MET Activation in Lung Cancer and Response to Targeted Therapies
Bibliographic record
Abstract
The hepatocyte growth factor receptor (MET) is a receptor tyrosine kinase (RTK) that mediates the activity of a variety of downstream pathways upon its activation. These pathways regulate various physiological processes within the cell, including growth, survival, proliferation, and motility. Under normal physiological conditions, this allows MET to regulate various development and regenerative processes; however, mutations resulting in aberrant MET activity and the consequent dysregulation of downstream signaling can contribute to cellular pathophysiology. Mutations within MET have been identified in a variety of cancers and have been shown to mediate tumorigenesis by increasing RTK activity and downstream signaling. In lung cancer specifically, a number of patients have been identified as possessing MET alterations, commonly receptor amplification (METamp) or splice site mutations resulting in loss of exon 14 (METex14). Due to MET's role in mediating oncogenesis, it has become an attractive clinical target and has led to the development of various targeted therapies, including MET tyrosine kinase inhibitors (TKIs). Unfortunately, these TKIs have demonstrated limited clinical efficacy, as patients often present with either primary or acquired resistance to these therapies. Mechanisms of resistance vary but often occur through off-target or bypass mechanisms that render downstream signaling pathways insensitive to MET inhibition. This review provides an overview of the therapeutic landscape for MET-positive cancers and explores the various mechanisms that contribute to therapeutic resistance in these cases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".