HER kinase activation confers resistance to MET tyrosine kinase inhibition in MET oncogene-addicted gastric cancer cells
Bibliographic record
Abstract
Tumor cells with genomic amplification of MET display constitutive activation of the MET tyrosine kinase, which renders them highly sensitive to MET inhibition. Several MET inhibitors have recently entered clinical trials; however, as with other molecularly targeted agents, resistance is likely to develop. Therefore, elucidating possible mechanisms of resistance is of clinical interest. We hypothesized that collateral growth factor receptor pathway activation can overcome the effects of MET inhibition in MET-amplified cancer cells by reactivating key survival pathways. Treatment of MET-amplified GTL-16 and MKN-45 gastric cancer cells with the highly selective MET inhibitor PHA-665752 abrogated MEK/mitogen-activated protein kinase (MAPK) and phosphoinositide 3-kinase (PI3K)/AKT signaling, resulting in cyclin D1 loss and G(1) arrest. PHA-665752 also inhibited baseline phosphorylation of epidermal growth factor receptor (EGFR) and HER-3, which are transactivated via MET-driven receptor cross-talk in these cells. However, MET-independent HER kinase activation using EGF (which binds to and activates EGFR) or heregulin-beta1 (which binds to and activates HER-3) was able to overcome the growth-inhibitory effects of MET inhibition by restimulating MEK/MAPK and/or PI3K/AKT signaling, suggesting a possible escape mechanism. Importantly, dual inhibition of MET and HER kinase signaling using PHA-665752 in combination with the EGFR inhibitor gefitinib or in combination with inhibitors of MEK and AKT prevented the above rescue effects. Our results illustrate that highly targeted MET tyrosine kinase inhibition leaves MET oncogene-"addicted" cancer cells vulnerable to HER kinase-mediated reactivation of the MEK/MAPK and PI3K/AKT pathways, providing a rationale for combined inhibition of MET and HER kinase signaling in MET-amplified tumors that coexpress EGFR and/or HER-3.
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 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".