes (and associated factors) to mRNA 5' cap structures. Hence, there is much interest in targeted therapies that block eIF4F activity to assess the consequences on tumor cell growth and chemotherapy response. We report here that hippuristanol (Hipp), a translation initiation inhibitor that selectively inhibits the eIF4F RNA helicase subunit, eIF4A, resensitizes Eμ-Myc lymphomas to DNA damaging agents, including those that overexpress eIF4E-a modifier of rapamycin responsiveness. As Mcl-1 levels are significantly affected by Hipp, combining its use with the Bcl-2 family inhibitor, ABT-737, leads to a potent synergistic response in triggering cell death in mouse and human lymphoma and leukemia cells. Suppression of eIF4AI using RNA interference also synergized with ABT-737 in murine lymphomas, highlighting eIF4AI as a therapeutic target for modulating tumor cell response to chemotherapy.
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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".