Abstract
Better biomarkers are urgently needed to improve diagnosis, guide molecularly targeted therapy and monitor activity and therapeutic response across a wide spectrum of disease. Proteomics methods based on mass spectrometry hold special promise for the discovery of novel biomarkers that might form the foundation for new clinical blood tests, but to date their contribution to the diagnostic armamentarium has been disappointing. This is due in part to the lack of a coherent pipeline connecting marker discovery with well-established methods for validation. Advances in methods and technology now enable construction of a comprehensive biomarker pipeline from six essential process components: candidate discovery, qualification, verification, research assay optimization, biomarker validation and commercialization. Better understanding of the overall process of biomarker discovery and validation and of the challenges and strategies inherent in each phase should improve experimental study design, in turn increasing the efficiency of biomarker development and facilitating the delivery and deployment of novel clinical tests.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Ramaswamy, S. & Perou, C.M. DNA microarrays in breast cancer: the promise of personalised medicine. Lancet 361, 1576–1577 (2003).
Fernie, A.R., Trethewey, R.N., Krotzky, A.J. & Willmitzer, L. Metabolite profiling: from diagnostics to systems biology. Nat. Rev. Mol. Cell Biol. 5, 763–769 (2004).
Etzioni, R. et al. The case for early detection. Nat. Rev. Cancer 3, 243–252 (2003).
Anderson, N.L. & Anderson, N.G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).
Gutman, S. & Kessler, L.G. The US Food and Drug Administration perspective on cancer biomarker development. Nat. Rev. Cancer 6, 565–571 (2006).
Anderson, N.L. The roles of multiple proteomic platforms in a pipeline for new diagnostics. Mol. Cell. Proteomics 4, 1441–1444 (2005).
Mor, G. et al. Serum protein markers for early detection of ovarian cancer. Proc. Natl. Acad. Sci. USA 102, 7677–7682 (2005).
Sabatine, M.S. et al. Multimarker approach to risk stratification in non-ST elevation acute coronary syndromes: simultaneous assessment of troponin I, C-reactive protein, and B-type natriuretic peptide. Circulation 105, 1760–1763 (2002).
de Wildt, R.M., Mundy, C.R., Gorick, B.D. & Tomlinson, I.M. Antibody arrays for high-throughput screening of antibody-antigen interactions. Nat. Biotechnol. 18, 989–994 (2000).
Kononen, J. et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4, 844–847 (1998).
MacBeath, G. & Schreiber, S.L. Printing proteins as microarrays for high-throughput function determination. Science 289, 1760–1763 (2000).
Kingsmore, S.F. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat. Rev. Drug Discov. 5, 310–320 (2006).
Gulmann, C., Sheehan, K.M., Kay, E.W., Liotta, L.A. & Petricoin, E.F., III. Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer. J. Pathol. 208, 595–606 (2006).
Nishizuka, S. et al. Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays. Proc. Natl. Acad. Sci. USA 100, 14229–14234 (2003).
Paweletz, C.P. et al. Reverse phase protein microarrays which capture disease progression show activation of pro-survival pathways at the cancer invasion front. Oncogene 20, 1981–1989 (2001).
Wang, X. et al. Autoantibody signatures in prostate cancer. N. Engl. J. Med. 353, 1224–1235 (2005).
Ong, S.E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).
Sabatine, M.S. et al. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875 (2005).
Burtis, C.A., Ashwood, E.R. & Bruns, D.E. (eds.). Tietz Textbook of Clinical Chemistry. (Elsevier Saunders Co., Philadelphia, 2005).
Rosty, C. et al. Identification of hepatocarcinoma-intestine-pancreas/pancreatitis-associated protein I as a biomarker for pancreatic ductal adenocarcinoma by protein biochip technology. Cancer Res. 62, 1868–1875 (2002).
Celis, J.E. et al. Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery. Mol. Cell. Proteomics 3, 327–344 (2004).
