Activation of Akt-1 (PKB-α) Can Accelerate ErbB-2-Mediated Mammary Tumorigenesis but Suppresses Tumor Invasion
Bibliographic record
Abstract
Elevated expression of Akt-1 (PKBalpha) has been noted in a significant percentage of primary human breast cancers. Another frequent event in the genesis of human breast cancers is amplification and overexpression of the ErbB-2 receptor tyrosine kinase, an event which is associated with activation of Akt-1. To directly assess the importance of Akt-1 activation in ErbB-2 mammary tumor progression, we interbred separate strains of transgenic mice carrying mouse mammary tumor virus/activated Akt-1 and mouse mammary tumor virus/activated ErbB-2 to derive progeny that coexpress the transgenes in the mammary epithelium. Female transgenic mice coexpressing activated Akt-1 and ErbB-2 develop multifocal mammary tumors with a significantly shorter latency period than mice expressing activated ErbB-2 alone. This dramatic acceleration of mammary tumor progression correlates with enhanced cellular proliferation, elevated Cyclin D1 protein levels, and phosphorylation of retinoblastoma protein. These bitransgenic mammary tumors also exhibit lower levels of invasion into the surrounding tissue and more differentiated phenotypes. Consistent with these observations, female mice coexpressing activated Akt-1 and ErbB-2 developed significantly fewer metastatic lesions than the activated ErbB-2 strain alone. Taken together, these observations suggest that activation of Akt-1 during ErbB-2-induced mammary tumorigenesis may have opposing effects on tumor growth and metastatic progression.
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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".