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

描述(由申请人提供)：巴雷特食道(BE)发生在美国人口的2%-5%，是唯一已知的食管腺癌(EA)的前驱症状。通过监视内窥镜筛查BE病例是昂贵的、侵入性的，而且由于大多数BE病例没有进展，因此基本上不成功。由于EA很快就会致命，5年内只有15%的存活率，我们的目标是 告知基础生物学和翻译策略，以改进BE的预防和检测，以防止进展为EA。流行病学研究已经确定，胃食道反流(GER)、肥胖和吸烟会增加BE和EA的风险，而非类固醇抗炎药(NSAID)的使用会降低风险。这些共同的因素都会影响促进细胞增殖和遗传不稳定的局部和系统炎症。鉴于与炎症相关的环境暴露很常见，但结果很少见，我们建议确定炎症途径中的基因变异的贡献，以确定那些疾病风险最高的人。我们将使用通过Beacon财团提供的丰富数据资源，该财团汇集了来自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(Illumina Omni-1M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(COX、氧化应激、细胞因子、人类白细胞抗原/KIR和核因子B)的基因变异是否与BE和EA的发生有关。将使用独立的数据集来验证整个基因发现。在目标2中，我们将评估基因变异的影响可能在多大程度上因已建立的炎症相关暴露而变化，包括反流、肥胖、吸烟和非甾体抗炎药的使用。我们建议探索的炎症途径基因代表了来自功能研究的信息的综合，以及在精心策划的数据库中途径知识的公共储存库。用标准的不可知论方法分析GWAS数据可能会遗漏一些遗传信号，这些信号低于关联研究中全基因组意义所需的高统计门槛。通径分析是对GWAS的补充，我们计划使用主成分分析(PCA)方法，通过将众多独立变量组合成几个综合因素来降低数据复杂性。对Gwas数据进行二次分析的积极结果将确定新的遗传信号，并确定在已确定的风险因素层内检查这些信号是否可以确定最有可能进展的因素。 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发现。

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.