Pretherapeutic Identification of High Risk AML Patients
AML patients are frequently stratified by prognostic parameters
into therapeutic subgroups
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 is sufficient for correct classifications (fig.4). The differences between the predictive (fig.5) and the prognostic (fig.6) disease classification masks illustrate that individualised and group oriented predictions refer to quite different parameter patterns.
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 which include the CD antigens of the disease classification masks identified in this study. In addition the degree of antigen expression, antigen ratios and scatter of antigen distributions have then to be evaulated in addition to cell frequency to further increase the predictive values to >95% for the purpose of individualized pretherapeutic risk assessment.
L1. Valet G, Repp R, Link H, Ehninger G, Gramatzki M and SHG-AML study group. Pretherapeutic identification of high risk acute myeloid leukemia (AML) patients from immunophenotype, cytogenetic and clinical parameters. Cytometry 53B:4-10, (2003) (PDF)
L2. Repp R, Schaekel U, Helm G, Thiede C, Soucek S, Pascheberg U, Wandt H, Aulitzky W, Bodenstein H, Kuse R, Link H, Ehninger G, Gramatzki M and AML-SHG Study Ehninger G, Gramatzki M and SHG-AML study group. Immunophenotyping is an independent factor for risk stratification in AML. Cytometry 53B:11-19, (2003) (PDF)
|© 2017 G.Valet|