Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
Department of Pharmacology, Harbin Medical University, Harbin, China.
Brief Bioinform. 2018 Jul 20;19(4):644-655. doi: 10.1093/bib/bbw142.
Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.
合成生存力是指可以挽救单个基因改变致死效应的基因改变的组合，它可能代表了癌细胞抵抗靶向药物的一种机制。目前，用于检测癌症基因组中合成生存力（SV）相互作用以研究耐药性的方法仍然很少。在这里，我们提出了一种通过整合多维数据集（包括拷贝数改变、全外显子突变、表达谱和临床数据）来检测合成生存力诱导的药物耐药性（SVDR）的计算方法。SVDR 通过整合癌症基因组图谱数据、小发夹 RNA 功能实验数据和酵母遗传相互作用数据，全面描述了 32 种癌症类型的 8580 个肿瘤中的 SV 相互作用景观。我们揭示了 SV 相互作用有利于细胞，并可以预测癌症患者的临床预后，这在一个独立的数据集中得到了稳健的观察。通过整合癌症药物基因组学数据集，包括癌症细胞系百科全书（CCLE）和 Broad Cancer Therapeutics Response Portal，我们已经证明，SVDR 能够进行耐药性预测，并且在两个数据库之间具有高度可靠性。据我们所知，SVDR 是第一个用于识别与癌细胞耐药性相关的 SV 相互作用的全基因组数据驱动方法。这种数据驱动方法为识别预测药物耐药性的基因组标志物奠定了基础，并成功推断出用于癌症治疗的潜在药物组合。
