This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Researchers from the University of Florida and PNNL are collaborating to develop quantitative proteomic platform for human blood plasma profiling with the goal of providing protein biomarkers capable of predicting the outcome of human breast cancer patients. It is hypothesized that: (1) the human plasma proteome changes in response to the surgical removal of human breast cancer; (2) there is a difference in the plasma proteome in patients with breast cancer compared to control patients without a diagnosis of cancer; (3) patterns of plasma protein concentrations can predict the outcome of human breast cancer in patients following operative removal of disease. The PNNL center is developing a quantitative proteomic biomarker discovery platform suitable for high throughput analyses of large number of patient plasma samples. The platform will involve sample fractionation, stable isotope labeling, high resolution liquid chromatography separations (LC) coupled with high performance Fourier transform ion cyclotron resonance (FTICR) mass spectrometry, and subsequent informatic data analysis. The plasma proteomes of pre- and post- resection in patients with invasive breast cancer will be analyzed using this platform and patterns of plasma protein concentrations related to the presence and recurrence of human breast cancer will be used for class determination. The differences in plasma proteome between breast cancer patients and control patients without cancer and the relationship between plasma proteome, and stage and disease progression in patients with invasive breast cancer will also been investigated with the purpose of identifying novel biomarkers for prognosis/diagnosis and prediction of disease progression.
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