Modeling High-grade serous ovarian carcinoma using a combination of <i>in vivo</i> electroporation and CRISPR/Cas9 mediated genome editing
Bibliographic record
Abstract
Abstract Ovarian cancer remains the most lethal gynecological cancer today. High-grade serous ovarian carcinoma (HGSC) is the most common and lethal type of ovarian cancer and is most frequently diagnosed at advanced stages. Here, we developed a novel strategy to generate somatic ovarian cancer mouse models using a combination of in vivo electroporation and CRISPR/Cas9 mediated genome editing. We mutated tumor suppressor genes associated with HGSC in two different combinations; Brca1, Tp53, Pten with/without Lkb1 and successfully generated HGSC, however, with different latencies and pathophysiology. By utilizing Cre lineage tracing in our system, we visualized peritoneal micrometastases in an immune-competent environment. Because our strategy is flexible in selecting mutation combinations and targeting areas, it would be useful for generating ovarian cancer mouse models. Significance Statement High grade serous ovarian cancer (HGSC) is the most common ovarian malignancy but our knowledge of early tumorigenesis is still quite limited due to late diagnosis of patients. We developed a new strategy of generating mouse ovarian cancer models using a combination of in vivo electroporation and CRISPR-mediated genome editing. We demonstrated that a combination of three tumor suppressor gene mutations ( Brca1, Tp53 and Pten ) is sufficient to develop HGSCs. Interesting, an additional mutation in Lkb1 drastically changed tumor latency, penetrance and pathophysiology through changing its cell-of-origin. Our strategy is highly flexible in selection of mutation combinations and targeting areas in immune competent mice and useful to study early tumorigenesis of ovarian cancer.
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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 | 0.000 | 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 | 0.000 |
| 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 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".