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Breast cancer is one of the most commonly diagnosed cancers in the United States of America. Tumors are usually removed and then the area is sometimes radiated. After removal, therapies such as chemo, endocrine, and trastuzumab have been shown to increase the chance of survival. Various patient factors such as age, tumor size, menopausal status, etc. are combined into prediction models and used as guidelines for treatment. However, patients with the same factors can have different outcomes. The prediction models have limited use in the ability to predict outcomes or to use to decide on the use of systemic adjuvant therapy. This is where DNA microarray technology comes in to play. Studying thousands of genes at a time and profiling the genetics of breast cancer will provide new ways in managing the treatments of breast cancer patients. Several studies were released showing that using different microarray platforms on the same RNA samples gave different results. However this was due to reasons other than the unreliability of the techniques. The United States Food and Drug Administration launched a project named the Microarray Quality Control project to assess the reproducibility of microarray results across different platforms. They found that the microarray results are in fact highly reproducible within and across different microarray platforms. They found that microarrays are reliable enough to be used for medical purposes. Other investigators came to the same conclusion. However, doubts were also raised in the ability for microarray technology to be used in the prediction of disease outcomes and treatment predications. This was because there were several microarray studies that generated different gene-expression classifiers wi... ... middle of paper ... ...long time however. The article proposes a model to help with this. The predictive sequences can be used in pre-op and post op settings to cut down on time. There are many other new technologies that are powerful tools to help with breast cancer. It will be a challenge to evaluate all the data and predictors. It may also be possible to combine predictors into one model with the currently used computer models. In the future trials should be tailored to specific subtypes instead of the breast cancer population as a whole. Clinical researchers will need to collaborate with scientists. There also need to be general standard operating procedures. Moving to specific treatments tailored to the patient instead of empirical treatment will save money in the long run. Right now the governments and other large organizations will need to bear the cost for the good of all people.

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