Angiogenic Acceleration of Neu Induced Mammary Tumor Progression and Metastasis
Bibliographic record
Abstract
The Neu (ErbB2, HER2) member of the epidermal growth factor receptor family is implicated in many human breast cancers. We have tested the importance of increased angiogenic signaling in the NeuYD [mouse mammary tumor virus (MMTV)-Neu(ndl)-YD5] mammary tumor model. Transgenic mice expressing vascular endothelial growth factor (VEGF)(164) from the MMTV promoter were generated. These mice expressed VEGF(164) RNA and protein at 20- to 40-fold higher levels throughout mammary gland development but exhibited normal mammary gland development and function. However, in combination with the NeuYD oncogene, VEGF(164) expression resulted in increased vascularization of hyperplastic mammary epithelium and dramatic acceleration of tumor appearance from 111 to 51 days. Gene expression profiling also indicated that the VEGF-accelerated tumors were substantially more vascularized and less hypoxic. The preferential vascularization of early hyperplastic portions of mammary epithelia in NeuYD;MMTV-VEGF animals was associated with NeuYD RNA expression, disorganization of the tight junctions, and overlapping transgenic VEGF expression. NeuYD;MMTV-VEGF(164) bigenic, tumor-bearing animals resulted in an average of 10 tumor cell colonies/lung lodged within vascular spaces. No similar lung colonies were found in control NeuYD mice with similar tumor burdens. Overall, these results demonstrate the angiogenic restriction of early hyperplastic mammary lesions. They also reinforce in vivo the importance of activated Neu in causing disorganization of mammary luminal epithelial cell junctions and provide support for an invasion-independent mechanism of metastasis.
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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".