Development of a drug repressible β-catenin mutant mouse to examine effect of activated β-catenin signaling on long-term maintenance of mammary tumor growth
Bibliographic record
Abstract
Abstract Stabilized β-catenin expression is a well described initiator of mammary tumorigenesis in the mouse and elevated nuclear expression of this protein has been observed in human triple-negative tumor samples. However, the importance of stabilized β-catenin to continued tumor growth after initiation, and in the context of other driver mutations, has yet to be elucidated. To ascertain the importance of stabilized β-catenin after tumor initiation, we generated a novel transgenic mouse model, utilizing the tet-off system, to control expression of a stabilized mutant of β-catenin. Pups from early litters carrying one allele of regulatable stabilized β-catenin at the Rosa26 locus, but not targeted by Cre, were smaller in size compared with wildtype littermates and also developed skin lesions, requiring euthanasia. Maintenance of breeding cages on doxycycline chow allowed for healthy transgenic pups to survive past 3 weeks of age and 6 animal cohorts were established. We used two different mammary Cre strains, WAP-Cre and MMTV-NLST-Cre, crossed with regulatable, stabilized β-catenin (bcatFL) animals, as well as animals expressing mutated p53-R270H. While mammary tumors developed in animals expressing Cre-targeted regulatable bcatFL, the penetrance and growth kinetics were lower than observed with other mammary mouse models expressing stabilized β-catenin. Since fewer animals developed tumors, we focused analysis on the effect of turning off stabilized β-catenin expression following tumor initiation. In the animals examined, a delay in tumor growth, but not regression, was observed in animals expressing Cretargeted bcatFL as well as those expressing both bcatFL and p53-R270H. These results indicate that in a less aggressive β-catenin model, additional mutations likely provide independence from the initiating event.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".