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Gene expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia.

ABSTRACT: Core binding factor (CBF) leukemias, characterized by either inv(16)/t(16;16) or t(8;21), constitute acute myeloid leukemia (AML) subgroups with favorable prognosis. However, there exists substantial biological and clinical heterogeneity within these cytogenetic groups, which is not fully reflected by the current classification system. To improve the molecular characterization we profiled gene expression in a large series (n=93) of AML patients with CBF leukemia [inv(16) n=55, t(8;21) n=38]. By unsupervised hierarchical clustering we were able to define a subgroup of CBF cases (n=35) characterized by shorter overall survival times (P=0.03). While there was no obvious correlation with fusion gene transcript levels, FLT3 tyrosine kinase domain, KIT, and NRAS mutations, the newly defined inv(16)/t(8;21)-subgroup was associated with elevated white blood cell counts and FLT3 internal tandem duplications (P=0.011 and P=0.026, respectively). Supervised analyses of gene expression suggested alternative cooperating pathways leading to transformation. In the ?favorable? CBF leukemias anti-apoptotic mechanisms and deregulated mTOR-signaling, and in the newly defined ?unfavorable? subgroup aberrant MAPKinase-signaling and chemotherapy-resistance mechanisms might play a role. While the leukemogenic relevance of these signatures remains to be validated, their existence nevertheless supports a prognostically relevant biological basis for the heterogeneity observed in CBF leukemia. A reference experiement design type is where all samples are compared to a common reference. Keywords: reference_design

ORGANISM(S): Homo sapiens

PROVIDER: GSE8653 | GEO | 2007/08/09

SECONDARY ACCESSION(S): PRJNA101847

REPOSITORIES: GEO

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Project description:Background: 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 also be correctly identified. Methods: Specific 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). Results: With 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. Conclusion: Gene 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. Specific 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).

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Gene expression profiling identifies distinct subclasses of core binding factor acute myeloid leukemia.

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