Sedlaczek, P. et al. Comparative analysis of CA125, tissue polypeptide specific antigen, and soluble interleukin-2 receptor alpha levels in sera, cyst, and ascitic fluids from patients with ovarian carcinoma. Cancer 95, 1886–1893 (2002).
Entertainment Industry Foundation, Women's Cancer Research Fund Breast Cancer Biomarker Discovery Project. http://www.eifoundation.org/national/wcrf/press/article2005-10-04.html.
Jackson, E.L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).
Holliday, R. Neoplastic transformation: the contrasting stability of human and mouse cells. Cancer Surv. 28, 103–115 (1996).
Balmain, A. & Harris, C.C. Carcinogenesis in mouse and human cells: parallels and paradoxes. Carcinogenesis 21, 371–377 (2000).
Kelland, L.R. Of mice and men: values and liabilities of the athymic nude mouse model in anticancer drug development. Eur. J. Cancer 40, 827–836 (2004).
Voskoglou-Nomikos, T., Pater, J.L. & Seymour, L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin. Cancer Res. 9, 4227–4239 (2003).
Resor, L., Bowen, T.J. & Wynshaw-Boris, A. Unraveling human cancer in the mouse: recent refinements to modeling and analysis. Hum. Mol. Genet. 10, 669–675 (2001).
Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
Petricoin, E.F. et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002).
Petricoin, E.F., Zoon, K.C., Kohn, E.C., Barrett, J.C. & Liotta, L.A. Clinical proteomics: translating benchside promise into bedside reality. Nat. Rev. Drug Discov. 1, 683–695 (2002).
Villanueva, J. et al. Serum peptide profiling by magnetic particle-assisted, automated sample processing and MALDI-TOF mass spectrometry. Anal. Chem. 76, 1560–1570 (2004).
VerBerkmoes, N.C. et al. Integrating “top-down” and “bottom-up” mass spectrometric approaches for proteomic analysis of Shewanella oneidensis. J. Proteome Res. 1, 239–252 (2002).
Adkins, J.N. et al. Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol. Cell. Proteomics 1, 947–955 (2002).
Shen, Y. et al. Ultra-high-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal. Chem. 76, 1134–1144 (2004).
Shen, Y. et al. High-efficiency on-line solid-phase extraction coupling to 15–150-microm-i.d. column liquid chromatography for proteomic analysis. Anal. Chem. 75, 3596–3605 (2003).
Tirumalai, R.S. et al. Characterization of the low molecular weight human serum proteome. Mol. Cell. Proteomics 2, 1096–1103 (2003).
Wang, W. et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal. Chem. 75, 4818–4826 (2003).
Gillette, M.A., Mani, D.R. & Carr, S.A. Place of pattern in proteomic biomarker discovery. J. Proteome Res. 4, 1143–1154 (2005).
Leptos, K.C., Sarracino, D.A., Jaffe, J.D., Krastins, B. & Church, G.M. MapQuant: open-source software for large-scale protein quantification. Proteomics 6, 1770–1782 (2006).
Zimmer, J.S., Monroe, M.E., Qian, W.J. & Smith, R.D. Advances in proteomics data analysis and display using an accurate mass and time tag approach. Mass Spectrom. Rev. 25, 450–482 (2006).
Clauser, K.R., Baker, P.R. & Burlingame, A.L. Role of accurate mass measurement (+/− 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Anal. Chem. 71, 2871–2882 (1999).
Spengler, B. De novo sequencing, peptide composition analysis, and composition-based sequencing: a new strategy employing accurate mass determination by fourier transform ion cyclotron resonance mass spectrometry. J. Am. Soc. Mass Spectrom. 15, 703–714 (2004).
Olsen, J.V. & Mann, M. Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation. Proc. Natl. Acad. Sci. USA 101, 13417–13422 (2004).
