Gene Ratios for Lung Cancer Detection and Prognosis

描述(申请人提供)：肺癌是美国男性和女性的头号癌症杀手。近156,000人(85%)的肺癌患者在发病5年内死于这种疾病。这些严峻的死亡率数字有几个原因，包括没有现有的筛查，以及大多数患者的治疗选择无效。即使是I期肺癌患者在接受手术治疗的同时，也有30%的五年内因转移疾病而死亡。美国正在开展对患者进行螺旋胸部CT筛查的早期努力，但由于发现了大量良性结节，这一努力受到了限制。我们最近描述了一种新的方法，将从微阵列的基因图谱获得的数据转换为利用基因比率进行的简单的临床可用的测试。在初步工作中，我们开发了模型来检测肺结节的微小样本中的肺癌，并区分可能影响肺部的不同类型的癌症。我们还开发了一个模型来预测早期肺癌手术后的患者结果，这种方法增加了现有的分期技术。所有这些测试都利用了基因表达谱数据，这些数据可以通过微阵列或定量RT-PCR获得。我们建议扩展我们的初步发现，以确定我们的基因比率测试是否可以：1.使用肿瘤库标本区分肺腺癌、间皮瘤和从乳房转移到肺的腺癌。2.使用从接受肺腺癌手术的患者身上获得的一组标本并与结果数据库相关联来准确预测结果。3.允许使用从肺结节获得的细针抽吸来检测肺癌。我们设想，我们的方法将适用于未来的临床情景，每个可疑结节将通过细针抽吸来评估，以确定诊断和患者的最佳治疗方案。

DESCRIPTION (provided by applicant): Lung cancer is the number one cancer killer in the US for both men and women. Almost 156,000 (85%) of patients who present with lung cancer die from the disease within 5 years of presentation. There are several reasons for these stark mortality figures including non-existing screening, and ineffective therapeutic options for most patients. Even patients with stage I lung cancer who are treated surgically with intent to cure have a 30% five-year mortality due to metastatic disease. Early efforts to screen patients with spiral chest CT are being undertaken in the US but are limited by the large number of benign nodules discovered. We recently described a new method to translate data obtained from gene profiling with microarrays into simple clinically usable tests utilizing gene ratios. In preliminary work we developed models to detect lung cancer in minute specimens of lung nodules, and distinguish between different types of cancers that may affect the lung. We also developed a model to predict patient outcome after surgery for early lung cancer in a way that adds to current staging techniques. All of these tests utilize gene expression profiling data that can be acquired with microarrays or quantitative RT-PCR. We propose to extend our preliminary findings in order to determine if our gene ratio tests can 1. Distinguish between lung adenocarcinoma, mesothelioma and adenocarcinoma metastatic from breast origin to the lung using tumor bank specimens. 2. Accurately predict outcome using a set of specimens obtained from patients who underwent surgery for lung adenocarcinoma and linked with outcomes database. 3. Allow detection of lung cancer using fine needle aspirations obtained from lung nodules. We envision that our methodology will be adaptable to a future clinical scenario where each suspicious nodule would be evaluated by fine needle aspiration determining the diagnosis and best therapeutic options for the patients.