T-cadherin Supports Angiogenesis and Adiponectin Association with the Vasculature in a Mouse Mammary Tumor Model
Bibliographic record
Abstract
T-cadherin delineates endothelial, myoepithelial, and ductal epithelial cells in the normal mouse mammary gland, and becomes progressively restricted to the vasculature during mammary tumorigenesis. To test the function of T-cadherin in breast cancer, we inactivated the T-cadherin (Cdh13) gene in mice and evaluated tumor development and pathology after crossing the mutation into the mouse mammary tumor virus (MMTV)-polyoma virus middle T (PyV-mT) transgenic model. We report that T-cadherin deficiency limits mammary tumor vascularization and reduces tumor growth. Tumor transplantation experiments confirm the stromal role of T-cadherin in tumorigenesis. In comparison with wild-type MMTV-PyV-mT controls, T-cadherin-deficient tumors are pathologically advanced and metastasize to the lungs. T-cadherin is a suggested binding partner for high molecular weight forms of the circulating, fat-secreted hormone adiponectin. We discern adiponectin in association with the T-cadherin-positive vasculature in the normal and malignant mammary glands and report that this interaction is lost in the T-cadherin null condition. This work establishes a role for T-cadherin in promoting tumor angiogenesis and raises the possibility that vascular T-cadherin-adiponectin association may contribute to the molecular cross-talk between tumor cells and the stromal compartment in breast cancer.
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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 | 0.001 | 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".