Expression genomics and cancer biology

Expression genomics is a term that is used to describe the investigation of transcription in a whole-genome manner and includes the investigation of gene regulation. Characteristic of this approach is its comprehensiveness and the highly multiplexed nature of its analysis. Thus, the range of technologies used include microarrays, tag library approaches, such as serial analysis of gene expression (SAGE), full-length cDNA cloning and sequencing, and chromatin immunoprecipitation coupled with cloning/sequencing or applied to genomic chips. These technologies are all complementary since they have different capabilities and attack different components of the transcriptome: transcriptional regulation, promoter usage, differential splicing, and gene expression. Unlike genome sequencing, the combinatorial complexity of the transcriptome is immense, making its complete characterization impossible. Instead, the strategy has shifted to the analysis of the whole transcriptome in a context-driven, cell biological framework where the fundamental truths emerge through multiple comparisons and pharmacological challenges. The density of non-redundant data generated with any experiment allows for the assessment of higher order relationships. This comprehensive data, in turn, permits information convergence across different experiments, organisms, and data sets. Surprising concordance in the underlying conclusions is observed with data of such complexity. Since microarrays have been the most widely used technology in expression genomics for the study of cancer biology, this paper will focus on studies using expression arrays, but will also touch on other transcriptome-directed technologies. The experience with these approaches is sufficiently mature to arrive at some generalizable observations. First, although expression genomics is a useful approach for the discovery of individual candidate genes, its greatest power is in defining class distinctions using the collective behavior of gene clusters. Therefore, expression genomic output can effectively uncover hierarchies of molecular importance. Second, in the hierarchy of factors that determine the expression footprint within a cell, cell lineage is the most important, followed by the activity of specific biochemical pathways and further followed by the effect of individual genes. Lastly, expression profiling is an effective clinical tool that can discern prognostic and therapeutic classes. The importance of these gene lists is that not only do they describe potential therapeutic targets, but they are effective monitors of therapeutic efficacy.