Abstract 4289: Using the right tools: A catalog of ovarian cancer cell lines by subtype
Bibliographic record
Abstract
Abstract Ovarian cancer cell lines provide an important tool in exploring disease phenotypes and biology. Within the spectrum of ovarian epithelial cancers at least five histologically distinguishable unique diseases exist: high-grade serous, clear cell, endometrioid, mucinous, and low-grade serous. While sharing a common site of presentation, these ovarian cancer types exhibit distinct genomic signatures, clinical characteristics and treatment responses. Thus, the critical interpretation of the results of cell based experiments performed in in-vitro and in-vivo model systems of ovarian cancer first requires characterization of the disease subtype each model represents. Our group has recently used a minimal panel of nine immunohistochemical (IHC) markers, termed “COSP” (Calculator for Ovarian carcinoma Subtype Prediction), to predict each ovarian cancer type. Application of COSP has suggested that as many as 19% of primary ovarian cancer samples may be misclassified prior to an expert review. Commonly used cell lines are rarely classified into ovarian cancer types, confounding interpretation of results and potentially delaying transition of basic research to clinically relevant application. We now apply COSP for typing commonly used ovarian cancer cell lines, as well as in-house derived xenografts, and primary culture models. The COSP immuno-predictive tool is combined with molecular and mutational data to support each cell line's subtype prediction while primary tumour types are compared to their derivative cell lines. We now present an extensive panel of accurately classified ovarian cancer cell lines to facilitate disease specific analysis of the biology of ovarian cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4289. doi:10.1158/1538-7445.AM2011-4289
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".