文摘
Personalized medicine allows the selection of treatments best suited to an individual patient and diseasephenotype. To implement personalized medicine, effective tests predictive of response to treatment orsusceptibility to adverse events are needed, and to develop a personalized medicine test, both highquality samples and reliable data are required. We review key features of state-of-the-art proteomicprofiling and introduce further analytic developments to build a proteomic toolkit for use in personalizedmedicine approaches. The combination of novel analytical approaches in proteomic data generation,alignment and comparison permit translation of identified biomarkers into practical assays. We furtherpropose an expanded statistical analysis to understand the sources of variability between individualsin terms of both protein expression and clinical variables and utilize this understanding in a predictivetest.