Pharmacologic Inhibition of EIF4A Blocks NRF2 Synthesis to Prevent Osteosarcoma Metastasis
Bibliographic record
Abstract
PURPOSE: Effective therapies for metastatic osteosarcoma (OS) remain a critical unmet need. Targeting mRNA translation in metastatic OS offers a promising option, as selective translation drives the synthesis of cytoprotective proteins under harsh microenvironmental conditions to facilitate metastatic competence. EXPERIMENTAL DESIGN: We assessed the expression levels of eukaryotic translation factors in OS, revealing the high expression of the eukaryotic initiation factor 4A1 (EIF4A1). Using a panel of metastatic OS cell lines and patient-derived xenograft (PDX) models, EIF4A1 inhibitors were evaluated for their ability to block proliferation and reduce survival under oxidative stress, mimicking harsh conditions of the lung microenvironment. Inhibitors were also evaluated for their antimetastatic activity using the ex vivo pulmonary metastasis assay and in vivo metastasis models. Proteomics was performed to catalog which cytoprotective proteins or pathways were affected by EIF4A1 inhibition. RESULTS: CR-1-31B, a rocaglate-based EIF4A1 inhibitor, exhibited nanomolar cytotoxicity against all metastatic OS models tested. CR-1-31B exacerbated oxidative stress and apoptosis when OS cells were co-treated with tert-butylhydroquinone, a chemical oxidative stress inducer. CR-1-31B potently inhibited OS growth in the pulmonary metastasis assay model and in experimental and spontaneous models of OS lung metastasis. Proteomic analysis revealed that tert-butylhydroquinone-mediated upregulation of the NRF2 antioxidant factor was blocked by co-treatment with CR-1-31B. Genetic inactivation of NRF2 phenocopied the antimetastatic activity of CR-1-31B. Finally, the clinical-grade EIF4A1 phase-1-to-2 inhibitor, zotatifin, similarly blocked NRF2 synthesis and the OS metastatic phenotype. CONCLUSIONS: Collectively, our data reveal that pharmacologic targeting of EIF4A1 is highly effective in blocking OS metastasis by blunting the NRF2 antioxidant response.
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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) | 0.002 | 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".