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Cell Biochemistry Martinsried |
1. Introduction:
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 problems 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 are major
goals in this research area.
2. Goal: 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.
3. CLASSIF1
Data Pattern Classification:
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.
4. Conclusion: Immunophenotype panels are usually devised for the detection of the frequency of abnormal cell populations e.g. for cell lineage assignment. 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.
Literature References:
L1.
G Valet, M Arland, A Franke, Ch Kahl, HG Höffkes
(2002) Discrimination of chronic lymphocytic leukemia of B-cell type
by computerized 3-color flow cytometric immunophenotypes of
bone marrow aspirates and peripheral blood.
Lab.Hematology 8:134-142(
L2.
R Bartsch, M Arland, St Lange, Ch Kahl, G Valet, HG Höffkes:
(2000) Lymphoma discrimination by computerized triple matrix analysis of
list mode data from three color flow cytometric immunophenotypes of bone
marrow aspirates.
Cytometry 41:9-18
L3.
G Valet, HG Höffkes: (1997)
Automated classification of patients with chronic lymphatic leukemia and
immunocytoma from flow cytometric three colour immunophenotypes.
Cytometry(CCC) 30:275-288
L4. G Valet, G Schmidtke, U Schmücker, G Brittinger, HG Höffkes:
(1996) Computer classification of 3-colour flow cytometric immunophenotypes of
low and high grade non-Hodgkin-lymphoma and chronic lymphatic leukemia
patients. Cytometry Suppl.8:78
L5.
HG Höffkes, G Schmidtke, G Brittinger, G Valet:
(1995) Computerized analysis of cells from patients with acute myelogenous
keukemia (AML) prepared by density gradient centrifugation (DGC) or
erythrocyte lysis (EL) and measured by multiparameter flow cytometry
(FC). Lab.Hematology 1:128-134
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