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Blood, 1 September 2007, Vol. 110, No. 5, pp. 1429-1438.
Prepublished online as a Blood First Edition Paper on May 10, 2007; DOI 10.1182/blood-2006-12-059790.
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Submitted December 6, 2006
Accepted April 11, 2007
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group
Stuart S. Winter*, Zeyu Jiang, Hadya Khawaja, Timothy Griffin, Meenakshi Devidas, Barbara L. Asselin, and Richard S. Larson
Department of Pediatrics, The University of New Mexico Health Sciences Center, Albuquerque, NM, United States
Department of Biochemistry and Molecular Biology, The University of New Mexico Health Sciences Center, Albuquerque, NM, United States
Department of Pathology, The University of New Mexico Health Sciences Center, Albuquerque, NM, United States
Hematology/Oncology, Memorial Hospital of South Bend, South Bend, IN, United States
Statistics, Children's Oncology Group and University of Florida, Gainesville, FL, United States
Pediatric Hematology Oncology, University of Rochester Medical Center and Golisano Children's Hospital at Strong, Rochester, NY, United States
* Corresponding author; email: swinter{at}salud.unm.edu.
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and Prognostic Multi-array Analysis (PAM) to profile 50 newly-diagnosed patients who were treated on Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly up-regulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
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