Signaling through ShcA Is Required for Transforming Growth Factor β- and Neu/ErbB-2-Induced Breast Cancer Cell Motility and Invasion
Bibliographic record
Abstract
Cooperation between the Neu/ErbB-2 and transforming growth factor beta (TGF-beta) signaling pathways enhances the invasive and metastatic capabilities of breast cancer cells; however, the underlying mechanisms mediating this synergy have yet to be fully explained. We demonstrate that TGF-beta induces the migration and invasion of mammary tumor explants expressing an activated Neu/ErbB-2 receptor, which requires signaling from autophosphorylation sites located in the C terminus. A systematic analysis of mammary tumor explants expressing Neu/ErbB-2 add-back receptors that couple to distinct signaling molecules has mapped the synergistic effect of TGF-beta-induced motility and invasion to signals emanating from tyrosine residues 1226/1227 and 1253 of Neu/ErbB-2. Given that the ShcA adaptor protein is known to interact with Neu/ErbB-2 through these residues, we investigated the importance of this signaling molecule in TGF-beta-induced cell motility and invasion. The reduction of ShcA expression rendered cells expressing activated Neu/ErbB-2, or add-back receptors signaling specifically through tyrosines 1226/1227 or 1253, unresponsive to TGF-beta-induced motility and invasion. In addition, a dominant-negative form of ShcA, lacking its three known tyrosine phosphorylation sites, completely abrogates the TGF-beta-induced migration and invasion of breast cancer cells expressing activated Neu/ErbB-2. Our results implicate signaling through the ShcA adaptor as a key component in the synergistic interaction between these pathways.
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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) | 0.000 | 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 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".