Abstract B17: Modeling ovarian cancer in mice using in vivo electroporation and CRISPR-mediated genome editing
Bibliographic record
Abstract
Abstract Ovarian cancer is the fifth largest cause of cancer-related deaths in women. The vast majority of ovarian cancers are epithelial ovarian cancers, and high-grade serous carcinoma (HGSC) is the most common and most lethal epithelial ovarian cancer. In the last 10 years, it has been recognized that the cell of origin of most HGSCs is within the fallopian tube epithelium, instead of the ovarian surface epithelium. However, the early events in disease progression still remain poorly defined, because HGSC is usually diagnosed at advanced stages and there is a lack of proper animal models recapitulating human disease progression. My group has recently developed a unique strategy for generating mouse ovarian cancer models, which is a combination of in vivo fallopian tube electroporation, Cre-mediated lineage tracing and CRISPR-mediated gene modifications. As proof of principle, we generated a highly metastatic HGSC model by targeting four tumor suppressor genes. The female mice targeted these four genes generated ovarian tumors within 5 months after electroporation and peritoneal metastasis within 6 months. After 6 months, ascites formation was observed in two third of those females. Interestingly, similar to human ovarian cancer patients, we observed two metastatic patterns, miliary and nonmiliary. Our unique strategy has several advantages over the current mouse cancer models: 1) high flexibility permitting many gene combinations/modifications and host genetic backgrounds to be tested; 2) control over the size and area of targeted cells (the low-frequency mosaic transfection pattern better recapitulates the sporadic nature of human tumorigenesis); 3) the ability to track genetically modified cells by fluorescent reporters, permitting analysis of tumor initiation and early metastasis; and 4) highly metastatic mouse models with immune competency. Citation Format: Keerthana Harwalkar, Katie Teng, Jocelyn Arceneau, Yifan Zhao, Dave Farnell, Matt Ford, Tuyet Nhung Tun Nu, David Huntsman, Yojiro Yamanaka. Modeling ovarian cancer in mice using in vivo electroporation and CRISPR-mediated genome editing [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr B17.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".