Abstract B12: The role of stromal cells in supporting tumor-initiating cells in serous ovarian carcinoma
Bibliographic record
Abstract
Abstract High grade serous ovarian carcinoma has been shown to be a highly heterogeneous disease. In vivo studies demonstrate that the tumor initiating frequency (TIF) varies substantially from case to case. Nonetheless, the tumor initiating subset remains a rare population within the epithelial compartment and can be enriched using the cell surface marker CD133 (Stewart et al., PNAS, 2011). The tumor microenvironment may play a role in supporting and maintaining tumor initiating cells (TIC) through direct and/ or indirect cross talk. The contribution of the mesenchymal component of the microenvironment may prove to be essential in providing such support as has been previously shown in studies on breast and prostate cancers. We hypothesize that cancer associated fibroblasts (CAFs) serve as a niche that supports and maintains the tumorigenic potential of TICs in SOC. We have derived and established CAF lines from bulk primary tumors and characterized their phenotype through immunostaining. Those lines stained positive for mesenchymal markers (Vimentin and Alpha-SMA), and stained negative for the epithelial marker Cytokertain. A profile of surface markers expressed on the surface of these CAF lines was then generated by running a flow cytometry-based high throughput screen (HTS) for 370 known cell surface proteins. Those markers are being validated for their specificity by immunostaining, FACS, qPCR, and in vivo work. Preliminary data obtained through immunoflourescnce and FACS support the specificity of our candidate stromal marker CD90. Furthermore, FACS data show that CD90 is expressed more brightly on fibroblasts than on epithelial cells (EpCAM+) in bulk SOC cases. Consequently, CD90 has been selected for sorting stromal cells for additional validation. Quantitative PCR analysis of CD90+EpCAM- sorted populations further validates their over-expression of mesenchymal genes when compared to CD90-EpCAM+ sorted populations. Moreover, functional validation of the influence of fibroblasts on the growth of tumor cells is currently being investigated through co-injection and co-culture assays. Preliminary data show that the presence of fibroblasts better supports the growth of epithelial colonies in culture than when compared to other conditions. Interplay between the niche and a specific subset of epithelial cells may promote those cells to become more tumorigenic. Such an interaction may be dependent on direct physical contact and/or indirect cross talk. Ultimately, we aim on understanding the mechanisms that govern these interactions. Citation Format: Ali Hussain, Jocelyn Stewart, Elzbieta Hyatt, Benjamin Neel, Laurie Ailles. The role of stromal cells in supporting tumor-initiating cells in serous ovarian carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Invasion and Metastasis; Jan 20-23, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;73(3 Suppl):Abstract nr B12.
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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) | 0.002 | 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".