Met synergizes with p53 loss to induce mammary tumors that possess features of claudin-low breast cancer
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) accounts for ∼20% of cases and contributes to basal and claudin-low molecular subclasses of the disease. TNBCs have poor prognosis, display frequent mutations in tumor suppressor gene p53 (TP53), and lack targeted therapies. The MET receptor tyrosine kinase is elevated in TNBC and transgenic Met models (Met(mt)) develop basal-like tumors. To investigate collaborating events in the genesis of TNBC, we generated Met(mt) mice with conditional loss of murine p53 (Trp53) in mammary epithelia. Somatic Trp53 loss, in combination with Met(mt), significantly increased tumor penetrance over Met(mt) or Trp53 loss alone. Unlike Met(mt) tumors, which are histologically diverse and enriched in a basal-like molecular signature, the majority of Met(mt) tumors with Trp53 loss displayed a spindloid pathology with a distinct molecular signature that resembles the human claudin-low subtype of TNBC, including diminished claudins, an epithelial-to-mesenchymal transition signature, and decreased expression of the microRNA-200 family. Moreover, although mammary specific loss of Trp53 promotes tumors with diverse pathologies, those with spindloid pathology and claudin-low signature display genomic Met amplification. In both models, MET activity is required for maintenance of the claudin-low morphological phenotype, in which MET inhibitors restore cell-cell junctions, rescue claudin 1 expression, and abrogate growth and dissemination of cells in vivo. Among human breast cancers, elevated levels of MET and stabilized TP53, indicative of mutation, correlate with highly proliferative TNBCs of poor outcome. This work shows synergy between MET and TP53 loss for claudin-low breast cancer, identifies a restricted claudin-low gene signature, and provides a rationale for anti-MET therapies in TNBC.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".