Post-genomic molecular biology is revolutionizing the study of cancer. New measurement technologies are giving us fresh insights into the molecular and genetic mechanisms underlying the disease, and modelling these data gives us ways to predict the likely progression and outcome of disease in a given patient. By identifying informative markers related to critical events, there is unprecedented potential for both the development of new prognostic/diagnostic tests, and also for furthering our understanding of cancer's key driving molecular and genetic mechanisms.
This revolution in data generation necessitates a corresponding advances is statistical, mathematical, and computational techniques, in order to be able to utilise these data. There is also a need for a translational focus, so that new methods can be translated from academic research into the clinic.