The area of precision (or personalized) medicine has been the focus of considerable current interest and vigorous research efforts, in part inspired the wealth of genetic and genomic information that is becoming more feasible to collect and interpret. Broadly speaking, precision medicine seeks to determine “the right treatment for the right patient;” that is, to make optimal treatment decisions for an individual patient based on all information available for that patient, including not only genetic and genomic characteristics but all demographic, physiological, and other clinical factors, thereby allowing treatment to be tailored to the patient. We use the term “treatment” to refer not only to therapeutic agents (e.g., drugs or biologics), but also to include interventions such as surgery, vaccination, and behavioral therapy; actions taken to prevent or forestall the onset of disease; and diagnostic and screening procedures.
One popular perspective on precision medicine is the identification of subpopulations of patients who share certain characteristics and who are more likely to do better on one particular treatment than another. Central to this goal are the development of biomarkers that can be used to identify such patients and the potential for developing treatments specifically targeting them. Pharmaceutical sponsors are particularly interested in this goal, which focuses on subgroups in whom clinically important improvements in outcome may be realized over comparator treatments.
An alternative perspective is to focus on determination of how best to treat all patients using the arsenal of available treatment options. Here, the goal is to synthesize accruing information on a patient to inform possibly sequential treatment decisions made over the course of the disease/disorder process. More formally, this perspective focuses on discovery of optimal sequential decision-making strategies that represent an evidence-based approach to guiding treatment. Development of such formal decision strategies complements the current interest in comparative effectiveness research, offering a systematic approach to the best use of existing and new treatments to benefit the population as a whole.
In both cases, key to these goals are science-based quantitative methods for discovery of optimal treatment decision strategies based on data and the development of innovative study designs for the collection of the data required and for the evaluation of the strategies so determined. Across the quantitative sciences, many approaches to optimal decision-making in the context of health are the focus of extensive research. Efforts to develop new methodologies in response to the evolving landscape of this area will be essential, as will translation of methods to practice in areas such as cancer, cardiovascular disease, and transplantation medicine through substantial, sustained collaboration between quantitative and clinical and biological scientists.
Through the Chancellor’s Faculty Excellence Program, NC State University has established a Faculty Cluster in Personalized Medicine Discovery focused on the development of quantitative methods toward the promise of precision medicine and their implementation in practice. Faculty in the NC State Department of Statistics, Department of Mathematics, and the Edward P. Fitts Department of Industrial and Systems Engineering have partnered to establish this innovative entity, whose goal is to leverage and integrate existing and new complementary quantitiative expertise, bringing together faculty who are separately pursuing methodological research relevant to precision medicine to draw strength from their diverse perspectives, fostering innovation. Linkages between quantitative and clinical and biological scientists both on and beyond the NC State campus will be forged, resulting in a synergistic framework for advances in this area, enhancing the ability of faculty to attract substantial external funding and to capitalize on opportunities for translating the methodology into practice. A further benefit will be interdisciplinary training of graduate students in the quantitative sciences, who will emerge with unique, cross-cutting methodological and collaborative skills that will be in high demand.