Introduction

1.1. BACKGROUND AND OVERVIEW A powerful suite of new technologies now makes it possible to measure simultaneously the expression of many thousands of genes directly from a clinical sample (1). Known alternately as DNA microarrays, gene arrays, expression arrays, "gene chips," or various permutations thereof, technologies for measuring gene expression are already beginning to have many applications in clinical diagnosis. The most promising to date are in cancer diagnosis, where microarray classification has suggested clinically meaningful distinctions that are difficult or impossible to make by conventional histopathology alone (2,3). As the experimental and analytical techniques have developed, applications have been suggested across the spectrum of human pathology, including infectious, vascular, genetic, inflammatory, psychiatric, and metabolic diseases.

Although generally not stated explicitly, the concept of gene expression analysis has been embedded in clinical diagnosis by conventional methods such as flow cytometry, fluorescent in situ hybridization (for amplification), immunohistochemistry, and other assays. For example, the characterization of lym-phoid malignancies depends on the measurement of surface protein abundance, as detected by labeled antibodies such as the CD group markers (4). Similarly, the presence or absence of estrogen receptor in breast cancer informs key treatment decisions, as does the presence or absence of Her-2/neu amplification (5).

Thus, the new microarray methods represent a quantitative leap in the ability to measure expression of multiple genes and their corresponding proteins, rather than a qualitatively new principle. The power of the microarray lies in its flexibility, quantitative reproducibility, efficiency, and ability to sample the genome in an unbiased fashion. The scope of gene expression arrays in clinical diagnosis is likely to expand dramatically in the near future as increasing numbers of genes and gene products are described with prognostic and treatment significance. In particular, the introduction of molecularly targeted therapies for cancer is likely to drive the demand. Expression arrays are an efficient and robust means of integrating many of these measurements in a single assay.

The goal of this chapter is to discuss the current tools used in functional genomics with an emphasis on clinical applications and focusing mainly on the various microarray platforms. We will discuss issues of study design, sample selection and preparation, and the contribution of measurement error. Whereas genomics analysis is heavily dependent on a variety of computer imaging, software, databases, and bioinformatics tools, we will sample a range of representative applications. We place particular emphasis on the processing of raw data into forms amenable to analysis of underlying biology. The analytic methods used in genomics assays are unlikely to be familiar to most readers and we describe their underlying principals. Acknowledging the technical limitations of the microarray, we will introduce a range of complementary DNA, RNA, and protein-based technologies. In all of these areas, we emphasize that the field of functional genomics is young and few conventions have emerged that apply to all situations the reader might encounter. Despite the ongoing evolution, the basic principles described in this chapter are hopefully sufficient to allow the reader confidence in evaluating current clinical genomics research. To illustrate this point, we identify in a modular way the components of two landmark publications, identifying the relevant methods in light of the lessons we review in this chapter.

1.2. BACKGROUND: GENE EXPRESSION DEFINES DIFFERENTIATED TISSUE The human genome is estimated to contain between 30,000 and 120,000 genes. However, in a given cell or tissue, only a subset of the genes is expressed. The identity of the expressed genes principally defines the character and function of a particular cell type. For example, red blood cells have a very high concentration of hemoglobin, whereas this protein is essentially undetectable in other cells. Accordingly, red blood cell precursors will express high levels of globin genes and other cells will not. Similarly, muscle, heart, and brain are characterized by expression of different isoforms of creatine kinase, a distinction that is useful for the diagnosis of myocardial infarction.

From: Molecular Diagnostics: For the Clinical Laboratorian, Second Edition Edited by: W. B. Coleman and G. J. Tsongalis © Humana Press Inc., Totowa, NJ

Measuring the overall pattern of expressed genes allows the distinction between distinct differentiated tissues. This is particularly valuable in cancer diagnosis, in which accurate diagnosis dictates treatment and diagnosis is often uninformative using current methods. The identification of unique gene expression signatures, associated with different cell differentiation states, permits precise cancer diagnosis. For example, the distinction between acute lymphoid leukemia and acute myeloid leukemia may be made on the basis of global gene expression patterns (6).

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