Abstract B47: Characterization of carcinoma-associated fibroblasts from head and neck squamous cell carcinoma
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».