EARLY DETECTION OF LUNG CANCER USING METABOLOMIC LIPID PROFILING

使用代谢脂质分析早期检测肺癌

• 批准号: 8617255
• 负责人: Youping Deng
• 金额: $ 19.37万
• 依托单位: RUSH UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-11 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8617255
• 关键词: Adenocarcinoma; Adopted; Algorithms; Benign; Biochemical Pathway; Biological Markers; Biological Process; Blood; Bronchoscopy; Cancer Etiology; Cancer Patient; Cells; Cessation of life; Classification; Data; Detection; Development; Diagnosis; Diagnostic; Disease; Gene Expression; Goals; Granuloma; Hamartoma; Human; Human body; Individual; Inflammatory; Knowledge; Lead; Lecithin; Lesion; Link; Lipids; Lung; Lung Neoplasms; Lung nodule; Lysophospholipids; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of lung; Malignant neoplasm of prostate; Mass Spectrum Analysis; Measures; Membrane; Metabolic Pathway; Methods; Mining; Molecular; Monitor; Neoplasm Metastasis; Newly Diagnosed; Nodule; Non-Malignant; Non-Neoplastic Lung Disorder; Patients; Pharmaceutical Preparations; Phenotype; Phosphatidylethanolamine; Plasma; Play; Predictive Value of Tests; Procedures; Proteomics; Public Health; Reporting; Role; Sampling; Sensitivity and Specificity; Signal Transduction; Specificity; Sputum Cytology Screening; Squamous cell carcinoma; Staging; Technology; Testing; Thoracic Radiography; Time; United States; Work; X-Ray Computed Tomography; base; blood lipid; cancer risk; cohort; human disease; improved; lung cancer screening; mass spectrometer; metabolomics; minimally invasive; mortality; new technology; novel; novel marker; prevent; psychologic; public health relevance; screening; tool; tumor

DESCRIPTION (provided by applicant): Our broad long-term goal is to develop a convenient, non-invasive, clinically-used blood biomarker test that can distinguish patients with lung cancer from patients with benign nodules for the early detection of lung cancer. Lung cancer is often diagnosed at an advanced stage. Detecting lung cancer at earlier stages could reduce mortality rates 10- to 50-fold. The current CT scan approach has difficulty distinguishing benign from malignant pulmonary nodules. Patients are frequently over-diagnosed with poor specificity and require further invasive screening, which adds both to their psychological and to their financial burden. It is urgent to develop new non-invasive methods such as identifying blood molecular biomarkers for early detection of lung cancer. Our immediate objective for this proposal is to identify blood lipid markers for the early detection of lung cancer. Lipids have numerous critical biological functions which include membrane structure, energy storage, and signal transduction. Lipids have also been implicated as playing roles in several human diseases, including lung cancer. However, studies generally have been focused on total levels of lipids in a class. Because individual species of a lipid class may have different functions, it is essential to measure their compositions. In the proposed study, we will adopt lipidomics technology which aims to quantify a cell's lipidome, identifying and quantifying individual lipid molecular species on a large scale using mass spectrometry. Our preliminary studies with lung and prostate cancer indicate this technology is robust and promising. We have identified a lipid profile difference between non-lung cancer and lung cancer plasma samples. The sensitivity and specificity of distinguishing non-cancer and lung cancer samples are over 90%. Our hypothesis is that lipidomics profiles will be different between lung cancer and non-malignant cancer plasma samples including benign pulmonary lesions and we will be able to define a lipid list as a predictive signature of lung cancer. To test this hypothesis, we propose to: 1) measure the levels of lipid species in human plasma from non-malignant and lung cancer biospecimens. 2) Mine lipid profile data to identify "lipid markers" that vary reproducibly between non-malignant samples and lung cancer samples. 3) Validate and test the predictive value of the lipid markers using independent samples. Lipidomics is a rapidly developing novel technology that has not been applied to lung cancer studies. This work could lead to potentially new clinically used markers for early detection of lung cancer. Our findings may provide information that will lead to the development of novel lipid-related drugs to treat lung cancer. In the long term, linking our data with gene expression and proteomics data in lung cancer will give us a complete view of lipid metabolic pathways and networks, as well as new knowledge about their role in lung cancer development.

描述（由申请人提供）：我们的广泛长期目标是开发一种方便，无创，临床中使用的血液生物标志物测试，可以将肺癌患者与良性结节患者区分开来，以早日检测到肺癌。 经常在晚期诊断出肺癌。在较早的阶段检测肺癌可能会降低死亡率10至50倍。当前的CT扫描方法很难区分良性和恶性肺结节。患者经常被过度诊断出较差的特异性，需要进一步的侵入性筛查，从而增加了心理和经济负担。迫切需要开发新的非侵入性方法，例如鉴定血液分子生物标志物以早期检测到肺癌。 我们对该提案的直接目标是确定早期发现肺癌的血脂标记。脂质具有许多关键的生物学功能，包括膜结构，储能和信号转导。脂质也被认为是在包括肺癌在内的多种人类疾病中发挥作用。但是，研究通常集中在类中脂质的总水平上。因为脂质类的个别物种可能具有不同的功能，因此必须测量其组成。在拟议的研究中，我们将采用脂肪组技术，旨在量化细胞的脂质组，使用质谱法大规模识别和量化单个脂质分子物种。我们对肺癌和前列腺癌的初步研究表明，这项技术是强大而有希望的。我们已经确定了非肺癌和肺癌血浆样品之间的脂质谱差。区分非癌症和肺癌样本的灵敏度和特异性超过90％。我们的假设是，肺癌和包括良性肺部病变在内的非恶性癌等离子体样品之间的脂肪组学特征将有所不同，我们将能够将脂质清单定义为肺癌的预测性特征。为了检验这一假设，我们建议：1）测量非恶性和肺癌生物质量的人血浆中脂质物种的水平。 2）地雷脂质剖面数据以识别“脂质标记”，这些标志物在非恶性样品和肺癌样品之间可重复变化。 3）使用独立样品验证和测试脂质标记的预测值。 脂肪组学是一项快速发展的新技术，尚未应用于肺癌研究。这项工作可能会导致潜在的临床使用标记，以早日检测肺癌。我们的发现可能会提供信息，从而导致新型脂质相关药物的发展以治疗肺癌。从长远来看，将我们的数据与肺癌中的基因表达和蛋白质组学数据联系起来，将使我们对脂质代谢途径和网络进行完整的了解，以及有关其在肺癌发育中的作用的新知识。