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 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".