Benstead-Hume Graeme, Wooller Sarah K, Pearl Frances M G
a Bioinformatics Group, School of Life Sciences , University of Sussex , Brighton , United Kingdom.
Expert Opin Drug Discov. 2017 Jun;12(6):599-609. doi: 10.1080/17460441.2017.1319356. Epub 2017 May 2.
The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.
改进癌症治疗方法的开发常被视为一项迫切未满足的医疗需求。平台技术的最新进展以及生物“大数据”可用性的不断提高，为系统识别肿瘤发生过程中涉及的关键基因和通路提供了前所未有的机会。利用这些新技术所取得的发现可能会带来新的治疗干预措施。涵盖领域：作者讨论了当前利用“大数据”识别癌症驱动因素的方法。这些方法包括基因组测序数据分析、通路数据分析、多平台数据分析、识别合成致死等基因相互作用以及使用细胞系数据。他们回顾了如何利用大数据识别新的药物靶点。作者随后概述了癌症药物发现前沿所使用的可用数据存储库和工具。专家观点：基于驱动肿瘤的基因组事件的靶向治疗最终将为治疗方案提供依据。然而，采用量身定制的方法治疗所有肿瘤患者可能需要开发大量的靶向药物。
