Differential Regulation of Tumor Angiogenesis by Distinct ErbB Homo- and Heterodimers
Bibliographic record
Abstract
Interactions between cancer cells and their microenvironment are critical for the development and progression of solid tumors. This study is the first to examine the role of all members of the ErbB tyrosine kinase receptors (epidermal growth factor receptor [EGFR], ErbB-2, ErbB-3, or ErbB-4), expressed singly or as paired receptor combinations, in the regulation of angiogenesis both in vitro and in vivo. Comparison of all receptor combinations reveals that EGFR/ErbB-2 and ErbB-2/ErbB-3 heterodimers are the most potent inducers of vascular endothelial growth factor (VEGF) mRNA expression compared with EGFR/ErbB-3, EGFR/ErbB-4, ErbB-2/ErbB-4, and ErbB-3/ErbB-4. Immunohistochemistry of tumor xenografts overexpressing these heterodimers shows increased VEGF expression and remarkably enhanced vascularity. Enhanced VEGF expression is associated with increased VEGF transcription. Deletional analysis reveals that ErbB-mediated transcriptional up-regulation of VEGF involves a hypoxia-inducible factor 1-independent responsive region located between nucleotides -88 to -66 of the VEGF promoter. Mutational analysis reveals that the Sp-1 and AP-2 transcription factor binding elements within this region are required for up-regulation of VEGF by heregulin beta1 and that this up-regulation is dependent on the activity of extracellular signal-related protein kinases. These results emphasize the biological implications of cell signaling diversity among members of the ErbB receptor family in regulation of the tumor microenvironment.
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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".