PTEN Deficiency in a Luminal ErbB-2 Mouse Model Results in Dramatic Acceleration of Mammary Tumorigenesis and Metastasis
Bibliographic record
Abstract
Overexpression and/or amplification of the ErbB-2 oncogene as well as inactivation of the PTEN tumor suppressor are two important genetic events in human breast carcinogenesis. To address the biological impact of conditional inactivation of PTEN on ErbB-2-induced mammary tumorigenesis, we generated a novel transgenic mouse model that utilizes the murine mammary tumor virus (MMTV) promoter to directly couple expression of activated ErbB-2 and Cre recombinase to the same mammary epithelial cell (MMTV-NIC). Disruption of PTEN in the mammary epithelium of the MMTV-NIC model system dramatically accelerated the formation of multifocal and highly metastatic mammary tumors, which exhibited homogenous pathology. PTEN-deficient/NIC-induced tumorigenesis was associated with an increase in angiogenesis. Moreover, inactivation of PTEN in the MMTV-NIC mouse model resulted in hyperactivation of the phosphatidylinositol 3'-kinase/Akt signaling pathway. However, like the parental strain, tumors obtained from PTEN-deficient/NIC mice displayed histopathological and molecular features of the luminal subtype of primary human breast cancer. Taken together, our findings provide important implications in understanding the molecular determinants of mammary tumorigenesis driven by PTEN deficiency and ErbB-2 activation and could provide a valuable tool for testing the efficacy of therapeutic strategies that target these critical signaling 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".