Baldwin, M.A. Protein identification by mass spectrometry. Mol. Cell. Proteomics 3, 1–9 (2004).
Carr, S. et al. The need for guidelines in publication of peptide and protein identification data. Mol. Cell. Proteomics 3, 531–533 (2004).
Nesvizhskii, A. & Aebersold, R. Analysis, statistical validation and dissemination of large-scale proteomics datasets generated by tandem MS. Drug Discov. Today 9, 173–181 (2004).
Sadygov, R., Liu, H. & Yates, J.R. Novel statistical models for protein validation using tandem mass spectral data and protein amino acid sequence databases, analytical chemistry. Anal. Chem. 76, 1664–1671 (2004).
States, D.J. et al. Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 24, 333–338 (2006).
Omenn, G.S. et al. Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226–3245 (2005).
Gatlin, C.L., Kleemann, G.R., Hays, L.G., Link, A.J. & Yates, J.R., III. Protein identification at the low femtomole level from silver-stained gels using a new fritless electrospray interface for liquid chromatography-microspray and nanospray mass spectrometry. Anal. Biochem. 263, 93–101 (1998).
Johnson, R.S., Davis, M.T., Taylor, J.A. & Patterson, S.D. Informatics for protein identification by mass spectrometry. Methods 35, 223–236 (2005).
Sadygov, R.G., Cociorva, D. & Yates, J.R., III. Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat. Methods 1, 195–202 (2004).
Lee, M.S. & Kerns, E.H. LC/MS applications in drug development. Mass Spectrom. Rev. 18, 187–279 (1999).
Perchalski, R., Yost, R. & Wilder, B. Structural elucidation of drug metabolites by triple-quadrupole mass spectrometry. Anal. Chem. 54, 1466–1471 (1982).
Tiller, P. et al. Drug quantitation on a benchtop liquid chromatograpy-tandem mass spectrometry system. J. Chromatogr. A. 771, 119–125 (1997b).
Wieboldt, R., Campbell, D. & Henion, J. Quantitative liquid chromatographic-tandem mass spectrometric determination of orlistat in plasma with a quadrupole ion trap. J. Chromatogr. B Biomed. Sci. Appl. 708, 121–129 (1998).
Yost, R.A. & Fetterolf, D.D. Tandem mass spectrometry (MS/MS) instrumentation. Mass Spectrom. Rev. 2, 1–45 (1983).
Zhu, X. & Desiderio, D. Peptide quantification by tandem mass spectrometry, Mass Spectrom. Rev. 15, 213–240 (1996).
Roschinger, W., Olgemoller, B., Fingerhut, R., Liebl, B. & Roscher, A. Advances in analytical mass spectrometry to improve screening for inherited metabolic diseases. Eur. J. Pediatr. 162, S67–S76 (2003).
Desiderio, D. & Kai, M. Preparation of stable-isotope incorporated peptide internal standards for field desorption mass spectrometry quantification of peptides in biologic tissue. Biomed. Mass Spectrom. 1983, 471–479 (1983).
Desiderio, D., Kai, M., Tanzer, F., Trimble, J. & Wakelyn, C. Measurement of enkephalin peptides in canine brain regions, teeth, and CSF with HPLC and mass spectrometry. J. Chromatogr. 297, 245–260 (1984).
Barr, J. et al. Isotope-dilution mass spectrometric quantification of specific proteins: model application with apolipoprotein A-1. Clin. Chem. 42, 1676–1682 (1996).
Gerber, S.A., Rush, J., Stemman, O., Kirschner, M.W. & Gygi, S.P. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl. Acad. Sci. USA 100, 6940–6945 (2003).
Kuhn, E. et al. Quantification of C-reactive protein in the serum of patients with rheumatoid arthritis using multiple reaction monitoring mass spectrometry and 13C-labeled peptide standards. Proteomics 4, 1175–1186 (2004).
