Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Clinical and Translational Medicine
Published in: Clinical and Translational Medicine 1/2014

Open Access 01-12-2014 | Review

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Authors: Maria Frantzi, Akshay Bhat, Agnieszka Latosinska

Published in: Clinical and Translational Medicine | Issue 1/2014

Login to get access

Abstract

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic–based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.
Appendix
Available only for authorised users
Literature
1.
Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer J Int Cancer 2010, 127: 2893–2917. 10.1002/ijc.25516CrossRef
2.
Kulasingam V, Diamandis EP: Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Prac Oncol 2008, 5: 588–599. 10.1038/ncponc1187CrossRef
3.
Frantzi M, Makridakis M, Vlahou A: Biomarkers for bladder cancer aggressiveness. Curr Opin Urol 2012, 5: 390–396.CrossRef
4.
Fertig EJ, Slebos R, Chung CH: Application of genomic and proteomic technologies in biomarker discovery. Am Soc Clin Oncol Educ Book/ASCO Am Soc Clin Oncol Meet 2012, 32: 377–382.
5.
Gupta S, Venkatesh A, Ray S, Srivastava S: Challenges and prospects for biomarker research: a current perspective from the developing world. Biochim Biophys Acta in press in press
6.
Henley SJ, King JB, German RR, Richardson LC, Plescia M, Centers for Disease C, Prevention: Surveillance of screening-detected cancers (colon and rectum, breast, and cervix) - United States, 2004–2006. Morb Mortal Wkly Rep Surveill Summ 2010, 59: 1–25.
7.
Iuga C, Seicean A, Iancu C, Buiga R, Kumar Sappa P, Volker U, Hammer E: Proteomic identification of potential prognostic biomarkers in resectable pancreatic ductal adenocarcinoma. Proteomics in press in press
8.
Fuzery AK, Levin J, Chan MM, Chan DW: Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin Proteomics 2013, 10: 13. 10.1186/1559-0275-10-13PubMedCentralPubMedCrossRef
9.
Pavlou MP, Diamandis EP, Blasutig IM: The long journey of cancer biomarkers from the bench to the clinic. Clin Chem 2013, 59: 147–157. 10.1373/clinchem.2012.184614PubMedCrossRef
10.
Rifai N, Gillette MA, Carr SA: Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 2006, 24: 971–983. 10.1038/nbt1235PubMedCrossRef
11.
Mischak H, Kolch W, Aivaliotis M, Bouyssié D, Court M, Dihazi H, Dihazi GH, Franke J, Garin J, Gonzalez de Peredo A, Iphöfer A, Jänsch L, Lacroix C, Makridakis M, Masselon C, Metzger J, Monsarrat B, Mrug M, Norling M, Novak J, Pich A, Pitt A, Bongcam-Rudloff E, Siwy J, Suzuki H, Thongboonkerd V, Wang LS, Zoidakis J, Zürbig P, Schanstra JP, et al.: Comprehensive human urine standards for comparability and standardization in clinical proteome analysis. Proteomics Clin Appl 2010, 4: 464–478. 10.1002/prca.200900189PubMedCentralPubMedCrossRef
12.
Liu D, Zhou XH: Covariate adjustment in estimating the area under ROC curve with partially missing gold standard. Biometrics 2013, 69: 91–100. 10.1111/biom.12001PubMedCentralPubMedCrossRef
13.
Ehdaie B, Theodorescu D: Molecular markers in transitional cell carcinoma of the bladder: new insights into mechanisms and prognosis. IJU 2008, 24: 61–67.PubMedCentralPubMed
14.
Lopez E, Madero L, Lopez-Pascual J, Latterich M: Clinical proteomics and OMICS clues useful in translational medicine research. Proteome Sci 2012, 10: 35. 10.1186/1477-5956-10-35PubMedCentralPubMedCrossRef
15.
Diamandis EP: The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med 2012, 10: 87. 10.1186/1741-7015-10-87PubMedCentralPubMedCrossRef
16.
Mischak H, Vlahou A, Righetti PG, Calvete JJ: Putting value in biomarker research and reporting. J Proteomics 2014, 96: A1-A3.PubMedCrossRef
17.
Vlahou A: Network views for personalized medicine. Proteomics Clin Appl 2013, 7: 384–387. 10.1002/prca.201200121PubMedCrossRef
18.
Volanis D, Papadopoulos G, Doumas K, Gkialas I, Delakas D: Molecular mechanisms in urinary bladder carcinogenesis. J BUON 2011, 16: 589–601.PubMed
19.
