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
2007 (English)In: Leukemia, ISSN 0887-6924, E-ISSN 1476-5551, Vol. 21, no 6, 1198-1203 p.Article in journal (Refereed) Published
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 (>0.1) 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.
Place, publisher, year, edition, pages
2007. Vol. 21, no 6, 1198-1203 p.
IdentifiersURN: urn:nbn:se:liu:diva-49655DOI: 10.1038/sj.leu.2404688OAI: oai:DiVA.org:liu-49655DiVA: diva2:270551