The MNK1/2-eIF4E axis drives melanoma plasticity, progression, and resistance to immunotherapy
Bibliographic record
Abstract
Abstract Melanomas commonly undergo a phenotype switch, from a proliferative to an invasive state. Melanoma plasticity exhibited as phenotype switching contributes to immunotherapy resistance, however the mechanisms are not completely understood and thus therapeutically unexploited. Here, using a transgenic melanoma mouse model, we demonstrated a critical role of the MNK1/2-eIF4E axis in melanoma plasticity and resistance to immunotherapy. We showed that phospho-eIF4E deficient murine melanomas express high levels of melanocytic antigens, with similar results verified in patient melanomas. Mechanistically, we identified that phospho-eIF4E controls the translation of NGFR , a critical effector of phenotype switching. In patients with melanoma, the expression of MKNK1 , the kinase for eIF4E, positively correlated with markers of immune exhaustion. Genetic ablation of phospho-eIF4E reprogrammed the immunosuppressive microenvironment, exemplified by lowered production of inflammatory factors and increased CD8 + T cell infiltrates. Blocking phospho-eIF4E, using MNK1/2 inhibitors, offers a new strategy to inhibit melanoma plasticity and improve the survival response to anti-PD-1 immunotherapy.
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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 | 0.001 |
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".