Germ-line expression of an oncogenic <i>erbB2</i> allele confers resistance to <i>erbB2</i> -induced mammary tumorigenesis
Bibliographic record
Abstract
We have previously shown that mammary epithelial specific expression of the activated erbB2 allele under the control of the endogenous promoter in mice resulted in the formation of mammary adenocarcinomas. To assess whether mammary tumorigenesis in this model is influenced by the developmental window of expression, we generated mice that expressed the activated erbB2 allele in the germ line. Although we were able to derive viable transgenic mice that were heterozygous for the activated erbB2 allele, mice homozygous for the activated erbB2 allele died at 12.5 days of embryogenesis. These two separate lines of mice expressed activated erbB2 at equal levels in the mammary gland. Surprisingly, unlike the tumor-prone mice expressing activated ErbB2 in the mammary epithelium, mice with the germ-line erbB2 allele failed to develop tumors. Gene expression analysis of the preneoplastic mammary glands revealed that there were a number of luminal epithelial markers expressed at higher levels in the tumor-prone mice. These data suggest either an expansion of a susceptible population in the tumor-prone mice or the loss of this population in the tumor-resistant mice. Taken together, these observations suggest that the temporal pattern of expression of activated ErbB2 is a critical determinant in mammary tumorigenesis. These results strongly suggest that there are feedback mechanisms present that can compensate for the expression of a potent oncogene.
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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) | 0.001 | 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".