ACCELERATION OF OVARIAN TUMORS IN FORKO MICE BRED IN A SUSCEPTIBLE SWXJ GENETIC BACKGROUND
Bibliographic record
Abstract
Ovarian cancer is an aggressive disease with poor prognosis and is usually diagnosed at an advanced stage because of the lack of sensitive tests for detecting early stages of the disease. Thus animal models that develop disease early are required to gain a better understanding. Previously we found that most (>90%) aging FSH receptor Knock-Out (FORKO) females (12+ months) in the SV129 background have profound ovarian pathology with tumors of different cell types. Just as humans show differences in disease susceptibility, mouse tumor phenotypes are also influenced by genetic background and special strains prone to certain diseases/conditions have been developed for experimental manipulations. We hypothesized that ovarian tumors might be accelerated if FORKO mice were bred in to a susceptible genetic background. To verify the hypothesis we selected SWXJ-9B mice that have 10% spontaneous ovarian tumors by 4–6 weeks and this increases to 40% after exogenous testosterone treatment. Thus SV129 FORKO females were bred by backcrossing up to 4 generations with mutants having >90% SWXJ genome. Our results show that (1) the sex ratio of pups in the SW-N4 females were skewed in favor of males (>60%). (2) SWXJ FORKO female mice acquire phenotypes similar to SV129 with more pronounced changes. Mutant ovaries and uteri are very severely atrophied at 2–3 months (weighing only half or a third of the already small SV129 tissues). (3) Early abdominal obesity was more pronounced. (4) Incidence of ovarian tumors increased significantly in SW-N4 FORKO mice (2–3 month ∼ 20%, 4–6 month ∼ 23%, 8–10 month old ∼42%), while around 10% in both wild type and heterozygous female had tumors. Nodular tumors in null mutants were rather large ranging from 500 mg to 3000 mg, and the uteri of all mice with heavy tumor burden were also big as compared to the atrophied organ in young mice suggesting that tumors were hormonally aggressive. We also found ascites with red blood cells in some of these mice. (5) Histologically these tumors resembled granulosa cell tumor of human ovaries. The tumors stained strongly for inhibin-α, COX-2 and PDGF receptor-α and -β. These results indicate that loss of FSH-R signaling is more profound in SWXJ mice and ovarian tumors (mainly granulosa cell tumor) can be accelerated in a background with high susceptibility to androgen imbalance. Androgens may also affect gender ratios (support from the Canadian cancer Society). (poster)
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".