Mechanistic insights into MET exon 14 skipping mutations and their role in tumor progression
Bibliographic record
Abstract
The MET receptor tyrosine kinase is a pivotal regulator of cellular survival, motility, and proliferation. Mutations leading to skipping of exon 14 (METΔex14) within the juxtamembrane domain of MET impair receptor degradation and prolong oncogenic signaling, contributing significantly to tumor progression across multiple cancer types. METΔex14 mutations are associated with aggressive clinical behavior, therapeutic resistance, and poor outcomes. Next-generation sequencing from both tissue and liquid biopsies has significantly improved the detection frequency of METΔex14 in lung and other cancers. However, clinical trials targeting METΔex14 have rendered partial responses and mixed outcomes due to the lack of a comprehensive mechanistic understanding of METΔex14 regulation and a diverse mutational landscape. This review synthesizes current knowledge on the mechanistic basis of METΔex14-driven oncogenesis, including alterations in receptor dynamics, downstream signaling perturbations, genomic alterations underlying this mutation, and mechanisms of acquired therapeutic resistance. We further discuss the clinical implications of these insights and highlight future research directions essential for optimizing targeted therapies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".