Met induces mammary tumors with diverse histologies and is associated with poor outcome and human basal breast cancer
Bibliographic record
Abstract
Elevated MET receptor tyrosine kinase correlates with poor outcome in breast cancer, yet the reasons for this are poorly understood. We thus generated a transgenic mouse model targeting expression of an oncogenic Met receptor (Met(mt)) to the mammary epithelium. We show that Met(mt) induces mammary tumors with multiple phenotypes. These reflect tumor subtypes with gene expression and immunostaining profiles sharing similarities to human basal and luminal breast cancers. Within the basal subtype, Met(mt) induces tumors with signatures of WNT and epithelial to mesenchymal transition (EMT). Among human breast cancers, MET is primarily elevated in basal and ERBB2-positive subtypes with poor prognosis, and we show that MET, together with EMT marker, SNAIL, are highly predictive of poor prognosis in lymph node-negative patients. By generating a unique mouse model in which the Met receptor tyrosine kinase is expressed in the mammary epithelium, along with the examination of MET expression in human breast cancer, we have established a specific link between MET and basal breast cancer. This work identifies basal breast cancers and, additionally, poor-outcome breast cancers, as those that may benefit from anti-MET receptor therapies.
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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) | 0.002 | 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".