Peptide Biomarker Discovery by Mass Spectrometry for Early Detection of Liver Can

通过质谱法发现肽生物标志物，用于早期检测肝细胞癌

• 批准号: 7531854
• 负责人: Habtom W Ressom
• 金额: $ 17.27万
• 依托单位: GEORGETOWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7531854
• 关键词: Accounting; Address; Algorithms; Bioinformatics; Biological; Biological Markers; Biometry; Calibration; Child; Cirrhosis; Clinical; Code; Commit; Complex; Comprehensive Cancer Center; Computing Methodologies; Data; Detection; Development; Diagnostic tests; Discipline; Disease; Disease Management; Early Detection Research Network; Early Diagnosis; Egypt; Ensure; Fuzzy Logic; Goals; Health; Heterogeneity; Human; Individual; Information Resources; Isotopes; Knowledge; Label; Laboratories; Lead; Letters; Linguistics; Liquid substance; Liver; MALDI-TOF Mass Spectrometry; Machine Learning; Mass Spectrum Analysis; Medical center; Methodology; Methods; Michigan; Molecular Weight; Morphologic artifacts; Newly Diagnosed; Noise; Numbers; Participant; Patients; Pattern; Peptides; Plasma; Population; Population Heterogeneity; Preparation; Primary carcinoma of the liver cells; Process; Proteins; Proteomics; Public Health; Research Personnel; Resolution; Risk; Running; Sampling; Sampling Studies; Screening for Hepatocellular Cancer; Screening procedure; Serum; Shoulder; Signal Transduction; Solutions; Source; Spectrometry; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Staging; Standards of Weights and Measures; Subgroup; System; Testing; Thailand; Time; United States; Universities; Urine; Validation; Work; analytical method; analytical tool; base; design; desire; disease classification; improved; innovation; instrument; liquid chromatography mass spectrometry; member; open source; outcome forecast; radius bone structure; research study; simulation; tandem mass spectrometry; tool

DESCRIPTION (provided by applicant): Mass spectrometry (MS) has the promise to provide a noninvasive screening mechanism on easily accessible fluids such as plasma, serum, and urine. The characterization of peptides in these biological fluids is one of the promising strategies for biomarker discovery. However, peptide profiles obtained through current mass spectrometric methods are characterized by their high dimensionality and complex patterns with substantial amount of noise. The presence of biological variability and disease heterogeneity in human samples from diverse populations adds to the complexity of the problem. Thus, in addition to innovative analytical methods desired for sample preparation, peptide identification, and validation, robust computational methods are needed for optimal selection of useful peptidic markers. This collaborative project brings together experts in bioinformatics, biostatistics, proteomics, and mass spectrometry to develop analytical tools that address the above challenges. The specific aims are the following: (1) To develop fuzzy logic based methods to detect and calibrate MS peaks. Our peak detection method will identify peaks in a way that is consistent with peaks detected manually by MS experts. Peaks will be calibrated to accommodate isotopic distributions and machine drifts. (2) To investigate machine learning- based peak selection methods that take into account biological variability and disease heterogeneity of the human population. Spike-in and simulation studies will be conducted to obtain spectra whose true inputs are known. The spectra from these studies will be used to optimize our peak detection/calibration and selection methods, and compare the methods with other existing solutions. The optimized analytical tools will be applied to find and validate markers that detect hepatocellular carcinoma (HCC) at a treatable stage. Serum samples collected from cirrhotic and HCC patients as well as healthy controls in Egypt, United States, and Thailand will be used in this study. Mass spectra will be generated using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS of enriched low molecular weight (LMW) serum fractions of the samples. From these spectra, the most useful panel of peaks will be identified using the proposed peak detection, calibration, and selection methods. The selected peaks will be sequenced to identify the peptides they represent. Finally, the identity of the peptides and their ability to detect HCC will be examined using isotope dilution by synthesizing 13C-labeled peptide standards. The synergetic interaction of diverse disciplines contributes to the intellectual merit of this project, leading to analytical tools that will make scientific knowledge discovery more efficient. Analytical tools developed in this project will be useful for other biomarker discovery studies, where the analysis of high-dimensional mass spectral data is needed. The tools will be freely available (open source) to mass spectrometry users. PUBLIC HEALTH RELEVANCE: Development of a diagnostic test would be of great benefit for detection of hepatocellular carcinoma (HCC) at a treatable stage. In particular, 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 present with advanced-stage disease and poor prognosis. There is a pressing need to identify biomarkers of HCC that could be used for early detection and more accurate classification of disease. This project will lead to the development of analytical tools to find and validate early-diagnosis candidate peptide biomarkers from high- dimensional MALDI-TOF spectra of low-molecular-weight serum fractions. 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)：质谱学(MS)有望为血浆、血清和尿液等容易获得的液体提供一种非侵入性的筛选机制。对这些生物体液中的多肽进行表征是发现生物标记物的一种很有前途的策略。然而，通过当前的质谱学方法获得的多肽图谱具有高维性和复杂模式的特点，并且含有大量的噪声。来自不同人群的人类样本中存在生物变异性和疾病异质性，这增加了问题的复杂性。因此，除了样品制备、多肽鉴定和验证所需的创新分析方法外，还需要稳健的计算方法来优化选择有用的多肽标记。这个合作项目汇集了生物信息学、生物统计学、蛋白质组学和质谱学方面的专家，以开发应对上述挑战的分析工具。具体目标如下：(1)建立基于模糊逻辑的质谱峰检测和校正方法。我们的峰值检测方法将以与MS专家手动检测的峰值一致的方式识别峰值。将对峰进行校准，以适应同位素分布和机器漂移。(2)研究基于机器学习的峰值选择方法，该方法考虑了人类群体的生物变异性和疾病异质性。将进行尖峰注入和模拟研究，以获得其真实输入已知的光谱。这些研究的光谱将用于优化我们的峰值检测/校准和选择方法，并将这些方法与其他现有解决方案进行比较。优化的分析工具将被应用于寻找和验证在可治疗阶段检测肝细胞癌(HCC)的标志物。这项研究将使用从埃及、美国和泰国的肝硬变和肝细胞癌患者以及健康对照收集的血清样本。将使用基质辅助激光解吸/电离飞行时间(MALDI-TOF)MS对样品的浓缩低分子量(LMW)血清部分进行质谱分析。从这些光谱中，将使用所提出的峰值检测、校准和选择方法来识别最有用的峰值面板。选定的峰将被测序，以确定它们所代表的多肽。最后，将通过合成13C标记的多肽标准，使用同位素稀释法检查这些多肽的一致性及其检测肝癌的能力。不同学科的协同互动有助于该项目的智力优势，导致分析工具将使科学知识发现更有效率。该项目开发的分析工具将对其他生物标志物发现研究有用，这些研究需要分析高维质量谱数据。这些工具将向质谱学用户免费提供(开源)。公共卫生相关性：诊断测试的发展将极大地有利于在可治疗阶段发现肝细胞癌(HCC)。特别是，定义临床上适用的生物标志物来检测高危人群中的肝硬变患者的早期肝细胞癌，对疾病管理和患者健康具有潜在的深远影响。这个项目很重要，因为大多数肝细胞癌患者都是晚期疾病，预后差。迫切需要确定可用于早期发现和更准确地对疾病进行分类的肝细胞癌的生物标志物。该项目将导致开发分析工具，从低分子血清组份的高维MALDI-TOF光谱中寻找和验证早期诊断候选多肽生物标记物。除了筛选高危人群的早期疾病迹象外，由此产生的生物标记物还可以用于设计和测试改进的治疗策略。