Ewald JA, Downs TM, Cetnar JP, Ricke WA: Expression microarray meta-analysis identifies genes associated with Ras/MAPK and related pathways in progression of muscle-invasive bladder transition cell carcinoma. PloS One 2013, 8: e55414. 10.1371/journal.pone.0055414PubMedCentralPubMedCrossRef
20.
Mitra AP, Cote RJ: Molecular pathogenesis and diagnostics of bladder cancer. Annu Rev Pathol 2009, 4: 251–285. 10.1146/annurev.pathol.4.110807.092230PubMedCrossRef
21.
Wu XR: Urothelial tumorigenesis: a tale of divergent pathways. Nat Rev Cancer 2005, 5: 713–725. 10.1038/nrc1697PubMedCrossRef
22.
van den Bosch S, Alfred Witjes J: Long-term cancer-specific survival in patients with high-risk, non-muscle-invasive bladder cancer and tumour progression: a systematic review. Eur Urol 2011, 60: 493–500. 10.1016/j.eururo.2011.05.045PubMedCrossRef
23.
May M, Brookman-Amissah S, Roigas J, Hartmann A, Storkel S, Kristiansen G, Gilfrich C, Borchardt R, Hoschke B, Kaufmann O, Gunia S: Prognostic accuracy of individual uropathologists in noninvasive urinary bladder carcinoma: a multicentre study comparing the 1973 and 2004 World Health Organisation classifications. Eur Urol 2010, 57: 850–858. 10.1016/j.eururo.2009.03.052PubMedCrossRef
24.
Dyrskjot L, Reinert T, Novoradovsky A, Zuiverloon TC, Beukers W, Zwarthoff E, Malats N, Real FX, Segersten U, Malmstrom PU, Knowles M, Hurst C, Sorge J, Borre M, Orntoft TF: Analysis of molecular intra-patient variation and delineation of a prognostic 12-gene signature in non-muscle invasive bladder cancer; technology transfer from microarrays to PCR. Br J Cancer 2012, 107: 1392–1398. 10.1038/bjc.2012.412PubMedCentralPubMedCrossRef
25.
de Ronde JJ, Rigaill G, Rottenberg S, Rodenhuis S, Wessels LF: Identifying subgroup markers in heterogeneous populations. Nucleic Acids Res 2013, 41: 1–10. 10.1093/nar/gks1039CrossRef
26.
27.
Gallo V, Egger M, McCormack V, Farmer PB, Ioannidis JP, Kirsch-Volders M, Matullo G, Phillips DH, Schoket B, Stromberg U, Vermeulen R, Wild C, Porta M, Vineis P: STrengthening the reporting of OBservational studies in Epidemiology-Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement. Eur J Epidemiol 2013, 26: 797–810.CrossRef
28.
Pepe MS, Feng Z, Janes H, Bossuyt PM, Potter JD: Pivotal evaluation of the accuracy of a biomarker used for classification or prediction: standards for study design. J Natl Cancer Inst 2008, 100: 1432–1438. 10.1093/jnci/djn326PubMedCentralPubMedCrossRef
29.
Altman DG, McShane LM, Sauerbrei W, Taube SE: Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med 2012, 10: 51. 10.1186/1741-7015-10-51PubMedCentralPubMedCrossRef
30.
Dillner J: A basis for translational cancer research on aetiology, pathogenesis and prognosis: guideline for standardised and population-based linkages of biobanks to cancer registries. Eur J Cancer in press in press
31.
Malm J, Fehniger TE, Danmyr P, Vegvari A, Welinder C, Lindberg H, Appelqvist R, Sjodin K, Wieslander E, Laurell T, Hober S, Berven FS, Fenyö D, Wang X, Andrén PE, Edula G, Carlsohn E, Fuentes M, Nilsson CL, Dahlbäck M, Rezeli M, Erlinge D, Marko-Varga G: Developments in biobanking workflow standardization providing sample integrity and stability. J Proteomics 2013, 95: 38–45.PubMedCrossRef
32.
33.
Bruggeman M, Verheyen L, Vidmar T: A dedicated LIMS for routine gamma-ray spectrometry. Appl Radiat Isot in press in press
34.
Morisawa H, Hirota M, Toda T: Development of an open source laboratory information management system for 2-D gel electrophoresis-based proteomics workflow. BMC Bioinform 2006, 7: 430. 10.1186/1471-2105-7-430CrossRef
35.
Stephan C, Kohl M, Turewicz M, Podwojski K, Meyer HE, Eisenacher M: Using laboratory information management systems as central part of a proteomics data workflow. Proteomics 2010, 10: 1230–1249. 10.1002/pmic.200900420PubMedCrossRef
36.
