|
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).
3. Results:
The
algorithmic
(non parametric) classification of the available
immunophenotype, cytogenetic and clinical
parameters
(fig.1)
shows that predictive values of 100% for 5-year nonsurvival
and of 88.6% for 2-year nonsurvival
(fig.2)
are obtained.
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 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.
Literature References:
L1.
Valet G., R.Repp, H.Link, G.Ehninger, M.Gramatzki 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)
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)
© 2024 G.Valet |