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

项目总结 肺癌(LC)是美国癌症相关死亡的主要原因。尽管全基因组 关联研究已经确定了许多LC易感基因座，其大部分遗传性仍然隐藏着，可能 通过拷贝数的变化进一步解释。到目前为止，研究已经提供了强有力的证据支持 拷贝数变异(CNV)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 LC的发病机制尚不清楚。此R21的总体目标是进行 综合研究利用来自癌症的大规模跨学科研究的数据集 肺(TRICL)联盟、一个肺eQTL数据集和两个公共数据资源以发现高 有信心的CNV易患不同组织学亚型的LC。中心假设是CNV 与LC易感性有关，调节基因表达，有可能成为新的生物标志物 用于LC的预测。这一假设将通过追求两个具体目标来检验：1)确定CNV的效果 关于LC风险；2)表征CNV对基因表达的调节影响。在目标1中，有一个很大的 从来自TRICL联盟的LC患者和对照组(总共36,068人)收集数据，我们将严格 评价CNV作为肺癌易感性的新生物标志物。首先，CNV将通过更改生成- 基于点的方法，modSaRa2，和基于隐马尔可夫模型的方法，PennCNV。然后用一种基因- 基于折叠关联测试，将确定与LC关联的重复或缺失。这些 重要的关联将通过独立的复制数据集、环境和遗传学进行验证 在肺癌病因学(EAGLE)数据集中(n=4,221)。我们将确定新的途径、网络和交互 潜在的LC，显著富含LC易感性CNV。洞察根本原因 CNV影响LC风险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对基因表达的调节作用是许多复杂表型的中间环节。基因组学 来自肺部eQTL研究(n=1,038)和公共数据集GTEx(n=383)的测量将用于评估 已鉴定的LC易感性CNV与其相应基因的表达之间的关联。功能界别 将按照实验设计进行研究，以测试新发现的CNV的下游功能 癌细胞生长和发展中的调控基因表达。这个项目有可能填补 目前关于CNV作为一种影响LC风险的新型遗传变异的效用的知识，并提供了一种 更好地理解潜在的分子机制。我们创新、一体化的遗传学、基因组学和 生物信息学方法将确定易患LC的新的遗传预测因子。这项研究有巨大的 提供关键的新方向的可能性，将允许探索一系列关于 如何将CNV特性用于人类复杂疾病的未来风险管理和治疗。

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.