Moreover, K-562 is also classified into the AML class, but it is derived from a patient with chronic myeloid leukemia (CML).
These encouraging results, on a limited set of poor quality samples, could also be related to the classification method used, as the GGA-based class prediction strategy aimed at finding the most influential markers for each AML class (top markers with the highest inter-class variance and the lowest intra-class variance).
Only the classifiers associated with one of the four AML classes were considered for this analysis.
Interestingly, even AML samples for which a third of the required cRNA amount was hybridized on BeadChips could be assigned to the correct class in the present study, suggesting that the biological variance between the AML classes was higher than the one related to technical variability or artifacts.
MEIS1 is a transcriptional regulator also included in the homeobox co-expression cluster and selected as one of the genes with best discriminant power for the nk-AML class.
This heterogeneity likely led to a higher variance of gene expression profiles within the NK-AML class, to the detriment of the interclass variance, hence influencing the class prediction confidence level more than the leukemic blast load.
This hypothesis was supported by the fact that, within the NK-AML class, the best predictive capacity of the GGA was achieved for NPM1-mutated samples, in accordance with previous studies, and the worst one for samples with no NPM1 and FLT3 mutations [ 28- 31].
The present results suggest that the use of MYH11 as the sole marker for the inv(16 -AML class could lead to unstable results in a class prediction model, especially in low leukemic blast load samples or in case of a clonal evolution at relapse.
For APL, t(8 21 -AML and inv(16)-AML classes, the blast load significantly influenced the confidence level of the class assignment.
First, using the Class Prediction Module of ArrayMiner 5.3.3 software, classifiers associated with the APL, t(8 21 -AML, inv(16)-AML, NK-AML classes were defined based on 71 samples containing at least 60% of leukemic blasts and characterized by good quality control criteria (Optimal samples - Training Set).
At the same time, we selected some genes rarely identified by other methods (e.g., TCRA, KIAA0159, and MB-1), which have been proven to correlate directly or indirectly with AML-ALL class prediction.
