Exploring liquid chromatography-mass spectrometry fingerprints of urine samples from patients with prostate or urinary bladder cancer

Chemometrics and Intelligent Laboratory Systems

Volumn 108, Issue 1, 2011, Pages 33-48

  Danielsson, Rolf ^a   Allard, Erik ^a   Sjöberg, Per Johan Ragnar ^a   Bergquist, Jonas ^a  

^a UPPSALA UNIVERSITY   (Sweden)

Author keywords

LC MS; Metabolic fingerprinting; Urine profile

Indexed keywords

BIOLOGICAL MARKER;

ARTICLE; BLADDER CANCER; CLINICAL ARTICLE; COMPUTER PROGRAM; CONTROLLED STUDY; DATA ANALYSIS; DNA FINGERPRINTING; HUMAN; IMAGE ANALYSIS; INFORMATION PROCESSING; LIQUID CHROMATOGRAPHY; MALE; MASS SPECTROMETRY; MATHEMATICAL ANALYSIS; METABOLOMICS; PRIORITY JOURNAL; PROSTATE CANCER; SCORING SYSTEM; SIGNAL NOISE RATIO; URINALYSIS; VALIDATION PROCESS;

EID: 79960141659     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2011.03.008     Document Type: Article

References (74)

1. Ellis D.I., Dunn W.B., Griffin J.L., Allwood J.W., Goodacre R. Metabolic fingerprinting as a diagnostic tool. Pharmacogenomics 2007, 8(9):1244-1266.
2. Dettmer K., Aronov P.A., Hammock B.D. Mass spectrometry-based metabolomics. Mass Spectrom. Rev. 2007, 26:51-78.
3. Lu X., Zhao X., Bai C., Zhao C., Lu G. G Xu, LC-MS-based metabonomics analysis, J. Chromatogr., B: Anal. Technol. Biomed. Life Sci. 2008, 866:64-76.
4. Roux A., Lison D., Juno C., Heilier J.-F. Applications of liquid chromatography coupled to mass spectrometry-based metabolomics in clinical chemistry and toxicology: a review. Clin. Biochem. 2011, 44:119-135.
5. Pasikanti K.K., Ho P.C., Chan E.C.Y. Gas chromatography/mass spectrometry in metabolic profiling of biological fluids. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2008, 871:202-211.
6. García-Péreza I., Vallejoa M., García A., Legido-Quigleyb C., Barbasa C. Metabolic fingerprinting with capillary electrophoresis. J. Chromatogr. A 2008, 1204:130-139.
7. Ramautar R., Somsen G.W., de Jong G.J. CE-MS in metabolomics. Electrophoresis 2009, 30:276-291.
8. Monton M.R.N., Soga T. Metabolome analysis by capillary electrophoresis-mass spectrometry. J. Chromatogr. A 2007, 1168:237-246.
9. Listgarten J., Emili A. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 2005, 4(4):419-434.
10. Katajamaa M., Orešič M. Data processing for mass spectrometry-based metabonomics. J. Chromatogr. A 2007, 1158:318-328.
11. America A.H.P., Cordewener J.H.G. Comparative LC-MS: a landscape of peaks and valleys. Proteomics 2008, 8:731-749.
12. Dakna M., He Z., Yu W.C., Mischak H., Kolch W. Technical, bioinformatical and statistical aspects of liquid chromatography-mass spectrometry (LC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) based clinical proteomics: a critical assessment. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2009, 877:1250-1258.
13. Want E., Masson P. Processing and analysis of GC/LC-MS-based metabolomics data. Metabolic Profiling: Methods and Protocols (Methods in Molecular Biology) 2011, 277-298. Humana Press. T.O. Metz (Ed.).
14. Danielsson R., Bäckström D., Ullsten S. Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment. Chemometr. Intell. Lab. Syst. 2006, 84:33-39.
15. Ullsten S., Danielsson R., Bäckström D., Sjöberg P., Bergquist J. Urine profiling using capillary electrophoresis-mass spectrometry and multivariate data analysis. J. Chromatogr. A 2006, 1117:87-93.
16. Bäckström D., Moberg M., Sjöberg P.J.R., Bergquist J., Danielsson R. Multivariate comparison between peptide mass fingerprints obtained by liquid chromatography-electrospray ionization-mass spectrometry with different trypsin digestion procedures. J. Chromatogr. A 2007, 1171:69-79.
17. Allard E., Bäckström D., Danielsson R., Sjöberg P.J.R., Bergquist J. Comparing capillary electrophoresis-mass spectrometry fingerprints of urine samples obtained after intake of coffee, tea, or water. Anal. Chem. 2008, 80:8946-8955.
