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

定量分析 LC-MS 数据以发现肽和聚糖生物标志物

• 批准号: 8104063
• 负责人: Habtom W Ressom
• 金额: $ 26.59万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8104063
• 关键词: Accounting; Address; Affect; Algorithms; Biochemistry; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Charge; Chromatography; Cirrhosis; Clinical; Cluster Analysis; Collaborations; Communities; Computer software; Computing Methodologies; Coupled; Coupling; Data; Detection; Development; Diagnosis; Dimensions; Disease; Disease Management; Disease Progression; Early Diagnosis; Electrospray Ionization; Ensure; Health; Heterogeneity; Human; Individual; Isotopes; Joints; Label; Lead; Maps; Marker Discovery; Mass Spectrum Analysis; Measurement; Metabolic Pathway; Methods; Metric; Modeling; Newly Diagnosed; Participant; Pathway interactions; Patients; Pattern; Peptides; Performance; Plasma; Polysaccharides; Population; Primary carcinoma of the liver cells; Process; Recruitment Activity; Reporting; Research; Role; Running; Sampling; Screening procedure; Signal Pathway; Solutions; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Staging; Statistical Methods; Subgroup; Technology; Testing; Time; University Hospitals; Work; analytical method; analytical tool; anticancer research; base; comparative; density; design; expectation; high risk; improved; instrument; liquid chromatography mass spectrometry; mass spectrometer; multiple reaction monitoring; novel; open source; public health relevance; sample collection; stem; synthetic peptide; tool; treatment strategy

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.

描述（由申请人提供）：使用液相色谱 - 质谱法（LC-MS）对分析物的项目摘要无标记量化正在成为生物标志物发现的一个很好的策略。但是，在特定于仪器的软件包中未充分解决此类量化。特别是，LC-MS数据的比对在无标签定量和生物分子的比较中提出了重大挑战。这一挑战以及人类种群中生物学变异性和疾病异质性的结合限制了基于LC-MS的生物标志物发现研究的最新进展。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，色谱和质谱法专家，以开发新的分析工具，用于基于LC-MS的无标签量化，并比较血清和血浆中的基于LC-MS的小聚糖和肽。具体而言，将研究一种基于概率的新型混合回归模型和一种新的基于聚类的方法，以对齐LC-MS数据并鉴定患者亚组。将利用来自Spike-In研究的LC-MS数据来开发和优化所提出的对准方法，并将其性能与其他现有解决方案进行比较。优化的算法和统计方法将用于识别肽和聚糖候选生物标志物，以早日检测HCC。这将通过使用两种LC-MS技术来评估从HCC患者和肝硬化对照中收集的血清和血浆样品中肽和聚糖的表达。候选生物标志物将使用同位素稀释质谱测定法进行验证。我们根据同一参与者的血清和血浆样本同时进行肽和聚糖分析研究的建议是探索标记发现的整合方法的独特机会。此外，该项目将利用本研究中确定的标记和其他研究，以研究可能与HCC进展有关的关键代谢和信号通路。这将增强我们对疾病进展的理解以及标记物在代谢和信号通路中的功能参与，这些途径可用于设计和测试改进的治疗策略。 公共卫生相关性：项目叙事该项目将通过液相色谱 - 质谱法（LC-MS）技术来开发新型的开源分析工具，用于在血清和血浆中无标记的肽和聚糖定量。此类工具的可用性将有助于研究社区推进有希望的基于LC的生物标志物发现研究。提出的工具将用于查找和验证肝细胞癌（HCC）的早期诊断生物标志物。定义临床适用的生物标志物，可在高危肝硬化患者中检测早期HCC，这可能对疾病管理和患者健康产生深远的影响。该项目很重要，因为大多数HCC患者在治疗选择受到限制的后期被诊断出。迫切需要识别可用于早期检测HCC的生物标志物。除了筛查早期疾病迹象的高风险种群外，所得的生物标志物还可用于设计和测试改进的治疗策略。