Suppr超能文献

从组学大数据中检测药物治疗中的个性化决定因素。

Detecting Personalized Determinants During Drug Treatment from Omics Big Data.

机构信息

Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy Science, Shanghai 200031, China.

出版信息

Curr Pharm Des. 2018;24(32):3727-3738. doi: 10.2174/1381612824666181106102111.

Abstract

BACKGROUNDS

Targeted therapy is the foundation of personalized medicine in cancer, which is often understood as the right patient using the right drug. Thinking from the viewpoint of determinants during personalized drug treatment, the genetics, epigenetics and metagenomics would provide individual-specific biological elements to characterize the personalized responses for therapy.

METHODS

Such personalized determinants should be not only understood as specific to one person, while they should have certain replicate observations in a group of individuals but not all, which actually provide more credible and reproducible personalized biological features. The requirement of detecting personalized determinants is well supported by novel high-throughput sequencing technologies and newly temporal-spatial experimental protocols, which quickly produce the omics big data.

RESULTS

In this mini-review, we would like to give a brief introduction firstly on the advanced drug or drug-like therapy with genetics, epigenetics and metagenomics, respectively, from the viewpoint of personalized determinants; then summarize the computational methods helpful to analyze the corresponding omics data under the consideration of personalized biological context; and particularly focus on metagenomics to discuss current data, method, and opportunity for personalized medicine.

CONCLUSION

Totally, detecting personalized determinants during drug treatment from omics big data will bring the precision medicine or personalized medicine from concept to application. More and more inspiring biotechnologies, data resources, and analytic approaches will benefit All of US in the near future.

摘要

背景

靶向治疗是癌症个体化医学的基础,通常被理解为正确的患者使用正确的药物。从个体化药物治疗的决定因素角度思考,遗传学、表观遗传学和宏基因组学将提供个体特有的生物学要素,以描述治疗的个体化反应。

方法

这些个体化决定因素不仅应被理解为特定于一个人,而且在一组个体中应该具有一定的重复观察,但不是全部,这实际上提供了更可信和可重复的个体化生物学特征。新型高通量测序技术和新的时空调验方案为检测个体化决定因素提供了很好的支持,这些方案可以快速产生组学大数据。

结果

在这篇迷你综述中,我们将首先简要介绍基于遗传学、表观遗传学和宏基因组学的先进药物或类药物治疗,分别从个体化决定因素的角度;然后总结有助于在个体化生物学背景下分析相应组学数据的计算方法;并特别关注宏基因组学,讨论个体化医学的当前数据、方法和机会。

结论

总的来说,从组学大数据中检测药物治疗中的个体化决定因素将使精准医学或个体化医学从概念走向应用。在不久的将来,越来越多令人振奋的生物技术、数据资源和分析方法将使我们所有人受益。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验