18. Smith C.A., Want E.J., O'Maille G., Abagyan R., Siuzdsak G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching and identification. Anal. Chem. 2006, 78:779-787.
19. Pluskal T., Castillo S., Villar-Briones A., Orešič M. MZmine 2: modular framework for processing, visualizing and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 2010, 11:395.
20. Stolt R., Torgrip R.J.O., Lindberg J., Csenki L., Kolmert J., Schuppe-Koistinen I., Jacobsson S.P. Second-order peak detection for multicomponent high-resolution LC/MS data. Anal. Chem. 2006, 78:975-983.
21. Åberg K.M., Torgrip R.J.O., Kolmert J., Schuppe-Kostinen I., Lindberg J. Feature detection and alignment of hyphenated chromatographic-mass spectrometric data; extraction of pure ion chromatograms using Kalman tracking. J. Chromatogr. A 2008, 1102:139-146.
22. Tautenhahn R., Böttcher C., Neumann S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 2008, 9:504.
23. Lommen A. MetAlign: interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal. Chem. 2009, 81:3079-3086.
24. Yu T., Park Y., Johnson J.M., Jones D.P. apLCMS - adaptive processing of high-resolution LC/MS data. Bioinformatics 2009, 25:1930-1936.
25. Wiener M.C., Sachs J.R., Deyanova E.G., Yates N.A. Differential mass spectrometry: a label-free LC-MS method for finding significant differences in complex peptide and protein mixtures. Anal. Chem. 2004, 76:6085-6096.
26. Palagi P.M., Walther D., Quaroni M., Catherinet S., Burgess J., Zimmermann-Ivol C.G., Sanchez J.-C. MSight: an image analysis software for liquid chromatography-mass spectrometry. Proteomics 2005, 5:2381-2384.
27. Prakash A., Mallick P., Whiteaker J., Zhang H., Paulovich A., Flory M., Lee H., Aebersold R., Schwikowski B. Signal maps for mass spectrometry-based comparative proteomics. Mol. Cell. Proteomics 2006, 5(3):423-432.
28. Listgarten J., Neal R.M., Roweis S.T., Wong P., Emili A. Difference detection in LC-MS data for protein biomarker discovery. Bioinformatics 2006, 23:e198-e204.
29. Baran R., Kochi H., Saito N., Suematsu M., Soga T., Nishioka T., Robert M., Tomita M. MathDAMP: a package for differential analysis of metabolite profiles. BMC Bioinform. 2006, 7:530.
30. Sugimoto M., Hirayama A., Ishikawa T., Robert M., Baran R., Uehara K., Kawai K., Soga T., Tomita M. Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis. Metabolomics 2010, 6:27-41.
31. Ruckstuhl A.F., Jacobson M.P., Field R.W., Dodd J.A. Baseline subtraction using robust local regression estimation. J. Quant. Spectrosc. Radiat. Transfer 2001, 68:179-193.
32. Mazet V., Carteret C., Brie D., Idier J., Humbert B. Background removal from spectra by designing and minimising a non-quadratic cost function. Chemometr. Intell. Lab. Syst. 2005, 76:121-133.
33. Vandenbogaert M., Li-Thiao-Té S., Kaltenbach H.-M., Zhang R., Aittokallio T., Schwikowski B. Alignment of LC-MS images, with applications to biomarker discovery and protein identification. Proteomics 2008, 8:650-672.
34. Torgrip R.J.O., Alm E., Åberg K.M. Warping and alignment technologies for inter-sample feature correspondence in 1D H NMR, chromatography-, and capillary electrophoresis-mass spectrometry data. Bioanal. Rev. 2010, 1:105-116.
35. Åberg K.M., Alm E., Torgrip R.J.O. The correspondence problem for metabonomics datasets. Anal. Bioanal. Chem. 2009, 394:151-162.
36. Lange E., Tautenhahn R., Neumann S., Gröpl C. Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinform. 2009, 9:375.
37. Bloemberg T.G., Gerretzen J., Wouters H.J.P., Gloerich J., van Dael M., Wessels H.J.C.T., van den Heuvel L.P., Eilers P.H.C., Buydens L.M.C., Wehrens R. Improved parametric time warping for proteomics. Chemometr. Intell. Lab. Syst. 2010, 104:65-74.
