Characterization of intraperitoneal, orthotopic, and metastatic xenograft models of human ovarian cancer
Bibliographic record
Abstract
Improvement of ovarian cancer patient outcome requires well-characterized animal models in which to evaluate novel therapeutics. Xenograft models are frequently used, but with little discussion of disease histology. The objectives of this study were to inject 11 ovarian cancer cell lines intraperitoneally (ip), and a subset intrabursally (ib; orthotopic), into nude mice and to analyze the resulting pathologies. Eight of 11 lines injected ip formed tumors within 3 months at variable rates with the following histological subtype distribution: one endometrioid, one serous, one clear cell, and five undifferentiated. Only mice injected with A2780-cp cells presented with ovarian-specific metastases (11 of 88), and the survival time of these animals was significantly shorter, which may be attributed to the higher proliferation rate as determined by Ki67 positivity. Additional analysis of the influence of the ovarian microenvironment on cell characteristics was conducted with ib injection of two cell lines (OVCA 429 and ES-2). The site of injection did not affect the tumor histology, the effect on proliferation was cell-type dependent, and the tumor take rate (cell survival) was negatively affected for OVCA 429 cells. The animal models described herein represent histologically distinct models of both early and late stage ovarian cancer useful for evaluation of therapeutics.
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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) | 0.001 | 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 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".