The Airway Transcriptome as a Biomarker for Lung Cancer

描述(申请人提供)：肺癌是美国男性和女性癌症死亡的主要原因。虽然吸烟是导致肺癌的主要原因，但只有10%-20%的重度吸烟者会患上肺癌。由于缺乏有效的生物标志物，无法确定哪些现任和前任吸烟者患癌症的风险最大，肺癌在扩散后最常被诊断为晚期；肺癌的五年死亡率为85%，这一数字在过去三四十年里变化不大。该项目的目标是利用呼吸道基因表达谱来开发一种相对非侵入性的方法来评估与烟草相关的肺癌的风险。我们的方法基于这样一个概念，即香烟烟雾影响整个呼吸道的呼吸道上皮细胞，从而产生一种呼吸道上皮细胞“场缺陷”，该缺陷定义了对烟草烟雾的反应的个体差异，并区分了患有和不患有肺癌的吸烟者。利用寡核苷酸基因表达阵列，这项提议中的实验将表征患有和不患有肺癌的吸烟者通过刷胸内(叶支气管)呼吸道获得的上皮细胞中的基因表达模式。将确定肺癌对呼吸道转录组的影响，以及影响患有肺癌的吸烟者上皮细胞损伤的性别和种族因素。使用一些类别预测算法，将识别可用于预测哪些吸烟者患有肺癌的胸腔内上皮细胞基因表达谱。 竞争性聚合酶链式反应和基质辅助激光解吸电离、飞行时间、质谱学将被用来确认在阵列实验中识别的选定基因的差异表达。这项研究的长期目标是开发一种非侵入性肺癌生物标记物，可以在随后的临床试验中进行测试，以确定其在识别患有或有患肺癌风险的吸烟者亚群方面的价值。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer death in both men and women in the US. While cigarette smoking is the major cause of lung cancer, only 10-20% of heavy smokers develop lung cancer. Because of the lack of effective biomarkers and the inability to identify which current and former smokers are at the greatest risk for developing cancer, lung cancer is most often diagnosed at a late state after it has spread; the five year mortality of lung cancer is 85%, a figure that has changed little over the past 3-4 decades. The goal of this project is to use airway gene expression profiles to develop a relatively non-invasive method for assessing risk of having tobacco-associated lung cancer. Our approach is based on the concept that cigarette smoke affects airway epithelial cells throughout the respiratory tract, thus producing an airway epithelial cell "field defect" that defines individual variations in the response to tobacco smoke and differentiates smokers with and without lung cancer. Using oligonucleotide gene expression arrays, experiments in this proposal will characterize the patterns of gene expression in epithelial cells obtained by brushing intrathoracic (lobar bronchi) airway in smokers with and without lung cancer. The impact of lung cancer on the airway transcriptome will be defined, along with gender and racial factors that influence epithelial cell damage in smokers with lung cancer. Using a number of class prediction algorithms, intrathoracic epithelial gene expression profiles that can be used to predict which smokers have lung cancer will be identified. Competitive PCR and matrix-assisted laser desorption ionization, time-of-flight, mass spectrometry will be used to confirm differential expression of selected genes identified in the array experiments. The long term objective of this study is to develop a non-invasive biomarker for lung cancer that can be tested in subsequent clinical trials to determine its value in identifying that subset of smokers who have, or are at risk for developing lung cancer.