Wu, S. et al. Targeted proteomics of low-level proteins in human plasma by LC/MSn: using human growth hormone as a model system. J. Proteome Res. 1, 459–465 (2002).
Barnidge, D., Goodmanson, M., Klee, G. & Muddiman, D. Absolute quantification of the model biomarker prostate-specific antigen in serum by LC-MS/MS using protein cleavage and isotope dilution MS. J. Proteome Res. 3, 644–652 (2004).
Anderson, L. & Hunter, C.L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006).
Anderson, N.L. et al. Mass spectrometric quantitation of peptides and proteins using stable isotope standards and capture by anti-peptide antibodies (SISCAPA). J. Proteome Res. 3, 235–244 (2004).
Vitzthum, F., Behrens, F., Anderson, N.L. & Shaw, J.H. Proteomics: from basic research to diagnostic application. A review of requirements & needs. J. Proteome Res. 4, 1086–1097 (2005).
Wild, D. The Immunoassay Handbook edn. 3 (Elsevier, Amsterdam; 2005).
Price, C. & Newman, D.J. Principles and Practice of Immunoassays, edn. 2. (Stockton Press, New York, 1996).
Johnson, A.M., Ledue, T.B. & Collins, M.F. Commutability of the CRM 470 C-reactive protein value in the Dade Behring N High Sensitivity CRP assay. Clin. Chem. Lab. Med. 41, 177–182 (2003).
Blirup-Jensen, S., Johnson, A.M. & Larsen, M. Protein standardization IV: value transfer procedure for the assignment of serum protein values from a reference preparation to a target material. Clin. Chem. Lab. Med. 39, 1110–1122 (2001).
Dati, F. & Brand, B. Standardization activities for harmonization of test results. Clin. Chim. Acta 297, 239–249 (2000).
Liu, M.Y. et al. Multiplexed analysis of biomarkers related to obesity and the metabolic syndrome in human plasma, using the Luminex-100 system. Clin. Chem. 51, 1102–1109 (2005).
Fraser, C.G. & Petersen, P.H. Analytical performance characteristics should be judged against objective quality specifications. Clin. Chem. 45, 321–323 (1999).
Dybkaer, R. Vocabulary for use in measurement procedures and description of reference materials in laboratory medicine. Eur. J. Clin. Chem. Clin. Biochem. 35, 141–173 (1997).
International Standards Organization, I.O.F.S. Accuracy (Trueness and Precision) of Measurement Methods and Results (ISO 5725)-Part 1: General Principles and Definitions (ISO, Geneva, 1994).
Linnet, K. Evaluation of regression procedures for methods comparison studies. Clin. Chem. 39, 424–432 (1993).
Bland, J.M. & Altman, D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310 (1986).
NCCLS. Interference testing in clinical chemistry; Approved guideline. NCCLS Document EP7-A (NCCLS, Wayne, PA, 2002).
Boscato, L.M. & Stuart, M.C. Heterophilic antibodies: a problem for all immuno-assays. Clin. Chem. 34, 27–33 (1988).
Kricka, L.J. Human anti-animal antibody interferences in immunological assays. Clin. Chem. 45, 942–956 (1999).
International Standards Organization, I.O.F.S. Statistics-vocabulary and Symbols-Part 1: Probability and General Statistical Terms (3534-1) (ISO, Geneva; 1993).
Miller, W.G. & Kaufman, H.W. College of American Pathologists Conference XXIII on matrix effects and accuracy assessment in clinical chemistry: introduction. Arch. Pathol. Lab. Med. 117, 343–344 (1993).
NCCLS 1–42. Evaluation of Precision Performance of Clinical Chemistry Devices; Approved Guideline. NCCLS Document EP6-A (NCCLS, Wayne, PA; 1999).
NCCLS. Evaluation of the linearity of quantitative measurement procedures: a statistical approach; approved guideline. NCCLS Document EP6-A (NCCLS. Wayne, PA; 2003).
