Cancer continues to be a formidable disease with an overall success rate of bringing new drugs to market of only 5-8%. This high failure rate is often attributed to a lack of translatability between preclinical knowledge and clinical efficacy. Clearly, new tools are needed to bridge this knowledge gap and improve success rates, to the benefit of the cancer patient population.
Cancer is an extraordinarily complex disease involving, a heterogeneous mix of genetically mercurial cancer cells, stroma, and microenvironment across at least 200 different cancer types. Even with a specific cancer type, heterogeneity within patients impairs clinical development. Thus, the clinical challenges are substantial —to produce meaningful regressions and survival benefits based on targeting important aspects of this complex ecology. The wealth of data generated in recent years has afforded the opportunity to rationally target the molecular basis of many cancers but preclinical models of cancer are frequently ineffective in translating promising molecularly targeted therapies into clinical success. Also these therapies often give way to resistance. Thus, there is a critical need to fully understand the molecular/metabolic context in which the target resides to begin to unravel the basis of sensitivity and resistance. This understanding will afford the opportunity to identify therapeutic combinations that will work in concert with molecularly targeted therapies.