Translation initiation factor eIF4F modifies the dexamethasone response in multiple myeloma
Bibliographic record
Abstract
Enhanced protein synthesis capacity is associated with increased tumor cell survival, proliferation, and resistance to chemotherapy. Cancers like multiple myeloma (MM), which display elevated activity in key translation regulatory nodes, such as the PI3K/mammalian target of rapamycin and MYC-eukaryotic initiation factor (eIF) 4E pathways, are predicted to be particularly sensitive to therapeutic strategies that target this process. To identify novel vulnerabilities in MM, we undertook a focused RNAi screen in which components of the translation apparatus were targeted. Our screen was designed to identify synthetic lethal relationships between translation factors or regulators and dexamethasone (DEX), a corticosteroid used as frontline therapy in this disease. We find that suppression of all three subunits of the eIF4F cap-binding complex synergizes with DEX in MM to induce cell death. Using a suite of small molecules that target various activities of eIF4F, we observed that cell survival and DEX resistance are attenuated upon eIF4F inhibition in MM cell lines and primary human samples. Levels of MYC and myeloid cell leukemia 1, two known eIF4F-responsive transcripts and key survival factors in MM, were reduced upon eIF4F inhibition, and their independent suppression also synergized with DEX. Inhibition of eIF4F in MM exerts pleotropic effects unraveling a unique therapeutic opportunity.
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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".