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Identification of Gene Expression Profiles That Segregate Patients with Childhood Leukemia¹

Center for Children at the Huntsman Cancer Institute [P. J. M., E. A. R., M. A. C., F. E. S., W. L. C.], Division of Pediatric Hematology/Oncology, Departments of Pediatrics [E. A. R., W. L. C.] and Oncological Sciences [P. J. M., A. S., W. L. C.], University of Utah School of Medicine, Salt Lake City, Utah 84112; University of New Mexico, Albuquerque, New Mexico 87131 [C. W.]; and University of Colorado Health Sciences Center [Q. W., S. P. H.] and the Children’s Hospital [S. P. H.], Denver, Colorado 80218

To identify genes whose expression correlated with biological features of childhood leukemia, we prospectively analyzed the expression profiles of 4608 genes using cDNA microarrays in 51 freshly processed bone marrow samples from children with acute leukemia, over a 24-month period, at a single institution. Two supervised methods of analysis were used to identify the 20 best discriminating genes between the following cohorts: acute myelogenous leukemia (AML) versus acute lymphoblastic leukemia (ALL); B-lineage versus T-lineage ALL; newly diagnosed B-lineage standard-risk versus high-risk ALL; and B-lineage leukemia harboring the TEL-AML1 fusion versus patients without a molecularly characterized translocation. These methods identified overlapping sets of genes that segregated patients within described subgroups. Cross-validation demonstrated that the majority of patients could be correctly classified based on these genes alone, and hierarchical clustering grouped patients with similar clinical and biological disease features. The potential for select genes to discriminate patients was validated using real-time PCR in samples that were analyzed by microarray profiling and in other uniformly processed leukemic marrow samples. As expected, microarray technology can successfully segregate patients defined by traditional measures such as immunophenotype and cytogenetic alterations. However, among specific subgroups, this preliminary analysis also suggests that microarrays can identify unanticipated similarities and diversity in individual patients and thus may be useful in augmenting risk-group stratification in the future.

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