Abstract
BackgroundGene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified.MethodsSpecific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n = 101) and/or poor quality control criteria (n = 10) (test set).ResultsWith 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load.ConclusionGene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.
Highlights
Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16)
Identification of classifiers from optimal acute promyelocytic leukemia (APL), t(8;21)-acute myeloid leukemia (AML), inv(16)-AML and NK-AML samples 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)
The current study focused on this kind of samples - either with a low leukemic blast load, poor quality control criteria or both and the ability to identify favorable-risk karyotype AMLs in such situations, using microarray-based gene expression profiling (GEP) and a class prediction method based on GGA
Summary
Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could be correctly identified. With samples containing a high leukemic blast load, microarray-based gene expression profiling (GEP) and class prediction analyses have demonstrated their ability to assign AML samples to one of these three well characterized favorable cytogenetic risk AML subtypes, with high accuracy and low error rates [10,11,12,13,14,15,16,17,18]. In the majority of those studies, the minimum percentage of leukemic cells within each sample is most often above 60 percent [10,11,12,13,14,15,16,17,18], an arbitrary threshold that is significantly different from the one used by cytologists for the diagnosis of AML, i.e., excess of blasts greater or equal to 20 percent
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