Abstract B24: Using a unique genetically modified ovarian cancer cell line model to identify the targets for siRNA directed therapies
Bibliographic record
Abstract
Abstract We reported that though >90% of high-grade serous ovarian carcinomas (HGSC) harbor somatic TP53 mutations, cases with missense mutations have significantly longer progression free and overall survival than cases with “null” mutations, and that HGSCs defined by TP53 mutation type exhibit unique differences in their genomic landscapes. Large-scale molecular genetic analyses by The Cancer Atlas Group (TCGA) have identified numerous genes/molecular pathways that could be targeted for therapy in HGSC. A large number of candidates were reported, whereby the results were affected by heterogeneity of samples. We have analyzed the transcriptomes from a unique HGSC cell line model with altered in vivo tumorigenic and in vitro growth characteristics to understand the biology of HGSC as we have shown in previous analyses that the pathways affected intersected those found altered in tumors. We posit that investigating phenotypically defined cancer cell line models could facilitate the identification of targets for siRNA-based therapeutics (as an example). Towards this goal, we have characterized the transcriptomes (n~700 RNAs) derived from our parental HGSC cell line model and derived genetically modified cell lines. The cell line was derived from long term passage of ovarian ascites, harbors a somatic missense TP53 mutation and exhibits suppression of tumorigenicity in murine models. The genetically modified derivative cell lines have lost tumorigenic potential and exhibit altered growth characteristics. We compared their transcriptomes, derived a list a genes correlated with these alterations and then compared them to transcriptomes from public data sets: ovarian surface epithelial cells (n=10) and HGSC (n=56); and (2) TCGA samples (n~300). A defined list of known and new genes was identified by these comparative analyses pointing to targets that could be used to affect tumorigenic potential. The genetically modified model provides a new avenue of research that has the potential to elucidate pathways important in HGSC, especially those involved in tumorigenicity, as well as define targets for the development of RNA-based targeted therapeutics. Citation Format: Patricia N. Tonin, Celia MT Greenwood, Diane MT Provencher, Anne-Marie Mes-Masson. Using a unique genetically modified ovarian cancer cell line model to identify the targets for siRNA directed therapies. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr B24.
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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 | 0.001 |
| 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".