Gene Ratios for Lung Cancer Detection and Prognosis

描述（由申请人提供）：肺癌是美国男性和女性的头号癌症杀手。近156，000（85%）患有肺癌的患者在5年内死于该病。造成这些死亡率的原因有几个，包括不存在筛查，以及大多数患者的无效治疗选择。即使是I期肺癌患者，如果进行手术治疗以治愈，由于转移性疾病，5年内的死亡率也有30%。早期的努力，筛选病人的螺旋胸部CT正在进行中，但受到限制的大量良性结节发现。我们最近描述了一种新的方法，利用基因比率将从微阵列基因图谱中获得的数据转化为简单的临床可用的测试。在初步工作中，我们开发了在肺结节的微小样本中检测肺癌的模型，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局，该模型增加了当前的分期技术。所有这些测试都利用了可以用微阵列或定量RT-PCR获得的基因表达谱数据。我们建议扩展我们的初步研究结果，以确定我们的基因比测试可以1。使用肿瘤库标本区分肺腺癌、间皮瘤和从乳腺起源转移到肺的腺癌。2.使用从接受肺腺癌手术的患者中获得的一组标本准确预测结局，并与结局数据库相关联。3.允许使用从肺结节获得的细针抽吸检测肺癌。我们设想，我们的方法将适用于未来的临床情况下，每个可疑结节将通过细针穿刺确定诊断和最佳治疗方案的患者进行评估。

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.