Genomics has enabled the examination of the totality of disease at the transcriptome level. Dependent upon a myriad of genetic aberrations for its pathogenesis, cancer has been the focus of gene expression profiling studies that have highlighted the potential clinical applications of this technology. This type of molecular profiling has the potential to enhance the ability of pathologists and oncologists to correctly classify tumors, not just into existing subgroups which may or may not have clear prognostic implications, but into new groups which carry predictable correlations with outcomes. Ultimately, these outcome predictions can be tied to specific treatment regimens, allowing clinicians to predict at the time of diagnosis to which therapy a given patient may best respond. Although this ultimate goal of personalized therapy remains in the future, the numerous studies to date have clearly demonstrated the overall feasibility of this approach. This review will showcase a few of these studies in several key tumor types with the goal of demonstrating which type of studies have been conducted and what types of results are currently possible.
Keywords: gene expression profiling, microarray, cancer classification, cancer prognosis, cancer treatment, hierarchical clustering, learning algorithms, responders
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