Microarrays For Cancer Research Historical Perspectives

Gene expression profiling in cancer has become routine in recent years and to date encompasses the largest category of research using DNA microarrays. Microarray analysis has been used to assess transcriptome-level (expression analysis) (1) and genome-level (single-nucleotide polymorphism (2), amplification/deletion (3)) differences in cancerous versus normal cell samples. Furthermore, microarray analysis has been used to identify putative diagnostic and prognostic markers that will hopefully translate into clinical tests that can be used to stratify patients and determine treatment regimens (4).

Comparison of treatment response at both the RNA and DNA levels has been conducted in an effort to further direct the most optimal therapy for an individual patient. Classically, many large-scale studies have revealed groups of genes differentially expressed between cancerous and normal cells, including genes known to be important for neoplastic transformation. However, despite the vast amounts of data generated to date, there has been limited translation from the research bench to the clinic. There are few drugs currently in the pipelines of the pharmaceutical industry that were developed because of a microarray-identified target (microarrays have been used to validate and test many potential pharamcophores, but few targets have actually been identified by microarrays).

One of the greatest early promises of microarrays was the ability to identify signatures of biomarkers (panels of genes), which would be able to diagnose or prognose complex diseases such as cancer. Despite some notable exceptions, however, this potential benefit has largely gone unmet. As researchers have looked to capitalize on their investments in these high-throughput technologies, there has been some shift recently toward genotyp-ing rather than transcript analysis in cancer biology. While these techniques are promising, there is still little sign at this early stage that suggests that this shift in focus will lead to revolutionary changes in the type of data obtained.

All of these developments have led to the discouragement of many researchers who have begun to re-evaluate their desire to use high-throughput technologies for oncology research. Despite this, it is our belief that the technology in-and-of itself is not inherently flawed, and rather it is the way that microarrays have typically been applied to cancer research that has led to a decrease in the potential power of the analyses. Recent advances in microarray design, labeling, and amplification technologies—and even our theory of the origin of cancerous lesions—are leading to changes in the way microarray experiments are conducted. These developments are likely to finally fulfil the realization of the promise of microarrays in oncology.

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