Regulation of multiple tumor microenvironment markers by overexpression of single or paired combinations of ErbB receptors.
Bibliographic record
Abstract
The progression of primary tumors to an invasive phenotype requires dynamic changes in multiple cellular and local tumor microenvironment markers. In this study, we report a genomic approach to assess gene transcriptional changes upon overexpression of ErbB receptors, in vitro and in vivo, focusing on markers involved in the regulation of the tumor microenvironment. ErbB receptors (ErbB-1/epidermal growth factor receptor, ErbB-2, ErbB-3, and ErbB-4) were stably overexpressed in a polyclonal cell population as single or paired combinations using murine and human breast cell models. The overall numbers of known genes that are up- or down-regulated was significantly higher in cells and tumors overexpressing paired combinations of receptors compared with cells and tumors overexpressing single ErbB receptors. Genes encoding components of cell-cell structures, extracellular matrix, coagulation factors, and angiogenesis were predominantly affected by the most active ErbB receptor combinations and were predictive of the aggressive in vivo tumorigenicity, a feature that was not always seen in vitro. Among ErbB-regulated tumor microenvironment markers detected by the genomic analysis, thrombospondin 1, an endogenous inhibitor of angiogenesis, was additionally validated in relation to tumor growth phenotype. Thrombospondin 1 mRNA and protein were down-regulated by specific ErbB receptors, in vitro and in both rodent and human ErbB-induced tumors, consistent with the extent of tumor growth and tumor vascularization associated with specific ErbB receptors. In summary, our genomic results highlight the broad diversity of ErbB-regulated cancer-associated genes and revealed several novel targets that may have potential therapeutic applications for targeting tumor progression involving aberrations of ErbB receptors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".