Currie, L. Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations 1995). Pure Appl. Chem. 67, 1699–1723 (1995).
International Standards Organization, I.O.F.S. Capability of Detection-Part 2: Methodology in the Linear Calibration Case (11843-2) (ISO, Geneva; 2000).
Linnet, K.B.J. in Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, edn. 4 (ed. Burtis, C.A., Ashwood, E.R.) 352–407 (Elsevier Saunders, Philadelphia, 2005).
Solberg, H.E. International Federation of Clinical Chemistry (IFCC), Scientific Committee, Clinical Section, Expert Panel on Theory of Reference Values, and International Committee for Standardization in Haematology (ICSH), Standing Committee on Reference Values. Approved recommendation (1986) on the theory of reference values. Part 1. The concept of reference values. J. Clin. Chem. Clin. Biochem. 25, 337–342 (1987).
Solberg, H.E. & PetitClerc, C. International Federation of Clinical Chemistry (IFCC), Scientific Committee, Clinical Section, Expert Panel on Theory of Reference Values. Approved recommendation (1988) on the theory of reference values. Part 3. Preparation of individuals and collection of specimens for the production of reference values. J. Clin. Chem. Clin. Biochem. 26, 593–598 (1988).
Harris, E. & Boyd, J.C. Statistical Bases of Reference Values in Laboratory Medicine (Marcel Dekker, New York, 1995).
Harris, E.K. & Boyd, J.C. On dividing reference data into subgroups to produce separate reference ranges. Clin. Chem. 36, 265–270 (1990).
Solberg, H.E. The theory of reference values Part 5. Statistical treatment of collected reference values. Determination of reference limits. J. Clin. Chem. Clin. Biochem. 21, 749–760 (1983).
Apple, F.S., Wu, A.H. & Jaffe, A.S. European Society of Cardiology and American College of Cardiology guidelines for redefinition of myocardial infarction: how to use existing assays clinically and for clinical trials. Am. Heart J. 144, 981–986 (2002).
Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). J. Am. Med. Assoc. 285, 2486–2497 (2001).
Gudewill, S. et al. Nocturnal plasma levels of cytokines in healthy men. Eur. Arch. Psychiatry Clin. Neurosci. 242, 53–56 (1992).
Meier-Ewert, H.K. et al. Absence of diurnal variation of C-reactive protein concentrations in healthy human subjects. Clin. Chem. 47, 426–430 (2001).
Tan, M.H., Wilmshurst, E.G., Gleason, R.E. & Soeldner, J.S. Effect of posture on serum lipids. N. Engl. J. Med. 289, 416–419 (1973).
Cloey, T. et al. Reevaluation of serum-plasma differences in total cholesterol concentration. J. Am. Med. Assoc. 263, 2788–2789 (1990).
Apple, F.S. et al. Future biomarkers for detection of ischemia and risk stratification in acute coronary syndrome. Clin. Chem. 51, 810–824 (2005).
Ledue, T.B. & Rifai, N. Preanalytic and analytic sources of variations in C-reactive protein measurement: implications for cardiovascular disease risk assessment. Clin. Chem. 49, 1258–1271 (2003).
Cooper, G.R., Smith, S.J., Myers, G.L., Sampson, E.J. & Magid, E. Biological variability in the concentration of serum lipids: sources, meta-analysis, estimation, and minimization by relative range measurements. J. Int. Fed. Clin. Chem. 7, 23–28 (1995).
Rifai, N. & Ridker, P.M. Population distributions of C-reactive protein in apparently healthy men and women in the United States: implication for clinical interpretation. Clin. Chem. 49, 666–669 (2003).
Bossuyt, P.M. et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin. Chem. 49, 7–18 (2003).
Albert, A. On the use and computation of likelihood ratios in clinical chemistry. Clin. Chem. 28, 1113–1119 (1982).
Zweig, M.H. & Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993).
Altman, D.G. Practical Statistics for Medical Research (Chapman & Hall, London, UK, 1991).
