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| IntroductionAs the medical community continues to work towards better treatment of cancer and other complex diseases such as Multiple Sclerosis and rheumatoid arthritis, it is becoming apparent that no single treatment will be ideal for the entire population of patients. Instead, the heterogeneity of these diseases will require treatments targeted to specific subpopulations of patients who will most benefit from the treatment while exhibiting the least severe side affects. Sometimes called personalized medicine, this goal is being aided by gene expression profiling. By comparing the gene expression profile of diseased cells to healthy cells, researchers have been better able to characterize the different forms of cancer. This can lead to more efficient drug discovery, novel drug targets, and earlier diagnosis. Of recent interest, and the primary focus of this site, is the comparison of gene expression profiles of a population of cancer patients. Where an accurate prognosis is often difficult based on current diagnostic techniques, clinical researchers are looking to this expression profiling as a tool to allow doctors to better tailor their patients treatment. Below are a couple of areas with which gene expression profiling has shown an ability to predict patients groups that could ultimately lead to more effective treatment. Risk level of the progression of the disease - Where a particular cancer may show a 50% survival rate over the entire population of patients, profile classification has shown a continuum of risk levels. The most aggressive forms of treatment, and their accompanying risk of side-affects or complications, could be reserved for the patients facing the most dire outcomes. Responsiveness to treatment - Some patients do not respond to traditional chemotherapy. Unfortunately, their health is often too compromised post-treatment to attempt alternative forms of treatment. Again profiling research has shown the potential to identify these patients. Likewise, where a new drug might be considered a clinical failure because of its low success rate overall, it might have had near 100% success for a particular sub-group. Drug toxicity - With many current treatments, there are a number of patients who react adversely to the treatment itself. Again, profile studies have shown a potential to identify these patients. What's on this site?Where patient stratification comes from powerful bioinformatic techniques, that raw data for such analysis is acquired with the microarray. Based on the amount of research over the last several years involving microarrays, it is cleared that it has become an indispensable tool. In the links to the left, the microarray is better described and it's use in gene expression profiling is reviewed. Additionally, the power of this technique is highlighted by reviewing some of the clinical research of Leukemia, a particularly complex and varied form of cancer. The links sections provides a number of sources for additional information on the above subject. Finally, the information on this site was garnered from a number of sources and are outlined in the references section.
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DNA Microarray: