Translating predictive biomarkers within oncology clinical development programs.
Predictive biomarkers provide essential information to enable personalized medicine, and hold the promise for enhancing the effectiveness and value of cancer therapies. However, they do not always work. This review provides a framework for managing the risk of predictive biomarkers and maximally harvesting their benefit. Methods are provided which permit data-driven, adaptive decision making about the use of predictive biomarkers during clinical development, applying them to the extent they are validated by the clinical data. Techniques for optimizing overall development efficiency, measured as the number of successful drug indications approved per patient utilized, are also presented.
Departments of Oncology & Biostatistics, Bioinformatics & Biomathematics, Lombardi Comprehensive Cancer Center & Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 4000 Reservoir Road NW, Suite 120 Washington, DC 20007, USA.
Robert A
RA
Beckman
Biostatistics & Research Decision Sciences, Merck Research Laboratories, Rahway, NJ, USA.
Cong
C
Chen
D014408:Biomarkers, Tumor; D002986:Clinical Trials as Topic; D006801:Humans; D009369:Neoplasms; D057285:Precision Medicine; D057170:Translational Research, Biomedical / Q000379:methods*
D014408:Biomarkers, Tumor
benefit–cost ratio; decision analysis; optimization; predictive biomarker