计算指导下针对 28 种肿瘤类型的癌症药物处方揭示了靶向机会。
In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities.
机构信息
Biomedical Genomics Lab, Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.
Systems Pharmacology, Research Program on Biomedical Informatics, IMIM Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain.
出版信息
Cancer Cell. 2015 Mar 9;27(3):382-96. doi: 10.1016/j.ccell.2015.02.007.
Large efforts dedicated to detect somatic alterations across tumor genomes/exomes are expected to produce significant improvements in precision cancer medicine. However, high inter-tumor heterogeneity is a major obstacle to developing and applying therapeutic targeted agents to treat most cancer patients. Here, we offer a comprehensive assessment of the scope of targeted therapeutic agents in a large pan-cancer cohort. We developed an in silico prescription strategy based on identification of the driver alterations in each tumor and their druggability options. Although relatively few tumors are tractable by approved agents following clinical guidelines (5.9%), up to 40.2% could benefit from different repurposing options, and up to 73.3% considering treatments currently under clinical investigation. We also identified 80 therapeutically targetable cancer genes.
致力于在肿瘤基因组/外显子中检测体细胞改变的大量努力有望在精准癌症医学方面取得重大进展。然而，肿瘤间的高度异质性是开发和应用治疗性靶向药物来治疗大多数癌症患者的主要障碍。在这里，我们对一个大型泛癌队列中的靶向治疗药物的范围进行了全面评估。我们开发了一种基于每个肿瘤中驱动突变的鉴定及其药物可开发性选择的虚拟处方策略。尽管根据临床指南，只有相对较少的肿瘤可以通过批准的药物治疗（5.9%），但多达 40.2%的肿瘤可以从不同的再利用选择中受益，考虑到目前正在临床研究中的治疗方法，多达 73.3%的肿瘤可以受益。我们还鉴定了 80 个具有治疗潜力的癌症基因。
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