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Blood, 15 August 2007, Vol. 110, No. 4, pp. 1271-1277.
Prepublished online as a Blood First Edition Paper on April 24, 2007; DOI 10.1182/blood-2007-01-068478.
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Submitted January 17, 2007
Accepted April 15, 2007
A set of genes that regulate cell proliferation predict treatment outcome in childhood acute lymphoblastic leukemia
Christian Flotho, Elaine Coustan-Smith, Deqing Pei, Cheng Cheng, Guangchun Song, Ching-Hon Pui, James R Downing, and Dario Campana*
Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, United States
Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, United States
* Corresponding author; email: dario.campana{at}stjude.org.
To identify novel predictors of outcome in childhood acute lymphoblastic leukemia (ALL), we analyzed gene expression in the leukemic cells of 187 children with newly diagnosed ALL and compared the findings with minimal residual disease (MRD) results obtained on day 19 of remission induction treatment. Genes that showed a significant relationship to MRD were then tested for their capacity to predict leukemic relapse in an independent cohort of 99 patients. We identified 674 probe sets that were associated with MRD on day 19 (P <0.006); 40 of the identified genes predicted relapse (P <0.03). Among these, 14 showed independent prognostic significance after adjustment for age, leukocyte count at diagnosis and genetic subtype. More than half of the 40 genes and nearly all of the 14 genes were functionally related, as indicated by their roles in the regulation of cell proliferation. Underexpression of genes promoting cell proliferation was associated with resistance to chemotherapy. The biologic processes regulated by the genes we identified appear to be key determinants of the early cytoreductive response to remission induction therapy and subsequent clinical outcome in childhood ALL. Incorporation of the expression levels of these genes into existing strategies of risk classification could improve clinical management.
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