Conditional Inactivation of Brca1, p53 and Rb in Mouse Ovaries Results in the Development of Leiomyosarcomas
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is thought to arise in part from the ovarian surface epithelium (OSE); however, the molecular events underlying this transformation are poorly understood. Germline mutations in the BRCA1 tumor suppressor gene result in a significantly increased risk of developing EOC and a large proportion of sporadic EOCs display some sort of BRCA1 dysfunction. To generate a model in which Brca1-mediated transformation can be studied, we previously inactivated Brca1 alone in murine OSE, which resulted in an increased accumulation of premalignant changes, but no tumor formation. In this study, we examined tumor formation in mice with conditionally expressed alleles of Brca1, p53 and Rb, alone or in combination. Intrabursal injection of adenovirus expressing Cre recombinase to inactivate p53 resulted in tumors in 100% of mice. Tumor progression was accelerated in mice with concomitant inactivation of Brca1 and p53, but not Rb and p53. Immunohistologic analyses classified the tumors as leiomyosarcomas that may be arising from the ovarian bursa. Brca1 inactivation in primary cultures of murine OSE cells led to a suppression of proliferation that could be rescued by concomitant inactivation of p53 and/or Rb. Brca1-deficient OSE cells displayed an increased sensitivity to the DNA damaging agent cisplatin, and this effect could be modulated by inactivation of p53 and/or Rb. These results indicate that Brca1 deficiency can accelerate tumor development and alter the sensitivity of OSE cells to chemotherapeutic agents. Intrabursal delivery of adenovirus intended to alter gene expression in the ovarian surface epithelium may, in some strains of mice, result in more rapid transformation of adjacent cells, resulting in leiomyosarcomas.
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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) | 0.002 | 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".