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FOR IMMEDIATE RELEASE
Contact:
Jim Parina
724-935-7029
jparina@expressionanalysis.com
Expression Analysis, Inc. (EA) Science Advisory Board Members Use Gene Expression Profiles to Predict the Clinical Status of Breast Cancer
EA Science Advisory Board members who are investigators at Duke University have developed a statistical approach to the classification of breast cancers based on the analysis of gene expression profiles.
Employing Affymetrix DNA microarrays, Drs. Joseph Nevins, Mike West, and Holly Dressman, who are also members of the Expression Analysis Scientific Advisory Board, were able to identify patterns of gene expression that could discriminate the estrogen receptor status of the tumors. In addition, the analyses also provided evidence for an ability to predict whether the tumors would spread to the lymph nodes, a measurement which constitutes the single most important risk factor in breast cancer.
The work was reported in the September 25, 2001 issue of the Proceedings of the National Academy of Sciences. The paper is available online at http://www.pnas.org.
Gene profiling using microarrays has been used in prior work to analyze and classify such cancers as leukemias and lymphomas as well as breast cancers. However, the Duke study goes beyond this past work by developing predictive analyses of breast cancer clinical status, an important aspect of the future use of this technology in the clinic.
The Duke analysis, employing Affymetrix GeneChips containing about 7,000 human genes, focused on 100 genes whose activity maximally reflected the outcomes in the tumors. They used their statistical analytical approach to analyze the gene expression profiles of 49 tumor samples previously tested to be either positive or negative for estrogen receptors. These gene expression profiles revealed clear differences in the patterns of gene expression in breast tumors that could predict the estrogen receptor status of the tumors with a high degree of accuracy. The determination of estrogen receptor status is an important aspect of breast cancer diagnosis because of its role in promoting tumor growth.
In addition, the study also reported the results of gene profiling of breast tumors to predict their lymph node involvement. In their profiling, the scientists compared tumors that had spread to lymph nodes at the time of diagnosis to those that had not. They found that statistical analysis of the profiles suggested the potential to classify tumors' lymph node status, although the accuracy was less than that of the estrogen receptor analysis.
The authors suggest that large scale gene expression information holds the promise of improved clinical diagnosis and treatment strategies, but it depends on developing statistical tools needed to analyze the data. This study provides the framework for statistical analyses that can be applied not only to the study of breast cancer but other clinical settings. The opportunities for these kind of data to aid clinical decision making has been recognized for some years, but the technology to develop the analytic methods needed to put it into practice hasn't been available.
In addition to isolating patterns of gene expression that accurately predict estrogen status of many tumors with high precision, the methods identify additional aspects of gene expression that characterize tumors that are not accurately predicted, so providing clues for further biological studies.
About Expression Analysis
Expression Analysis, Inc., (EA) (www.expressionanalysis.com) is a full-service microarray genomics testing and analysis organization dedicated to providing clients with the highest quality genomic processing and data analysis services using Affymetrix GeneChip® brand technology. In 2004, EA became the first microarray services facility, worldwide, to provide GLP-compliant services. EA is a U.S.-based authorized service provider for Affymetrix (www.affymetrix.com), a California-based company that develops and commercializes systems to help researchers explore the relationship between genes and human health.
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