用于模拟癌症药物反应和耐药性的大数据方法
Big Data Approaches for Modeling Response and Resistance to Cancer Drugs.
作者信息
Jiang Peng, Sellers William R, Liu X Shirley
机构信息
Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.
Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
出版信息
Annu Rev Biomed Data Sci. 2018 Jul;1:1-27. doi: 10.1146/annurev-biodatasci-080917-013350. Epub 2018 Apr 25.
Abstract
Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.
摘要
尽管癌症研究取得了重大进展，但目前的标准护理药物仍无法治愈多种类型的癌症。因此，迫切需要识别更好的预测生物标志物和治疗方案。传统上，假设驱动研究的见解是癌症生物学和治疗发现的主要推动力。最近，高通量技术的突破催化了大数据资源的快速增长，导致癌症治疗研究发生了范式转变。计算方法与基因组学数据的结合已带来了多项成功的临床应用。在本综述中，我们关注数据驱动方法在抗癌药物疗效建模方面的最新进展，并阐述数据科学在癌症治疗研究中的挑战与机遇。
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