ErbB-2 Receptor Cooperates with E6/E7 Oncoproteins of HPV Type 16 in Breast Tumorigenesis
Bibliographic record
Abstract
The ErbB-2 receptor is overexpressed in roughly 30% of human breast cancers. Moreover, approximately 50% of breast cancers are positive for high-risk human papillomaviruses (HPVs), specifically types 16 and 18. Recently, we reported that ErbB-2 cooperates with E6/E7 oncoproteins of HPV type 16 to induce neoplastic transformation of human normal oral epithelial cells. We also demonstrated that E6/E7 of HPV type 16 converts non-invasive breast cancer cells to an invasive form. In order to investigate the effect of ErbB-2/E6/E7 cooperation in breast carcinogenesis, we generated double transgenic mice carrying ErbB-2 and E6/E7 of HPV type 16 under mouse mammary tumor virus (MMTV) and human keratin 14 promoters, respectively. Within six months, these double transgenic mice developed large and extensive invasive breast cancer in comparison to ErbB-2 or E6/E7 singly transgenic mice. Histological analysis of ErbB-2/E6/E7 transgenic mice tumors showed the presence of invasive breast carcinomas. However, the breast tissues from ErbB-2 and E6/E7 transgenic mice showed only in-situ cancer and normal mammary phenotype, respectively. In parallel, we examined the cooperation effect of ErbB-2 and E6/E7 in the human breast cancer cell line, BT20; in comparison to ErbB-2 and E6/E7 alone as well as wild type cells, we found that ErbB 2/E6/E7 together stimulate colony formation and cell migration in the BT20 cell line. Furthermore, we found that beta-catenin is constitutively phosphorylated by c-Src and consequently trans-located to the nucleus in ErbB-2/E6/E7-breast cancer cells. These findings provide evidence that the ErbB-2 receptor cooperates with high-risk HPVs in breast tumorigenesis via beta-catenin activation.
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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".