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Science 15 October 1999:
Vol. 286. no. 5439, pp. 531 - 537
DOI: 10.1126/science.286.5439.531
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Reports

Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring

T. R. Golub, 12*dagger D. K. Slonim, 1dagger P. Tamayo, 1 C. Huard, 1 M. Gaasenbeek, 1 J. P. Mesirov, 1 H. Coller, 1 M. L. Loh, 2 J. R. Downing, 3 M. A. Caligiuri, 4 C. D. Bloomfield, 4 E. S. Lander 15*

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

1 Whitehead Institute/Massachusetts Institute of Technology Center for Genome Research, Cambridge, MA 02139, USA.
2 Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA 02115, USA.
3 St. Jude Children's Research Hospital, Memphis, TN 38105, USA.
4 Comprehensive Cancer Center and Cancer and Leukemia Group B, Ohio State University, Columbus, OH 43210, USA.
5 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA.
*   To whom correspondence should be addressed. E-mail: golub{at}genome.wi.mit.edu; lander{at}genome.wi.mit.edu.

dagger    These authors contributed equally to this work.

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Genome-wide DNA copy number predictors of lapatinib sensitivity in tumor-derived cell lines.
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Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits.
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Genetics 178, 2289-2303
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Current Status of Prognostic Profiling in Breast Cancer.
L. Pusztai (2008)
Oncologist 13, 350-360
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A simple and exact Laplacian clustering of complex networking phenomena: Application to gene expression profiles.
C. Kim, M. Cheon, M. Kang, and I. Chang (2008)
PNAS 105, 4083-4087
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