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      Revisiting biomarker discovery by plasma proteomics

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          Abstract

          Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry ( MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.

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

          M H Zweig, G. Campbell (1993)
          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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            Circulating angiogenic factors and the risk of preeclampsia.

            Richard J. Levine, Sharon E. Maynard, Cong Qian … (2004)
            The cause of preeclampsia remains unclear. Limited data suggest that excess circulating soluble fms-like tyrosine kinase 1 (sFlt-1), which binds placental growth factor (PlGF) and vascular endothelial growth factor (VEGF), may have a pathogenic role. We performed a nested case-control study within the Calcium for Preeclampsia Prevention trial, which involved healthy nulliparous women. Each woman with preeclampsia was matched to one normotensive control. A total of 120 pairs of women were randomly chosen. Serum concentrations of angiogenic factors (total sFlt-1, free PlGF, and free VEGF) were measured throughout pregnancy; there were a total of 655 serum specimens. The data were analyzed cross-sectionally within intervals of gestational age and according to the time before the onset of preeclampsia. During the last two months of pregnancy in the normotensive controls, the level of sFlt-1 increased and the level of PlGF decreased. These changes occurred earlier and were more pronounced in the women in whom preeclampsia later developed. The sFlt-1 level increased beginning approximately five weeks before the onset of preeclampsia. At the onset of clinical disease, the mean serum level in the women with preeclampsia was 4382 pg per milliliter, as compared with 1643 pg per milliliter in controls with fetuses of similar gestational age (P<0.001). The PlGF levels were significantly lower in the women who later had preeclampsia than in the controls beginning at 13 to 16 weeks of gestation (mean, 90 pg per milliliter vs. 142 pg per milliliter, P=0.01), with the greatest difference occurring during the weeks before the onset of preeclampsia, coincident with the increase in the sFlt-1 level. Alterations in the levels of sFlt-1 and free PlGF were greater in women with an earlier onset of preeclampsia and in women in whom preeclampsia was associated with a small-for-gestational-age infant. Increased levels of sFlt-1 and reduced levels of PlGF predict the subsequent development of preeclampsia. Copyright 2004 Massachusetts Medical Society
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              Use of proteomic patterns in serum to identify ovarian cancer.

              Emanuel Petricoin, Ali M. Ardekani, Ben Hitt … (2002)
              New technologies for the detection of early-stage ovarian cancer are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. We developed a bioinformatics tool and used it to identify proteomic patterns in serum that distinguish neoplastic from non-neoplastic disease within the ovary. Proteomic spectra were generated by mass spectroscopy (surface-enhanced laser desorption and ionisation). A preliminary "training" set of spectra derived from analysis of serum from 50 unaffected women and 50 patients with ovarian cancer were analysed by an iterative searching algorithm that identified a proteomic pattern that completely discriminated cancer from non-cancer. The discovered pattern was then used to classify an independent set of 116 masked serum samples: 50 from women with ovarian cancer, and 66 from unaffected women or those with non-malignant disorders. The algorithm identified a cluster pattern that, in the training set, completely segregated cancer from non-cancer. The discriminatory pattern correctly identified all 50 ovarian cancer cases in the masked set, including all 18 stage I cases. Of the 66 cases of non-malignant disease, 63 were recognised as not cancer. This result yielded a sensitivity of 100% (95% CI 93--100), specificity of 95% (87--99), and positive predictive value of 94% (84--99). These findings justify a prospective population-based assessment of proteomic pattern technology as a screening tool for all stages of ovarian cancer in high-risk and general populations.
              Article 0 comments Cited 335 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Matthias Mann:
                ORCID: http://orcid.org/0000-0003-1292-4799
                mmann@biochem.mpg.de
                Journal
                Journal ID (nlm-ta): Mol Syst Biol
                Journal ID (iso-abbrev): Mol. Syst. Biol
                Journal ID (doi): 10.1002/(ISSN)1744-4292
                Journal ID (publisher-id): MSB
                Journal ID (hwp): msb
                Title: Molecular Systems Biology
                Publisher: John Wiley and Sons Inc. (Hoboken )
                ISSN (Electronic): 1744-4292
                Publication date (Electronic): 26 September 2017
                Publication date Collection: September 2017
                Volume: 13
                Issue: 9 ( doiID: 10.1002/msb.v13.9 )
                Electronic Location Identifier: 942
                Affiliations
                [ 1 ] Department of Proteomics and Signal Transduction Max Planck Institute of Biochemistry Martinsried Germany
                [ 2 ] Faculty of Health Sciences NNF Center for Protein Research University of Copenhagen Copenhagen Denmark
                [ 3 ] Institute of Laboratory Medicine University Hospital LMU Munich Munich Germany
                Author notes
                [*] [* ]Corresponding author. Tel: +49 89 8578 2557; E‐mail: mmann@ 123456biochem.mpg.de
                Author information
                http://orcid.org/0000-0003-1292-4799
                Article
                Publisher ID: MSB156297
                DOI: 10.15252/msb.20156297
                PMC ID: 5615924
                PubMed ID: 28951502
                SO-VID: 3e1d695d-659e-4951-a0ad-07c2da28e1fc
                Copyright © © 2017 The Authors. Published under the terms of the CC BY 4.0 license
                License:

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 07 June 2017
                Date revision received : 04 August 2017
                Date accepted : 15 August 2017
                Page count
                Figures: 6, Tables: 0, Pages: 15, Words: 11941
                Funding
                Funded by: Max‐Planck‐Gesellschaft (MPG)
                Funded by: Novo Nordisk UK Research Foundation
                Award ID: NNF15CC0001
                Categories
                Subject: Review
                Subject: Reviews
                Custom metadata
                source-schema-version-number 2.0
                component-id msb156297
                cover-date September 2017
                details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_NLMPMC version:5.2.0 mode:remove_FC converted:27.09.2017

                ScienceOpen disciplines: Quantitative & Systems biology
                Keywords: biomarkers,diagnostic,mass spectrometry,plasma proteomics,systems medicine,molecular biology of disease,post-translational modifications, proteolysis & proteomics
                Data availability:
                ScienceOpen disciplines: Quantitative & Systems biology
                Keywords: biomarkers, diagnostic, mass spectrometry, plasma proteomics, systems medicine, molecular biology of disease, post-translational modifications, proteolysis & proteomics

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