Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Weill Cornell Medical College, New York, NY, USA.
Nature. 2019 Nov;575(7782):299-309. doi: 10.1038/s41586-019-1730-1. Epub 2019 Nov 13.
The problem of resistance to therapy in cancer is multifaceted. Here we take a reductionist approach to define and separate the key determinants of drug resistance, which include tumour burden and growth kinetics; tumour heterogeneity; physical barriers; the immune system and the microenvironment; undruggable cancer drivers; and the many consequences of applying therapeutic pressures. We propose four general solutions to drug resistance that are based on earlier detection of tumours permitting cancer interception; adaptive monitoring during therapy; the addition of novel drugs and improved pharmacological principles that result in deeper responses; and the identification of cancer cell dependencies by high-throughput synthetic lethality screens, integration of clinico-genomic data and computational modelling. These different approaches could eventually be synthesized for each tumour at any decision point and used to inform the choice of therapy.
癌症治疗耐药性问题是多方面的。在这里,我们采用简化论的方法来定义和分离耐药性的关键决定因素,包括肿瘤负担和生长动力学;肿瘤异质性;物理屏障;免疫系统和微环境;不可用药的癌症驱动因素;以及施加治疗压力的许多后果。我们提出了四种克服耐药性的一般解决方案,这些方案基于更早地发现肿瘤从而可以进行肿瘤干预;治疗过程中的适应性监测;添加新的药物和改进药理学原则,从而产生更深入的反应;以及通过高通量合成致死性筛选、临床基因组数据整合和计算建模来确定癌细胞的依赖性。这些不同的方法最终可以在每个肿瘤的任何决策点进行综合,并用于指导治疗选择。