Expression profiling of human tumors: diagnostic and research applications. Marc Ladanyi and William Gerald eds. Humana Press (2003).. Published: 2003.03.31
Pablo Tamayo and Sridhar RamaswamyRead Manuscript
Cancer is a genetic malady, mostly resulting from acquired mutations and epigenetic changes that influence gene expression. Accordingly, a major focus in cancer research is identifying genetic markers that can be used for precise diagnosis or therapy. Over the last half-century, investigators have used reductionism to discover such markers through the study of simple genetic changes like balanced chromosomal translocations. For example, fundamental insights into the nature of the bcr-abl gene translocation product resulted in the precise molecular classification of chronic myelogenous leukemia and recently led to the development of the molecularly targeted tyrosine kinase inhibitor STI571 (Gleevec; Novartis, East Hanover, NJ) for the treatment of this disease. Ninety percent of human cancers, however, are epithelial in origin and display marked aneuploidy, multiple gene amplifications and deletions, and genetic instability, making resulting downstream effects difficult to study with traditional methods. Because this complexity probably explains the clinical diversity of histologically similar tumors, a comprehensive understanding of the genetic alterations present in all tumors is required. The initial sequencing of the human genome, coupled with technologic advances, now make it possible to embrace the genetic complexity of common human cancers in a global fashion. Tools are currently available, or are being developed, for the identification of all changes that take place in cancer at the DNA, RNA, and protein levels. In particular, the use of DNA microarrays for the comprehensive analysis of RNA expression (expression profiling) in human tumor samples holds much promise (see review articles in the Chipping Forecast 1999). A major challenge with this approach, however, remains the interpretation of complex and biologically ?noisy? data in a way that yields new knowledge. We have therefore focused on developing first-generation approaches to gene expression data analysis that are suitable for this purpose. Without such analytic tools, DNA microarray data are useless. This chapter is meant to serve as an introduction to fundamental concepts and techniques that have been developed in gene expression data mining over the last three years. It is not meant to be a comprehensive review of this rapidly expanding field, nor is it a step-by-step set of recipes. Most of the examples described come from our experience in cancer gene expression data analysis at the Whitehead / MIT Center for Genome Research over the last five years, but references to other works are also given when relevant to the discussion.
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