Skip to main navigation menu Skip to main content Skip to site footer

Review article: Biomedical intelligence

Vol. 143 No. 2324 (2013)

Potentials and pitfalls of clinical peptidomics and metabolomics

  • Alexander Benedikt Leichtle
  • Jean-François Dufour
  • Georg Martin Fiedler
DOI
https://doi.org/10.4414/smw.2013.13801
Cite this as:
Swiss Med Wkly. 2013;143:w13801
Published
02.06.2013

Abstract

Clinical peptidomics and metabolomics are two emerging “-omics” technologies with the potential not only to detect disease-specific markers, but also to give insight into the disease dependency of degradation processes and metabolic pathway alterations. However, despite their rapid evolution and major investments, a clinical breakthrough, such as the approval of a major cancer biomarker, is still out of sight. What are the reasons for this failure? In this review we focus on three important factors: sensitivity, specificity and the avoidance of bias.

The way to clinical implementation of peptidomics and metabolomics is still hampered by many of the problems that had to be solved for genomics and proteomics in the past, as well as new ones that require the creation of new analytic, computational and interpretative techniques. The greatest challenge, however, will be the integration of information from different “-omics” subdisciplines into straightforward answers to clinical questions, for example, in the form of new, superior “meta-markers”.

References

  1. Master SR. Diagnostic proteomics: back to basics? Clin Chem. 2005;51(8):1333–4.
  2. Petricoin EF, Ardekani AM, Hitt BA, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359(9306):572–7.
  3. Ala-Korpela M, Kangas AJ, Soininen P. Quantitative high-throughput metabolomics: a new era in epidemiology and genetics. Genome Med. 2012;4(4):36.
  4. Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature. 2009;457(7231):910–4.
  5. Diamandis EP. Cancer biomarkers: can we turn recent failures into success? J Natl Cancer Inst. 2010;102(19):1462–7.
  6. Konforte D, Diamandis EP. Is early detection of cancer with circulating biomarkers feasible? Clin Chem. 2013;59(1):35–7.
  7. Diamandis EP. Letter to the Editor about Differential exoprotease activities confer tumor-specific serum peptidome. J Clin Invest. 2006;116(1).
  8. Sorace JM, Zhan M. A data review and re-assessment of ovarian cancer serum proteomic profiling. BMC Bioinformatics. 2003;4:24.
  9. Struys EA, Heijboer AC, van Moorselaar J, et al. Serum sarcosine is not a marker for prostate cancer. Ann Clin Biochem. 2010;47(Pt 3):282.
  10. Ziegler A, Koch A, Krockenberger K, et al. Personalized medicine using DNA biomarkers: a review. Hum Genet. 2012;131(10):1627–38.
  11. Villanueva J, Shaffer DR, Philip J, et al. Differential exoprotease activities confer tumor-specific serum peptidome patterns. J Clin Invest. 2006;116(1):271–84.
  12. Fernie AR, Trethewey RN, Krotzky AJ, et al. Metabolite profiling: from diagnostics to systems biology. Nat Rev Mol Cell Biol. 2004;5(9):763–9.
  13. Warburg O. Über den Stoffwechsel der Tumorzelle. J Mol Med. 1925;4(12):534–6. German.
  14. Zhang A, Sun H, Wang X. Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal Bioanal Chem. 2012;404(4):1239–45.
  15. Boulesteix AL, Sauerbrei W. Added predictive value of high-throughput molecular data to clinical data and its validation. Brief Bioinform 2011;12(3):215–29.
  16. Zhu CS, Pinsky PF, Cramer DW, et al. A framework for evaluating biomarkers for early detection: validation of biomarker panels for ovarian cancer. Cancer Prev Res. (Phila) 2011;4(3):375–83.
