Targeting c-MET Alterations in Cancer: A Review of Genetic Drivers and Therapeutic Implications
Bibliographic record
Abstract
Background: Recent research has increasingly highlighted alterations in the proto-oncogene MET, whose abnormal activation has been implicated in multiple cancers. MET encodes c-MET, a receptor tyrosine kinase critical for cellular growth, survival, and migration. Aberrant c-MET signaling, driven by mutations or gene amplification, promotes proliferation and invasion, contributing to tumorigenesis. Scope of the Review: While MET mutations are most often observed in non-small cell lung cancer (NSCLC), they also occur in other malignancies, including breast and gastric cancers. This review highlights key MET alterations, such as gene amplification, gene fusions, and exon 14 skipping deletions, and examines their prevalence across various tumor types. Major Conclusions: We discuss the clinical significance of c-MET as a therapeutic target and identify gaps in knowledge that could inform the development of alternative treatment strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".