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Protein biomarker discovery and validation: the long and uncertain path to clinical utility

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.

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Figure 1: Process flow for the development of novel protein biomarker candidates.

Katie Ris

Figure 2: Process flow for candidate protein biomarker verification by multiple reaction monitoring/stable isotope dilution liquid chromatography-tandem mass spectrometry (MRM/SID LC-MS/MS).

Katie Ris

References

  1. Ramaswamy, S. & Perou, C.M. DNA microarrays in breast cancer: the promise of personalised medicine. Lancet 361, 1576–1577 (2003).

    Article  PubMed  Google Scholar 

  2. 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).

    Article  CAS  PubMed  Google Scholar 

  3. Etzioni, R. et al. The case for early detection. Nat. Rev. Cancer 3, 243–252 (2003).

    Article  CAS  PubMed  Google Scholar 

  4. Anderson, N.L. & Anderson, N.G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 1, 845–867 (2002).

    Article  CAS  PubMed  Google Scholar 

  5. Gutman, S. & Kessler, L.G. The US Food and Drug Administration perspective on cancer biomarker development. Nat. Rev. Cancer 6, 565–571 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Anderson, N.L. The roles of multiple proteomic platforms in a pipeline for new diagnostics. Mol. Cell. Proteomics 4, 1441–1444 (2005).

    Article  CAS  PubMed  Google Scholar 

  7. Mor, G. et al. Serum protein markers for early detection of ovarian cancer. Proc. Natl. Acad. Sci. USA 102, 7677–7682 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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).

    Article  CAS  PubMed  Google Scholar 

  9. 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).

    Article  CAS  PubMed  Google Scholar 

  10. Kononen, J. et al. Tissue microarrays for high-throughput molecular profiling of tumor specimens. Nat. Med. 4, 844–847 (1998).

    Article  CAS  PubMed  Google Scholar 

  11. MacBeath, G. & Schreiber, S.L. Printing proteins as microarrays for high-throughput function determination. Science 289, 1760–1763 (2000).

    CAS  PubMed  Google Scholar 

  12. Kingsmore, S.F. Multiplexed protein measurement: technologies and applications of protein and antibody arrays. Nat. Rev. Drug Discov. 5, 310–320 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  CAS  PubMed  Google Scholar 

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. 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).

    Article  CAS  PubMed  Google Scholar 

  16. Wang, X. et al. Autoantibody signatures in prostate cancer. N. Engl. J. Med. 353, 1224–1235 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Ong, S.E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).

    Article  CAS  PubMed  Google Scholar 

  18. Sabatine, M.S. et al. Metabolomic identification of novel biomarkers of myocardial ischemia. Circulation 112, 3868–3875 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Burtis, C.A., Ashwood, E.R. & Bruns, D.E. (eds.). Tietz Textbook of Clinical Chemistry. (Elsevier Saunders Co., Philadelphia, 2005).

    Google Scholar 

  20. 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).

    CAS  PubMed  Google Scholar 

  21. 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).

    Article  CAS  PubMed  Google Scholar 

  22. 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).

    Article  PubMed  Google Scholar 

  23. Entertainment Industry Foundation, Women's Cancer Research Fund Breast Cancer Biomarker Discovery Project. http://www.eifoundation.org/national/wcrf/press/article2005-10-04.html.

  24. Jackson, E.L. et al. The differential effects of mutant p53 alleles on advanced murine lung cancer. Cancer Res. 65, 10280–10288 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Holliday, R. Neoplastic transformation: the contrasting stability of human and mouse cells. Cancer Surv. 28, 103–115 (1996).

    CAS  PubMed  Google Scholar 

  26. Balmain, A. & Harris, C.C. Carcinogenesis in mouse and human cells: parallels and paradoxes. Carcinogenesis 21, 371–377 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. 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).

    Article  CAS  PubMed  Google Scholar 

  28. 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).

    PubMed  Google Scholar 

  29. 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).

    Article  CAS  PubMed  Google Scholar 

  30. Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Petricoin, E.F. et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002).

    Article  CAS  PubMed  Google Scholar 

  32. 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).

    Article  CAS  PubMed  Google Scholar 

  33. 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).

    Article  CAS  PubMed  Google Scholar 

  34. 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).

    Article  CAS  PubMed  Google Scholar 

  35. 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).

    Article  CAS  PubMed  Google Scholar 

  36. 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).

    Article  CAS  PubMed  Google Scholar 

  37. 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).

    Article  CAS  PubMed  Google Scholar 

  38. Tirumalai, R.S. et al. Characterization of the low molecular weight human serum proteome. Mol. Cell. Proteomics 2, 1096–1103 (2003).

    Article  CAS  PubMed  Google Scholar 

  39. Wang, W. et al. Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Anal. Chem. 75, 4818–4826 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Gillette, M.A., Mani, D.R. & Carr, S.A. Place of pattern in proteomic biomarker discovery. J. Proteome Res. 4, 1143–1154 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. 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).