Obuchowski, N.A., Lieber, M.L. & Wians, F.H., Jr. ROC curves in clinical chemistry: uses, misuses, and possible solutions. Clin. Chem. 50, 1118–1125 (2004).
van der Helm, H.J. & Hische, E.A. Application of Bayes's theorem to results of quantitative clinical chemical determinations. Clin. Chem. 25, 985–988 (1979).
Sackett, D.L. & Haynes, R.B. The architecture of diagnostic research. Br. Med. J. 324, 539–541 (2002).
Phillips, K.A., Bebber, S.V. & Issa, A.M. Diagnostics and biomarker development: priming the pipeline. Nat. Rev. Drug Disc. 5, 463–469 (2006).
Code Federal Regulations, vol. 21 CFR807 http://frwebgate.access.gpo.gov/cgi-bin/get-cfr.cgi?YEAR=current&TITLE=21&PART=807&SECTION=81&SUBPART=&TYPE=TEXT
Code Federal Regulations, vol. 21 CFR814 http://frwebgate.access.gpo.gov/cgi-bin/get-cfr.cgi?YEAR=current&TITLE=21&PART=814&SECTION=1&SUBPART=&TYPE=TEXT
Ministerial ordinance on standards for manufacturing control and quality control for medical devices and in-vitro diagnostic reagents. (Pharmaceuticals and Medical Devices Agency, Tokyo) http://www.pmda.go.jp/pal-e.html.
Dati, F. The new European directive on in vitro diagnostics. Clin. Chem. Lab. Med. 41, 1289–1298 (2003).
Greenberg, R. Medical device amendments of 1976. Am. J. Hosp. Pharm. 33, 1308–1311 (1976).
Code Federal Register, vol. 21 USC 1998. Regulations and interpretive guidelines for laboratories and laboratory services. Centers for Medicare and Medicaid Services. http://www.cms.hhs.gov/CLIA/downloads/apcindex.pdf
Acknowledgements
We want to thank Leigh Anderson for sharing his insights on the economics and healthcare impact of in vitro diagnostics, Francesco Dati and Neil Greenberg for their input and advice on validation and clinical assay development, and Todd Golub and Eric Lander for their continuing support and encouragement to S.A.C. and M.A.G. S.A.C also thanks the many members of his laboratory who have contributed to developing and applying the conceptual framework for protein biomarker discovery and verification, including Jake Jaffe, Terri Addona, Karl Clauser, Shao-En Ong, Betty Chang, Eric Kuhn, Veronica Saenz-Vash, Hasmik Keshishian and Mike Burgess. This work was supported in part by grants to S.A.C. from the Women's Cancer Research Fund of the Entertainment Industry Foundation, the Bill and Melinda Gates Foundation and the National Institutes of Health, National Heart, Lung, and Blood Institute, and to M.A.G. from the National Institutes of Health, National Cancer Institute.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Rights and permissions
About this article
Cite this article
Rifai, N., Gillette, M. & Carr, S. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24, 971–983 (2006). https://doi.org/10.1038/nbt1235
Published:
Issue Date:
DOI: https://doi.org/10.1038/nbt1235
This article is cited by
-
Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers
Clinical Proteomics (2024)
-
Intelligent salivary biosensors for periodontitis: in vitro simulation of oral oxidative stress conditions
Medical & Biological Engineering & Computing (2024)
-
Prognostic utility of serum NT-proBNP (fragments 1-76aa and 13-71aa) and galectin-3 in predicting death and re-hospitalisation due to cardiovascular events in patients with heart failure
Heart and Vessels (2024)
-
Orthogonal proteomics methods warrant the development of Duchenne muscular dystrophy biomarkers
Clinical Proteomics (2023)
-
Quantifiable peptide library bridges the gap for proteomics based biomarker discovery and validation on breast cancer
Scientific Reports (2023)