Classification of pediatric acute myeloid leukemia based on miRNA expression profiles
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Askar Obulkasim1, Jenny E. Katsman-Kuipers1, Lonneke Verboon1, Mathijs Sanders2, Ivo Touw2, Mojca Jongen-Lavrencic2, Rob Pieters1,3, Jan-Henning Klusmann4, C. Michel Zwaan1, Marry M. van den Heuvel-Eibrink1,3, Maarten Fornerod1
1Pediatric Oncology-Hematology, Erasmus MC, Sophia Children’s Hospital, The Netherlands
2Department of Hematology, ErasmusMC, Rotterdam, The Netherlands
3Prinses Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
4Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany
Maarten Fornerod, email: [email protected]
Keywords: microRNA, acute myeloid leukemia, pediatric, classification, cytogenetic aberration
Received: October 27, 2016 Accepted: March 01, 2017 Published: March 23, 2017
Pediatric acute myeloid leukemia (AML) is a heterogeneous disease with respect to biology as well as outcome. In this study, we investigated whether known biological subgroups of pediatric AML are reflected by a common microRNA (miRNA) expression pattern. We assayed 665 miRNAs on 165 pediatric AML samples. First, unsupervised clustering was performed to identify patient clusters with common miRNA expression profiles. Our analysis unraveled 14 clusters, seven of which had a known (cyto-)genetic denominator. Finally, a robust classifier was constructed to discriminate six molecular aberration groups: 11q23-rearrangements, t(8;21)(q22;q22), inv(16)(p13q22), t(15;17) (q21;q22), NPM1 and CEBPA mutations. The classifier achieved accuracies of 89%, 95%, 95%, 98%, 91% and 96%, respectively. Although lower sensitivities were obtained for the NPM1 and CEBPA (32% and 66%), relatively high sensitivities (84%−94%) were attained for the rest. Specificity was high in all groups (87%−100%). Due to a robust double-loop cross validation procedure employed, the classifier only employed 47 miRNAs to achieve the aforementioned accuracies. To validate the 47 miRNA signatures, we applied them to a publicly available adult AML dataset. Albeit partial overlap of the array platforms and molecular differences between pediatric and adult AML, the signatures performed reasonably well. This corroborates our claim that the identified miRNA signatures are not dominated by sample size bias in the pediatric AML dataset. In conclusion, cytogenetic subtypes of pediatric AML have distinct miRNA expression patterns. Reproducibility of the miRNA signatures in adult dataset suggests that the respective aberrations have a similar biology both in pediatric and adult AML.
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