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Prepublished online as a Blood First Edition Paper on May 1, 2003; DOI 10.1182/blood-2003-01-0338. This Article 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 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 Social Bookmarking                   What's this? Submitted February 3, 2003 Accepted April 10, 2003 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* Department of Hematology and Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA * Corresponding author; email: jim.downing{at}stjude.org. 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-ALL, E2A-PBX1, TEL-AML1, MLL rearrangements, BCR-ABL, and hyperdiploid >50 chromosomes. As the next step toward developing this methodology into a front-line diagnostic tool, we have now analyzed leukemic blasts from 132 diagnostic samples using higher density oligonucleotide arrays that allow the interrogation of a majority of the identified genes in the human genome. Almost 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. 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