Transforming Growth Factor β Induces Clustering of HER2 and Integrins by Activating Src-Focal Adhesion Kinase and Receptor Association to the Cytoskeleton
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it