Identification of pathways associated with chemosensitivity through network embedding

Basal gene expression levels have been shown to be predictive of cellular response to cytotoxic treatments. However, such analyses do not fully reveal complex genotype- phenotype relationships, which are partly encoded in highly interconnected molecular networks. Biological pathways provide a complementary way of understanding drug response variation among individuals. In this study, we integrate chemosensitivity data from a large-scale pharmacogenomics study with basal gene expression data from the CCLE project and prior knowledge of molecular networks to identify specific pathways mediating chemical response. We first develop a computational method called PACER, which ranks pathways for enrichment in a given set of genes using a novel network embedding method. It examines a molecular network that encodes known gene-gene as well as gene-pathway relationships, and determines a vector representation of each gene and pathway in the same low-dimensional vector space. The relevance of a pathway to the given gene set is then captured by the similarity between the pathway vector and gene vectors. To apply this approach to chemosensitivity data, we identify genes whose basal expression levels in a panel of cell lines are correlated with cytotoxic response to a compound, and then rank pathways for relevance to these response-correlated genes using PACER. Extensive evaluation of this approach on benchmarks constructed from databases of compound target genes and large collections of drug response signatures demonstrates its advantages in identifying compound-pathway associations compared to existing statistical methods of pathway enrichment analysis. The associations identified by PACER can serve as testable hypotheses on chemosensitivity pathways and help further study the mechanisms of action of specific cytotoxic drugs. More broadly, PACER represents a novel technique of identifying enriched properties of any gene set of interest while also taking into account networks of known gene-gene relationships and interactions.已有研究表明，基础基因表达水平可预测细胞对细胞毒性治疗的应答反应。然而，此类分析无法完全揭示复杂的基因型-表型关联，这类关联部分编码于高度互联的分子网络之中。生物通路为理解个体间药物应答差异提供了互补的研究路径。本研究整合了大规模药物基因组学研究获得的化疗敏感性数据、来自癌症细胞系百科全书（Cancer Cell Line Encyclopedia, CCLE）项目的基础基因表达数据，以及分子网络的先验知识，以识别介导化学应答的特定通路。我们首先开发了一种名为PACER的计算方法，该方法通过一种新型的网络嵌入（network embedding）方法，对给定基因集合中富集的通路进行排序。该方法会分析包含已知基因-基因以及基因-通路关联的分子网络，并在同一低维向量空间中为每个基因和通路生成向量表征。随后，通路向量与基因向量之间的相似度即可用于表征该通路与给定基因集合的相关性。为将该方法应用于化疗敏感性数据，我们首先在一组细胞系中筛选出基础表达水平与某化合物的细胞毒性应答存在相关性的基因，随后利用PACER对这些与应答相关的基因的关联通路进行排序。本研究基于化合物靶基因数据库与大规模药物应答特征集构建了基准数据集，并在该数据集上对该方法进行了全面评估，结果显示，相较于现有的通路富集分析（pathway enrichment analysis）统计方法，PACER在识别化合物-通路关联方面具有显著优势。PACER所识别的关联可作为化疗敏感性通路的可验证假说，助力后续对特定细胞毒性药物的作用机制展开研究。从更广泛的层面来看，PACER是一种新型技术，可在考虑已知基因-基因关联与相互作用网络的同时，识别任意目标基因集合的富集特征。