Howard Hughes Medical Institute, Dana-Farber Cancer Institute and Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02215, USA.
Nat Rev Cancer. 2017 Jul;17(7):425-440. doi: 10.1038/nrc.2017.32. Epub 2017 May 19.
An alarming number of papers from laboratories nominating new cancer drug targets contain findings that cannot be reproduced by others or are simply not robust enough to justify drug discovery efforts. This problem probably has many causes, including an underappreciation of the danger of being misled by off-target effects when using pharmacological or genetic perturbants in complex biological assays. This danger is particularly acute when, as is often the case in cancer pharmacology, the biological phenotype being measured is a 'down' readout (such as decreased proliferation, decreased viability or decreased tumour growth) that could simply reflect a nonspecific loss of cellular fitness. These problems are compounded by multiple hypothesis testing, such as when candidate targets emerge from high-throughput screens that interrogate multiple targets in parallel, and by a publication and promotion system that preferentially rewards positive findings. In this Perspective, I outline some of the common pitfalls in preclinical cancer target identification and some potential approaches to mitigate them.
令人震惊的是,相当数量的实验室提名新癌症药物靶点的论文中的发现无法被其他人重现,或者根本不够稳健,无法证明药物研发工作的合理性。这个问题可能有多种原因,包括在使用药理学或遗传扰动剂进行复杂的生物测定时,对靶点脱靶效应的误导的危险认识不足。当所测量的生物表型是“下降”读数(如增殖减少、活力降低或肿瘤生长减少)时,这种危险尤其严重,因为这种情况很常见于癌症药理学中,而这种下降读数可能仅仅反映了细胞适应性的非特异性丧失。这些问题因多重假设检验而更加复杂,例如当候选靶点从平行检测多个靶点的高通量筛选中出现时,以及当出版和推广系统优先奖励阳性发现时。在这篇观点文章中,我概述了临床前癌症靶点识别中的一些常见陷阱,以及一些潜在的减轻这些陷阱的方法。