Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Ovarian cancers often exhibit high rates of recurrence and poor treatment response. Preclinical models that recapitulate the heterogeneity of human disease are critical to develop new therapeutic approaches. While patient-derived models are a powerful tool for testing various therapeutics, their dependence on immune-compromised mice is severely limiting. Syngeneic mouse models, however, allow for the generation of tumours comprising the full repertoire of non-malignant cell types. Here we have performed a comparative analysis of diverse models of high-grade serous ovarian cancer based on transcriptomic profiling of 22 cell line models, and intrabursal and intraperitoneal tumours from 12 models. Among cell lines, we identify distinct features in signalling activity, such as elevated inflammatory signalling in STOSE and OVE16 models, and MAPK/ERK signalling in ID8 and OVE4 models; metabolic features, such as predicted reduction in glycolysis associated with subsets of engineered ID8 subclones; and relevant functional properties, including differences in EMT activation, PD-L1 and MHC class I expression, and predicted chemosensitivity. Finally, we evaluate variability in properties of the tumour microenvironment among models. We anticipate that this work will serve as a valuable resource, providing new insight to help in the selection of models for specific experimental objectives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".