Inhibiting the MNK1/2-eIF4E axis impairs melanoma phenotype switching and potentiates antitumor immune responses
Bibliographic record
Abstract
Melanomas commonly undergo a phenotype switch, from a proliferative to an invasive state. Such tumor cell plasticity contributes to immunotherapy resistance; however, the mechanisms are not completely understood and thus are therapeutically unexploited. Using melanoma mouse models, we demonstrated that blocking the MNK1/2-eIF4E axis inhibited melanoma phenotype switching and sensitized melanoma to anti-PD-1 immunotherapy. We showed that phospho-eIF4E-deficient murine melanomas expressed high levels of melanocytic antigens, with similar results verified in patient melanomas. Mechanistically, we identified phospho-eIF4E-mediated translational control of NGFR, a critical effector of phenotype switching. Genetic ablation of phospho-eIF4E reprogrammed the immunosuppressive microenvironment, exemplified by lowered production of inflammatory factors, decreased PD-L1 expression on dendritic cells and myeloid-derived suppressor cells, and increased CD8+ T cell infiltrates. Finally, dual blockade of the MNK1/2-eIF4E axis and the PD-1/PD-L1 immune checkpoint demonstrated efficacy in multiple melanoma models regardless of their genomic classification. An increase in the presence of intratumoral stem-like TCF1+PD-1+CD8+ T cells, a characteristic essential for durable antitumor immunity, was detected in mice given a MNK1/2 inhibitor and anti-PD-1 therapy. Using MNK1/2 inhibitors to repress phospho-eIF4E thus offers a strategy to inhibit melanoma plasticity and improve response to anti-PD-1 immunotherapy.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".