A second-generation eIF4A RNA helicase inhibitor exploits translational reprogramming as a vulnerability in triple-negative breast cancer
Bibliographic record
Abstract
In this study, we aimed to address the current limitations of therapies for macro-metastatic triple-negative breast cancer (TNBC) and provide a therapeutic lead that overcomes the high degree of heterogeneity associated with this disease. Specifically, we focused on well-documented but clinically underexploited cancer-fueling perturbations in mRNA translation as a potential therapeutic vulnerability. We therefore developed an orally bioavailable rocaglate-based molecule, MG-002, which hinders ribosome recruitment and scanning via unscheduled and non-productive RNA clamping by the eukaryotic translation initiation factor (eIF) 4A RNA helicase. We demonstrate that MG-002 potently inhibits mRNA translation and primary TNBC tumor growth without causing overt toxicity in mice. Importantly, given that metastatic spread is a major cause of mortality in TNBC, we show that MG-002 attenuates metastasis in pre-clinical models. We report on MG-002, a rocaglate that shows superior properties relative to existing eIF4A inhibitors in pre-clinical models. Our study also paves the way for future clinical trials exploring the potential of MG-002 in TNBC and other oncological indications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".