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G.Valet, H.-G.Höffkes1)
Various forms of leukemias and lymphomas are commonly identified by the flow cytometric determination of leukocyte surface antigens by fluorescent antibodies in two, three or four colour flow cytometric immunophenotypes
Practical problem of the current "manual" evaluation of an increasing number of two parameter flow cytometric immunophenotype histograms emerge from the currently variable interpretation of the results in different immunophenotyping centers. Consensus formation on suitable antibody combinations as well as the automated result interpretation which shortens the otherwise time consuming manual evaluations are two major goals in this research area.
The goal of this study was the clinically relevant discrimination between chronic lymphocytic leukaemia (B-CLL), the more aggressive lymphoplasmocytoid immunocytoma (LP-IC) and other low-grade non-Hodgkin lymphomas (NHL) of the B-cell type by automated analysis of the flow cytometric immunophenotypes CD45/14/20, CD4/8/3, Kappa/CD19/5, Lambda/CD19/5 and CD10/23/19 of peripheral blood and bone marrow leukocytes by the standardized and automated evaluation with the multiparameter classification program CLASSIF1 (2) (Ann.NY.Acad.Sci 677,233-251(1993)).
This analysis consists in the exhaustive evaluation of immunophenotype list mode files by a self gating procedure for lympho-, mono- and granulocyte (LMG providing a total of 1110 result parameters. They were introduced into databases and CLASSIF1 triple matrix classifiers were learned without human interference. The resulting 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 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 five antibody triplets. This demonstrates the usefulness of standardized classification for the optimization of immunophenotype panels.
Immunophenotype panels are usually devised for the detection of the frequency of abnormal cell populations. As shown by computer classification, the majority of the highly discriminant information is, however, not contained in percent frequency values of cell populations but rather in cell parameter values like total antibody binding, antibody binding ratios and relative antibody surface density parameters of various lympho-, mono-, and granulocyte populations.
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