EARLY DETECTION OF LUNG CANCER USING METABOLOMIC LIPID PROFILING

描述（由申请人提供）：我们广泛的长期目标是开发一种方便，无创，临床使用的血液生物标志物测试，可以区分肺癌患者和良性结节患者，以早期发现肺癌。肺癌通常在晚期才被诊断出来。在早期发现肺癌可以将死亡率降低10到50倍。目前的CT扫描方法难以区分肺结节的良恶性。患者经常被过度诊断，特异性差，需要进一步的侵入性筛查，这增加了他们的心理和经济负担。目前迫切需要开发新的无创方法，如鉴定血液分子生物标志物，以早期发现肺癌。我们这个提议的直接目标是确定早期检测肺癌的血脂标志物。脂质具有许多重要的生物学功能，包括膜结构、能量储存和信号转导。脂质也被认为在包括肺癌在内的几种人类疾病中起着重要作用。然而，研究通常集中在一个班级的总脂质水平上。由于脂类的不同种类可能具有不同的功能，因此测量它们的组成是必要的。在本研究中，我们将采用脂质组学技术，旨在定量细胞的脂质组，利用质谱法大规模地鉴定和定量单个脂质分子种类。我们对肺癌和前列腺癌的初步研究表明，这项技术是可靠和有前途的。我们已经确定了非肺癌和肺癌血浆样本之间的脂质谱差异。鉴别非癌和肺癌样本的敏感性和特异性均在90%以上。我们的假设是，在肺癌和包括良性肺病变在内的非恶性癌症血浆样本中，脂质组学特征将是不同的，我们将能够将脂质列表定义为肺癌的预测特征。为了验证这一假设，我们建议：1)测量非恶性和肺癌生物标本中人类血浆中脂质种类的水平。2)挖掘脂质谱数据，识别非恶性样本和肺癌样本之间可重复变化的“脂质标志物”。3)使用独立样本验证和测试脂质标志物的预测价值。脂质组学是一项发展迅速的新技术，但尚未应用于肺癌研究。这项工作可能会导致潜在的新的临床用于早期检测肺癌的标志物。我们的研究结果可能会为开发治疗肺癌的新型脂质相关药物提供信息。从长远来看，将我们的数据与肺癌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有一个完整的认识，并对它们在肺癌发展中的作用有新的认识。

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.