Title Microarray-based classification of a consecutive series of 121 childhood acute leukemias: prediction of leukemic and genetic subtype as well as of minimal residual disease status.
Authors Anna Andersson, Cecilia Ritz, David Lindgren, Patrik Edén, Carin Lassen, Jesper Heldrup, Tor Olofsson, Johan Råde, Magnus Fontes, A Porwit-Macdonald, M Behrendtz, Mattias Höglund, Bertil Johansson, Thoas Fioretos
Alternative Location http://www.ncbi.nlm.nih.gov..., Restricted Access
Alternative Location http://dx.doi.org/10.1038/s..., Restricted Access
Publication Leukemia
Year 2007
Volume 21
Issue 6
Pages 1198 - 1203
Document type Article
Status Published
Quality controlled Yes
Language eng
Publisher Nature Publishing Group
Abstract English Gene expression analyses were performed on 121 consecutive childhood leukemias (87 B-lineage acute lymphoblastic leukemias (ALLs), 11 T-cell ALLs and 23 acute myeloid leukemias (AMLs)), investigated during an 8-year period at a single center. The supervised learning algorithm k-nearest neighbor was utilized to build gene expression predictors that could classify the ALLs/AMLs according to clinically important subtypes with high accuracy. Validation experiments in an independent data set verified the high prediction accuracies of our classifiers. B-lineage ALLs with uncharacteristic cytogenetic aberrations or with a normal karyotype displayed heterogeneous gene expression profiles, resulting in low prediction accuracies. Minimal residual disease status (MRD) in T-cell ALLs with a high (40.1%) MRD at day 29 could be classified with 100% accuracy already at the time of diagnosis. In pediatric leukemias with uncharacteristic cytogenetic aberrations or with a normal karyotype, unsupervised analysis identified two novel subgroups: one consisting mainly of cases remaining in complete remission (CR) and one containing a few patients in CR and all but one of the patients who relapsed. This study of a consecutive series of childhood leukemias confirms and extends further previous reports demonstrating that global gene expression profiling provides a valuable tool for genetic and clinical classification of childhood leukemias.
Keywords gene expression profiling, pediatric leukemia, supervised, classification, ALL, AML,
ISBN/ISSN/Other ISSN: 0887-6924

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