Abstract A101: A novel ovarian cancer organotypic tumor slice culture model
Bibliographic record
Abstract
Abstract High-grade serous ovarian carcinoma (HGSOC) is the most common form of ovarian cancer (OC). This heterogeneous disease is associated with various molecular alterations influencing the disease course. Unfortunately, the impact of those molecular alterations on the response to therapy is still misunderstood. Few OC models allow control of genetic background in the cancer cells while preserving the tumor's architecture and microenvironment, including the immune cells. Furthermore, there is currently no high-throughput model that retains those characteristics and is suitable for drug screens. This project aims to develop a high-throughput syngeneic murine organotypic tumor slice culture model to study the impact of common HGSOC molecular alterations on anticancer drug responses. The pre-established Trp53−/− syngeneic OC mouse model has been genetically engineered to represent molecular alterations frequently found in HGSC. Ten variants were produced by overexpressing Tp53R172h and overexpressing oncogenes such as Ccne, Brd4, Myc, Ndrg1, and Pik3ca and/or deleting Brca1, Pten, and Nf1. After validation and characterization of the cells in vitro, we performed allografts in immunocompetent C57/bl6 mice and obtained tumors with HGSOC histology. The tumors were used to develop an organotypic tumor slice culture model. Briefly, each tumor is collected and thinly sliced with a vibratome. Each slice can be cultured for several days without significant cell death induction and can be used for a drug screen. Our preliminary data shows that the model preserves the HGSOC histology over six days and remains viable. A viability assay has also been developed to assess the sensitivity of the tumor slices to various anticancer drugs. In conclusion, this project will allow the development of a platform for screening anticancer therapies against HGSOC and will lead to a better understanding of the relationship between HGSOC's common molecular alterations and the responses to therapy. Citation Format: Violaine Pourcel, Emile Létourneau, Dominique Jean, Marilyne Labrie. A novel ovarian cancer organotypic tumor slice culture model [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A101.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".