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

描述（由申请人提供）：早期发现癌症可提高患者生存率。表征多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前途的策略之一。本研究利用液相色谱-质谱联用技术评估慢性肝病（CLD）向肝细胞癌（HCC）发展过程中肽和聚糖的表达谱。目的是寻找和验证肽和聚糖生物标志物，用于在高危CLD患者的可治疗阶段检测HCC。无标签LC-MS定量允许比较多肽和聚糖具有良好的吞吐量，这使我们能够比较大量的患者。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数据的校准和规范化对生物分子的无标记定量和比较提出了重大挑战。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、生物化学和质谱学方面的专家，开发了一套新的分析工具，用于基于lc - ms的血清和血浆中肽和聚糖的无标签定量和比较。具体而言，将研究一种新的贝叶斯层次模型，用于LC-MS数据的同步校准和规范化以及患者亚组的识别。贝叶斯框架涉及固定和随机效应，以解释亚种群的同质行为（固定的系统变化），同时允许在一个群体内建模异质性（随机效应）。将进行尖峰研究，以获得具有已知肽和聚糖浓度的重复LC-MS运行。这些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。优化的框架和基于机器学习的特征选择方法将被应用于识别一套完整的肽和聚糖候选生物标志物，用于HCC的早期检测。据我们所知，LC-MS分析HCC患者血清和血浆中的整合肽和聚糖是前所未有的。将使用来自埃及和美国的HCC患者和CLD对照患者的血液样本。生物标记物将使用同位素稀释质谱分析进行验证。

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. PUBLIC HEALTH RELEVANCE: This project will lead to the development of a suite 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 patients with chronic liver disease 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 of HCC that could be used for early detection and more accurate classification of disease. 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.