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June 15, 2008 (Vol. 28, No. 12)

Planning for Success in Biomarker Discovery

Appropriate Proteomics Platform and Careful Study Design Can Improve Positive Results

  • Protein biomarkers have been used for many years for population screening, disease diagnosis, and prediction of therapeutic response. The increased focus on proteomics research over the last several years has led to advances in protein analytical technologies that are increasing the pace and scope of efforts to discover new biomarkers. There is an ever-increasing awareness that single biomarkers are limited in their ability to provide high predictive-value assays with clinical utility. Biological pathways that lead to disease are complex, and the ability to detect and monitor multiple biomarkers is required to achieve more robust, accurate, and predictive assays.

    Proteomics technologies that enable the simultaneous analysis of hundreds of proteins hold the promise of biomarker panels that could be used to accurately detect and predict human disease states.

    Human biological fluids, especially serum and plasma, contain many thousands of proteins and peptides, with concentrations varying as much as 11 orders of magnitude. The new proteomics technologies must meet the challenge of being sensitive enough to detect large numbers of proteins present at low concentrations in the presence of a small number of proteins that may comprise as much as 99% of the protein mass of the sample.

    In addition, they must be highly reproducible, while providing the throughput and vigor required to rapidly analyze thousands of samples, in order to provide statistically relevant data on biomarker candidates.

    Despite intensified interest and investment, however, the rate of introduction of novel protein biomarkers is falling, with only an average of one per year being approved by the FDA. This trend reflects not only the long and difficult path from candidate discovery to clinical assay but also the frequent lack of a coherent, rigorous, and comprehensive process for biomarker development.

  • Five Phases of the Research Process

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    Figure 1

    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.

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