Quantitative analysis of LC-MS data for peptide and glycan biomarker discovery

项目概述：使用液相色谱-质谱（LC-MS）对分析物进行无标签定量是发现生物标志物的一种非常好的策略。然而，这种量化在特定仪器的软件包中没有得到充分的解决。特别是，LC-MS数据的比对在生物分子的无标记定量和比较中提出了重大挑战。这一挑战加上人类群体的生物学变异性和疾病异质性限制了最近基于LC- ms的生物标志物发现研究的进展。该项目汇集了生物信息学、生物统计学、生物化学、临床癌症研究、色谱和质谱方面的专家，开发了基于lc - ms的新型分析工具，用于血清和血浆中聚糖和肽的无标签定量和比较。具体而言，将研究一种新的基于概率的混合回归模型和一种新的基于聚类的方法，用于LC-MS数据的对齐和患者亚组的识别。来自峰值研究的LC-MS数据将用于开发和优化拟议的校准方法，并将其性能与其他现有解决方案进行比较。优化后的算法和统计方法将应用于肝癌早期检测的肽和聚糖候选生物标志物的鉴定。这将通过使用两种LC-MS技术来评估从HCC患者和肝硬化对照组收集的血清和血浆样本中肽和聚糖的表达来完成。候选生物标志物将使用同位素稀释质谱分析进行验证。我们建议基于来自同一参与者的血清和血浆样本进行肽和聚糖分析研究，这是探索标记物发现的整体方法的独特机会。此外，该项目将利用本研究和其他先前研究中发现的标志物来研究可能与HCC进展相关的关键代谢和信号通路。这将增强我们对疾病进展和代谢和信号通路中标记物功能参与的理解，可用于设计和测试改进的治疗策略。

DESCRIPTION (provided by applicant): PROJECT SUMMARY Label-free quantification of analytes using liquid chromatography-mass spectrometry (LC-MS) is gaining recognition as a very good strategy for biomarker discovery. However, such quantification is not addressed adequately in the instrument-specific software packages. In particular, alignment of LC-MS data presents a significant challenge in label-free quantification and comparison of biomolecules. This challenge coupled with biological variability and disease heterogeneity in human populations has restricted recent advances in LC- MS-based biomarker discovery studies. This project brings together experts in bioinformatics, biostatistics, biochemistry, clinical cancer research, chromatography, and mass spectrometry to develop novel analytical tools for LC-MS-based label-free quantification and comparison of glycans and peptides in serum and plasma. Specifically, a novel probabilistic-based mixture regression model and a new clustering-based method will be investigated for alignment of LC-MS data and for identification of patient subgroups. LC-MS data from spike-in studies will be utilized to develop and optimize the proposed alignment methods and to compare their performance with other existing solutions. The optimized algorithms and statistical methods will be applied to identify peptide and glycan candidate biomarkers for early detection of HCC. This will be accomplished by using two LC-MS technologies to evaluate the expression of peptides and glycans in serum and plasma samples collected from HCC patients and cirrhotic controls. The candidate biomarkers will be validated using isotope dilution mass spectrometric assays. Our proposal to perform both peptide and glycan profiling studies based on serum and plasma samples from the same participants is a unique opportunity to explore an integromic approach for marker discovery. Furthermore, this project will capitalize on markers identified in this study and other previous studies to investigate key metabolic and signaling pathways that may be related to the progression of HCC. This will enhance our understanding of the disease progression and the functional involvement of the markers in metabolic and signaling pathways, which could be used to design and test improved treatment strategies. PUBLIC HEALTH RELEVANCE: PROJECT NARRATIVE This project will lead to the development of novel open source analytical tools for label-free quantification of peptides and glycans in serum and plasma using liquid chromatography-mass spectrometry (LC-MS) technologies. The availability of such tools will assist the research community in advancing the promising LC- MS-based biomarker discovery research. The proposed tools will be utilized to find and validate early- diagnosis biomarkers of hepatocellular carcinoma (HCC). Defining clinically applicable biomarkers that detect early-stage HCC in a high-risk population of cirrhotic patients has potentially far-reaching consequences for disease management and patient health. This project is important because most HCC patients are diagnosed at a late stage, where the treatment options are limited. There is a pressing need to identify biomarkers that could be used for early detection of HCC. In addition to screening high-risk populations for early signs of disease, the resulting biomarkers could be used to design and test improved treatment strategies.