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    Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.

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    Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.

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    Description

    Title : Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.
    Author(s) : Eng-Juh Yeoh, Mary E Ross, Sheila A Shurtleff, W Kent Williams, Divyen Patel, Rami Mahfouz, Fred G Behm, Susana C Raimondi, Mary V Relling, Anami Patel, Cheng Cheng, Dario Campana, Dawn Wilkins, Xiaodong Zhou, Jinyan Li, Huiqing Liu, Ching-Hon Pui, Willia
    Abstract : Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.
    Keywords : algorithms, child, computational biology, gene expression profiling, humans, immunophenotyping, leukemia, myeloid, acute, acute classification, acute diagnosis, acute genetics, acute pathology, oligonucleotide array sequence analysis, oligonucleotide arra

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2002

    Affiliations Dept of Computer Science, National University of Singapore
    Journal : Cancer Cell
    Volume : 1
    Issue : 2
    Publisher : Elsevier
    Pages : 133-143
    Url : http://www.ncbi.nlm.nih.gov/pubmed/12086872

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    Limsoon's Peer Evaluation activity

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