@article{3481,
  keywords = {Antineoplastic Agents, Data Interpretation, Statistical, Drug Discovery, Drug Evaluation, Preclinical, Humans, Molecular Targeted Therapy, Neoplasms, Reproducibility of Results, Research Design, Signal-To-Noise Ratio},
  author = {William G. Kaelin},
  title = {Common pitfalls in preclinical cancer target validation},
  abstract = {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.},
  year = {2017},
  journal = {Nature Reviews. Cancer},
  volume = {17},
  pages = {425-440},
  month = {2017-07},
  issn = {1474-1768},
  doi = {10.1038/nrc.2017.32},
  language = {eng},
}