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

描述（由申请人提供）：质谱（MS）有望为容易获得的液体（如血浆，血清和尿液）提供无创筛查机制。这些生物液体中多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点是它们的高维数和复杂的模式与大量的噪声。来自不同人群的人类样本中存在的生物变异和疾病异质性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)开发基于模糊逻辑的质谱峰检测和校准方法。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。将对峰值进行校准，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。将进行峰值输入和模拟研究，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检测/校准和选择方法，并与其他现有解决方案进行比较。优化的分析工具将用于发现和验证在可治疗阶段检测肝细胞癌（HCC）的标志物。本研究将使用来自埃及、美国和泰国的肝硬化和HCC患者以及健康对照者的血清样本。将利用基质辅助激光解吸/电离飞行时间（MALDI-TOF） MS对样品的富集低分子量（LMW）血清组分进行质谱分析。从这些光谱中，将使用提出的峰检测、校准和选择方法确定最有用的峰面板。所选的峰将被测序以确定它们所代表的肽。最后，通过合成13c标记的肽标准物，使用同位素稀释来检测肽的特性及其检测HCC的能力。不同学科的协同互动有助于这个项目的智力价值，导致分析工具，将使科学知识发现更有效。本项目开发的分析工具将对其他需要高维质谱数据分析的生物标志物发现研究有用。这些工具将免费提供（开源）给质谱用户。公共卫生相关性：开发一种诊断检测方法将对处于可治疗阶段的肝细胞癌（HCC）的检测大有裨益。特别是，确定临床适用的生物标志物，以检测肝硬化患者高危人群中的早期HCC，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HCC患者表现为疾病晚期，预后较差。目前迫切需要确定HCC的生物标志物，用于早期检测和更准确的疾病分类。该项目将开发分析工具，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还可用于设计和测试改进的治疗策略。

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.