We previously considered how inheritance of chromosomal regions in affected individuals or in tumor tissue can be used to build hypotheses about gene function. Instead of studying variation in gene sequence, a complementary approach is to analyze variation in gene expression as mRNA and protein. Because altering gene transcription is a ubiquitous mechanism for cell signaling and regulation, many cellular behaviors are reflected in altered patterns of gene expression. Investigating this phenomenon has traditionally been limited, however, by two fundamental constraints: only a small subset of genes was known (and thus could be studied) and, even among known genes, it was necessary to assay them one at a time. With increasingly complete databases of gene sequences, however, it has become possible to design expression probes for essentially all human genes. When combined with high-throughput methods, such as high-density arrays to measure transcript levels, these sequence resources can transform expression monitoring into an unbiased, genomewide approach.
The development of high-density array technology has dramatically expanded the breadth of methods for studying variation in gene expression. Specifically, nucleic acids of known sequence are deposited in specified locations on a solid support, and labeled experimental samples are hybridized to the array. After washing to eliminate nonspecific binding, the concentration of each mRNA can be determined based on the location and intensity of the label. The power and generality of this approach has been dramatically enhanced by the development of methods (photolithographic and robotic) that place tens to hundreds of thousands of such probes on individual glass slides.
Already, high-density arrays are finding diverse application in biomedical research.48 For example, expression profiling has been used to identify genes whose expression is characteristic of cells in a given developmental or clinical state. Using this approach, there are now multiple examples where tumor diagnosis and classification can be made based solely on gene expression profiles, including leukemia,49 breast cancer,50 and B-cell lymphomas.51 More importantly, gene expression profiles can be used to identify not only cells characteristic of known states but also previously unrecognized subtypes of cancer. This has made it possible to identify subgroups of patients with greater homogeneity of outcomes and etiological mechanisms.51-56
The clinical nosology of cancer has traditionally been based on site of origin and appearance under the light microscope and may be only weakly correlated with the diversity of underly ing biological mechanisms. If so, then mechanistic heterogeneity within a single diagnostic category may be responsible for much of the complexity of "complex" diseases. Under this hypothesis, genomic tools that reveal biologically relevant subgroups of tumors may play an important role in revealing the underlying diagnostic categorization of disease, resulting in more homogeneous groups with simpler genetic architecture. Thus, a new taxonomy of cancer, based on molecular signatures rather than visual inspection, may prove to be a critical step in the path toward identifying the genes responsible for cancer.
Gene expression monitoring is useful not only for developing markers and classifying disease but also for generating functional hypotheses. For example, microarrays offer an efficient approach to capturing the complete list of tran-scriptional changes accompanying the transition from carcinoma in situ to a more invasive form or after the experimental manipulation of a hormonal pathway or expression of an oncogene. In this way, an initial hypothesis about a functional pathway of interest can be expanded to include other genes that are transcriptionally coregu-lated or downstream targets.
In a few cases, it has been possible to confirm functional hypotheses first suggested by expression analysis. For example, the gene encoding RhoC (ARHC) was identified as an expression correlate of tumor metastasis in a melanoma model; critically, blockade of RhoC diminished metastasis and activation enhanced metastasis in this model.57 As yet, few such functional hypotheses have been validated, due to the relative mismatch between our ability to rapidly generate such hypotheses using expression microarrays and our more limited ability to test them in the laboratory. However, methods for systematically testing gene function on a global scale are rapidly evolving and are discussed briefly in the next section.
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