Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer
Bibliographic record
Abstract
Ovarian cancers exhibit high rates of recurrence and poor treatment response. Preclinical models that recapitulate human disease are critical to develop new therapeutic approaches. Syngeneic mouse models allow for the generation of tumours comprising the full repertoire of non-malignant cell types but have expanded in number, varying in the cell type of origin, method for transformation, and ultimately, the properties of the tumours they produce. Here we have performed a comparative analysis of high-grade serous ovarian cancer models based on transcriptomic profiling of 22 cell line models, and intrabursal and intraperitoneal tumours from 12. Among cell lines, we identify distinct signalling activity, such as elevated inflammatory signalling in STOSE and OVE16 models, and MAPK/ERK signalling in ID8 and OVE4 models; metabolic differences, such as reduced glycolysis-associated expression in several engineered ID8 subclones; and relevant functional properties, including differences in EMT activation, PD-L1 and MHC class I expression, and predicted chemosensitivity. Among tumour samples, we observe increased variability and stromal content among intrabursal tumours. Finally, we predict differences in the microenvironment of ID8 models engineered with clinically relevant mutations. We anticipate that this work will serve as a valuable resource, providing new insight to help select models for specific experimental objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".