Abstract B47: Characterization of carcinoma-associated fibroblasts from head and neck squamous cell carcinoma
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with over 350,000 deaths annually. Metastasis remains a major cause of mortality for HNSCC patients. Elucidating the mechanisms leading to invasion and spread of tumor cells is required to find new therapeutic approaches for controlling this disease. The stroma of HNSCCs consists of several non-tumor cell types, of which carcinoma-associated fibroblasts (CAFs) are prominent. We hypothesize that CAFs facilitate the invasion and migration of tumor cells leading to metastasis. Primary cultures of CAFs and their normal adjacent fibroblast (NAF) counterparts were established from 12 matched patient tissue samples using selective conditions. Each resulting cell line was confirmed to be vimentin positive and pan-cytokeratin negative by IHC staining. Characterization of the 12 pairs was performed by microarray analysis, high throughput flow cytometry cell surface marker profiling and intracellular flow cytometry for α-smooth muscle actin (αSMA) expression, a marker of fibroblast activation. The level of α-SMA expression was consistently low in the NAFs but varied widely in the CAFs where lines displayed 5-40% positive cells. Microarray gene expression analysis indentified over 500 differentially expressed genes between CAFs and NAFs. Extracellular matrix proteins (e.g. FN1 & COL11A1) and cell-cell or cell-matrix adhesion proteins (e.g. ITGA11 & ITGB5) were the most highly up-regulated genes in the CAFs, while immuno-modulatory receptors (e.g. IL6R, PTGER4, TGFβR, IL1R) were down-regulated. Co-injection of CAFs with SCC4 tongue carcinoma cells into immuno-compromised mice, resulted in decreased latency of tumor development and a reduced number of cells required to initiate a tumor – 1000 cells with CAF, compared to 10,000 SCC4 cells alone. In vitro co-culture of SCC4 and SCC9 tumor cells in several conditions including CAF and NAF-derived extracellular matrix (ECM), CAF and NAF-conditioned media (CM), and live CAF and NAF feeder layers, resulted in different growth phenotypes. Cells grown on ECM gave rise to tightly packed colonies with defined edges. Cells grown in CM were more scattered with some loose colonies. Cells grown on NAFs formed normal colonies, however, cells grown on matched CAFs took on a migratory phenotype, forming lines of cells along the length of the fibroblasts. Transwell migration assays were also performed where matched CAF and NAF or CAF-CM and NAF-CM were placed in the bottom chamber and SCC4 were seeded in the upper chamber. CAFs and NAFs induced more migration over the control conditions of no cells and un-conditioned media. CAFs tended to induce more cells to migrate compared to NAFs; though in one pair, the NAFs promoted more migration. Interestingly, gene expression data from this pair indicate that the expression of HGF, a potent migration inducer, is almost twice as high in the NAFs versus the CAFs. These results show that CAFs are able to enhance tumor formation and facilitate tumor cell migration. Further experiments using primary patient tumor cells are necessary to confirm this effect. Laser-capture microdissection of tumor and stromal cells, as well as cell sorting of these cell compartments from patient tissue is underway. Microarrays will be performed to identify a mechanism of interaction between these cell populations and will be integrated with our current data. Citation Format: Keira Pereira, Christina Karamboulas, Elzbieta Hyatt, Joshua Paterson, Laurie Ailles. Characterization of carcinoma-associated fibroblasts from head and neck squamous cell carcinoma. [abstract]. In: Proceedings of the Third AACR International Conference on Frontiers in Basic Cancer Research; Sep 18-22, 2013; National Harbor, MD. Philadelphia (PA): AACR; Cancer Res 2013;73(19 Suppl):Abstract nr B47.
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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 | 0.001 |
| 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".