Ets2 in Tumor Fibroblasts Promotes Angiogenesis in Breast Cancer
Bibliographic record
Abstract
Tumor fibroblasts are active partners in tumor progression, but the genes and pathways that mediate this collaboration are ill-defined. Previous work demonstrates that Ets2 function in stromal cells significantly contributes to breast tumor progression. Conditional mouse models were used to study the function of Ets2 in both mammary stromal fibroblasts and epithelial cells. Conditional inactivation of Ets2 in stromal fibroblasts in PyMT and ErbB2 driven tumors significantly reduced tumor growth, however deletion of Ets2 in epithelial cells in the PyMT model had no significant effect. Analysis of gene expression in fibroblasts revealed a tumor- and Ets2-dependent gene signature that was enriched in genes important for ECM remodeling, cell migration, and angiogenesis in both PyMT and ErbB2 driven-tumors. Consistent with these results, PyMT and ErbB2 tumors lacking Ets2 in fibroblasts had fewer functional blood vessels, and Ets2 in fibroblasts elicited changes in gene expression in tumor endothelial cells consistent with this phenotype. An in vivo angiogenesis assay revealed the ability of Ets2 in fibroblasts to promote blood vessel formation in the absence of tumor cells. Importantly, the Ets2-dependent gene expression signatures from both mouse models were able to distinguish human breast tumor stroma from normal stroma, and correlated with patient outcomes in two whole tumor breast cancer data sets. The data reveals a key function for Ets2 in tumor fibroblasts in signaling to endothelial cells to promote tumor angiogenesis. The results highlight the collaborative networks that orchestrate communication between stromal cells and tumor cells, and suggest that targeting tumor fibroblasts may be an effective strategy for developing novel anti-angiogenic therapies.
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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 | 0.000 |
| 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) | 0.000 | 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".