Automated classification of patients with chronic lymphocytic leukemia and immunocytoma from flow cytometric three-color immunophenotypes

Abstract

The goal of this study was the discrimination between chronic lymphocytic leukemia (B-CLL), clinically more aggressive lymphoplasmocytoid immunocytoma (LP-IC) and other low-grade non-Hodgkin's lymphomas (NHL) of the B-cell type by automated analysis of flow cytometric immunophenotypes CD45/14/20, CD4/8/3, kappa/CD19/5, lambda/CD19/5 and CD10/23/19 from peripheral blood and bone marrow aspirate leukocytes using the multiparameter classification program CLASSIF1. The immunophenotype list mode files were exhaustively evaluated by combined lymphocyte, monocyte, and granulocyte (LMG) analysis. The results were introduced into databases and automatically classified in a standardized way. The resulting triple matrix classifiers are laboratory and instrument independent, error tolerant, and robust in the classification of unknown test samples. Practically 100% correct individual patient classification was achievable, and most manually unclassifiable patients were unambiguously classified. It is of interest that the single lambda/CD19/5 antibody triplet provided practically the same information as the full set of the five antibody triplets. This demonstrates that standardized classification can be used to optimize immunophenotype panels. On-line classification of test samples is accessible on the internet: http://www.biochem.mpg.de/valet/leukaem1.html. Immunophenotype panels are usually devised for the detection of the frequency of abnormal cell populations. As shown by computer classification, most the highly discriminant information is, however, not contained in percentage frequency values of cell populations, but rather in total antibody binding, antibody binding ratios, and relative antibody surface density parameters of various lymphocyte, monocyte, and granulocyte cell populations.