Tuning into the Genetic Orchestra Using Microarrays
Tuning into the Genetic Orchestra Using Microarrays
Scientific advances in the field of genetics and gene-expression profiling have revolutionized the concept of patient-tailored treatment. Analysis of differential gene-expression patterns across thousands of biological samples in a single experiment (as opposed to hundreds to thousands of experiments measuring the expression of one gene at a time), and extrapolation of these data to answer clinically pertinent questions such as those relating to tumor metastatic potential, can help define the best therapeutic regimens for particular patient subgroups. The use of microarrays provides a powerful technology, allowing in-depth analysis of gene-expression profiles. Currently, microarray technology is in a transition phase whereby scientific information is beginning to guide clinical practice decisions. Before microarrays qualify as a useful clinical tool, however, they must demonstrate reliability and reproducibility. The high-throughput nature of microarray experiments imposes numerous limitations, which apply to simple issues such as sample acquisition and data mining, to more controversial issues that relate to the methods of biostatistical analysis required to analyze the enormous quantities of data obtained. Methods for validating proposed gene-expression profiles and those for improving trial designs represent some of the recommendations that have been suggested. This Review focuses on the limitations of microarray analysis that are continuously being recognized, and discusses how these limitations are being addressed.
The discovery of 25,000 human genes as a result of the completion of the Human Genome Project has greatly accelerated the research into genotypic-phenotypic correlations in an aim to elucidate the functional taxonomy of genes in both normal tissues and disease states. The use of microarray technology to perform gene-expression profiling is an important adjunct to our current knowledge of genetics, because microarrays allow the study of thousands of genes simultaneously in a single, standardized, and cost-efficient experiment. Gene expression is a broad term used to describe the transcription of information encoded within DNA sequences into mRNA, and the subsequent translation of the mRNA information into proteins that regulate cell function. The gene expression of all cells is a highly dynamic process that alters in response to cell requirements and environment changes, and as a consequence of disease. A brief summary of the applications of gene-expression profiling by microarray analysis and additional application of microarray platforms are described in Table 1 .
Gene-expression profiling has been used to study infectious and immunological diseases, but the predominant focus of microarray-based research has been in the study of cancer. Microarray analysis has made it possible to identify groups of genes according to their expression pattern or 'genetic signature', and this could potentially ameliorate the clinical management of a patient. Despite the vast amount of research in this field, a disparity exists between the quantity of publications and the quality of their interpretation. The quantity of articles that document the discovery of new gene profiles is as plentiful as the number of publications that scrutinize their interpretation.
In this article we will briefly describe the methods of microarray-based gene-expression profiling and highlight the technique's limitations, demonstrate the various levels at which both inter-experimental and intra-experimental variability can occur, summarize the suggestions that have been proposed to limit these problems, and describe the current status of genetic profiling in oncological practice. The different steps involved in gene-expression-profiling using microarray technology and the major limitations of this method are depicted in Figure 1.
The different steps in this process and their limitations are summarized. There are seven major steps in the development of a gene-expression signature using microarrays before it is ready for implementation as a test in clinical practice. The first step consists of defining the scientific question that should be answered by the output data. The accompanying hypothesis will help to construct a solid study design and to determine the type of samples and the applicable inclusion criteria (steps 2 and 3). Step 4 includes the actual microarray experiment by which the gene-expression data are obtained. The statistical analysis of the data generated in the experiment and the construction of the actual gene-expression profile is addressed in step 5. Step 6 includes the independent validation of the results, which is indispensable for the transition to step 7, the clinical implementation of the gene-expression profile. Figure modified with permission from reference 21 © (2002) Nature Publishing Group.
Summary and Introduction
Summary
Scientific advances in the field of genetics and gene-expression profiling have revolutionized the concept of patient-tailored treatment. Analysis of differential gene-expression patterns across thousands of biological samples in a single experiment (as opposed to hundreds to thousands of experiments measuring the expression of one gene at a time), and extrapolation of these data to answer clinically pertinent questions such as those relating to tumor metastatic potential, can help define the best therapeutic regimens for particular patient subgroups. The use of microarrays provides a powerful technology, allowing in-depth analysis of gene-expression profiles. Currently, microarray technology is in a transition phase whereby scientific information is beginning to guide clinical practice decisions. Before microarrays qualify as a useful clinical tool, however, they must demonstrate reliability and reproducibility. The high-throughput nature of microarray experiments imposes numerous limitations, which apply to simple issues such as sample acquisition and data mining, to more controversial issues that relate to the methods of biostatistical analysis required to analyze the enormous quantities of data obtained. Methods for validating proposed gene-expression profiles and those for improving trial designs represent some of the recommendations that have been suggested. This Review focuses on the limitations of microarray analysis that are continuously being recognized, and discusses how these limitations are being addressed.
Introduction
The discovery of 25,000 human genes as a result of the completion of the Human Genome Project has greatly accelerated the research into genotypic-phenotypic correlations in an aim to elucidate the functional taxonomy of genes in both normal tissues and disease states. The use of microarray technology to perform gene-expression profiling is an important adjunct to our current knowledge of genetics, because microarrays allow the study of thousands of genes simultaneously in a single, standardized, and cost-efficient experiment. Gene expression is a broad term used to describe the transcription of information encoded within DNA sequences into mRNA, and the subsequent translation of the mRNA information into proteins that regulate cell function. The gene expression of all cells is a highly dynamic process that alters in response to cell requirements and environment changes, and as a consequence of disease. A brief summary of the applications of gene-expression profiling by microarray analysis and additional application of microarray platforms are described in Table 1 .
Gene-expression profiling has been used to study infectious and immunological diseases, but the predominant focus of microarray-based research has been in the study of cancer. Microarray analysis has made it possible to identify groups of genes according to their expression pattern or 'genetic signature', and this could potentially ameliorate the clinical management of a patient. Despite the vast amount of research in this field, a disparity exists between the quantity of publications and the quality of their interpretation. The quantity of articles that document the discovery of new gene profiles is as plentiful as the number of publications that scrutinize their interpretation.
In this article we will briefly describe the methods of microarray-based gene-expression profiling and highlight the technique's limitations, demonstrate the various levels at which both inter-experimental and intra-experimental variability can occur, summarize the suggestions that have been proposed to limit these problems, and describe the current status of genetic profiling in oncological practice. The different steps involved in gene-expression-profiling using microarray technology and the major limitations of this method are depicted in Figure 1.
The different steps in this process and their limitations are summarized. There are seven major steps in the development of a gene-expression signature using microarrays before it is ready for implementation as a test in clinical practice. The first step consists of defining the scientific question that should be answered by the output data. The accompanying hypothesis will help to construct a solid study design and to determine the type of samples and the applicable inclusion criteria (steps 2 and 3). Step 4 includes the actual microarray experiment by which the gene-expression data are obtained. The statistical analysis of the data generated in the experiment and the construction of the actual gene-expression profile is addressed in step 5. Step 6 includes the independent validation of the results, which is indispensable for the transition to step 7, the clinical implementation of the gene-expression profile. Figure modified with permission from reference 21 © (2002) Nature Publishing Group.
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