A Platform for Pattern-Based Proteomic Biomarker Discovery-R01

描述(由申请人提供)： 蛋白质或多肽生物标记物在癌症的早期检测、监测和靶向治疗方面提供了巨大的希望。蛋白质组生物标记物的发现主要采用两种策略。基于身份的方法使用高质量的串联质谱仪，并在测序的多肽中识别潜在的生物标记物。另一方面，基于模式或无标记的方法在质谱图中寻找区分峰的模式，而不考虑它们的身份-从而实现更高的吞吐量分析。尽管这些方法提供了发现生物标记物的潜力，但有效地发现强大的生物标记物仍然是一个重大而未完成的挑战。在这里，我们建议开发一个稳健的、高通量的生物标志物发现分析平台，将在高分辨率和高质量精度下获得的身份和模式结合在一起。我们方法的一个关键创新是使用序列识别的多肽来指导未识别的m/z峰的比对(这两个峰都是在同一个LC-MS实验中获得的)并校正层析变异。该软件将使用数学和统计上合理的算法来匹配多个样本中的未识别峰，将多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该算法，以从经过额外分级的样品中获得定量数据，例如在多肽水平上进行强阳离子交换。我们预计，与现有的以身份为中心的生物标记物发现方法相比，我们开发的方法将使检测到的差异调节峰的数量至少增加一个数量级，并随后进行序列识别。拟议平台的成功开发和验证有可能大大加快癌症和其他疾病的生物标记物发现工作，无论是在布罗德研究所还是在其他地方。此外，部署生物标记物发现平台作为caBIG服务将提供对该平台的广泛访问，从而最大限度地扩大在研究界的影响。

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.