HGF-independent regulation of MET and GAB1 by nonreceptor tyrosine kinase FER potentiates metastasis in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer cells disseminate readily within the peritoneal cavity, which promotes metastasis, and are often resistant to chemotherapy. Ovarian cancer patients tend to present with advanced disease, which also limits treatment options; consequently, new therapies are required. The oncoprotein tyrosine kinase MET, which is the receptor for hepatocyte growth factor (HGF), has been implicated in ovarian tumorigenesis and has been the subject of extensive drug development efforts. Here, we report a novel ligand- and autophosphorylation-independent activation of MET through the nonreceptor tyrosine kinase feline sarcoma-related (FER). We demonstrated that the levels of FER were elevated in ovarian cancer cell lines relative to those in immortalized normal surface epithelial cells and that suppression of FER attenuated the motility and invasive properties of these cancer cells. Furthermore, loss of FER impaired the metastasis of ovarian cancer cells in vivo. Mechanistically, we demonstrated that FER phosphorylated a signaling site in MET: Tyr1349. This enhanced activation of RAC1/PAK1 and promoted a kinase-independent scaffolding function that led to recruitment and phosphorylation of GAB1 and the specific activation of the SHP2-ERK signaling pathway. Overall, this analysis provides new insights into signaling events that underlie metastasis in ovarian cancer cells, consistent with a prometastatic role of FER and highlighting its potential as a novel therapeutic target for metastatic ovarian cancer.
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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 | 0.000 | 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 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".