<p><em>MET</em> Inhibitors for the Treatment of Gastric Cancer: What’s Their Potential?</p>
Bibliographic record
Abstract
Abstract: Gastric cancer remains a disease with a dismal prognosis. Extensive efforts to find targetable disease drivers in gastric cancer were implemented to improve patient outcomes. Beyond anti- HER2 therapy, MET pathway seems to be culprit of cancer invasiveness with MET -overexpressing tumors having poorer prognosis. Tyrosine kinase inhibitors targeting the HGF/MET pathway were studied in MET -positive gastric cancer, but no substantial benefit was proven. Some patients responded in early phase trials but later developed resistance. Others failed to show any benefit at all. Etiologies of resistance may entail inappropriate patient selection with a lack of MET detection standardization, tumor alternative pathways, variable MET amplification, and genetic variation. Optimizing MET detection techniques and better understanding the MET pathway, as well as tumor bypass mechanisms, are an absolute need to devise means to overcome resistance using targeted therapy alone, or in combination with other synergistic agents to improve outcomes of patients with MET -positive GC. Keywords: gastric cancer, MET over-expression, MET amplification, HGF , tyrosine kinase inhibitors, monoclonal antibodies
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".