LAUSR

The concept of precision medicine has existed for a long time. In the Corpus Hippocraticum, an ancient collection of the Hippocratic texts, the essence of precision medicine was clearly discussed: each human body is different and responds differently to therapy; therefore, the same treatment cannot be suitable for everyone and hence the physician should choose the appropriate treatment that tailors to patients’ individual characteristics (Konstantinidou et al., 2017). To achieve the goal of choosing the right treatment for the right patient at the right time, the key is to discover patient-level characteristics, either demographic and clinical factors or imaging, molecular and genetic biomarkers that are able to predict patients’ disease condition, prognosis and response to potential treatments. The Precision Medicine Initiative was announced by the President of United States of America, Barrack Obama (www.whitehouse.gov/precisionmedicine), to accelerate progress (Collins and Varmus, 2015). This initiative aims to identify genetically based drivers of disease to develop new, more effective treatments. While a long-term goal of the initiative is applicable to all therapeutic areas, oncology has been in the forefront of the initiative as carcinogenesis and treatment are intertwined with molecular and genetic mutations that vary significantly from patient to patient even for the same diagnosis. Oncology drugs and biologics are increasingly targeting specific genetic mutant or pathways. Meanwhile, with the availability of large-scale-omics databases and recent development in patient-characterizing methods and computational tools for high-dimensional data, further advances in cancer research will require novel statistical and computing methods and clinical trial designs that can meet the rapidly changing landscape of precision medicine. Dr. Lisa LaVange, Director of CDER – Office of Biostatistics, delivered a keynote speech at the 2016 DukeIndustry Statistics Symposium (DISS) highlighting FDA precision medicine-related initiatives and some challenging statistical issues for study design for testing the target agents and developing and validating omics-based tests. An IOM committee focused on biomarkers for molecularly targeted therapies has issued recommendations to guide precision medicine in advancing the care of patients with cancer and other diseases (Lyman and Moses, 2016). As co-guest editors for this special issue, we invited statisticians, many of them participants in the 2016 DISS, from US government agencies, academia and industry to submit reviews and novel approaches to precision medicine in oncology. The issue starts with three review articles that summarize a number of new, innovative clinical trial designs in oncology precision medicine. Renfro and Mandrekar discuss the definition and properties of a class of master protocol (basket, umbrella and platform) trials. Zhang, Wang and Menon provide a review of biomarker types and how to integrate these biomarkers, well-established or not, in oncology clinical trial designs and their impact on sample size and power analysis. Basket trials play an important role in evaluating multiple agents of the same target of different diseases. A review paper by Simon describes non-randomized