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Philipp Kiewe, Department of Hematology and Oncology, University Hospital Benjamin Franklin, Berlin, Germany,
Wolf-Karsten Hofmann, Department of Hematology and Oncology, University Hospital Benjamin Franklin, Berlin, Germany
For many decades, drug therapy in medicine has been an empirical science, largely based on trial and error. Even today we cannot predict how effective a particular drug will be in an individual patient. To find the most suitable antihypertensive agent, for example, it may take more than one attempt. Often, only careful evaluation of a large number of clinical trials has enhanced treatment success and patient benefit.
Testing bacterial sensitivity towards antibiotic drugs (resistogram) was among the first attempts to a “proof of principle” before the onset of treatment. It is still an unsurpassed method for targeted antimicrobial therapy and a prime example of an educated approach to treatment.
With the possibility of evaluating the expression of thousands of genes at a time using commercially available or customized gene arrays and applying sophisticated statistical algorithms, a new era has dawned for prognostic assessment of diseases as well as for therapeutic implications. In treating infectious diseases, microarrays and mapping of single nucleotide polymorphisms (SNPs) have already enhanced drug discovery and understanding of resistance mechanisms [1–3]. However, all efforts to define gene expression profiles for disease are limited by available representative tissue samples. Although recently it has been shown that differentially expressed genes in heart failure patients can be found within white blood cells , progress is most pronounced in hematology and oncology.
Microarray analysis is a highly efficient tool for assessing the expression of a large number of genes simultaneously, and offers a new means to classify cancer and other diseases. Gene expression profiling can also be used to predict clinical outcome and response to specific therapeutic agents. This survey spans recent applications of microarrays in clinical medicine, covering malignant disease including acute leukaemias, lymphoid malignancies and breast cancer, together with diabetes and heart disease. Investigators in oncology, pharmacology and related clinical sciences, as well as basic scientists, will value this review of a promising new diagnostic and prognostic technology.
The development of methods to measure gene expression was revolutionized in the early 90th of the last century by Kary Banks Mullis who introduced the polymerase chain reaction (PCR). Total RNA was amplified using specific primers resulting in the detection of a gene specific PCR-product which could be visualized by gel electrophoresis. To detect specific gene expression in all different kinds of human cells, millions of PCR reactions were performed during the last 15 years. Today, PCR can be called a standard method for gene expression analysis which is used for diagnostic purpose as well as for analysis of physiological and pathophysiological gene expression in all organisms including humans.
Common PCR can help to detect the expression of single genes within one reaction. By optimizing the technique of PCR, the number of genes which can be detected within one reaction could be increased to a maximum of six by using fluorescence labeled primers or probes. High-throughput analysis of multiple genes, e.g., in hundreds of patients samples by PCR is very time consuming and requires a lot of technical and personell power. As a example, it would require about 625 days of work (24 hours a day) to analyze all the human genes which are known at this time by PCR using a singleplex reaction.
In 1995, Patrick O. Brown published the first paper about a new technique which could be used to simultaneously analyze gene expression of 45 genes within on experiment by using a microarray which was prepared by high-speed robotic printing of complementary DNA's on glass slides.