Abstract 4688: Met receptor tyrosine kinase cross-talk in tumorigenesis
Bibliographic record
Abstract
Abstract The Met receptor tyrosine kinase (RTK) is a cell surface, transmembrane protein that is activated in response to the growth factor Hepatocyte Growth Factor (HGF) and initiates multiple downstream signaling pathways that predominantly promote cell migration and invasive cell growth. These processes are essential for Met-dependent signals during development. Dysregulated signaling of Met has been characterized in cancers, and include mechanisms such as point mutations, gene amplification, overexpression, and loss of negative regulation and more recently evidence of Met cross talk with other RTKs has been established. However, the role of Met cross-talk with other cell surface receptors in tumorigenesis, is still poorly understood. Recent studies in non-small cell lung cancer cell lines have established that amplification of MET contributes to acquired resistance to the EGFR inhibitor gefitinib through phosphorylation of HER3, a member of the EGF receptor family of RTKs. As HER3 phosphorylation was demonstrated to occur under conditions of MET amplification, we have investigated whether HER3 phosphorylation and signaling is a common event in other cell lines where MET is amplified. In gastric cancer cell lines with MET amplification where Met is overexpressed and constitutively active, HER3 is phosphorylated. Met and HER3 are also found in a complex, as established through co-immunoprecipitation. Interestingly, upon treatment with a small molecule kinase inhibitor targeting Met, HER3 phosphorylation and association between the two receptors decreases. Thus, in cells that express high levels of constitutively active Met, cross-talk with other receptors, such as HER3, may be another mechanism through which Met promotes tumorigenesis. The interaction of Met with HER3 will be addressed at the mechanistic level and these data will be presented. Kinase inhibitors that target single RTKs have proven susceptible to resistance. Inhibition of one RTK often results in upregulation of signaling through another receptor, and in this way, re-establish pathways essential for tumorigenesis. Determining how and under what conditions RTKs cross-talk with one another may allow identification of potential resistance mechanisms, as well as other targets for multi-targeted therapies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4688. doi:10.1158/1538-7445.AM2011-4688
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".