Dynamic Reprogramming of Signaling Upon Met Inhibition Reveals a Mechanism of Drug Resistance in Gastric Cancer
Bibliographic record
Abstract
The Met receptor tyrosine kinase is activated or genetically amplified in some gastric cancers, but resistance to small-molecule inhibitors of Met often emerges in patients. We found that Met abundance correlated with a proliferation marker in patient gastric tumor sections, and gastric cancer cell lines that have MET amplifications depended on Met for proliferation and anchorage-independent growth in culture. Inhibition of Met induced temporal changes in gene expression in the cell lines, initiated by a rapid decrease in the expression of genes encoding transcription factors, followed by those encoding proteins involved in epithelial-mesenchymal transition, and finally those encoding cell cycle-related proteins. In the gastric cancer cell lines, microarray and chromatin immunoprecipitation analysis revealed considerable overlap between genes regulated in response to Met stimulation and those regulated by signal transducer and activator of transcription 3 (STAT3). The activity of STAT3, extracellular signal-regulated kinase (ERK), and the kinase Akt was decreased by Met inhibition, but only inhibitors of STAT3 were as effective as the Met inhibitor in decreasing tumor cell proliferation in culture and in xenografts, suggesting that STAT3 mediates the pro-proliferative program induced by Met. However, the phosphorylation of ERK increased after prolonged Met inhibition in culture, correlating with decreased abundance of the phosphatases DUSP4 and DUSP6, which inhibit ERK. Combined inhibition of Met and the mitogen-activated protein kinase kinase (MEK)-ERK pathway induced greater cell death in cultured gastric cancer cells than did either inhibitor alone. These findings indicate combination therapies that may counteract resistance to Met inhibitors.
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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 | 0.000 | 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) | 0.001 | 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".