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Physicians need improved tools for selecting treatments for individual patients. Many diagnostic entities hat were traditionally viewed as individual diseases are heterogeneous in their molecular pathogenesis and treatment responsiveness. This results in the treatment of many patients with ineffective drugs, incursion of substantial medical costs for the treatment of patients who do not benefit and the conducting of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. In oncology, new genomic technologies provide powerful tools for the selection of patients who require systemic treatment and are most (or least) likely to benefit from a molecularly targeted therapeutic. In the large amount of literature on biomarkers, there is considerable uncertainty and confusion regarding the specifics involved in the development and evaluation of prognostic and predictive biomarker diagnostics. There is a lack of appreciation that the development of drugs with companion diagnostics increases the complexity of clinical development. Adapting to the fundamental importance of tumor heterogeneity and achieving the benefits of personalized oncology for patients and healthcare costs will require paradigm changes for clinical and statistical investigators in academia, industry and regulatory agencies. In this review, I attempt to address some of these issues and provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.
Richard Simon (Mon,) studied this question.