Distinct ErbB-2–Coupled Signaling Pathways Promote Mammary Tumors with Unique Pathologic and Transcriptional Profiles
Bibliographic record
Abstract
ErbB-2 overexpression and amplification occurs in 15% to 30% of human invasive breast carcinomas associated with poor clinical prognosis. Previously, we have shown that four ErbB-2/Neu tyrosine-autophosphorylation sites within the cytoplasmic tail of the receptor recruit distinct adaptor proteins and are sufficient to mediate transforming signals in vitro. Two of these sites, representing the growth factor receptor binding protein 2 (Grb2; Neu-YB) and the Src homology and collagen (Shc; Neu-YD) binding sites, can induce mammary tumorigenesis and metastasis. Here, we show that transgenic mice bearing the two other ErbB-2 autophosphorylation sites (Neu-YC and Neu-YE) develop metastatic mammary tumors. A detailed comparison of biological profiles among all Neu mutant mouse models revealed that Neu-YC, Neu-YD, and Neu-YE mammary tumors shared similar pathologic and transcriptional features. By contrast, the Neu-YB mouse model displayed a unique pathology with a high metastatic potential that correlates with a distinct transcriptional profile, including genes that promote malignant tumor progression such as metalloproteinases and chemokines. Furthermore, Neu-YB tumor epithelial cells showed abundant intracellular protein level of the chemokine CXCL12/SDF-1alpha, which may reflect the aggressive nature of this Neu mutant mouse model. Taken together, these findings indicate that activation of distinct Neu-coupled signaling pathways has an important impact on the biological behavior of Neu-induced tumors.
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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".