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Free keywords:
acute myeloid leukemia (AML); predictive medicine; personalized medicine; clinical cytomics; data sieving; medical bioinformatics
Abstract:
Background: The goal of this study concerned the pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients by data pattern analysis from flow cytometric immunophenotype, cytogenetic, and clinical data. Methods: Sixty-seven parameters of AML patients at diagnosis were classified for predictive information by algorithmic data sieving using iteratively self optimizing triple matrix data pattern analysis (http://www.biochem.mpg.de/valet/classif1.html). Results: Pretherapeutic predictive values for nonsurvival within five years and two years were 100.0% and 83.2%, respectively, compared to 13.9% and 47.4% for the prediction of survival at five years and two years, respectively. At diagnosis, five-year nonsurvivors showed increased patient age and higher concentration of cells in the analyzed specimen, as well as increased levels of % CD2, CD4, CD13, CD36, and CD45 positive AML blasts. Two-year nonsurvivors were characterized by a data pattern of increased patient age and levels of % CD4, CD7, CD11b, CD24, CD45, TH126, and HLA-DR positive AML blasts and decreased levels of % CD1, CD65, CD95, and TC25 positive AML blasts. Cytogenetic abnormalities were not selected for the optimized discriminatory data patterns. Conclusions: The comparatively accurate pretherapeutic identification of high- risk AML patients may prove useful for the development of individualized therapy protocols in stratified clinical patients groups. (C) 2003 Wiley-Liss, Inc.
