Novel Pathway Analysis Methods for Identifying Genomic Causes of Cancer

描述（由申请人提供）：癌症是一个重大的公共卫生负担，在美国，发病率为每10万人467.4例，死亡率为每10万人189.8例。要改善这些数字，就需要改进检测和治疗。这些努力的一个重要方面是鉴定与癌症相关的遗传变异，这些变异已经产生了允许个性化癌症预防和治疗方法的翻译结果。现代高通量生物学实验，包括基因表达和SNP阵列，提供了一种前所未有的能力，通过同时测定105-106个标记物来详细研究癌症的遗传原因。然而，癌症是一种涉及多个基因的异质性和复杂原因的疾病。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多基因，系统水平变化驱动致癌作用的分析技术。为了填补这一方法上的空白，我提出了三种新的技术，基于途径的基因组数据分析。这些方法利用了我们目前对生物分子相互作用网络（途径）的了解。通过总结每个途径的数据，可以在病例和对照中比较整个途径行为，而不需要强的单基因关联。在目标1中，我提出了一种方法来正式的途径总结全基因组关联研究（GWAS）SNP数据，而不依赖于显着的单位点的关联，从而允许比较的途径作为一个整体之间的情况下和对照组使用基因型数据。在目标2和3中，我提出了两种不同的方法，使用通路拓扑结构从基因表达数据中创建汇总统计量，同样不依赖于单基因关联的显著性，从而不仅捕获通路中存在的基因，还捕获它们潜在的相互作用。这些方法都是非常新颖的：GWAS的这种类型的途径总结尚未报道，我用于目标2和3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，使其有可能确定癌症预防和治疗的靶向途径。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，从而推进个性化医疗。

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.