Abstract 5064: The non-receptor tyrosine kinase Fer is required for activated ErbB2-mediated tumorigenesis
Bibliographic record
Abstract
Abstract The fer proto-onogene encodes a non-receptor protein-tyrosine kinase (PTK) which together with the parologous Fps/Fes kinase makes up a distinct F-BAR domain containing subclass of PTKs. F-BARs have recently been shown to mediate membrane binding and bending properties that confer roles in receptor endocytosis and vesicle trafficking. Among known substrates of the Fer PTK are cortactin and F-BAR/SH3 containing adaptor proteins that regulate cytoskeletal remodeling and membrane dynamics important to cellular functions such as polarity determination, migration and mitogenesis. Mice deficient in Fer, Fps/Fes or both kinases are overtly healthy; although they do display enhanced responses to endotoxin challenges. Biochemical studies have implicated Fer in growth factor signaling, cadherin-dependent cell-cell adhesion, integrin-mediated adhesion and redox signaling. Cell biological studies have suggested a role for Fer in potentiating proliferation and migration. Recent results in our laboratory have provided compelling biological proof-in-principle that Fer is required for mammary tumorigenesis. RNAi-based knockdown of Fer reduced the tumorigenesis of the metastatic MDA-MB-231 breast cancer cells in mouse xenografting studies. In addition, tumor development in a transgenic mouse model of Her2/Neu/ErbB2-mediated breast tumorigenesis was significantly delayed in Fer-deficient mice by virtue of a targeted knock-in kinase-inactivating mutation in the mouse fer locus. Biochemical internalization assays suggested that EGFR endocytosis was enhanced in Fer-deficient tumor-derived epithelial cell cultures. These data point to a tumor promoting function for Fer in mammary tumorigenesis through a mechanism involving prolongation of ErbB2 signaling. Taken together, these studies suggest that pharmacological intervention of Fer may have therapeutic benefit in human ErbB2 positive breast cancer and potentially, in other epithelial cancers. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5064.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".