MNK Inhibition Sensitizes <i>KRAS</i> -Mutant Colorectal Cancer to mTORC1 Inhibition by Reducing eIF4E Phosphorylation and c-MYC Expression
Bibliographic record
Abstract
Abstract KRAS-mutant colorectal cancers are resistant to therapeutics, presenting a significant problem for ∼40% of cases. Rapalogs, which inhibit mTORC1 and thus protein synthesis, are significantly less potent in KRAS-mutant colorectal cancer. Using Kras-mutant mouse models and mouse- and patient-derived organoids, we demonstrate that KRAS with G12D mutation fundamentally rewires translation to increase both bulk and mRNA-specific translation initiation. This occurs via the MNK/eIF4E pathway culminating in sustained expression of c-MYC. By genetic and small-molecule targeting of this pathway, we acutely sensitize KRASG12D models to rapamycin via suppression of c-MYC. We show that 45% of colorectal cancers have high signaling through mTORC1 and the MNKs, with this signature correlating with a 3.5-year shorter cancer-specific survival in a subset of patients. This work provides a c-MYC–dependent cotargeting strategy with remarkable potency in multiple Kras-mutant mouse models and metastatic human organoids and identifies a patient population that may benefit from its clinical application. Significance: KRAS mutation and elevated c-MYC are widespread in many tumors but remain predominantly untargetable. We find that mutant KRAS modulates translation, culminating in increased expression of c-MYC. We describe an effective strategy targeting mTORC1 and MNK in KRAS-mutant mouse and human models, pathways that are also commonly co-upregulated in colorectal cancer. This article is highlighted in the In This Issue feature, p. 995
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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".