  17. Blair RH, Kliebenstein DJ, Churchill GA. What can causal networks tell us about metabolic pathways? PLoS Comput Biol. 2012;8(4):e1002458.
  18. Ransohoff DF. Bias as a threat to the validity of cancer molecular-marker research. Nat Rev Cancer. 2005;5(2):142–9.
  19. Conrad TOF, Leichtle A, Hagehülsmann A, et al. Beating the Noise: New Statistical Methods for Detecting Signals in MALDI-TOF Spectra Below Noise Level. In: R. Berthold M, Glen R, Fischer I, (eds). Computational Life Sciences II: Springer Berlin Heidelberg; 2006, 119–128.
  20. Diamandis EP. Oncopeptidomics: a useful approach for cancer diagnosis? Clin Chem. 2007;53(6):1004–6.
  21. Makawita S, Diamandis EP. The bottleneck in the cancer biomarker pipeline and protein quantification through mass spectrometry-based approaches: current strategies for candidate verification. Clin Chem. 2010;56(2):212–22.
  22. Hori SS, Gambhir SS. Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations. Sci Transl Med. 2011;3(109):109ra116.
  23. Becker S, Kortz L, Helmschrodt C, et al. LC-MS-based metabolomics in the clinical laboratory. J Chromatogr B Analyt Technol Biomed Life Sci. 2012;883–4:68–75.
  24. Fiedler GM, Leichtle AB, Kase J, et al. Serum peptidome profiling revealed platelet factor 4 as a potential discriminating Peptide associated with pancreatic cancer. Clin Cancer Res. 2009;15(11):3812–9.
  25. Pencina MJ. Caution is needed in the interpretation of added value of biomarkers analyzed in matched case control studies. Clinical Chemistry. 2012;8(58):1176–8.
  26. Pencina MJ, D’Agostino RB, Sr., D’Agostino RB, Jr., et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72; discussion 207–12.
  27. Pepe MS, Fan J, Seymour CW, et al. Biases introduced by choosing controls to match risk factors of cases in biomarker research. Clin Chem. 2012;58(8):1242–51.
  28. Leichtle AB, Nuoffer JM, Ceglarek U, et al. Serum amino acid profiles and their alterations in colorectal cancer. Metabolomics 2012;8(4):643–53.
  29. Vuckovic D. Current trends and challenges in sample preparation for global metabolomics using liquid chromatography-mass spectrometry. Anal Bioanal Chem. 2012;403(6):1523–48.
  30. Dong X, Tang H, Hess KR, et al. Glucose metabolism gene polymorphisms and clinical outcome in pancreatic cancer. Cancer. 2011;117(3):480–91.
  31. Tsoli M, Robertson G. Cancer cachexia: malignant inflammation, tumorkines, and metabolic mayhem. Trends Endocrinol Metab. 2012.
  32. Patterson AD, Maurhofer O, Beyoglu D, et al. Aberrant lipid metabolism in hepatocellular carcinoma revealed by plasma metabolomics and lipid profiling. Cancer Res. 2011;71(21):6590–600.
  33. Steyerberg EW, Harrell FE, Jr., Borsboom GJ, et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–81.
  34. Castaldi PJ, Dahabreh IJ, Ioannidis JP. An empirical assessment of validation practices for molecular classifiers. Brief Bioinform. 2011;12(3):189–202.
  35. Steyerberg EW, Pencina MJ, Lingsma HF, et al. Assessing the incremental value of diagnostic and prognostic markers: a review and illustration. Eur J Clin Invest. 2012;42(2):216–28.
  36. Demler OV, Pencina MJ, D’Agostino RB, Sr. Misuse of DeLong test to compare AUCs for nested models. Stat Med. 2012;31(23):2577–87.
  37. Christians U, Klawitter J, Hornberger A. How unbiased is non-targeted metabolomics and is targeted pathway screening the solution? Curr Pharm Biotechnol. 2011;12(7):1053–66.
  38. Ransohoff DF, Gourlay ML. Sources of bias in specimens for research about molecular markers for cancer. J Clin Oncol. 2010;28(4):698–704.
  39. Knottnerus JA, Muris JW. Assessment of the accuracy of diagnostic tests: the cross-sectional study. J Clin Epidemiol. 2003;56(11):1118–28.
