Development of a Bioluminescent BRCA1-Deficient Xenograft Model of Disseminated, High-Grade Serous Ovarian Cancer
Bibliographic record
Abstract
Successful translation of preclinical data relies on valid and comprehensive animal models. While high-grade serous ovarian cancer (HGSOC) is the most prevalent subtype, the most commonly used ovarian cancer cell lines are not representative of HGSOC. In addition, 50% of ovarian cancer patients present with dysfunctional BRCA1/2, however currently there is a shortage of BRCA-deficient models. By utilizing the OVCAR8 cell line, which contains a hypermethylated BRCA1 promoter, the aim of the current study was to establish and characterize an animal model for BRCA-deficient HGSOC. Transfection of the luciferase gene to OVCAR8 cells enabled bioluminescent imaging for real-time, non-invasive monitoring of tumor growth. The resulting model was characterized by peritoneal metastasis and ascites formation at late stages of disease. Immunohistochemical staining revealed high-grade serous histology in all resected tumor nodules. Immunoblotting and qPCR analysis demonstrated BRCA1 deficiency was maintained in vivo. Moderate to strong correlations were observed between bioluminescent signal and tumor weight. Lastly, intraperitoneal administration of carboplatin significantly reduced tumor growth as measured by bioluminescence. The current model demonstrated BRCA1 deficiency and a high resemblance of the clinical features of HGSOC. This model may be well-suited for evaluation of therapeutic efficacy in BRCA-deficient HGSOC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".