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Outcome Prediction for High Risk AML Patients |
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1. Background:
AML patients are frequently stratified by prognostic parameters
into therapeutic subgroups
(
L2).
Stratification is useful for therapy improvement in patient subgroups
but does not provide indivualized outcome predictions.
2. Goal:
Classification of the SHG-96 multicenter AML trial database
(
L2)
to identify high risk AML-patients prior to therapy
(L1.).
3. Results:
Algorithmic
classification of the available
immunophenotype, cytogenetic and clinical parameters
(fig.1)
provides predictive values of 100% for 5-year
and of 88.6% for 2-year nonsurvival
(fig.2)
of individual patients.
Discriminatory data patterns (disease classification masks)
contain 7 parameters for 5-year prediction and 12 parameters
for 2-year classifications
(fig.3) .
Patient age and %CD4, %CD45 positive AML blasts are equally selected
in both classifications.
The reclassification of the learning set
indicates correct patient classifications with mask
coincidence factors between 0.57-1.00
(fig.4)
despite only partial coincidence of patient classification
masks with the two disease classification masks >5-year survivors
and <5-year nonsurvivors.
Individually predictive
(fig.3)
and group oriented
prognostic (fig.5)
disease classification masks contain different parameters.
4. Conclusion and Outlook:
Immunophenotype parameter pattern analysis
identifies early on individual high risk patients with high predictive
values.
Cytogenetic parameters were not
selected, probably because they occur only in about half of the patients
as opposed to CD antigens expressed on all cells.
It seems promising to routinely evaluate multiparameter CD antigen expression
intensity, antigen ratios and antigen spread of
cell populations to enrich the percent cell
frequency determination of the present study with additionally relevant
information.
This might further increase predictive values beyond the 95% level,
aiming at individualized outcome predictions for all considered
AML patients.
Literature References:
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)
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)
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