Aguilar-Mahecha A, Kuzyk MA, Domanski D, Borchers CH, Basik M: The effect of pre-analytical variability on the measurement of MRM-MS-based mid- to high-abundance plasma protein biomarkers and a panel of cytokines. PloS One 2012, 7: e38290. 10.1371/journal.pone.0038290PubMedCentralPubMedCrossRef
37.
Percy AJ, Parker CE, Borchers CH: Pre-analytical and analytical variability in absolute quantitative MRM-based plasma proteomic studies. Bioanalysis 2013, 5: 2837–2856. 10.4155/bio.13.245PubMedCrossRef
38.
Lescuyer P, Hochstrasser D, Rabilloud T: How shall we use the proteomics toolbox for biomarker discovery? J Proteome Res 2007, 6: 3371–3376. 10.1021/pr0702060PubMedCrossRef
39.
Good DM, Thongboonkerd V, Novak J, Bascands JL, Schanstra JP, Coon JJ, Dominiczak A, Mischak H: Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future. J Proteome Res 2007, 6: 4549–4555. 10.1021/pr070529wPubMedCrossRef
40.
McGuire JN, Overgaard J, Pociot F: Mass spectrometry is only one piece of the puzzle in clinical proteomics. Brief Funct Genom Proteomic 2008, 7: 74–83. 10.1093/bfgp/eln005CrossRef
41.
Fiedler GM, Ceglarek U, Leichtle A, Thiery J: Standardized preprocessing of urine for proteome analysis. In The Urinary Proteome. Edited by: Rai AJ. New York: Humana Press; 2010:47–63.CrossRef
42.
Mischak H, Vlahou A, Ioannidis JP: Technical aspects and inter-laboratory variability in native peptide profiling: the CE-MS experience. Clin Biochem 2013, 46: 432–443. 10.1016/j.clinbiochem.2012.09.025PubMedCrossRef
43.
Skates SJ, Gillette MA, Labaer J, Carr SA, Anderson L, Liebler DC, Ransohof D, Rifai N, Kondratovich M, Tezak Z, Mansfield E, Oberg AL, Wright I, Barnes G, Gail M, Mesri M, Kinsinger CR, Rodriguez H, Boja ES: Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies. J Proteome Res 2013, 12: 5383–5394. 10.1021/pr400132jPubMedCentralPubMedCrossRef
44.
Shariat SF, Lotan Y, Vickers A, Karakiewicz PI, Schmitz-Drager BJ, Goebell PJ, Malats N: Statistical consideration for clinical biomarker research in bladder cancer. Urol Oncol 2010, 28: 389–400. 10.1016/j.urolonc.2010.02.011PubMedCentralPubMedCrossRef
45.
Behrens T, Bonberg N, Casjens S, Pesch B, Bruning T: A practical guide to epidemiological practice and standards in the identification and validation of diagnostic markers using a bladder cancer example. Biochim Biophys Acta 2013, 1844: 145–155.PubMedCrossRef
46.
Riley RD, Sauerbrei W, Altman DG: Prognostic markers in cancer: the evolution of evidence from single studies to meta-analysis, and beyond. Br J Cancer 2009, 100: 1219–1229. 10.1038/sj.bjc.6604999PubMedCentralPubMedCrossRef
47.
Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AJ, Bunk DM, Kilpatrick LE, Billheimer DD, Blackman RK, Cardasis HL, Carr SA, Clauser KR, Jaffe JD, Kowalski KA, Neubert TA, Regnier FE, Schilling B, Tegeler TJ, Wang M, Wang P, Whiteaker JR, Zimmerman LJ, Fisher SJ, Gibson BW, Kinsinger CR, Mesri M, Rodriguez H, Stein SE, Tempst P, Paulovich AG, et al.: Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J Proteome Res 2010, 9: 761–776. 10.1021/pr9006365PubMedCentralPubMedCrossRef
48.
49.
White NM, Masui O, Desouza LV, Krakovska O, Metias S, Romaschin AD, Honey RJ, Stewart R, Pace K, Lee J, Jewett MA, Bjarnason GA, Siu KW, Yousef GM: Quantitative proteomic analysis reveals potential diagnostic markers and pathways involved in pathogenesis of renal cell carcinoma. Oncotarget 2014, 5: 506–518.PubMedCentralPubMedCrossRef
50.
Zou X, Feng B, Dong T, Yan G, Tan B, Shen H, Huang A, Zhang X, Zhang M, Yang P, Zheng M, Zhang Y: Up-regulation of type I collagen during tumorigenesis of colorectal cancer revealed by quantitative proteomic analysis. J Proteomics 2013, 94: 473–485.PubMedCrossRef
51.
