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Gene expression profiling studies using cDNA and oligonucleotide microarrays have yielded troves of information about the biology, classification, and prognostication of human cancers, with much of it currently accessible in public databases. A simple but elegant statistical model now offers the potential to unite data sets across these two technology platforms.Since the start of the new millennium, the scientific community has witnessed an explosion in the use of DNA microarray technology to study gene expression (1). A casual inspection of PubMed citations reveals that, since the year 2000, the number of scientific papers related to microarrays has dramatically increased with each passing year, with well over 1,000 publications in the first half of 2003. However, this powerful technology is still in its infancy. There is no single, standardized array platform on which to conduct experiments, and the lack of a standard data analysis protocol further confounds experimental results (2, 3). In a recent issue of PNAS, Wright et al. (4) proposed a statistical model that can be used to translate experimental results across microarray platforms. The method is used to assign tumor samples to one of two subgroups based on gene expression delineated by using cDNA arrays, and the validity of this predictor is confirmed in publicly available data from a set of similar tumor specimens studied independently by using oligonucleotide arrays.There are many types of microarrays, but the literature is dominated by two distinct platform technologies, cDNA and oligonucleotide arrays (5). Both methods share the feature of a solid support “chip” to which hundreds of thousands of gene fragments are attached. Major differences between these array platforms include the immobilized probe used to detect specific mRNA transcripts and the number of different biological samples used within a single chip experiment.cDNA microarrays probe the biological sample …

Extending the utility of gene profiling data by bridging microarray platforms