A paradigm for class prediction using gene expression profiles.
We propose a general framework for prediction of predefined tumor classes using gene expression profiles from microarray experiments. The framework consists of 1) evaluating the appropriateness of class prediction for the given data set, 2) selecting the prediction method, 3) performing cross-validated class prediction, and 4) assessing the significance of prediction results by permutation testing. We describe an application of the prediction paradigm to gene expression profiles from human breast cancers, with specimens classified as positive or negative for BRCA1 mutations and also for BRCA2 mutations. In both cases, the accuracy of class prediction was statistically significant when compared to the accuracy of prediction expected by chance. The framework proposed here for the application of class prediction is designed to reduce the occurrence of spurious findings, a legitimate concern for high-dimensional microarray data. The prediction paradigm will serve as a good framework for comparing different prediction methods and may accelerate the development of molecular classifiers that are clinically useful.
Radmacher
Michael D
MD
Biometric Research Branch, National Cancer Institute, 6130 Executive Boulevard, Bethesda, MD 20892-7434, USA. mdradac@helix.nih.gov
McShane
Lisa M
LM
Simon
Richard
R
D000465:Algorithms; D019313:BRCA1 Protein / Q000235:genetics*; D024682:BRCA2 Protein / Q000235:genetics*; D001943:Breast Neoplasms / Q000145:classification* / Q000235:genetics* / Q000473:pathology; D019295:Computational Biology; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D005838:Genotype; D018095:Germ-Line Mutation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis
D019313:BRCA1 Protein; D024682:BRCA2 Protein