Molina L, Salvetat N, Ameur RB, Peres S, Sommerer N, Jarraya F, Ayadi H, Molina F, Granier C: Analysis of the variability of human normal urine by 2D-GE reveals a “public” and a “private” proteome. J Proteomics 2011, 75: 70–80. 10.1016/j.jprot.2011.06.031PubMedCrossRef
52.
Mischak H, Allmaier G, Apweiler R, Attwood T, Baumann M, Benigni A, Bennett SE, Bischoff R, Bongcam-Rudloff E, Capasso G, Kuczyk MA, Mischak H, Vlahou A, Theodorescu D, Merseburger AS: Recommendations for biomarker identification and qualification in clinical proteomics. Sci Transl Med 2010, 2(46):42.CrossRef
53.
Anderson NL, Anderson NG: The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics 2002, 1: 845–867. 10.1074/mcp.R200007-MCP200PubMedCrossRef
54.
Zubarev RA: The challenge of the proteome dynamic range and its implications for in-depth proteomics. Proteomics 2013, 13: 723–726. 10.1002/pmic.201200451PubMedCrossRef
55.
Leth-Larsen R, Lund RR, Ditzel HJ: Plasma membrane proteomics and its application in clinical cancer biomarker discovery. Mol Cell Proteomics 2010, 9: 1369–1382. 10.1074/mcp.R900006-MCP200PubMedCentralPubMedCrossRef
56.
Yang Y, Toy W, Choong LY, Hou P, Ashktorab H, Smoot DT, Yeoh KG, Lim YP: Discovery of SLC3A2 cell membrane protein as a potential gastric cancer biomarker: implications in molecular imaging. J Proteome Res 2012, 11: 5736–5747.PubMedCentralPubMed
57.
de Wit M, Kant H, Piersma SR, Pham TV, Mongera S, van Berkel MP, Boven E, Ponten F, Meijer GA, Jimenez CR, Fijneman RJ: Colorectal cancer candidate biomarkers identified by tissue secretome proteome profiling. J Proteomics 2014, 99C: 26–39.CrossRef
58.
Makridakis M, Vlahou A: Secretome proteomics for discovery of cancer biomarkers. J Proteomics 2010, 73: 2291–2305. 10.1016/j.jprot.2010.07.001PubMedCrossRef
59.
Albrethsen J, Knol JC, Piersma SR, Pham TV, de Wit M, Mongera S, Carvalho B, Verheul HM, Fijneman RJ, Meijer GA, Jimenez CR: Subnuclear proteomics in colorectal cancer: identification of proteins enriched in the nuclear matrix fraction and regulation in adenoma to carcinoma progression. Mol Cell Proteomics 2010, 9: 988–1005. 10.1074/mcp.M900546-MCP200PubMedCentralPubMedCrossRef
60.
Ha GH, Baek KH, Kim HS, Jeong SJ, Kim CM, McKeon F, Lee CW: p53 activation in response to mitotic spindle damage requires signaling via BubR1-mediated phosphorylation. Cancer Res 2007, 67: 7155–7164. 10.1158/0008-5472.CAN-06-3392PubMedCrossRef
61.
Chen J, Xi J, Tian Y, Bova GS, Zhang H: Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody-based assays on tissue specimens. Proteomics 2013, 13: 2268–2277. 10.1002/pmic.201200541PubMedCentralPubMedCrossRef
62.
Li QK, Gabrielson E, Zhang H: Application of glycoproteomics for the discovery of biomarkers in lung cancer. Proteomics Clin Appl 2012, 6: 244–256. 10.1002/prca.201100042PubMedCrossRef
63.
Kullolli M, Warren J, Arampatzidou M, Pitteri SJ: Performance evaluation of affinity ligands for depletion of abundant plasma proteins. J Chromatogr B Analyt Technol Biomed Life Sci 2013, 939: 10–16.PubMedCrossRef
64.
Patel BB, Barrero CA, Braverman A, Kim PD, Jones KA, Chen DE, Bowler RP, Merali S, Kelsen SG, Yeung AT: Assessment of two immunodepletion methods: off-target effects and variations in immunodepletion efficiency may confound plasma proteomics. J Proteome Res 2012, 11: 5947–5958.PubMedCentralPubMedCrossRef
65.
Polaskova V, Kapur A, Khan A, Molloy MP, Baker MS: High-abundance protein depletion: comparison of methods for human plasma biomarker discovery. Electrophoresis 2010, 31: 471–482. 10.1002/elps.200900286PubMedCrossRef
66.
