Analysis of LC-MS data to identify peptide and glycan biomarkers for hepatocellul

分析 LC-MS 数据以鉴定肝细胞的肽和聚糖生物标志物

• 批准号: 8658019
• 负责人: Habtom W Ressom
• 金额: $ 26.97万
• 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: 2010-09-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8658019
• 关键词: Accounting; Address; Bayesian Modeling; Behavior; Biochemistry; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Blood specimen; Cirrhosis; Collaborations; Communities; Complex; Computer software; Computing Methodologies; Coupled; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Management; Early Diagnosis; Egypt; Ensure; Exhibits; Fibrosis; Goals; Health; Heterogeneity; Human; Individual; Isotopes; Label; Lead; Machine Learning; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Methods; Metric; Michigan; Modeling; Molecular Profiling; Newly Diagnosed; Patients; Pattern; Peptides; Performance; Plasma; Polysaccharides; Population; Primary carcinoma of the liver cells; Process; Proteins; Recruitment Activity; Research; Running; Sampling; Screening for cancer; Serum; Solutions; Source; Staging; Subgroup; System; Technology; Testing; United States; Universities; University Hospitals; Work; analytical tool; base; chronic liver disease; comparative; design; disease classification; disorder control; high risk; improved; instrument; liquid chromatography mass spectrometry; mass spectrometer; multiple reaction monitoring; novel; open source; patient population; public health relevance; sample collection; screening; stem; synthetic peptide; tool; treatment strategy

DESCRIPTION (provided by applicant): Early detection of cancer improves patient survival. Characterizing the association of peptides and glycans with cancer is one of the most promising strategies to discover early-diagnosis cancer biomarkers. This study evaluates peptide and glycan expression profiles in the progression of chronic liver disease (CLD) to hepatocellular carcinoma (HCC) by using the liquid chromatography-mass spectrometry (LC-MS) technology. The goal is to find and validate peptide and glycan biomarkers for detection of HCC at a treatable stage in a high-risk population of patients with CLD. Label-free LC-MS quantification allows comparison of peptides and glycans with good throughput which allows us to compare a large population of patients. However, such quantification is not addressed adequately in the instrument-specific software packages. In particular, alignment and normalization of LC-MS data present 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, and mass spectrometry to develop a suite of novel analytical tools for LC-MS-based label-free quantification and comparison of peptides and glycans in serum and plasma. Specifically, a novel Bayesian hierarchical model will be investigated for simultaneous alignment and normalization of LC-MS data and for identification of patient subgroups. The Bayesian framework involves fixed and random effects to account for subpopulation homogeneous behavior (fixed systematic changes), while allowing for modeling heterogeneity within a group (random effects). A spike-in study will be conducted to obtain replicate LC-MS runs with known peptide and glycan concentrations. The data will be utilized to develop and optimize the proposed Bayesian framework and to compare its performance with other existing solutions. The optimized framework and a machine learning-based feature selection method will be applied to identify an integrated set of peptide and glycan candidate biomarkers for early detection of HCC. LC-MS analysis of integrated peptides and glycans in both serum and plasma of patients with HCC is to our knowledge unprecedented. Blood samples from patients with HCC and CLD controls in Egypt and United States will be used. The biomarkers will be validated using isotope dilution mass spectrometric assays.

描述（由申请人提供）：癌症的早期发现可改善患者的存活。表征肽和聚糖与癌症的关联是发现早期诊断癌症生物标志物的最有希望的策略之一。这项研究通过使用液相色谱 - 质谱法（LC-MS）技术评估了慢性肝病（CLD）对肝细胞癌（HCC）进展的肽和聚糖表达谱。目的是在高危CLD患者的高危患者中找到并验证在可治疗阶段检测HCC的肽和聚糖生物标志物。无标签的LC-MS定量允许比较具有良好吞吐量的肽和聚糖，这使我们能够比较大量的患者。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数据的比对和归一化在无标签的定量和生物分子比较中构成了重大挑战。这一挑战再加上人群中的生物变异性和疾病异质性，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，生物化学和质谱的专家，以开发一套新型的分析工具，用于基于LC-MS的无标签定量，并比较血清和血浆中的肽和聚糖。具体而言，将研究一种新型的贝叶斯分层模型，以同时对齐和识别LC-MS数据和鉴定患者亚组。贝叶斯框架涉及固定和随机效应，以说明亚群的同质行为（固定的系统变化），同时允许在组内建模异质性（随机效应）。将进行一项尖峰研究，以获得具有已知肽和聚糖浓度的重复LC-MS运行。数据将用于开发和优化所提出的贝叶斯框架，并将其性能与其他现有解决方案进行比较。将应用优化的框架和基于机器学习的功能选择方法来识别一组集成的肽和聚糖候选生物标志物，用于早期检测HCC。据我们所知，HCC患者的血清和血浆中综合肽和聚糖的LC-MS分析是前所未有的。将使用来自埃及和美国HCC和CLD对照患者的血液样本。将使用同位素稀释质谱测定法对生物标志物进行验证。