Activation of HER family members in gastric carcinoma cells mediates resistance to MET inhibition
Bibliographic record
Abstract
BACKGROUND: Gastric cancer is the second leading cause of cancer mortality in the world. The receptor tyrosine kinase MET is constitutively activated in many gastric cancers and its expression is strictly required for survival of some gastric cancer cells. Thus, MET is considered a good candidate for targeted therapeutic intervention in this type of tumor, and MET inhibitors recently entered clinical trials. One of the major problems of therapies targeting tyrosine kinases is that many tumors are not responsive to treatment or eventually develop resistance to the drugs. Perspective studies are thus mandatory to identify the molecular mechanisms that could cause resistance to these therapies. RESULTS: Our in vitro and in vivo results demonstrate that, in MET-addicted gastric cancer cells, the activation of HER (Human Epidermal Receptor) family members induces resistance to MET silencing or inhibition by PHA-665752 (a selective kinase inhibitor). We provide molecular evidences highlighting the role of EGFR, HER3, and downstream signaling pathways common to MET and HER family in resistance to MET inhibitors. Moreover, we show that an in vitro generated gastric cancer cell line resistant to MET-inhibition displays overexpression of HER family members, whose activation contributes to maintenance of resistance. CONCLUSIONS: Our findings predict that gastric cancer tumors bearing constitutive activation of HER family members are poorly responsive to MET inhibition, even if this receptor is constitutively active. Moreover, the appearance of these alterations might also be responsible for the onset of resistance in initially responsive tumors.
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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) | 0.002 | 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".