Gene expression profiling of human cancers

Abstract

DNA microarrays allow us to visualize simultaneously the expression of potentially all genes within a cell population or tissue sample-revealing the "transcriptome." The analysis of this type of data is commonly called "gene expression profiling" (GEP) because it provides a comprehensive picture of the pattern of gene expression in a particular biological sample. For this reason microarrays are revolutionizing life sciences research and are leading to the development of novel and powerful methods for investigating cancer biology, classifying cancers, and predicting clinical outcome of cancers. Several recent high-profile reports have revealed how clustering of GEP data can clearly identify clinically (and prognostically) important subtypes of cancer among patients considered by established clinicopathological criteria to have similar tumors. Accurate "prognostic signatures" can be obtained from GEP data, which represent relatively small numbers of genes. These signatures can be valuable in directing appropriate treatment and in predicting clinical outcome, and they generally outperform other systems based on clinical and histological criteria. In this paper the basic principles of DNA microarray technology and the different types of microarray platforms available will be introduced, and the power of the technique will be illustrated by reviewing some recent GEP studies on selected cancers, including a preliminary analysis of hepatocellular carcinoma from our Palermo laboratory. GEP is likely to be adopted in the future as a key decision-making tool in the clinical arena. However, several issues relating to data analysis, reproducibility, cross-comparability, validation, and cost need to be resolved before the technology can be adopted broadly in this context.