Personalized medicine using DNA biomarkers: a review

Abstract

Biomarkers are of increasing importance for personalized medicine, with applications including diagnosis, prognosis, and selection of targeted therapies. Their use is extremely diverse, ranging from pharmacodynamics to treatment monitoring. Following a concise review of terminology, we provide examples and current applications of three broad categories of biomarkers-DNA biomarkers, DNA tumor biomarkers, and other general biomarkers. We outline clinical trial phases for identifying and validating diagnostic and prognostic biomarkers. Predictive biomarkers, more generally termed companion diagnostic tests predict treatment response in terms of efficacy and/or safety. We consider suitability of clinical trial designs for predictive biomarkers, including a detailed discussion of validation study designs, with emphasis on interpretation of study results. We specifically discuss the interpretability of treatment effects if a large set of DNA biomarker profiles is available and the number of therapies is identical to the number of different profiles.

Fig. 1

Common study designs for biomarker studies. a Traditional randomized-control design where the randomization (R) to standard (STD) or experimental (EXP) therapy is independent from the results of the biomarker test. A retrospective evaluation of this design is possible for DNA biomarkers. b So-called “Gold standard” design. Randomization to STD or EXP is performed in the total patient population stratified by the result of the biomarker results. c Restricted design. To reduce effort, only biomarker-positive patients are randomized to STD or EXP. Claims about the utility of a biomarker cannot be made from this trial alone. d Patients are randomized according to treatment based on biomarker or non-biomarker-dependent strategy. This is the most flexible design that provides information about specific individualized treatment rules according to, e.g., a DNA profile