Current Concepts and New Insights from Mouse Models of Mammary Tumors on Epithelial Mesenchymal Transition and its Synergy with Mutant p53
Bibliographic record
Abstract
Epithelial Mesenchymal Transition (EMT) is the transdifferentiation of epithelial cells into a mesenchymal phenotype. This process occurs during embryogenesis but also in wound healing and in tumors. The neoplastic EMT is characterized by variably complete shedding of epithelial architectural features and acquisition of mesenchymal traits. In immunohistochemistry a variable coexpression of cytokeratins, vimentin or alpha-smooth muscle actin with loss of E-cadherin and other interepithelial adhesion molecules is characteristic. Such transition is associated with mutations both at the genetic (somatic) and epigenetic levels and is believed to confer a more advantageous phenotype for local and distant spread of cancer cells. Mammary carcinoma can exhibit EMT features in humans and mice and it tends to occur more frequently in women with tumors bearing a worse prognosis such as the claudin low subtype within the triple negative cancer. Missense mutation of TP53 is one of the most common mutations in cancer and it is frequently found in EMT tumor types, often with a more aggressive behavior. The current literature and survey of our mouse EMT cases in the Genomic Pathology Center image archives demonstrate a synergy between p53 and EMT that is independent of the initiating oncogene. However, p53 mutation is not sufficient or causal for EMT. Moreover, despite the local malignant behavior, processes such as spontaneous metastases and Mesenchymal Epithelial Transition (MET) appear not to be as frequent and obvious as previously hypothesized.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".