Novel Pathway Analysis Methods for Identifying Genomic Causes of Cancer

描述(申请人提供)：癌症是一个重大的公共卫生负担，在美国的发病率为467.4/10万，死亡率为189.8/100,000。改善这些数字需要同时提高检测和治疗水平。这些努力的一个重要方面是识别与癌症相关的基因变异，这些变异已经产生了翻译结果，使个性化的癌症预防和治疗方法成为可能。现代高通量生物学实验，包括基因表达和SNP阵列，通过同时分析105-106个标记，提供了前所未有的能力来详细研究癌症的遗传原因。然而，癌症是一种异质性和复杂的疾病，涉及多个基因。由于这些研究中通常使用的单标记分析方法可能会错过复杂的多基因效应，因此迫切需要能够揭示推动癌症发生的多基因、系统水平变化的分析技术。为了填补这一方法论空白，我提出了三种基于途径的基因组数据分析新技术。这些方法利用了我们目前对生物分子相互作用网络(路径)的了解。通过总结每条途径的数据，可以在病例和对照中比较整个途径的行为，而不需要强烈的单基因关联。在目标1中，我提出了一种方法来形式化全基因组关联研究(GWAS)SNP数据的路径总结，而不依赖于显著的单基因座关联，从而允许使用基因数据来比较病例组和对照组之间的整个路径。在目标2和3中，我提出了两种不同的方法，使用路径拓扑学从基因表达数据中创建汇总统计数据，同样不依赖于单基因关联的重要性，从而不仅捕获存在于路径中的基因，还捕获它们的潜在相互作用。这些方法中的每一种都是高度新颖的：这种类型的GWAS路径总结尚未被报道，我在AIMS 2和3中使用的网络特征从未应用于生物数据。这些方法补充了现有的分析技术，使确定癌症预防和治疗的目标途径成为可能。通过填补一个重要的方法学空白，拟议的目标将提供以患者为中心的分析技术，认识到癌症遗传学的内在复杂性和多样性，从而促进个性化医学。 公共卫生相关性：了解癌症的复杂基因决定因素对于改进早期发现和设计个性化治疗至关重要。识别促进癌症发生的基因差异的复杂模式的分析方法将显著提高对癌症易感性的预测，并指明合理药物设计的目标，从而减少癌症对公众健康的负担。

DESCRIPTION (provided by applicant): Cancer presents a significant public health burden, with an incidence rate of 467.4 per 100,000 and a mortality rate of 189.8 per 100,000 in the in the United States. Improving these figures requires both improving detection and treatment. An important aspect of these efforts is the identification of genetic variants associated with cancer, which have already yielded translational results that permit personalized approaches to cancer prevention and treatment. Modern high-throughput biological experiments, including gene expression and SNP arrays, provide an unprecedented ability to investigate the genetic causes of cancer in fine detail by simultaneously assaying 105-106 markers. However, cancer is a disease with heterogeneous and complex causes that involve multiple genes. Because the single-marker analytical approaches typically used in these studies are likely to miss complex multi-gene effects, there is a pressing need for analysis techniques that have the power to reveal multi-gene, system-level changes driving carcinogenesis. To fill this methodological gap, I propose three novel techniques for pathway based analysis of genomic data. These methods harness our current knowledge of biomolecular interaction networks (pathways). By summarizing the data across each pathway, the pathway behavior as a whole may be compared in cases and controls without requiring strong single-gene associations. In Aim 1, I propose a method to formalize pathway summarization for genome-wide association study (GWAS) SNP data without relying on the significance single-locus associations, thereby allowing comparisons of pathway as a whole between case and control groups using genotype data. In Aims 2 and 3, I propose two distinct methods for using pathway topology to create a summary statistic from gene expression data, again without relying on the significance single-gene associations, thereby capturing not only the genes present in a pathway but their potential interactions as well. Each of these methods is highly novel: pathway summarization of this type for GWAS has not yet been reported, and the network characteristics I use for Aims 2 & 3 have never been applied to biological data. These methods complement existing analytical techniques and make it possible to identify target pathways for cancer prevention and treatment. By filling an important methodological gap, the proposed Aims would provide patient-centric analysis techniques that recognize the inherent complexity and diversity of cancer genetics, and thereby advance personalized medicine. PUBLIC HEALTH RELEVANCE: An understanding of the complex genetic determinants of cancer is crucial to improving early detection and designing personalized therapies. Analytical methods that identify complex patterns of genetic differences which promote carcinogenesis will significantly improve predictions of cancer susceptibility and indicate targets for rational drug design, thereby reducing the public health burden of cancer.