title: Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotypic, cytogenetic, and clinical parameters.
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.
authors.affiliation: Max-Planck-Institut für Biochemie, Martinsried, Germany. valet@biochem.mpg.de
authors.forename: G
authors.initials: G
authors.lastname: Valet
authors.forename: R
authors.initials: R
authors.lastname: Repp
authors.forename: H
authors.initials: H
authors.lastname: Link
authors.forename: A
authors.initials: A
authors.lastname: Ehninger
authors.forename: M
authors.initials: M
authors.lastname: Gramatzki M
mesh_terms: D000208:Acute Disease; D019295:Computational Biology; D005434:Flow Cytometry; D006801:Humans; D016130:Immunophenotyping; D007951:Leukemia, Myeloid / Q000401:mortality* / Q000473:pathology*; D011237:Predictive Value of Tests; D011379:Prognosis; D012307:Risk Factors; D016019:Survival Analysis