Moving from correlative science to predictive oncology.

Many diagnostic entities traditionally viewed as individual diseases are heterogeneous in molecular pathogenesis and treatment responsiveness. This results in treatment of many patients with ineffective drugs, the conduct of large clinical trials to identify small average treatment benefits for heterogeneous groups of patients. In oncology, genomic technologies provide powerful tools for identification of patients who require systemic treatment and for selecting the most appropriate drug. Development of drugs with companion diagnostics, however, increases the complexity of clinical development and requires new approaches to the design and analysis of clinical trials. Adapting to the fundamental importance of tumor genomics will require paradigm changes for clinical and statistical investigators in academia, industry and government. In this paper we attempt to address some of these issues and to comment specifically on the design of clinical studies for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.