Collaborative Research: Proteomics Biomarker Information System (ProBIS)

以微创和可重复的方式测量的生物标志物可以加快疾病的早期诊断、疾病预后的指示以及新的治疗药物靶点的发现。用于生物标志物检测的血清蛋白质组学分析可以通过在患病个体和健康个体之间不同表达的蛋白质/肽峰来反映各种疾病的异常或病理状态。由于程序简单，廉价，微创，血清蛋白质组学方法很容易借给自己的筛选测试的发展，其稳健性和易用性的承诺，转化为常规的临床实践。本项目的目标是构建一个高通量的蛋白质组学分析工具集--蛋白质组学生物标志物信息系统（Proteomics Biomarker Information System，ProBIS），为临床蛋白质组学医疗保健提供早期生物标志物检测和鉴定。我们的主要贡献是：（1）高分辨率蛋白质组分析：与传统的低分辨率质谱分析不同，我们将使用高分辨率谱分析蛋白质组谱的低分子量（LMW）端，以进行更精确的分析;以及（2）生物标志物发现和鉴定集成系统：潜在的区别性蛋白质/肽的鉴定和测序将揭示生物标志物的见解并表征疾病途径。该项目的结果将对计算机科学界产生影响，并对需要这种蛋白质组学分析数据分析工具的广泛医学界产生同等（如果不是更大）的影响。这些实验将由一系列案例研究驱动，包括卵巢癌早期复发监测和前列腺癌诊断。这些案例研究的解决方案将对各个领域产生直接影响。我们的源代码和数据将在互联网上广泛传播，发现将被整合到课堂上。

Biomarkers measured in a minimally invasive and repeatable way can expedite the early diagnosis of disease, the indication of disease prognosis, and the discovery of new drug targets for therapy. Serum proteomic profiling for biomarker detection may reflect the abnormality or pathologic state of various diseases through the protein/peptide peaks that are expressed differently between diseased and healthy individuals. Since the procedures are simple, inexpensive, and minimally invasive, serum proteomic methods readily lend themselves to screening-test development; their robustness and ease promise to translate into routine clinical practice. The objective of this project is to construct a high-throughput proteomic profiling analysis toolset, Proteomics Biomarker Information System (ProBIS), which can provide early biomarker detection and identification for clinical proteomics healthcare. Our main contributions are: (1) High Resolution Proteomic Profiling: different from traditional low resolution mass spectrometry analysis, we will use high resolution profile to analyze the low-molecular weight (LMW) end of the proteomic spectrum for more precise analysis; and (2) Integrated System for Biomarker Discovery and Identification: identification and sequencing of the underlying discriminatory proteins/peptides will reveal the insights of biomarkers and characterize disease pathway. The results of this project will have an impact on the computer science community and an equal, if not greater, impact on the broad medical community that is in need of such a proteomic profiling data analysis tool. The experiments will be driven by a series of case studies, including ovarian cancer early relapse monitoring and prostate cancer diagnosis. The solutions for these case studies will have a direct impact on individual areas. Our source code and data will be available for general dissemination over the internet, and discoveries will be integrated into the classroom.