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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

基本信息

批准号：
7640849
负责人：
Habtom W Ressom
金额：
$ 20.72万
依托单位：
GEORGETOWN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-07-01 至 2012-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7640849
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 Participant Patients Pattern Peptides Plasma Population Population Heterogeneity Preparation Primary carcinoma of the liver cells Process Proteins Proteomics Radial Research Personnel Resolution Running Sampling Sampling Studies Screening for Hepatocellular Cancer Screening procedure Serum Shoulder Signal Transduction Solutions Source Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Staging Subgroup System Testing Thailand Time United States Universities Urine Validation Work analytical method analytical tool base candidate identification design disease classification high risk improved innovation instrument liquid chromatography mass spectrometry member open source outcome forecast public health relevance research study simulation tandem mass spectrometry tool treatment strategy

项目摘要

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）：质谱法（MS）有望为易获取的液体（如血浆、血清和尿液）提供无创筛选机制。这些生物体液中的肽的表征是生物标志物发现的有前途的策略之一。然而，通过目前的质谱方法获得的肽谱的特征在于它们的高维数和具有大量噪声的复杂模式。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、肽鉴定和验证所需的创新分析方法之外，还需要稳健的计算方法来最佳选择有用的肽标记物。该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，以开发解决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与校正方法。我们的峰检测方法将以与MS专家手动检测的峰一致的方式识别峰。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学变异性和疾病异质性。将进行加标和模拟研究，以获得真实输入已知的光谱。来自这些研究的光谱将用于优化我们的峰检测/校准和选择方法，并将该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。本研究将使用从埃及、美国和泰国的阿尔茨海默病和HCC患者以及健康对照中采集的血清样本。将使用基质辅助激光解吸/电离飞行时间（MALDI-TOF）MS生成样品富集低分子量（LMW）血清组分的质谱。从这些光谱，最有用的面板的峰将被确定使用所提出的峰检测，校准和选择方法。将对所选峰进行测序，以鉴别其代表的肽。最后，通过合成13 C标记的肽标准品，使用同位素稀释法检查肽的身份及其检测HCC的能力。不同学科的协同互动有助于该项目的智力价值，导致分析工具，使科学知识的发现更有效。本项目开发的分析工具将有助于其他生物标志物发现研究，其中需要分析高维质谱数据。这些工具将免费提供给质谱用户（开源）。公共卫生相关性：发展一种诊断测试将是非常有益的检测肝细胞癌（HCC）在可治疗的阶段。特别是，定义临床上适用的生物标志物，检测早期肝癌的高风险人群中的患者有潜在的疾病管理和患者健康的深远影响。这个项目很重要，因为大多数HCC患者都是晚期疾病和预后不良。目前迫切需要鉴定HCC的生物标志物，以用于早期检测和更准确的疾病分类。该项目将导致分析工具的开发，以从低分子量血清组分的高维MALDI-TOF光谱中发现和验证早期诊断候选肽生物标志物。除了筛查高风险人群的早期疾病迹象外，由此产生的生物标志物可用于设计和测试改进的治疗策略。