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High-Throughput Evaluation of Breast Cancer Markers

Rick Zangar, Principal Investigator

Funding Agency: National Institutes of Health, National Cancer Institute

Breast cancer is one of the most prevalent cancers in the United States, resulting in the death of approximately 43,000 women per year. Current methods for early detection (e.g., mammography and breast exam) of this disease rely on physical means to detect a tumor and are unreliable. Because a number of blood proteins have been reported to be altered in women with breast cancer, a more useful and accurate evaluation of breast cancer may be obtained by an analysis of these proteins. Because breast cancer is a multifaceted disease, it seems likely that analysis of more than one protein will be needed to detect all forms of this disease. In addition, the normal levels of many cancer markers will be affected by age, reproductive history, menopausal status, and other epidemiological factors. Therefore, we hypothesize that it will be necessary to use a profile of markers to accurately detect breast cancer and that the accuracy of this profile will be improved by accounting for predictable effects of epidemiological factors.

To test this hypothesis, the interdisclipinary High-Throughput Evaluation of Breast Cancer Markers project team at Pacific Northwest National Laboratory (PNNL) is undertaking a three-phase study. In Phase 1, we are conducting biomarker discovery analysis using sophisticated proteomic methodologies. Based on results of this first study and other information, we will conduct a Phase 2 analysis of up to 50 proteins in approximately 1000 plasma samples using enzyme-linked immunosorbent assay (ELISA) microarray technology. Finally, we will undertake an extensive Phase 3 retrospective study with the goal of determining whether a selected subset of plasma markers can be used to predict the presence of breast cancer in a population of high-risk women prior to detection of that disease by conventional methods. Our research will effectively use new technologies to significantly accelerate the pace of biomarker research. The final result of these analyses will be an extensive characterization of a whole profile of protein levels. We will use sufficient numbers of samples to draw statistically valid conclusions about the ability of this biomarker profile to detect the presence of early disease. These studies will also determine whether incorporation of epidemiological factors can improve the accuracy of this analysis.

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