HER3 Is Required for HER2-Induced Preneoplastic Changes to the Breast Epithelium and Tumor Formation
Bibliographic record
Abstract
Increasing evidence suggests that HER2-amplified breast cancer cells use HER3/ErbB3 to drive therapeutic resistance to HER2 inhibitors. However, the role of ErbB3 in the earliest events of breast epithelial transformation remains unknown. Using mouse mammary specific models of Cre-mediated ErbB3 ablation, we show that ErbB3 loss prevents the progressive transformation of HER2-overexpressing mammary epithelium. Decreased proliferation and increased apoptosis were seen in MMTV-HER2 and MMTV-Neu mammary glands lacking ErbB3, thus inhibiting premalignant HER2-induced hyperplasia. Using a transgenic model in which HER2 and Cre are expressed from a single polycistronic transcript, we showed that palpable tumor penetrance decreased from 93.3% to 6.7% upon ErbB3 ablation. Penetrance of ductal carcinomas in situ was also decreased. In addition, loss of ErbB3 impaired Akt and p44/42 phosphorylation in preneoplastic HER2-overexpressing mammary glands and in tumors, decreased growth of preexisting HER2-overexpressing tumors, and improved tumor response to the HER2 tyrosine kinase inhibitor lapatinib. These events were rescued by reexpression of ErbB3, but were only partially rescued by ErbB36F, an ErbB3 mutant harboring six tyrosine-to-phenylalanine mutations that block its interaction with phosphatidyl inositol 3-kinase. Taken together, our findings suggest that ErbB3 promotes HER2-induced changes in the breast epithelium before, during, and after tumor formation. These results may have important translational implications for the treatment and prevention of HER2-amplified breast tumors through ErbB3 inhibition.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".