COPY NUMBER VARIATION AND LUNG CANCER: DISEASE RISK, PREDICTION AND MECHANISM

项目摘要 肺癌（LC）是美国癌症相关死亡的主要原因。虽然全基因组 关联研究已经确定了许多LC易感基因座，其大部分遗传力仍然隐藏，可能 可以通过拷贝数变化进一步解释。迄今为止，研究提供了有力的证据来支持 拷贝数变异体（CNVs）在许多癌症类型中的独特作用，然而，它们的风险效应和分子生物学效应， 导致LC的机制尚不清楚。R21的总体目标是进行一次 综合研究，利用大规模跨学科癌症研究的数据集， Lung（TRICL）联盟，一个Lung eQTL数据集和两个公共数据资源， 在不同组织学亚型中，CNV倾向于LC的置信度。核心假设是CNVs 与LC易感性相关，调节基因表达，并有可能作为新的生物标志物 预测LC。这一假设将通过追求两个具体目标进行检验：1）确定CNV的影响 2）表征CNV对基因表达的调控影响。在目标1中， 从TRICL联盟收集LC患者和对照组（共36，068例）的数据，我们将严格 评估CNVs作为肺癌易感性的新生物标志物。首先，CNV将由变化产生- 基于点的方法modSaRa 2和基于隐马尔可夫模型的方法PennCNV。再用一种基因- 基于崩溃关联检验，将确定与LC相关的重复或缺失。这些 重要的关联将由一个独立的复制数据集，环境和遗传学来验证 肺癌病因学（EAGLE）数据集（n= 4，221）。我们将确定新的途径，网络和相互作用 潜在的LC，其被LC敏感性CNV显著富集。深入了解底层 CNV对LC风险影响的生物学机制是至关重要的;因此，在目标2中，我们将评估CNV对LC风险的影响。 CNV对基因表达的调节影响，这是许多复杂表型的中间过程。基因组 来自肺eQTL研究（n= 1，038）和公共数据集GTEx（n=383）的测量将用于评估 鉴定的LC易感性CNV与其相应基因表达之间的关联。的功能 将进行一项实验设计研究，以检测新发现的CNV的下游功能 调节癌细胞生长和进展中的基因表达。该项目有可能填补 目前关于CNV作为一种新型遗传变异影响LC风险的实用性的知识，并提供了一个 更好地理解潜在的分子机制。我们的创新，整合遗传学，基因组学和 生物信息学方法将鉴定易患LC的新的遗传预测因子。这项研究具有巨大的 提供关键新方向的潜力，将允许探索一系列研究问题， CNV特征如何用于未来的风险管理和人类复杂疾病的治疗。

PROJECT SUMMARY Lung cancer (LC) is the leading cause of cancer related death in the United States. Although genome-wide association studies have identified many LC susceptibility loci, most of its heritability remains hidden and might be further explained by copy number variation. To date, studies have provided robust evidence to support the unique roles of copy number variants (CNVs) in many cancer types, however, their risk effect and molecular mechanisms contributing to LC is still unclear. The overall objective of this R21 is to conduct a comprehensive study leveraging datasets from the large-scale Transdisciplinary Research in Cancer of the Lung (TRICL) consortium, a Lung eQTL dataset and two public data resources to discover high confidence CNVs predisposing to LC across histological subtypes. The central hypothesis is that CNVs are associated with LC susceptibility, regulate gene expression and have a potential to serve as novel biomarkers for prediction of LC. This hypothesis will be tested by pursuing two specific aims: 1) Determine the effect of CNVs on LC risk; and 2) Characterize the regulatory impact of CNVs on gene expression. In Aim 1, with a large collection of data from LC patients and controls (n=36,068 total) from the TRICL consortium, we will rigorously evaluate CNVs as novel biomarkers for lung cancer predisposition. First, CNVs will be generated by a change- point based method, modSaRa2, and a Hidden Markov Model based approach, PennCNV. Then using a gene- based collapsing association test, duplications or deletions associated with LC will be determined. These significant associations will be validated by an independent replication dataset, the Environment and Genetics in Lung cancer Etiology (EAGLE) dataset (n=4,221). We will identify novel pathways, networks, and interactions underlying LC, which are significantly enriched by LC-susceptibility CNVs. Gaining insight into the underlying biological mechanisms of the influence of CNVs on LC risk is critical; therefore, in Aim 2, we will evaluate the regulatory impact of CNVs on gene expression, which is intermediate to many complex phenotypes. Genomic measures from the Lung eQTL study (n=1,038) and the public dataset GTEx (n=383) will be used to evaluate the associations between the identified LC-susceptibility CNVs and expression of their corresponding genes. A functional study with experimental design will be followed to test the downstream functions of the newly identified CNV regulated gene expression in growth and progression of cancer cells. This project has the potential to fill a gap in current knowledge about the utility of CNV as a new type of genetic variation influencing the risk of LC and provide a better understanding of the underlying molecular mechanisms. Our innovative, integrative genetics, genomics and bioinformatics approaches will identify novel genetic predictors that predispose to LC. This study has enormous potential for providing critical new directions that will allow exploration of a range of research questions about how CNV characteristics can be utilized for future risk management and treatment of human complex diseases.