    Article  CAS  PubMed  Google Scholar 

  42. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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).

    Article  CAS  PubMed  Google Scholar 

  44. 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).

    Article  CAS  PubMed  Google Scholar 

  45. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Baldwin, M.A. Protein identification by mass spectrometry. Mol. Cell. Proteomics 3, 1–9 (2004).

    Article  CAS  PubMed  Google Scholar 

  47. Carr, S. et al. The need for guidelines in publication of peptide and protein identification data. Mol. Cell. Proteomics 3, 531–533 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. 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).

    Article  CAS  PubMed  Google Scholar 

  49. 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).

    Article  CAS  PubMed  Google Scholar 

  50. 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).

    Article  CAS  PubMed  Google Scholar 

  51. 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).

    Article  CAS  PubMed  Google Scholar 

  52. 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).

    Article  CAS  PubMed  Google Scholar 

  53. Johnson, R.S., Davis, M.T., Taylor, J.A. & Patterson, S.D. Informatics for protein identification by mass spectrometry. Methods 35, 223–236 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. 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).

    Article  CAS  PubMed  Google Scholar 

  55. Lee, M.S. & Kerns, E.H. LC/MS applications in drug development. Mass Spectrom. Rev. 18, 187–279 (1999).

    Article  CAS  PubMed  Google Scholar 

  56. Perchalski, R., Yost, R. & Wilder, B. Structural elucidation of drug metabolites by triple-quadrupole mass spectrometry. Anal. Chem. 54, 1466–1471 (1982).

    Article  CAS  Google Scholar 

  57. Tiller, P. et al. Drug quantitation on a benchtop liquid chromatograpy-tandem mass spectrometry system. J. Chromatogr. A. 771, 119–125 (1997b).

    Article  CAS  PubMed  Google Scholar 

  58. 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).

    Article  CAS  PubMed  Google Scholar 

  59. Yost, R.A. & Fetterolf, D.D. Tandem mass spectrometry (MS/MS) instrumentation. Mass Spectrom. Rev. 2, 1–45 (1983).

    Article  CAS  Google Scholar 

  60. Zhu, X. & Desiderio, D. Peptide quantification by tandem mass spectrometry, Mass Spectrom. Rev. 15, 213–240 (1996).

    Article  CAS  PubMed  Google Scholar 

  61. 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).

    Article  PubMed  CAS  Google Scholar 

  62. 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).

    Article  Google Scholar 

  63. 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).

    Article  CAS  PubMed  Google Scholar 

  64. Barr, J. et al. Isotope-dilution mass spectrometric quantification of specific proteins: model application with apolipoprotein A-1. Clin. Chem. 42, 1676–1682 (1996).

    Article  CAS  PubMed  Google Scholar 

  65. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 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).

    Article  CAS  PubMed  Google Scholar 

  67. 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).

    Article  CAS  PubMed  Google Scholar 

  68. 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).

    Article  CAS  PubMed  Google Scholar 

  69. Anderson, L. & Hunter, C.L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006).

    Article  CAS  PubMed  Google Scholar 

  70. 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).

    Article  CAS  PubMed  Google Scholar 

  71. 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).

    Article  CAS  PubMed  Google Scholar 

  72. Wild, D. The Immunoassay Handbook edn. 3 (Elsevier, Amsterdam; 2005).

    Google Scholar 

  73. Price, C. & Newman, D.J. Principles and Practice of Immunoassays, edn. 2. (Stockton Press, New York, 1996).

    Google Scholar 

  74. 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).

    Article  CAS  PubMed  Google Scholar 

  75. 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).

    CAS  PubMed  Google Scholar 

  76. Dati, F. & Brand, B. Standardization activities for harmonization of test results. Clin. Chim. Acta 297, 239–249 (2000).

    Article  CAS  PubMed  Google Scholar 

  77. 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).

    Article  CAS  PubMed  Google Scholar 

  78. Fraser, C.G. & Petersen, P.H. Analytical performance characteristics should be judged against objective quality specifications. Clin. Chem. 45, 321–323 (1999).

    Article  CAS  PubMed  Google Scholar 

  79. 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).

    CAS  PubMed  Google Scholar 

  80. 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).

    Google Scholar 

  81. Linnet, K. Evaluation of regression procedures for methods comparison studies. Clin. Chem. 39, 424–432 (1993).

    Article  CAS  PubMed  Google Scholar 

  82. Bland, J.M. & Altman, D.G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310 (1986).

    Article  CAS  PubMed  Google Scholar 

  83. NCCLS. Interference testing in clinical chemistry; Approved guideline. NCCLS Document EP7-A (NCCLS, Wayne, PA, 2002).

