Cancer-specific gene set testing

项目摘要 当控制细胞存活、细胞命运或基因组维持的途径被破坏时， 通过关键驱动基因的体细胞改变。了解途径疾病的机制和影响- 因此，破裂对于癌症生物学的准确表征和治疗的鉴定是必不可少的。 瞄准目标。研究癌症中通路失调的一种常见方法是分析 肿瘤基因表达数据的基因集测试或途径分析技术。基因集测试 是一种有效且广泛应用的假设聚合方法， 基因功能，以测试更少的生物学上有意义的假设，从而提高 解释，复制和权力相对于基因水平的分析。虽然基因组分析 的大型癌症基因表达数据集已经成功地确定了在癌症中通常受影响的途径。 对于人类癌症，现有的途径分析方法在应用于癌症时具有两个重要的局限性， cer基因表达数据。首先，大多数现有的基因集集合对基因活动的模式进行建模 在正常组织中发现，这可能与肿瘤中发现的模式显著不同。使用这些 分析癌症基因表达数据的基因集可能导致误导性结果， 因为第二类错误率被大大夸大了第二，标准基因集测试方法仅利用 分析样品的基因表达数据。虽然有一些癌症特异性途径 考虑多种组学模式的分析方法，例如，表达和突变，信息 关于相关正常组织中的基因表达的信息没有被现有技术利用。 忽略正常组织基因表达可能导致以癌症为重点的分析， 相关正常组织的表型，而不是捕获癌症特异性活性。解决 这些挑战，我们将开发新颖和创新的生物信息学算法，1）优化现有的- 收集基因集以反映发育异常组织中发现的基因活性模式，以及2）利用 在基因组分析期间关于正常组织基因活性的信息。

PROJECT SUMMARY Cancer develops when pathways controlling cell survival, cell fate or genome maintenance are disrupted by the somatic alteration of key driver genes. Understanding the mechanism and impact of pathway dis- ruption is therefore essential for an accurate characterization of cancer biology and identification of ther- apeutic targets. A common approach for studying pathway dysregulation in cancer involves the analysis of tumor gene expression data using gene set testing or pathway analysis techniques. Gene set testing is an effective and widely applied hypothesis aggregation method that uses prior knowledge regarding gene function to test a smaller number of more biologically meaningful hypotheses and thereby improve interpretation, replication and power relative to a gene-level analysis. Although the gene set analysis of large cancer gene expression data sets has successfully identified pathways commonly impacted in human cancer, existing pathway analysis methods have two important limitations when applied to can- cer gene expression data. First, most existing gene set collections model the pattern of gene activity found in normal tissues, which can differ significantly from the pattern found within tumors. Using these gene sets to analyze cancer gene expression data can result in misleading results with the potential for a significantly inflated type II error rate. Second, standard gene set testing methods leverage only the gene expression data for the analyzed samples. Although there are some cancer-specific pathway analysis methods that consider multiple omics modalities, e.g., expression and mutations, information regarding the expression of genes in the associated normal tissue is not utilized by existing techniques. Ignoring normal tissue gene expression can result in a cancer-focused analysis that simply recapitulates the phenotype of the associated normal tissue rather than capturing cancer-specific activity. To address these challenges, we will develop novel and innovative bioinformatics algorithms that 1) optimize exist- ing gene set collections to reflect the pattern of gene activity found in dysplastic tissue, and 2) leverage information regarding normal tissue gene activity during gene set analysis.