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: 7484093
• 负责人: Denkanikota R Mani
• 金额: $ 9.24万
• 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 2009-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7484093
• 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）： 蛋白质或肽生物标志物在癌症的早期检测、监测和靶向治疗方面具有很大的前景。在蛋白质组学生物标志物发现中采用了两种主要策略。基于身份的方法使用高质量的串联质谱法，并在测序的肽中鉴定潜在的生物标志物。另一方面，基于模式或无标记的方法在质谱中寻找可区分的峰模式，而不考虑它们的身份，从而实现更高的通量分析。尽管这些方法提供了生物标志物发现的潜力，但有效发现稳健的生物标志物仍然是一个重大且未实现的挑战。在这里，我们建议开发一个强大的，高通量的生物标志物发现的分析平台，结合身份和模式获得高分辨率和高质量的准确性。我们的方法的一个关键创新是使用序列识别肽来指导未识别的m/z峰的对齐（两者都在相同的LC-MS实验中获得）并校正色谱变化。该软件将采用数学和统计上合理的算法来匹配多个样品中未识别的峰，将肽强度整合到相关的蛋白质丰度中，并使用先进的模式识别工具进行差异标记选择和定量。重要的是，我们打算调整和扩展算法，以获得定量数据，从样品经历了额外的分级，如强阳离子交换在肽水平。我们预期，我们开发的方法将提供相对于现有的以身份为中心的生物标志物发现方法，检测为差异调节的峰的数量增加至少一个数量级，并且随后进行序列鉴定。该平台的成功开发和验证有可能大大加快布罗德研究所和其他地方的癌症和其他疾病的生物标志物发现工作。此外，将生物标志物发现平台部署为caBIG服务将提供对该平台的广泛访问，从而最大限度地提高对研究界的影响。