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Translational biomarker discovery in clinical metabolomics: an introductory tutorial

By:
Contributors: David Broadhurst, PhD

Metabolomics. 2013 Apr;9(2):280-299. Epub 2012 Dec 4.

Xia J1, Broadhurst DI, Wilson M, Wishart DS.

 

Abstract

Metabolomics is increasingly being applied towards the identification of biomarkers for disease diagnosis, prognosis and risk prediction. Unfortunately among the many published metabolomic studies focusing on biomarker discovery, there is very little consistency and relatively little rigor in how researchers select, assess or report their candidate biomarkers. In particular, few studies report any measure of sensitivity, specificity, or provide receiver operator characteristic (ROC) curves with associated confidence intervals. Even fewer studies explicitly describe or release the biomarker model used to generate their ROC curves. This is surprising given that for biomarker studies in most other biomedical fields, ROC curve analysis is generally considered the standard method for performance assessment. Because the ultimate goal of biomarker discovery is the translation of those biomarkers to clinical practice, it is clear that the metabolomics community needs to start “speaking the same language” in terms of biomarker analysis and reporting-especially if it wants to see metabolite markers being routinely used in the clinic. In this tutorial, we will first introduce the concept of ROC curves and describe their use in single biomarker analysis for clinical chemistry. This includes the construction of ROC curves, understanding the meaning of area under ROC curves (AUC) and partial AUC, as well as the calculation of confidence intervals. The second part of the tutorial focuses on biomarker analyses within the context of metabolomics. This section describes different statistical and machine learning strategies that can be used to create multimetabolite biomarker models and explains how these models can be assessed using ROC curves. In the third part of the tutorial we discuss common issues and potential pitfalls associated with different analysis methods and provide readers with a list of nine recommendations for biomarker analysis and reporting. To help readers test, visualize and explore the concepts presented in this tutorial, we also introduce a web-based tool called ROCCET (ROC Curve Explorer & Tester, http://www.roccet.ca). ROCCET was originally developed as a teaching aid but it can also serve as a training and testing resource to assist metabolomics researchers build biomarker models and conduct a range of common ROC curve analyses for biomarker studies.

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Raja Singh

Raja Singh

APCaRI welcomes our new Director, Raja B. Singh who brings a wealth of precision health networking expertise to APCaRI.
With over 12 years of experience in innovation, commercialization, and translational research, Raja has helped Canadian collaborative researchers and clinicians translate novel research from bench to market. We are excited to have Raja on board!

 

Catalina Vasquez

APCaRI also sends thanks to outgoing Interim Director Catalina Vasquez for her dedication and service to APCaRI. Catalina worked tirelessly with APCaRI researchers, clinicians, funders, and patients to help shape APCaRI into what it is today. We greatly appreciate her commitment to improving the lives of Albertan patients and their families and her continued commitment to improving the outcomes of cancer patients.

- Perrin Beatty