Transforming Growth Factor β Induces Clustering of HER2 and Integrins by Activating Src-Focal Adhesion Kinase and Receptor Association to the Cytoskeleton
Bibliographic record
Abstract
It has been proposed that cross talk between integrin and growth factor receptor signaling such as ErbB2 (HER2) is required for activation of downstream effectors and ErbB2-mediated mammary tumorigenesis. Here we show that transforming growth factor beta (TGF-beta) induced focal adhesion kinase (FAK)-dependent clustering of HER2 and integrins alpha(6), beta(1), and beta(4) in HER2-overexpressing mammary epithelial cells without altering the total and surface levels of HER2 receptors. This effect was mediated by ligand-induced epidermal growth factor receptor (EGFR) activation and the subsequent phosphorylation of Src and FAK. We have previously reported that TGF-beta up-regulates EGFR ligand shedding through a mechanism involving the phosphorylation of tumor necrosis factor-alpha-converting enzyme (TACE/ADAM17). Knockdown of TACE, FAK, or integrin alpha(6) by siRNA or inhibition of EGFR or Src by specific inhibitors abrogated TGF-beta-induced receptor clustering and signaling to phosphatidylinositol 3-kinase-Akt. Finally, inhibition of Src-FAK reversed TGF-beta-induced resistance to the therapeutic HER2 inhibitor trastuzumab in HER2-overexpressing breast cancer cells. Taken together, these data suggest that, by activating Src-FAK, TGF-beta integrates ErbB receptor and integrin signaling to induce cell migration and survival during breast cancer progression.
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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) | 0.001 | 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".