  40. Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24(8):971–83.
  41. Thomas L, Peterson ED. The value of statistical analysis plans in observational research: defining high-quality research from the start. JAMA. 2012;308(8):773–4.
  42. Andre F, McShane LM, Michiels S, et al. Biomarker studies: a call for a comprehensive biomarker study registry. Nat Rev Clin Oncol. 2011;8(3):171–6.
  43. Ioannidis JP. The importance of potential studies that have not existed and registration of observational data sets. JAMA. 2012;308(6):575–6.
  44. Baggerly KA, Morris JS, Coombes KR. Reproducibility of SELDI-TOF protein patterns in serum: comparing datasets from different experiments. Bioinformatics. 2004;20(5):777–85.
  45. Xu Y, Shen Z, Wiper DW, et al. Lysophosphatidic acid as a potential biomarker for ovarian and other gynecologic cancers. JAMA. 1998;280(8):719–23.
  46. Baker DL, Morrison P, Miller B, et al. Plasma lysophosphatidic acid concentration and ovarian cancer. JAMA. 2002;287(23):3081–2.
  47. Jentzmik F, Stephan C, Lein M, et al. Sarcosine in prostate cancer tissue is not a differential metabolite for prostate cancer aggressiveness and biochemical progression. J Urol. 2011;185(2):706–11.
  48. Jentzmik F, Stephan C, Miller K, et al. Sarcosine in urine after digital rectal examination fails as a marker in prostate cancer detection and identification of aggressive tumours. Eur Urol. 2010;58(1):12–8; discussion 20–1.
  49. Baumann S, Ceglarek U, Fiedler GM, et al. Standardized approach to proteome profiling of human serum based on magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin Chem. 2005;51(6):973–80.
  50. Bruegel M, Planert M, Baumann S, et al. Standardized peptidome profiling of human cerebrospinal fluid by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. J Proteomics. 2009;72(4):608–15.
  51. Fiedler GM, Baumann S, Leichtle A, et al. Standardized peptidome profiling of human urine by magnetic bead separation and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Clin Chem. 2007;53(3):421–8.
  52. Brauer R, Leichtle A, Fiedler G, et al. Preanalytical standardization of amino acid and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry. Metabolomics. 2011;7(3):344–52.
  53. Fiedler GM, Ceglarek U, Leichtle A, et al. Standardized preprocessing of urine for proteome analysis. Methods Mol Biol. 2010;641:47–63.
  54. Zhou L, Lu Z, Yang A, et al. Comparative proteomic analysis of human pancreatic juice: methodological study. Proteomics. 2007;7(8):1345–55.
  55. Findeisen P, Costina V, Yepes D, et al. Functional protease profiling with reporter peptides in serum specimens of colorectal cancer patients: demonstration of its routine diagnostic applicability. J Exp Clin Cancer Res. 2012;31:56.
  56. Pelikan R, Bigbee WL, Malehorn D, et al. Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models. Bioinformatics. 2007;23(22):3065–72.
  57. Goodacre R, Broadhurst D, Smilde A, et al. Proposed minimum reporting standards for data analysis in metabolomics. Metabolomics. 2007;3(3):231–41.
  58. Taylor CF, Paton NW, Lilley KS, et al. The minimum information about a proteomics experiment (MIAPE). Nat Biotechnol. 2007;25(8):887–93.
  59. Ekblad L, Baldetorp B, Ferno M, et al. In-source decay causes artifacts in SELDI-TOF MS spectra. J Proteome Res. 2007;6(4):1609–14.
  60. Beasley-Green A, Bunk D, Rudnick P, et al. A proteomics performance standard to support measurement quality in proteomics. Proteomics. 2012;12(7):923–31.
  61. Petersen PH, Stockl D, Westgard JO, et al. Models for combining random and systematic errors. assumptions and consequences for different models. Clinical chemistry and laboratory medicine : CCLM / FESCC. 2001;39(7):589–95.
