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      Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges

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          Abstract

          Tremendous efforts have been made over the past few decades to discover novel cancer biomarkers for use in clinical practice. However, a striking discrepancy exists between the effort directed toward biomarker discovery and the number of markers that make it into clinical practice. One of the confounding issues in translating a novel discovery into clinical practice is that quite often the scientists working on biomarker discovery have limited knowledge of the analytical, diagnostic, and regulatory requirements for a clinical assay. This review provides an introduction to such considerations with the aim of generating more extensive discussion for study design, assay performance, and regulatory approval in the process of translating new proteomic biomarkers from discovery into cancer diagnostics. We first describe the analytical requirements for a robust clinical biomarker assay, including concepts of precision, trueness, specificity and analytical interference, and carryover. We next introduce the clinical considerations of diagnostic accuracy, receiver operating characteristic analysis, positive and negative predictive values, and clinical utility. We finish the review by describing components of the FDA approval process for protein-based biomarkers, including classification of biomarker assays as medical devices, analytical and clinical performance requirements, and the approval process workflow. While we recognize that the road from biomarker discovery, validation, and regulatory approval to the translation into the clinical setting could be long and difficult, the reward for patients, clinicians and scientists could be rather significant.

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          Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            Estimation of the Youden Index and its associated cutoff point.

            The Youden Index is a frequently used summary measure of the ROC (Receiver Operating Characteristic) curve. It both, measures the effectiveness of a diagnostic marker and enables the selection of an optimal threshold value (cutoff point) for the marker. In this paper we compare several estimation procedures for the Youden Index and its associated cutoff point. These are based on (1) normal assumptions; (2) transformations to normality; (3) the empirical distribution function; (4) kernel smoothing. These are compared in terms of bias and root mean square error in a large variety of scenarios by means of an extensive simulation study. We find that the empirical method which is the most commonly used has the overall worst performance. In the estimation of the Youden Index the kernel is generally the best unless the data can be well transformed to achieve normality whereas in estimation of the optimal threshold value results are more variable.
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              Clinical tests: sensitivity and specificity: Fig 1

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                Author and article information

                Contributors
                Journal
                Clin Proteomics
                Clin Proteomics
                Clinical proteomics
                Springer
                1542-6416
                1559-0275
                2013
                2 October 2013
                : 10
                : 1
                : 13
                Affiliations
                [1 ]Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
                [2 ]Division of Immunology and Hematology Devices, Office of In Vitro Diagnostic Devices and Radiological Health, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
                [3 ]Center for Biomarker Discovery, Department of Pathology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, USA
                [4 ]Current address: Department of Pathology and Laboratory Medicine, Royal Alexandra Hospital, Diagnostic Treatment Center, Room 5047, Edmonton, AB T5H 3V9, Canada
                Article
                1559-0275-10-13
                10.1186/1559-0275-10-13
                3850675
                24088261
                168fb5b7-f776-410c-b992-88575dca222e
                Copyright © 2013 Füzéry et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 April 2013
                : 31 August 2013
                Categories
                Review

                Molecular medicine
                proteomic biomarker,analytical performance,clinical performance,food and drug administration

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