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Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotypic, cytogenetic, and clinical parameters

  1. G. Valet1,*,
  2. R. Repp2,
  3. H. Link3,
  4. A. Ehninger4,
  5. M. Gramatzki M2

Article first published online: 21 APR 2003

DOI: 10.1002/cyto.b.10028

Issue

Cytometry Part B: Clinical Cytometry

Cytometry Part B: Clinical Cytometry

Special Issue: Cytomics in Predictive Medicine

Volume 53B, Issue 1, pages 4–10, May 2003

Additional Information

How to Cite

Valet, G., Repp, R., Link, H., Ehninger, A. and Gramatzki M, M. (2003), Pretherapeutic identification of high-risk acute myeloid leukemia (AML) patients from immunophenotypic, cytogenetic, and clinical parameters. Cytometry, 53B: 4–10. doi: 10.1002/cyto.b.10028

Author Information

  1. 1

    Max-Planck-Institut für Biochemie, Martinsried, Germany

  2. 2

    Med.Klinik III der Universität Erlangen, Germany

  3. 3

    Klinikum Kaiserslautern, Germany

  4. 4

    Med.Klinik und Poliklinik I der Universität Dresden, Dresden, Germany

*Max-Planck-Institut für Biochemie, Arbeitsgruppe Zellbiochemie, Am Klopferspitz 18a, D-82152 Martinsried, Germany

Publication History

  1. Issue published online: 21 APR 2003
  2. Article first published online: 21 APR 2003
  3. Manuscript Accepted: 17 FEB 2003
  4. Manuscript Received: 15 JAN 2003

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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. Cytometry Part B (Clin. Cytometry) 53B:4–10, 2003. © 2003 Wiley-Liss, Inc.

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