38. Sysi-Aho M., Katajamaa M., Yetukuri L., Orešič M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinform. 2007, 8:93.
39. Warrack B.M., Hnatyshyna S., Otta K.-H., Reily M.D., Sanders M., Zhanga H., Drexler D.M. Normalization strategies for metabonomic analysis of urine samples. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2009, 877:547-552.
40. van den Berg R.A., Hoefsloot H.C.J., Westerhuis J.A., Smilde A.K., van der Werf M.J. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 2006, 7:142.
41. Mueller L.N., Rinner O., Schmidt A., Letarte S., Bodenmiller B., Brusniak M.-Y., Vitek O., Aebersold R., Müller M. SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 2007, 7:3470-3480.
42. Grigsby C.C., Rizki M.M., Tamburino L.A., Pitsch R.L., Shiyanov P.A., Cool D.R. Metabolite differentiation and discovery dab (MeDDL): a new tool for biomarker discovery and mass spectral visualization. Anal. Chem. 2010, 82:4386-4395.
43. Marengo E., Robottia E., Righetti P.G., Antonucci F. New approach based on fuzzy logic and principal component analysis for the classification of two-dimensional maps in health and disease; application to lymphomas. J. Chromatogr. A 2003, 1004:13-28.
44. Marengo E., Robotti E., Bobba M., Liparota M.C., Rustichelli C., Zamò A., Chilosi M., Righetti P.G. Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel electrophoresis. Electrophoresis 2006, 27:484-494.
45. Beck Rye M., Færgestad E.M., Martens H., Wold J.P., Alsberg B.K. An improved pixel-based approach for analyzing images in two-dimensional gel electrophoresis. Electrophoresis 2008, 29:1382-1393.
46. Dowsey A.W., English J.A., Lisacek F., Morris J.S., Yang G.-Z., Dunn M.J. Image analysis tools and emerging algorithms for expression proteomics. Proteomics 2010, 10:4226-4257.
47. Wu W., Massart D.L., de Jong S. The kernel PCA algortihms for wide data. Part I: theory and algorithms. Chem. Intell. Lab. Syst. 1997, 36:165-172.
48. Daszykowski M., Danielsson R., Walczak B. No-alignment-strategies for exploring a set of two-way data tables obtained from capillary electrophoresis-mass spectrometry. J. Chromatogr. A 2008, 1192:157-165.
49. Daszykowski M., Walczak B. Methods for the exploratory analysis of two-dimensional chromatographic signals. Talanta 2011, 83:1088-1097.
50. van der Kloet F.M., Bobeldijk I., Verheij E.R., Jellema R.H. Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J. Proteome Res. 2009, 8:5132-5141.
51. Harrington P.B., Vieira N.E., Espinoza J., Nien J.K., Romeroc R., Yergey A.L. Analysis of variance-principal component analysis: a soft tool for proteomic discovery. Anal. Chim. Acta 2005, 544:118-127.
52. Kamangar F., Dores G.M., Anderson W.F. Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J. Clin. Oncol. 2006, 24:2137-2150.
53. Harris R., Lohr K.N. Screening for prostate cancer: an update of the evidence for the U.S. preventive services task force. Ann. Intern. Med. 2002, 137:917-929.
54. Crawford E.D. PSA testing: what is the use?. Lancet 2005, 365:1447-1449.
55. Thompson I.M., Pauler D.K., Goodman P.J., Tangen C.M., Lucia M.S., Parnes H.L., Minasian L.M., Ford L.G., Lippman S.M., Crawford E.D., Crowley J.J., Coltman C.A. Prevalence of prostate cancer among men with a prostate-specific antigen level <=4.0ng per milliliter. N. Engl. J. Med. 2004, 350:2239-2246.
56. Serkova N.J., Gamito E.J., Jones R.H., O'Donell C., Brown J.L., Green S., Sullivan H., Hedlund T., Crawford E.D. The metabolites citrate, myo-inositol, and spermine are potential age-independent markers of prostate cancer in human expressed prostatic secretions. Prostate 2008, 68:620-628.
