Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA, 02118, USA.
Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
Sci Rep. 2019 Nov 15;9(1):16904. doi: 10.1038/s41598-019-52886-z.
The emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators - such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others - as predictive features of cancer outcome. However, identification of "driver genes" associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.
大规模多组学数据的出现需要开发数据集成方法。来自癌症患者的基因组研究已经确定了表观遗传和遗传调节剂 - 如甲基化标记、体细胞突变和体细胞拷贝数改变 (SCNAs) 等 - 作为癌症结果的预测特征。然而，确定与给定改变相关的“驱动基因”仍然是一个挑战。为此，我们开发了一种计算工具 iEDGE，用于模拟 (epi-)DNA 改变的顺式和反式效应，并识别潜在的顺式驱动基因，其中顺式和反式基因分别表示那些位于给定 (epi-)遗传改变的基因组边界内和外的基因。iEDGE 首先确定与特定 epi-DNA 改变在样本中存在/不存在相关的顺式和反式基因表达特征。然后，它应用统计中介检验来确定预测反式基因表达的顺式基因。最后，通过途径富集分析注释顺式和反式效应，以深入了解潜在的调控网络。我们使用 iEDGE 对来自乳腺癌和 TCGA 中包含的 18 种其他癌症类型的 SCNAs 和基因表达数据进行综合分析。值得注意的是，iEDGE 鉴定的顺式基因驱动因子被发现明显富集了来自多个验证的致癌基因和肿瘤抑制因子综合目录的已知驱动基因，这表明其余的同样重要。此外，预测的驱动因子富集了具有扩增驱动依赖性的功能相关癌症基因，这些基因具有潜在的预后和治疗价值。所有分析结果均可在 https://montilab.bu.edu/iEDGE 上获取。总之，使用 iEDGE 对 SCNAs 和基因表达进行综合分析成功地在 TCGA 中的 19 种癌症类型中识别了已知的癌症驱动基因和潜在的癌症治疗靶点。该方法可以很容易地应用于在各种疾病情况下将基因表达谱与其他 epi-DNA 检测相结合。
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