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      Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine

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          Abstract

          Since the emergence of the so-called omics technology, thousands of putative biomarkers have been identified and published, which have dramatically increased the opportunities for developing more effective therapeutics. These opportunities can have profound benefits for patients and for the economics of healthcare. However, the transfer of biomarkers from discovery to clinical practice is still a process filled with lots of pitfalls and limitations, mostly limited by structural and scientific factors. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific and sensitive. Besides the lack of quality in biomarker validation, a number of other key issues can be identified and should be addressed. Therefore, the aim of this article is to discuss a series of interpretative and practical issues that need to be understood and resolved before potential biomarkers become a clinically approved test or are already on the diagnostic market. Some of these issues are shortly discussed here.

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          Bring on the biomarkers.

          George Poste (2011)
          Article 0 comments Cited 180 times – based on 0 reviews     Review now
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            Analysis of the adult human plasma metabolome.

            Matthew W. Mitchell, Benjamin Evans, Michael V. Milburn … (2008)
            It is well established that disease states are associated with biochemical changes (e.g., diabetes/glucose, cardiovascular disease/cholesterol), as are responses to chemical agents (e.g., medications, toxins, xenobiotics). Recently, nontargeted methods have been used to identify the small molecules (metabolites) in a biological sample to uncover many of the biochemical changes associated with a disease state or chemical response. Given that these experimental results may be influenced by the composition of the cohort, in the present study we assessed the effects of age, sex and race on the relative concentrations of small molecules (metabolites) in the blood of healthy adults. Using gas- and liquid-chromatography in combination with mass spectrometry, a nontargeted metabolomic analysis was performed on plasma collected from an age- and sex-balanced cohort of 269 individuals. Of the more than 300 unique compounds that were detected, significant changes in the relative concentration of more than 100 metabolites were associated with age. Many fewer differences were associated with sex and fewer still with race. Changes in protein, energy and lipid metabolism, as well as oxidative stress, were observed with increasing age. Tricarboxylic acid intermediates, creatine, essential and nonessential amino acids, urea, ornithine, polyamines and oxidative stress markers (e.g., oxoproline, hippurate) increased with age. Compounds related to lipid metabolism, including fatty acids, carnitine, beta-hydroxybutyrate and cholesterol, were lower in the blood of younger individuals. By contrast, relative concentrations of dehydroepiandrosterone-sulfate (a proposed antiaging androgen) were lowest in the oldest age group. Certain xenobiotics (e.g., caffeine) were higher in older subjects, possibly reflecting decreases in hepatic cytochrome P450 activity. Our nontargeted analytical approach detected a large number of metabolites, including those that were found to be statistically altered with age, sex or race. Age-associated changes were more pronounced than those related to differences in sex or race in the population group we studied. Age, sex and race can be confounding factors when comparing different groups in clinical studies. Future studies to determine the influence of diet, lifestyle and medication are also warranted.
            Article 0 comments Cited 160 times – based on 0 reviews     Review now
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              The human plasma proteome: a nonredundant list developed by combination of four separate sources.

              Anna Lobley, Richard D. Veenstra, Rembert Pieper … (2004)
              We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.
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                Author and article information

                Contributors
                Elisabeth Drucker
                Kurt Krapfenbauer
                Journal
                Journal ID (nlm-ta): EPMA J
                Journal ID (iso-abbrev): EPMA J
                Title: The EPMA Journal
                Publisher: BioMed Central
                ISSN (Print): 1878-5077
                ISSN (Electronic): 1878-5085
                Publication date Collection: 2013
                Publication date (Electronic): 25 February 2013
                Volume: 4
                Issue: 1
                Page: 7
                Affiliations
                [1 ]Department of Molecular Biotechnology, University of Applied Science Vienna, Helmut-Qualtinger-Gasse 2, Vienna A-1030, Austria
                [2 ]Department of Otorhinolaryngology, Medical University of Vienna, Head and Neck Surgery, Waehringer Guertel 18-20, Vienna A-1090, Austria
                Article
                Publisher ID: 1878-5085-4-7
                DOI: 10.1186/1878-5085-4-7
                PMC ID: 3599714
                PubMed ID: 23442211
                SO-VID: 70e75467-f86e-4a99-81e2-ebf48acb4c61
                Copyright © Copyright ©2013 Drucker and Krapfenbauer; licensee BioMed Central Ltd.
                License:

                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
                Date received : 10 December 2012
                Date accepted : 14 January 2013
                Categories
                Subject: Review

                ScienceOpen disciplines: Molecular medicine
                Keywords: predictive medicine,targeted prevention,validation strategies,regulatory overview,biomarker perspectives,tailored therapy
                Data availability:
                ScienceOpen disciplines: Molecular medicine
                Keywords: predictive medicine, targeted prevention, validation strategies, regulatory overview, biomarker perspectives, tailored therapy

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