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Previous Volume 350:1605-1616 April 15, 2004 Number 16 Next

	Full Text PDF PDA Full Text PowerPoint Slide Set Supplementary Material Perspective by Liu, E. T. Editorial by Grimwade, D. Add to Personal Archive Add to Citation Manager Notify a Friend E-mail When Cited E-mail When Letters Appear PubMed Citation

ABSTRACT

Background In patients with acute myeloid leukemia (AML), the presence or absence of recurrent cytogenetic aberrations is used to identify the appropriate therapy. However, the current classification system does not fully reflect the molecular heterogeneity of the disease, and treatment stratification is difficult, especially for patients with intermediate-risk AML with a normal karyotype.

Methods We used complementary-DNA microarrays to determine the levels of gene expression in peripheral-blood samples or bone marrow samples from 116 adults with AML (including 45 with a normal karyotype). We used unsupervised hierarchical clustering analysis to identify molecular subgroups with distinct gene-expression signatures. Using a training set of samples from 59 patients, we applied a novel supervised learning algorithm to devise a gene-expression–based clinical-outcome predictor, which we then tested using an independent validation group comprising the 57 remaining patients.

Results Unsupervised analysis identified new molecular subtypes of AML, including two prognostically relevant subgroups in AML with a normal karyotype. Using the supervised learning algorithm, we constructed an optimal 133-gene clinical-outcome predictor, which accurately predicted overall survival among patients in the independent validation group (P=0.006), including the subgroup of patients with AML with a normal karyotype (P=0.046). In multivariate analysis, the gene-expression predictor was a strong independent prognostic factor (odds ratio, 8.8; 95 percent confidence interval, 2.6 to 29.3; P<0.001).

Conclusions The use of gene-expression profiling improves the molecular classification of adult AML.

Source Information

From the Departments of Pathology (L.B., J.R.P.), Health Research and Policy (E.B., R.T.), and Statistics (E.B., R.T.), Stanford University, Stanford, Calif.; and the Department of Internal Medicine III, University of Ulm, Ulm, Germany (K.D., S.F., R.F.S., H.D.).

Address reprint requests to Dr. Pollack at the Department of Pathology, Stanford University, 269 Campus Dr., CCSR 3245A, Stanford, CA 94305, or at pollack1{at}stanford.edu.

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• Lee, S., Chen, J., Zhou, G., Shi, R. Z., Bouffard, G. G., Kocherginsky, M., Ge, X., Sun, M., Jayathilaka, N., Kim, Y. C., Emmanuel, N., Bohlander, S. K., Minden, M., Kline, J., Ozer, O., Larson, R. A., LeBeau, M. M., Green, E. D., Trent, J., Karrison, T., Liu, P. P., Wang, S. M., Rowley, J. D. (2006). Gene expression profiles in acute myeloid leukemia with common translocations using SAGE. Proc. Natl. Acad. Sci. USA 103: 1030-1035 [Abstract] [Full Text]  
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• Mrozek, K., Bloomfield, C. D. (2006). Chromosome Aberrations, Gene Mutations and Expression Changes, and Prognosis in Adult Acute Myeloid Leukemia. ASH Education Book 2006: 169-177 [Abstract] [Full Text]  
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