  62. Perkins NJ, Schisterman EF, Vexler A. Generalized ROC curve inference for a biomarker subject to a limit of detection and measurement error. Stat Med. 2009;28(13):1841–60.
  63. Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics. 2012;28(17):2270–1.
  64. Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
  65. Leichtle AB, Helmschrodt C, Ceglarek U, et al. Effects of a 2-y dietary weight-loss intervention on cholesterol metabolism in moderately obese men. Am J Clin Nutr. 2011;94(5):1189–95.
  66. Zhu D, Li Y, Li H. Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data. Bioinformatics. 2007;23(17):2298–305.
  67. Leichtle A, Ceglarek U, Weinert P, et al. Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma. Metabolomics. 2013;9:677–87.
  68. Field D, Sansone SA, Collis A, et al. Megascience. Omics data sharing. Science 2009;326(5950):234–6.
  69. Ioannidis JP, Khoury MJ. Improving validation practices in “omics” research. Science. 2011;334(6060):1230–2.
  70. Mohammed Y, Mostovenko E, Henneman AA, et al. Cloud parallel processing of tandem mass spectrometry based proteomics data. J Proteome Res. 2012;11(10):5101–8.
  71. Conrad T. New statistical algorithms for the analysis of mass spectrometry time-of-flight mass data with applications in clinical diagnostics. Free University of Berlin Institute of Mathematics 2008.
  72. Xia J, Mandal R, Sinelnikov IV, et al. MetaboAnalyst 2.0 – a comprehensive server for metabolomic data analysis. Nucleic Acids Res. 2012;40(Web Server issue):W127–33.
  73. Le Cao KA, Gonzalez I, Dejean S. integrOmics: an R package to unravel relationships between two omics datasets. Bioinformatics. 2009;25(21):2855–6.
  74. de Bruin JS, Deelder AM, Palmblad M. Scientific workflow management in proteomics. Mol Cell Proteomics. 2012;11(7):M111 010595.
  75. Junker J, Bielow C, Bertsch A, et al. TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J Proteome Res 2012;11(7):3914-20.
  76. Sung J, Wang Y, Chandrasekaran S, et al. Molecular signatures from omics data: from chaos to consensus. Biotechnol J. 2012;7(8):946–57.
  77. Quackenbush J. Microarray data normalization and transformation. Nat Genet. 2002;32(Suppl):496–501.
  78. Long Q, Zhang X, Zhao Y, et al. Modeling clinical outcome using multiple correlated functional biomarkers: A Bayesian approach. Stat Methods Med Res. 2012.
  79. Luo J, Xiong C. DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups. Journal of Statistical Software. 2012;51(3).
  80. Nakas CT, Alonzo TA, Yiannoutsos CT. Accuracy and cut-off point selection in three-class classification problems using a generalization of the Youden index. Stat Med. 2010;29(28):2946–55.
  81. Moons KG, de Groot JA, Linnet K, et al. Quantifying the added value of a diagnostic test or marker. Clin Chem. 2012;58(10):1408–17.
  82. Tunes da Silva G, Logan BR, Klein JP. Methods for equivalence and noninferiority testing. Biol Blood Marrow Transplant. 2009;15(1 Suppl):120–7.
  83. Jesneck JL, Mukherjee S, Yurkovetsky Z, et al. Do serum biomarkers really measure breast cancer? BMC Cancer. 2009;9:164.
  84. Yeung KY, Bumgarner RE, Raftery AE. Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics. 2005;21(10):2394–402.
  85. Broadhurst D, Kell D. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics. 2006;2(4):171–96.
  86. Krumsiek J, Suhre K, Illig T, et al. Bayesian independent component analysis recovers pathway signatures from blood metabolomics data. J Proteome Res. 2012;11(8):4120–31.

Most read articles by the same author(s)