Abstract B45: Met-Dependent Positive and Negative Signaling Cascades in Gastric Cancer Cells
Bibliographic record
Abstract
Abstract Signaling by the Met, hepatocyte growth factor (HGF) receptor tyrosine kinase (RTK) activates multiple downstream signaling pathways that promote cell migration and invasive growth. Cells that overexpress and are “addicted” to Met also require Met signaling to sustain cell survival. Thus, a number of specific small-molecule inhibitors have been developed to target Met in the clinic. Although several successes of RTK-targeted therapies are acknowledged, several have limited long-term success clinically, due to development of drug resistance. Initiation of RTK signaling cascades leads to the activation of a number of downstream signaling molecules, but prolonged activation also triggers negative feedback loops that function in part to abrogate this signaling. Hence, targeted inhibition of Met, in addition to suppressing Met-dependent signaling, may also release cells from negative feedback mechanisms and allow the reactivation of signaling pathways that may promote resistance to Met inhibition. As MET amplification occurs in 20–40% of gastric cancers, to identify core Met dependent signaling pathways activated in Met dependent cancers we have used four different gastric cancer cell lines that exhibit amplification, overexpression, and constitutive activation of Met. Upon inhibition of Met with a small-molecule inhibitor, we observe abrogation of several downstream signaling pathways at both the protein phosphorylation and transcript level. Interestingly, as Met has been demonstrated to cross-talk with the EGF receptor family, we also observe a decrease in the phosphorylation of EGFR and HER3 upon treatment with Met inhibitor, and a decrease in expression of EGFR ligands. Conversely, Met inhibition results in an elevation in expression of HER3 transcript and protein in all 4 cell lines, implicating Met signaling in HER3 repression, and inhibition of Met may release HER3 from this negative regulation. As increases in HER3 phosphorylation and expression occur in other models (such as breast or lung cancer cell lines) upon treatment with EGFR, HER2, or AKT inhibitors, and high HER3 expression is strongly associated with tumor progression and poor prognosis in gastric cancer; hence, the loss of negative regulation of HER3, downstream from Met, may ultimately contribute to clinical efficacy, or lack thereof, of 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) | 0.002 | 0.001 |
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".