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基于患者个体的多组学模型及其在个体化联合治疗中的应用。

Patient-specific multi-omics models and the application in personalized combination therapy.

机构信息

Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA.

Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.

出版信息

Future Oncol. 2020 Aug;16(23):1737-1750. doi: 10.2217/fon-2020-0119. Epub 2020 May 28.

Abstract

The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.

摘要

高通量技术的快速发展和成本的急剧下降使得在个体基础上生成大量多组学数据成为可能。包括基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学和微生物组学在内的高通量组学的发展,使得多组学技术在临床环境中的应用成为可能。联合治疗是指用两种或两种以上药物治疗疾病,以达到低剂量或低药物毒性的疗效,是癌症等疾病治疗的基础。基于患者的多组学数据整合有助于识别和开发联合疗法。在这篇综述中,我们概述了不同的组学平台,并讨论了高通量、数据整合、个性化联合治疗的多组学方法。

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