A Gene Expression Classifier for Improved Risk Classification and Outcome Prediction in Pediatric Acute Lymphoblastic Leukemia (ALL)

• Published in: Blood 2005-11, Vol.106 (11), p.762-762
• Main Authors: Willman, Cheryl L., Kang, Huining, Potter, Jeffrey W., Harvey, Richard C., Atlas, Susan R., Bedrick, Ed, Helman, Paul, Veroff, Robert L., Chen, I.-Ming, Carroll, Andrew J., Ar, Kerem, Xu, Yuexian, Murphy, Sharon B., Bhojwani, Deepa, Moskowitz, Naomi, Carroll, William L., Camitta, Bruce
• Format: Article
• Language: English
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood.V106.11.762.762

Significant advances in the treatment of pediatric ALL have been achieved through the use of risk classification schemes that target children to increasing therapeutic intensities based on their relapse risk. However, current classification schemes do not fully reflect the molecular heterogeneity of the disease and do not precisely identify those children more prone to relapse or those who could be cured with less intensive regimens. To improve risk classification and outcome prediction in ALL, gene expression profiles were obtained using oligonucleotide arrays in a retrospective case control study of 220 children with B precursor ALL, balanced for outcome (continuous complete remission (CCR) vs. failure at 4 years) across several established prognostic variables (age, sex, WBC, karyotype). Using multiple statistical methods and computational tools, these comprehensive gene expression profiles were reduced to a 26 gene expression classifier that was highly predictive of overall outcome (two tailed p values ranging from 0.00001–0.001). Each of these 26 genes was shown to provide additional prognostic information relative to established prognostic variables (p