Post GWA Analysis of Inflammation Pathways in Barrett's and Esophageal Cancer

描述（由申请方提供）：Barrett食管（BE）发生在2-5%的美国人群中，是唯一已知的食管腺癌（EA）前体。通过监测内窥镜筛查BE病例是昂贵的、侵入性的，并且由于大多数BE病例没有进展而在很大程度上是不成功的。由于EA是快速致命的，5年生存率仅为15%，我们的目标是 为基础生物学和翻译策略提供信息，以改善BE的预防和检测，防止进展为EA。流行病学研究表明，胃食管反流（格尔）、肥胖和吸烟会增加BE和EA的风险，而非甾体抗炎药（NSAID）的使用会降低风险。这些共同的因素都影响促进细胞增殖和遗传不稳定性的局部和全身炎症。鉴于炎症相关的环境暴露是常见的，但结果是罕见的，我们建议确定遗传变异在炎症途径中的贡献，以确定那些疾病的最高风险。我们将利用BEACON联盟提供的丰富数据资源，该联盟汇集了来自14项研究的约2400例BE病例、1500例EA病例和2200例对照的广泛基因型（Illumina Omni-1 M阵列）和环境暴露数据。在目标1中，我们将确定包括五个关键炎症途径（考克斯，氧化应激，细胞因子，HLA/KIR和NF B）的基因变异是否与BE和EA的发展相关。独立的数据集将用于验证总体遗传学结果。在目标2中，我们将评估遗传变异的影响可能因已建立的炎症相关暴露而异的程度，包括反流、肥胖、吸烟和NSAID的使用。我们建议探索的炎症通路基因代表了来自功能研究和精选数据库中通路知识公共库的信息的综合。分析GWAS数据的标准不可知方法可能会错过一些低于关联研究中全基因组显著性所需的高统计阈值的遗传信号。途径分析是对GWAS的补充，我们计划使用主成分分析（PCA）方法，通过将众多个体变异组合成几个综合因子来降低数据复杂性。GWAS数据的二次分析的阳性结果将识别新的遗传信号，并确定在已确定的风险因素层中检查这些信号是否可以识别最有可能进展的信号。 未来的研究将建立在这项研究的基础上，通过对BE和EA病例进行基于组织的表观遗传学分析来扩展我们的研究结果。

DESCRIPTION (provided by applicant): Barrett's esophagus (BE) occurs in 2-5% of the US population, and is the only known precursor to esophageal adenocarcinoma (EA). Screening BE cases by surveillance endoscopy is costly, invasive, and largely unsuccessful since most BE cases do not progress. Since EA is quickly fatal, with only 15% survival at 5 years, our goal is to inform both basic biology and translational strategies for improved prevention and detection of BE to prevent progression to EA. Epidemiologic research has determined that gastroesophageal reflux (GER), obesity, and smoking increase risk of BE and EA, and non-steroidal anti-inflammatory (NSAID) use decrease risk. These shared factors all impact local and systemic inflammation that promotes cell proliferation and genetic instability. Given that the inflammation-related environmental exposures are common but the outcomes are rare, we propose to identify the contribution of genetic variation in inflammation pathways to identify those at highest risk of disease. We will use the rich data resource available to us through the BEACON consortium that pools extensive genotype (Illumina Omni-1M array) and environmental exposure data on approximately 2400 BE cases, 1500 EA cases, and 2200 controls from 14 studies. In Aim 1 we will determine whether variation in genes comprising five key inflammation pathways (COX, oxidative stress, cytokines, HLA/KIR, and NF¿B) is associated with the development of BE and EA. Independent datasets will be used to validate the overall genetic findings. In Aim 2 we will assess the extent to which the impact of genetic variation may vary by established inflammation-related exposures, including reflux, obesity, smoking, and NSAID use. The inflammation pathway genes we propose to explore represent a synthesis of information derived from functional studies and public repositories of pathway knowledge in curated databases. Standard agnostic approaches to analyzing GWAS data may miss some genetic signals that are below the high statistical threshold required for genome- wide significance in association studies. Pathway analysis is complementary to GWAS, and we plan to use a principal component analysis (PCA) approach that will reduce data complexity by combining numerous individual variants into a few integrated factors. Positive results from this secondary analysis of GWAS data will identify new genetic signals, and determine if examining those signals within strata of established risk factors can identify those most likely to progress. Future studies will build on this study by extending our findings through tissue-based epigenetic analysis of BE and EA cases.