College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
J Transl Med. 2019 Aug 6;17(1):255. doi: 10.1186/s12967-019-2010-4.
Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.
In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.
Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP ( http://bio-bigdata.hrbmu.edu.cn/CancerDAP/ ) available to explore 2751 subpathways relevant with 191 anticancer drugs response.
Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions.
个性化药物反应预测对于实现癌症的个体化治疗和推动精准医学的发展至关重要。大规模的多组学图谱为精准癌症治疗提供了前所未有的机会。
在本研究中,我们提出了一种通过整合多种遗传和表观遗传(基因表达、拷贝数变异和 DNA 甲基化)改变的综合贡献来识别个体抗癌药物反应的亚途径特征的工作流程。
总共基于五个癌症药物反应数据集,鉴定了与个体对不同抗癌药物反应相关的 46 个亚途径特征。我们在两个独立的数据集验证了亚途径特征的可靠性。此外,我们还证明这些多组学亚途径特征可以显著提高抗癌药物反应预测的性能。对这 46 个亚途径特征的深入分析揭示了三种组学类型的重要作用以及不同抗癌药物反应背后的功能关联。基于涉及抗癌药物反应的亚途径特征的患者分层确定了具有不同临床结局的亚型,表明它们作为预后生物标志物的潜在作用。此外,还提供了与 CellMiner 中 191 种抗癌药物的细胞反应相关的亚途径图谱,并基于这些亚途径准确揭示了药物作用的机制相似性。最后,我们构建了一个用户友好的网络界面-CancerDAP(http://bio-bigdata.hrbmu.edu.cn/CancerDAP/),可用于探索与 191 种抗癌药物反应相关的 2751 个亚途径。
综上所述,我们的研究确定并系统地描述了个体化抗癌药物反应预测的亚途径特征,这可能促进癌症的精确治疗和药物作用分子机制的研究。