Five Phases of the Research Process
The success rate of biomarker development programs using any proteomics platform can be increased by first dividing biomarker research into five phases that address each of the key steps of the process: study design, discovery, validation, identification, and clinical assay implementation (Figure 1).
In the initial phase of study design, the objective is to detail the clinical question being asked and the types and number of samples, experimental workflow, and technologies to be used. This phase is particularly critical to successful biomarker discovery.
The purpose of the discovery phase is to elucidate candidate biomarker proteins by screening a large number of conditions to detect the maximum number of proteins including low-abundance proteins.
Samples must be carefully chosen and in sufficient numbers to produce statistical significance. Those proteins that show significant group- or time-dependent differences are described as candidate biomarkers, which can be used alone (univariate analysis) or in combination (multivariate analysis) to produce predictive models.
The validation phase assesses the validity of a biomarker against a larger, more heterogeneous population. The robustness of the candidate markers is tested against a level of biological variability that more accurately represents the variability present in the target population. This phase may be designed to confirm the findings from the discovery phase or it may explore different variables affecting the validity of the markers for a large population. In the identification phase, the most promising markers are first enriched and purified and then subsequently identified by tryptic digestion and sequencing by tandem mass spectrometry.
The clinical assay implementation phase entails the development and optimization of assays for the validated biomarkers that are robust, sensitive, and quantitative enough to be of clinical utility. This phase can be performed at multiple points in a study, and the assays may be either chromatography or antibody based.
Understanding and managing sources of bias are also key to successful biomarker development during all five phases of the process. Small changes in protein expression levels can be detected with current proteomics technologies. Some of these changes can be due to the biological differences related to a disease or treatment under study or may reflect the heterogeneity of patients across multiple sites, the inherent complexity and diversity of different sample types, and even small differences in the sample collection, processing, and analysis techniques used. As a consequence, results may be site, study, population, or sample specific, and thus not of clinical use.
Preanalytical bias can arise from systematic differences in patient populations or sample characteristics as well as the procedures used for sample collection, handling, and storage. Differences in the manner in which samples are processed and analyzed can produce analytical bias, which can have profound effects on the outcome of a discovery study. Careful management of sources of variability and bias can help ensure reproducible results.
Preanalytical bias can be minimized by careful definition of the biological question and selection of appropriate samples, evaluation of patient and sample histories, establishment of rigorous criteria for sample inclusion and exclusion, development of standard operating procedures (SOPs) for sample collection, handling, and storage, and measurement and documentation of all potential sources of uncontrollable variation.
Analytical bias can be controlled through rigorous training, instrument qualification, and the use of SOPs, resulting in the elucidation of true biological differences. Best practices to minimize analytical bias include using sufficient numbers of replicates, processing all samples together under the same conditions including reference and quality control samples, analyzing all data using consistent parameters for processing, and maintaining detailed records of all sample-processing and data- analysis steps.