  84. Boscato, L.M. & Stuart, M.C. Heterophilic antibodies: a problem for all immuno-assays. Clin. Chem. 34, 27–33 (1988).

    Article  CAS  PubMed  Google Scholar 

  85. Kricka, L.J. Human anti-animal antibody interferences in immunological assays. Clin. Chem. 45, 942–956 (1999).

    CAS  PubMed  Google Scholar 

  86. International Standards Organization, I.O.F.S. Statistics-vocabulary and Symbols-Part 1: Probability and General Statistical Terms (3534-1) (ISO, Geneva; 1993).

  87. 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).

    CAS  PubMed  Google Scholar 

  88. NCCLS 1–42. Evaluation of Precision Performance of Clinical Chemistry Devices; Approved Guideline. NCCLS Document EP6-A (NCCLS, Wayne, PA; 1999).

  89. NCCLS. Evaluation of the linearity of quantitative measurement procedures: a statistical approach; approved guideline. NCCLS Document EP6-A (NCCLS. Wayne, PA; 2003).

  90. Currie, L. Nomenclature in evaluation of analytical methods including detection and quantification capabilities (IUPAC Recommendations 1995). Pure Appl. Chem. 67, 1699–1723 (1995).

    Article  CAS  Google Scholar 

  91. International Standards Organization, I.O.F.S. Capability of Detection-Part 2: Methodology in the Linear Calibration Case (11843-2) (ISO, Geneva; 2000).

  92. 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).

    Google Scholar 

  93. 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).

    CAS  PubMed  Google Scholar 

  94. 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).

    CAS  PubMed  Google Scholar 

  95. Harris, E. & Boyd, J.C. Statistical Bases of Reference Values in Laboratory Medicine (Marcel Dekker, New York, 1995).

    Book  Google Scholar 

  96. Harris, E.K. & Boyd, J.C. On dividing reference data into subgroups to produce separate reference ranges. Clin. Chem. 36, 265–270 (1990).

    Article  CAS  PubMed  Google Scholar 

  97. 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).

    CAS  PubMed  Google Scholar 

  98. 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).

    Article  PubMed  Google Scholar 

  99. 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).

  100. Gudewill, S. et al. Nocturnal plasma levels of cytokines in healthy men. Eur. Arch. Psychiatry Clin. Neurosci. 242, 53–56 (1992).

    Article  CAS  PubMed  Google Scholar 

  101. 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).

    Article  CAS  PubMed  Google Scholar 

  102. 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).

    Article  CAS  PubMed  Google Scholar 

  103. Cloey, T. et al. Reevaluation of serum-plasma differences in total cholesterol concentration. J. Am. Med. Assoc. 263, 2788–2789 (1990).

    Article  CAS  Google Scholar 

  104. Apple, F.S. et al. Future biomarkers for detection of ischemia and risk stratification in acute coronary syndrome. Clin. Chem. 51, 810–824 (2005).

    Article  CAS  PubMed  Google Scholar 

  105. 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).

    Article  PubMed  Google Scholar 

  106. 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).

    CAS  PubMed  Google Scholar 

  107. 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).

    Article  CAS  PubMed  Google Scholar 

  108. Bossuyt, P.M. et al. The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration. Clin. Chem. 49, 7–18 (2003).

    Article  CAS  PubMed  Google Scholar 

  109. Albert, A. On the use and computation of likelihood ratios in clinical chemistry. Clin. Chem. 28, 1113–1119 (1982).

    Article  CAS  PubMed  Google Scholar 

  110. Zweig, M.H. & Campbell, G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin. Chem. 39, 561–577 (1993).

    Article  CAS  PubMed  Google Scholar 

  111. Altman, D.G. Practical Statistics for Medical Research (Chapman & Hall, London, UK, 1991).

    Google Scholar 

  112. 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).

    Article  CAS  PubMed  Google Scholar 

  113. 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).

    Article  CAS  PubMed  Google Scholar 

  114. Sackett, D.L. & Haynes, R.B. The architecture of diagnostic research. Br. Med. J. 324, 539–541 (2002).

    Article  CAS  Google Scholar 

  115. Phillips, K.A., Bebber, S.V. & Issa, A.M. Diagnostics and biomarker development: priming the pipeline. Nat. Rev. Drug Disc. 5, 463–469 (2006).

    Article  CAS  Google Scholar 

  116. 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

  117. 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

  118. 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.

  119. Dati, F. The new European directive on in vitro diagnostics. Clin. Chem. Lab. Med. 41, 1289–1298 (2003).

    Article  CAS  PubMed  Google Scholar 

  120. Greenberg, R. Medical device amendments of 1976. Am. J. Hosp. Pharm. 33, 1308–1311 (1976).

    CAS  PubMed  Google Scholar 

  121. 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

Download references

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.

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Correspondence to Steven A Carr.

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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

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