Targeting the translation initiation complex in therapy-resistant and metastatic melanoma
Bibliographic record
Abstract
Abstract Expression of components of the translation initiation complex (eIF4F) is frequently elevated in cancer, resulting in enhanced synthesis of oncogenic proteins. We thus set out to limit eIF4F pro-oncogenic activity, a notable challenge given its essential role in normal tissues. CRISPR-Cas9-based functional screen using tiling sgRNAs identified the eIF4G1 MA3 domain, a subunit of the eIF4F complex, as a target for developing small molecule inhibitors. Combination of structure-guided in silico modeling and chemical library screening led to the identification of small molecule candidates M19 and its analog M19-6 that binds to the MA3 domain of eIF4G1 and disrupts eIF4F complex. M19-6 treatment reprograms the melanoma translatome, limiting synthesis of factors that promote cell proliferation and neoplastic growth, as well as reducing translation of mRNAs encoding ferroptosis suppressors. Whole genome CRISPR screen indentified ferroptosis activators to augment M19-6 activity, which was confirmed in cultured melanoma cells. M19-6 alleviates melanoma resistance to BRAF and MEK inhibitors, while eliciting anti-tumor and anti-metastatic effects in preclinical mouse models. Among several biomarkers found in M19-6 sensitive cell lines, THBS1 and TGFβI expression were elevated in patients who are non-responders to PD-1 therapy as in patients with metastasis. Our studies identify M19-6 as a therapeutic candidate, offering a novel insights into targeting the eIF4F complex to overcome melanoma resistance to therapy and metastasis. Significance We identify M19-6 as a small molecule that disrupts the eIF4F, translation initiation complex, by targeting the MA3 domain of eIF4G1, resulting in elimination of melanoma cells in culture, overcoming therapy resistance while inhibiting melanoma growth and metastasis in vivo . As M19-6 causes minimal toxicity to melanocytes, it offers a therapeutic modality for melanoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".