Cell Biochemistry Martinsried
1. Background: AML patients are frequently stratified by prognostic parameters into therapeutic subgroups (L2). Stratification helps therapy susceptible patients but is of no use to non responders. There is significant clinical interest to identify non responder patients pretherapeutically for individualised therapy adaptation or switch to alternative therapies.
2. Goal: Classification of the SHG-96 multicenter AML trial database (L2) to identify high risk AML-patients prior to therapy (L1).
(non parametric) classification of the available
immunophenotype, cytogenetic and clinical
shows that predictive values of 100% for 5-year nonsurvival
and of 88.6% for 2-year nonsurvival
The discriminatory data patterns (disease classification masks) contain 7 parameters for the 5-year classification and 12 parameters for the 2-year classifications (fig.3) . Patient age and %CD4, %CD45 positive AML blasts are equally selected in both classifications. The other parameters of the disease classification masks are different.
The reclassification of the learning set shows that correct classification is obtained in most instances with mask coincidence factors between 0.57-1.00. This indicates that already a partial fit of the patient classification masks with the two disease classification masks >5-year survivors and 5-year nonsurvivors may be sufficient for correct classifications (fig.4).
4. Conclusion and Outlook:
Immunophenotype parameter patterns
identify high risk patients with high predictive values.
Cytogenetic parameters were not
selected, probably because they occur
in only about half of the patients
as opposed to the CD antigen expression on all cells.
It seems promising to perform multiparametric CD measurements according to the disease classification masks of this study. Antigen expression, antigen ratios and scatter of the antigen distributions have then to be determined in addition to cell frequency to further increase the predictive values to >95% in the learning and unknown test sets of patients.
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