Cluster analysis of immunophenotypic data: the example of chronic lymphocytic leukemia.

Studies of gene expression profiling have been successfully used for the identification of molecules to be employed as potential prognosticators. In analogy with gene expression profiling, we have previously proposed an original method to identify the immunophenotypic signature of chronic lymphocytic leukemia (CLL) subsets with different prognosis, named surface-antigen expression profiling. According to this method, expression data for surface markers can be successfully analyzed by data mining tools identical to those employed in gene expression profiling studies, including unsupervised and supervised algorithms, with the aim to identify the immunophenotypic signature of CLL subsets with different prognosis. By employing an identical approach for investigating the reactivity of a wide panel of monoclonal antibodies provided by the "Ninth International Workshop on Leukocyte Differentiation Antigens", we were able to identify some of them (i.e. TCL1, CCR7, FCRL2, FCRL3, and CD150) as additional potential markers with prognostic relevance in CLL. These suggestions need to be confirmed: (i) in a new set of clinically characterized CLL cases; (ii) in combination with other prognostic markers in the context of comprehensive scoring systems for clinical outcome prediction.