A Platform for Pattern-Based Proteomic Biomarker Discovery-R01

基于模式的蛋白质组生物标志物发现平台-R01

• 批准号: 7293576
• 负责人: Denkanikota R Mani
• 金额: $ 32.04万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-27 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7293576
• 关键词: Algorithms; Arts; Biogenesis; Biological; Biological Markers; Cations; Class; Collaborations; Communities; Complex; Computer software; Data; Development; Disease; Early Diagnosis; Equilibrium; Fractionation; Goals; Hand; Institutes; Label; Liquid Chromatography; Malignant Neoplasms; Mass Spectrum Analysis; Methodology; Methods; Mind; Monitor; Mus; Numbers; Outcome; Parents; Pattern; Pattern Recognition; Peptides; Performance; Plasma; Process; Proteins; Proteomics; Relative (related person); Reproducibility; Research; Research Personnel; Resolution; Resources; Sampling; Services; Validation; Variant; Work; base; cancer Biomedical Informatics Grid; cancer therapy; computerized data processing; data acquisition; design; high throughput analysis; innovation; instrumentation; mass spectrometer; mouse model; programs; protein aminoacid sequence; research study; sound; tandem mass spectrometry; tool

DESCRIPTION (provided by applicant): Protein or peptide biomarkers offer great promise in early detection, monitoring and targeted treatment of cancer. Two main strategies have been employed in proteomic biomarker discovery. Identity-based methods use high quality tandem mass spectrometry and identify potential biomarkers among sequenced peptides. Pattern-based, or label-free, approaches, on the other hand, look for discriminating peak patterns in mass spectra, without regard to their identity-enabling higher throughput analysis. In spite of the potential for biomarker discovery afforded by these methods, efficient discovery of robust biomarkers has remained a significant and unfulfilled challenge. Here we propose to develop a robust, high throughput analytical platform for biomarker discovery that combines identity and pattern obtained at high resolution and high mass accuracy. A key innovation of our approach is the use of sequence identified peptides to guide the alignment of unidentified m/z peaks (both obtained in the same LC-MS experiment) and to correct for chromatographic variation. The software will employ mathematically and statistically sound algorithms to match unidentified peaks across multiple samples, integrate peptide intensities into associated protein abundance, and use advanced pattern recognition tools for differential marker selection and quantitation. Importantly, we intend to adapt and extend the algorithm to derive quantitative data from samples that have undergone additional levels of fractionation such as strong-cation exchange at the peptide level. We anticipate that the methods we develop will provide at least an order of magnitude increase in the number of peaks detected as differentially regulated and subsequently sequence identified relative to existing identity- centric biomarker discovery approaches. Successful development and validation of the proposed platform has the potential to significantly accelerate biomarker discovery efforts for cancer as well as for other diseases, both at the Broad Institute and elsewhere. Furthermore, deployment of the biomarker discovery platform as a caBIG service will provide wide access to the platform, thereby maximizing impact in the research community.

描述（由申请人提供）：