Surface-antigen expression profiling of B cell chronic lymphocytic leukemia: from the signature of specific disease subsets to the identification of markers with prognostic relevance
1 Clinical and Experimental Hematology Research Unit, Centro di Riferimento Oncologico, I.R.C.C.S., Aviano (PN), Italy
2 Medical Oncology, Centro di Riferimento Oncologico, I.R.C.C.S., Aviano (PN), Italy
3 Chair of Hematology, University of Tor Vergata, S. Eugenio Hospital, Rome, Italy
4 Department of Physics, University of Bologna, INFN-Sezione di Bologna, Bologna; Italy
Journal of Translational Medicine 2006, 4:11 doi:10.1186/1479-5876-4-11Published: 1 March 2006
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 recently proposed a novel method to identify the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis, named surface-antigen expression profiling. According to this approach, surface marker expression data can be analysed by data mining tools identical to those employed in gene expression profiling studies, including unsupervised and supervised algorithms, with the aim of identifying the immunophenotypic signature of B-cell chronic lymphocytic leukemia subsets with different prognosis. Here we provide an overview of the overall strategy employed for the development of such an "outcome class-predictor" based on surface-antigen expression signatures. In addition, we will also discuss how to transfer the obtained information into the routine clinical practice by providing a flow-chart indicating how to select the most relevant antigens and build-up a prognostic scoring system by weighing each antigen according to its predictive power. Although referred to B-cell chronic lymphocytic leukemia, the methodology discussed here can be also useful in the study of diseases other than B-cell chronic lymphocytic leukemia, when the purpose is to identify novel prognostic determinants.