Phosphatase and Tensin Homologue Deleted on Chromosome 10 Deficiency Accelerates Tumor Induction in a Mouse Model of ErbB-2 Mammary Tumorigenesis
Bibliographic record
Abstract
Loss of the tumor suppressor phosphatase and tensin homologue deleted on chromosome 10 (PTEN) and amplification or elevated expression of ErbB-2 are both involved in human breast cancer. To directly test the importance of these genetic events in mammary tumorigenesis, we have assessed whether mammary-specific disruption of PTEN could cooperate with activation of ErbB-2. Transgenic mice expressing ErbB-2 under the transcriptional control of its endogenous promoter (ErbB-2(KI)) were interbred with mice carrying conditional PTEN alleles and an MMTV/Cre transgene. Loss of one or both PTEN alleles resulted in a dramatic acceleration of mammary tumor onset and an increased occurrence of lung metastases in the ErbB-2(KI) strain. Tumor progression in PTEN-deficient/ErbB-2(KI) strains was associated with elevated ErbB-2 protein levels, which were not due to ErbB-2 amplification or to a dramatic increase in ErbB-2 transcripts. Moreover, the PTEN-deficient/ErbB-2(KI)-derived mouse mammary tumors display striking morphologic heterogeneity in comparison with the homogeneous pathology of the ErbB-2(KI) parental strain. Therefore, inactivation of PTEN would not only have a dramatic effect on ErbB-2-induced mammary tumorigenesis but would also lead to the formation of mammary tumors that, in part, display pathologic and molecular features associated with the basal-like subtype of primary human breast cancer.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".