Therapeutic Inhibition of MAP Kinase Interacting Kinase Blocks Eukaryotic Initiation Factor 4E Phosphorylation and Suppresses Outgrowth of Experimental Lung Metastases
Bibliographic record
Abstract
Activation of the translation initiation factor 4E (eIF4E) promotes malignant transformation and metastasis. Signaling through the AKT-mTOR pathway activates eIF4E by phosphorylating the inhibitory 4E binding proteins (4E-BP). This liberates eIF4E and allows binding to eIF4G. eIF4E can then be phosphorylated at serine 209 by the MAPK-interacting kinases (Mnk), which also interact with eIF4G. Although dispensable for normal development, Mnk function and eIF4E phosphorylation promote cellular proliferation and survival and are critical for malignant transformation. Accordingly, Mnk inhibition may serve as an attractive cancer therapy. We now report the identification of a potent, selective and orally bioavailable Mnk inhibitor that effectively blocks 4E phosphorylation both in vitro and in vivo. In cultured cancer cell lines, Mnk inhibitor treatment induces apoptosis and suppresses proliferation and soft agar colonization. Importantly, a single, orally administered dose of this Mnk inhibitor substantially suppresses eIF4E phosphorylation for at least 4 hours in human xenograft tumor tissue and mouse liver tissue. Moreover, oral dosing with the Mnk inhibitor significantly suppresses outgrowth of experimental B16 melanoma pulmonary metastases as well as growth of subcutaneous HCT116 colon carcinoma xenograft tumors, without affecting body weight. These findings offer the first description of a novel, orally bioavailable MNK inhibitor and the first preclinical proof-of-concept that MNK inhibition may provide a tractable cancer therapeutic approach.
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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) | 0.001 | 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) | 0.001 | 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".