57. Kline E.E., Treat E.G., Averna T.A., Davis M.S., Smith A.Y., Sillerud L.O. Citrate concentrations in human seminal fluid and expressed prostatic fluid determined via 1H nuclear magnetic resonance spectroscopy outperform prostate specific antigen in prostate cancer detection. J. Urol. 2006, 176:2274-2279.
58. Sreekumar A., Poisson L.M., Rajendiran T.M., Khan A.P., Cao Q., Yu J., Laxman B., Mehra R., Lonigro R.J., Li Y., Nyati M.K., Ahsan A., Kalyana-Sundaram S., Han B., Cao X., Byun J., Omenn G.S., Ghosh D., Pennathur S., Alexander D.C., Berger A., Shuster J.R., Wei J.T., Varambally S., Beecher C., Chinnaiyan A.M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457:910-915.
59. Cao D.-L., Ye D.-W., Zhu Y., Zhang H.-L., Wang Y.-X., Yao X.-D. Efforts to resolve the contradictions in early diagnosis of prostate cancer: a comparison of different algorithms of sarcosine in urine. Prostate Cancer Prostatic Dis. 2011, advance online publication, 15 February 2011. 10.1038/pcan.2011.2.
60. Kirkali Z., Chan T., Manoharan M., Algaba F., Busch C., Cheng L., Kiemeney L., Kriegmair M., Montironi R., Murphy W.M., Sesterhenn I.A., Tachibana M., Weider J. Bladder cancer: epidemiology, staging and grading, and diagnosis. Urology 2005, 66:6(Supplement 1):4-34.
61. Badalament R.A., Hermansen D.K., Kimmel M., Gay H., Herr H.W., Fair W.R., Whitmore W.F., Melamed M.R. The sensitivity of bladder wash flow cytometry, bladder wash cytology, and voided cytology in the detection of bladder carcinoma. Cancer 1987, 60:1423-1427.
62. Issaq H.J., Nativ O., Waybright T., Luke B., Veenstra T.D., Issaq E.J., Kravstov A., Mullerad M. Detection of bladder cancer in human urine by metabolomic profiling using high performance liquid chromatography/mass spectrometry. J. Urol. 2008, 179:2422-2426.
63. Pasikanti K.K., Esuvaranathan K., Ho P.C., Mahendran R., Kamaraj R., Wu Q.H., Chiong E., Chan E.C.Y. Noninvasive urinary metabonomic diagnosis of human bladder cancer. J. Proteome Res. 2010, 9:2988-2995.
64. http://www.unidata.ucar.edu/software/netcdf/.
65. Pedrioli P.G.A., Eng J.K., Hubley R., Vogelzang M., Deutsch E.W., Raught B., Pratt B., Nilsson E., Angeletti R.H., Apweiler R., Cheung K., Costello C.E., Hermjakob H., Huang S., Julian R.K., Kapp E., McComb M.E., Oliver S.G., Omenn G., Paton N.W., Simpson R., Smith R., Taylor C.F., Zhu W., Aebersold R. A common open representation of mass spectrometry data and its application to proteomics research. Nat. Biotechnol. 2004, 22:1459-1466.
66. Deutsch E. mzML: a single, unifying data format for mass spectrometer output. Proteomics 2008, 8:2776-2777.
67. Cox G.B. The influence of silica structure on reversed-phase retention. J. Chromatogr. A 1993, 656:353-367.
68. Nawrocki J. The silanol group and its role in liquid chromatography. J. Chromatogr. A 1997, 779:29-71.
69. Barker M., Rayens W. Partial least squares for discrimination. J. Chemometr. 2003, 17:166-173.
70. Bylesjö M., Rantalainen M., Cloarec O., Nicholson J.K., Holmes E., Trygg J. OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification. J. Chemometr. 2006, 20:341-351.
71. Rajalahti T., Arneberg R., Kroksveen A.C., Berle M., Myhr K.-M., Kvalheim O.M. Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles. Anal. Chem. 2009, 81:2581-2590.
72. Rajalahti T., Kroksveen A.C., Arneberg R., Berven F.S., Vedeler C.A., Myhr K.-M., Kvalheim O.M. A multivariate approach to reveal biomarker signatures for disease classification: application to mass spectral profiles of cerebrospinal fluid from patients with multiple sclerosis. J. Proteome Res. 2010, 9:3608-3620.
73. Broadhurst D., Kell D. Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2006, 2:171-196.
74. Kultima K., Nilsson A., Scholz B., Rossbach U.L., Fälth Maria, Andrén P.E. Development and evaluation of normalization methods for label-free relative quantification of endogenous peptides. Mol. Cell. Proteomics 2009, 8(10):2285-2295.