MNK1/2 inhibition limits oncogenicity and metastasis of KIT-mutant melanoma
Bibliographic record
Abstract
Melanoma can be stratified into unique subtypes based on distinct pathologies. The acral/mucosal melanoma subtype is characterized by aberrant and constitutive activation of the proto-oncogene receptor tyrosine kinase C-KIT, which drives tumorigenesis. Treatment of these melanoma patients with C-KIT inhibitors has proven challenging, prompting us to investigate the downstream effectors of the C-KIT receptor. We determined that C-KIT stimulates MAP kinase-interacting serine/threonine kinases 1 and 2 (MNK1/2), which phosphorylate eukaryotic translation initiation factor 4E (eIF4E) and render it oncogenic. Depletion of MNK1/2 in melanoma cells with oncogenic C-KIT inhibited cell migration and mRNA translation of the transcriptional repressor SNAI1 and the cell cycle gene CCNE1. This suggested that blocking MNK1/2 activity may inhibit tumor progression, at least in part, by blocking translation initiation of mRNAs encoding cell migration proteins. Moreover, we developed an MNK1/2 inhibitor (SEL201), and found that SEL201-treated KIT-mutant melanoma cells had lower oncogenicity and reduced metastatic ability. Clinically, tumors from melanoma patients harboring KIT mutations displayed a marked increase in MNK1 and phospho-eIF4E. Thus, our studies indicate that blocking MNK1/2 exerts potent antimelanoma effects and support blocking MNK1/2 as a potential strategy to treat patients positive for KIT mutations.
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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".