Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

Diaz-Romero, J; Romeo, S; Bovée, JV; Hogendoorn, PC; Heini, PF; Mainil-Varlet, P (2010). Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma. Journal of cellular physiology, 225(2), pp. 601-11. Hoboken, N.J.: Wiley 10.1002/jcp.22245

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We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Service Sector > Institute of Pathology
04 Faculty of Medicine > Department of Orthopaedic, Plastic and Hand Surgery (DOPH) > Clinic of Orthopaedic Surgery

UniBE Contributor:

Diaz Romero, Jose; Heini, Paul Ferdinand and Mainil, Pierre








Factscience Import

Date Deposited:

04 Oct 2013 14:07

Last Modified:

04 May 2014 23:04

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https://boris.unibe.ch/id/eprint/248 (FactScience: 197101)

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