Abstract IA20: Analyzing the cellular basis for heterogeneity in serous ovarian carcinoma
Bibliographic record
Abstract
Abstract Serous ovarian cancer (SOC) is the leading cause of morbidity/mortality from gynecologic cancer. Current therapies increase survival, yet the majority of SOC patients die of their disease. The cancer stem cell (CSC) hypothesis holds that only a subset of tumor cells can initiate/maintain the tumor and might be more resistant to chemotherapy. In the CSC model, it should be possible to purify a stable population with the ability to generate transplantable tumors that recreate the heterogeneity of the initial malignancy. We previously reported that CD133 marks all TIC from several primary SOC, but in some cases, TIC activity is also found in the CD133– fraction. Also, the TIC phenotype is unstable in xenografts. To address whether the expression of an unidentified protein marks all TIC in SOC, we performed high-throughput flow cytometry (profiling 365 cell surface markers) and combined these analyses with mass cytometry, a transformative technology allowing examination of 35 cell surface/intracellular markers on a single cell. Analysis of 40 primary SOC samples provided further evidence for inter- and intra-patient heterogeneity. Current analyses focus on functional validation of subpopulations marked by combinations of cell surface/stem cell genes. In the course of these studies, we have generated a large cohort of SOC xenografts. Xenografts recapitulate the inter- and intra-patient heterogeneity of SOC when their gene expression and copy number profiles are compared. Indeed, they also recapitulate the patient's sensitivity to standard of care chemotherapy. These xenografts might provide a useful model l for more efficient and personalized testing of investigational drugs for SOC. Citation Format: Benjamin G. Neel. Analyzing the cellular basis for heterogeneity in serous ovarian carcinoma. [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 IA20.
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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 | 0.001 | 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".