Advances in the preclinical testing of cancer therapeutic hypotheses

The reduction in attrition rates of cancer therapeutics in the clinic requires robust translational strategies. In their Perspective, Caponigro and Sellers review historical and current uses of preclinical model systems, and discuss how these systems can be optimized for rationally predicting the therapeutic benefit of drug candidates. The genetic and epigenetic underpinnings of cancer are becoming increasingly clear owing to impressive and well-coordinated ventures occurring worldwide. As our understanding of the molecular alterations driving human cancer increases, there is an opportunity to direct the clinical application of cancer therapeutics with improved accuracy. The often empirical treatment of cancer — which was initially based on inhibiting DNA synthesis and cellular division — while having led to a number of remarkable successes, remains prone to a high rate of clinical failure that results partly from a lack of understanding of how best to implement drugs in the clinic. Consequently, it is vital that robust translational strategies be developed preclinically to both reduce failure rates in the clinic and shorten the time required to identify patient populations most likely to benefit from a given therapeutic. Here, we review both historical and current uses of preclinical model systems, being mindful that a combination of approaches will be needed to address all meritorious therapeutic hypotheses.