Comparative functional genomics for lung cancer gene discovery

描述(申请人提供)：自20世纪90年代中期以来，每年约有15万美国人死于肺癌，而癌症总死亡人数的上升趋势在很大程度上是由于肺癌死亡率的上升。即使我们今天能够防止吸烟和接触其他致癌物质，在接下来的几十年里，仍有数十万肺癌病例需要治疗。目前肺癌领域的一个主要努力是在早期阶段发现和治疗肺癌，以提高患者的存活率。此外，确定新的药物靶点将有助于开发更有效的治疗肺癌的策略。最后，肺癌患者是按照既定的治疗方案进行治疗的，这些方案大多没有考虑到他们肿瘤的遗传多样性，而且对个别肺癌患者的预后因素知之甚少。更好地了解肺癌发生发展中的分子事件将有助于识别肺癌患者的诊断和预后标志物。Rb、P53和Kras是人类癌症中最常见的突变基因。特别是，这三个基因的突变组合经常在人类肺癌中发现，并定义了重要的临床亚型。利用先进的基因打靶方法，我们和其他人已经培育出了具有这些基因突变的基因工程小鼠。这些突变小鼠的肿瘤与人类肺癌非常相似，并提供了一个遗传上易于处理的系统来研究体内的肺肿瘤发生。在这里，我们建议使用比较基因表达分析来确定使用这些肺癌小鼠模型的基因型特异性致癌特征。我们的具体目标是：-开发小鼠肿瘤的基因表达特征，并将它们与人类数据进行比较，以识别新的人类肺癌亚型。根据这些特征对亚型特异性基因的验证将使用人体组织阵列进行。-使用传统的生物信息学方法以及我们将开发的以事件为中心的新型基因网络，识别这些基因表达特征的关键调节因素和“驱动因素”。特别是，我们将在肺癌基因网络中引入有序因果事件的概念，以确定这些基因网络中的关键节点。-从功能上分析这些肺癌基因表达特征的潜在关键调控因素。为此，我们将首先使用基于基因表达的高通量筛选来识别这些调控因子，然后我们将在体内测试它们在肺癌发生中的功能作用。我们工作的总体目标是开始定义基因特异性肿瘤发生所需的关键途径。这些通路的特征可能为识别肺癌患者的诊断、预后和靶向治疗的新途径提供有用的方法。 公共卫生相关性：我们建议使用一种新的基因网络，通过比较来自基因定义的小鼠模型的肺癌基因表达谱与来自人类肺癌的基因表达谱，来识别肺癌关键致癌“驱动”突变下游的分子事件。我们将在小鼠模型和人类肿瘤细胞系和组织中测试肺癌候选调节因子的功能作用。我们的实验将为开发检测和治疗肺癌的新策略奠定必要的基础。肺癌是美国男性和女性的头号癌症杀手。

DESCRIPTION (provided by applicant): Abstract Since the mid-1990s, approximately 150,000 Americans have died of lung cancer every year, and the upward trend in total cancer deaths is largely due to the increasing rate of lung cancer mortality. Even if we could prevent cigarette smoking and exposure to other carcinogens today, hundreds of thousands of lung cancer cases would still need to be treated in the next decades. A major current effort in the lung cancer field is to detect and treat lung cancer at earlier stages to improve the survival of patients. In addition, identifying novel drug targets would allow for the development of more efficacious therapeutic strategies against lung tumors. Finally, lung cancer patients are treated following well-established protocols that most often do not take into account the genetic diversity of their tumors, and very little is known about prognostic factors for individual lung cancer patients. A better knowledge of the molecular events in lung cancer development would help to identify diagnostic and prognostic markers in lung cancer patients. Rb, p53 and Kras are among the most frequently mutated genes in human cancer. In particular, combinations of mutations in these three genes are often found in human lung cancer and define important clinical subtypes. Using advanced gene-targeting approaches, we and others have generated genetically engineered mice with mutations in these genes. These mutant mice develop tumors that closely resemble human lung tumors and provide a genetically tractable system to study lung tumorigenesis in vivo. Here, we propose to use comparative gene expression analysis to define genotype-specific oncogenic signatures using these mouse models of lung cancer. Our specific goals are: - To develop gene expression signatures from mouse tumors and compare them to human data to identify new human lung cancer subtypes. The validation of subtype-specific genes from these signatures will be performed using human tissue arrays. - To identify key regulators and "drivers" of these gene expression signatures using conventional bioinformatics approaches as well as a novel "event" centered gene network that we will develop. In particular, we will introduce the notion of ordered, causal events in lung cancer gene networks to identify key nodes in these gene networks. - To functionally analyze potential key regulators of these lung cancer gene expression signatures. To this end, we will first use gene expression-based high throughput screening to identify such regulators and we will then test their functional role in lung cancer development in vivo. The overall goal of our work is to begin to define critical pathways that are required for genotype-specific oncogenesis. Characterization of these pathways may provide a useful approach for identification of new approaches for diagnosis, prognosis, and targeted therapy in lung cancer patients. PUBLIC HEALTH RELEVANCE: We propose to use a novel gene network to identify molecular events downstream of key oncogenic "driver" mutations for lung cancer by comparing gene expression profiles in lung tumors from genetically defined mouse models to gene expression profiles from human lung tumors. We will test the functional role of candidate regulators of lung cancer in mouse models and human tumor cell lines and tissues. Our experiments will lay the foundation needed for the development of novel strategies to detect and treat lung cancer, the number one cancer killer in both men and women in the United States.