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Blood, 15 October 2003, Vol. 102, No. 8, pp. 2951-2959. Prepublished online as a Blood First Edition Paper on May 1, 2003; DOI 10.1182/blood-2003-01-0338. This Article Full Text Full Text (PDF) Supplemental Data All Versions of this Article: 2003-01-0338v1 102/8/2951    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Rights and Permissions Citing Articles Citing Articles via HighWire Citing Articles via CrossRef Citing Articles via Google Scholar Google Scholar Articles by Ross, M. E. Articles by Downing, J. R. Search for Related Content PubMed PubMed Citation Articles by Ross, M. E. Articles by Downing, J. R. Related Collections Neoplasia Oncogenes and Tumor Suppressors Gene Expression Genomics Social Bookmarking                   What's this?  Previous Article  |  Table of Contents  |  Next Article  NEOPLASIA Classification of pediatric acute lymphoblastic leukemia by gene expression profiling Mary E. Ross, Xiaodong Zhou, Guangchun Song, Sheila A. Shurtleff, Kevin Girtman, W. Kent Williams, Hsi-Che Liu, Rami Mahfouz, Susana C. Raimondi, Noel Lenny, Anami Patel, and James R. Downing From the Department of Hematology-Oncology, Department of Pathology, and the Hartwell Center for Bioinformatics and Biotechnology, St Jude Children's Research Hospital (SJCRH), Memphis, TN. Contemporary treatment of pediatric acute lymphoblastic leukemia (ALL) requires the assignment of patients to specific risk groups. We have recently demonstrated that expression profiling of leukemic blasts can accurately identify the known prognostic subtypes of ALL, including T-cell lineage ALL (T-ALL), E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid karyotypes with more than 50 chromosomes. As the next step toward developing this methodology into a frontline diagnostic tool, we have now analyzed leukemic blasts from 132 diagnostic samples using higher density oligonucleotide arrays that allow the interrogation of most of the identified genes in the human genome. Nearly 60% of the newly identified subtype discriminating genes are novel markers not identified in our previous study, and thus should provide new insights into the altered biology underlying these leukemias. Moreover, a proportion of the newly selected genes are highly ranked as class discriminators, and when incorporated into class-predicting algorithms resulted in an overall diagnostic accuracy of 97%. The performance of an array containing the identified discriminating genes should now be assessed in frontline clinical trials in order to determine the accuracy, practicality, and cost effectiveness of this methodology in the clinical setting. CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this? This article has been cited by other articles: Y. I. Ingster, C. Pouet, and A. B. Tsybakov Classification of sparse high-dimensional vectors Phil Trans R Soc A, November 13, 2009; 367(1906): 4427 - 4448. [Abstract] [Full Text] [PDF] D. Trageser, I. Iacobucci, R. Nahar, C. Duy, G. von Levetzow, L. Klemm, E. Park, W. Schuh, T. Gruber, S. Herzog, et al. Pre-B cell receptor-mediated cell cycle arrest in Philadelphia chromosome-positive acute lymphoblastic leukemia requires IKAROS function J. Exp. Med., August 3, 2009; 206(8): 1739 - 1753. [Abstract] [Full Text] [PDF] R. K. 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