Tu C, Rudnick PA, Martinez MY, Cheek KL, Stein SE, Slebos RJ, Liebler DC: Depletion of abundant plasma proteins and limitations of plasma proteomics. J Proteome Res 2010, 9: 4982–4991. 10.1021/pr100646wPubMedCentralPubMedCrossRef
67.
Atanassov I, Urlaub H: Increased proteome coverage by combining PAGE and peptide isoelectric focusing: comparative study of gel-based separation approaches. Proteomics 2013, 13: 2947–2955.PubMedCentralPubMed
68.
Zhang L, Yao L, Zhang Y, Xue T, Dai G, Chen K, Hu X, Xu LX: Protein pre-fractionation with a mixed-bed ion exchange column in 3D LC-MS/MS proteome analysis. J Chromatogr B Analyt Technol Biomed Life Sci 2012, 905: 96–104.PubMedCrossRef
69.
Pirmoradian M, Budamgunta H, Chingin K, Zhang B, Astorga-Wells J, Zubarev RA: Rapid and deep human proteome analysis by single-dimension shotgun proteomics. Mol Cell Proteomics 2013, 12: 3330–3338. 10.1074/mcp.O113.028787PubMedCentralPubMedCrossRef
70.
Chanthammachat P, Promwikorn W, Pruegsanusak K, Roytrakul S, Srisomsap C, Chokchaichamnankit D, Svasti J, Boonyaphiphat PKS, Thongsuksai P: Comparative proteomic analysis of oral squamous cell carcinoma and adjacent non-tumour tissue from Thailand. Arch Oral Biol 2013, 58: 1677–1685. 10.1016/j.archoralbio.2013.08.002PubMedCrossRef
71.
Chen JH, Ni RZ, Xiao MB, Guo JG, Zhou JW: Comparative proteomic analysis of differentially expressed proteins in human pancreatic cancer tissue. Hepatob Pancreat Dis Int 2009, 8: 193–200.
72.
Petrak J, Ivanek R, Toman O, Cmejla R, Cmejlova J, Vyoral D, Zivny J, Vulpe CD: Deja vu in proteomics: a hit parade of repeatedly identified differentially expressed proteins. Proteomics 2008, 8: 1744–1749. 10.1002/pmic.200700919PubMedCrossRef
73.
Bjellqvist B, Ek K, Righetti PG, Gianazza E, Gorg A, Westermeier R, Postel W: Isoelectric focusing in immobilized pH gradients: principle, methodology and some applications. J Biochem Biophys Methods 1982, 6: 317–339. 10.1016/0165-022X(82)90013-6PubMedCrossRef
74.
75.
Marcus K, Joppich C, May C, Pfeiffer K, Sitek B, Meyer H, Stuehler K: High-resolution 2DE. Methods Mol Biol 2009, 519: 221–240. 10.1007/978-1-59745-281-6_14PubMedCrossRef
76.
Caldini A, Moneti G, Fanelli A, Bruschettini A, Mercurio S, Pieraccini G, Cini E, Ognibene A, Luceri F, Messeri G: Epoetin alpha, epoetin beta and darbepoetin alfa: two-dimensional gel electrophoresis isoforms characterization and mass spectrometry analysis. Proteomics 2003, 3: 937–941. 10.1002/pmic.200300405PubMedCrossRef
77.
Zugaro LM, Reid GE, Ji H, Eddes JS, Murphy AC, Burgess AW, Simpson RJ: Characterization of rat brain stathmin isoforms by two-dimensional gel electrophoresis-matrix assisted laser desorption/ionization and electrospray ionization-ion trap mass spectrometry. Electrophoresis 1998, 19: 867–876. 10.1002/elps.1150190544PubMedCrossRef
78.
79.
Unlu M, Morgan ME, Minden JS: Difference gel electrophoresis: a single gel method for detecting changes in protein extracts. Electrophoresis 1997, 18: 2071–2077. 10.1002/elps.1150181133PubMedCrossRef
80.
Minden J: Comparative proteomics and difference gel electrophoresis. BioTechniques 2007, 43: 739–741. 743 passim 743 passim 10.2144/000112653PubMedCrossRef
81.
Zhang Y, Fonslow BR, Shan B, Baek MC, Yates JR III: Protein analysis by shotgun/bottom-up proteomics. Chem Rev 2013, 113: 2343–2394. 10.1021/cr3003533PubMedCentralPubMedCrossRef
82.
DeSouza LV, Siu KW: Mass spectrometry-based quantification. Clin Biochem 2013, 46: 421–431. 10.1016/j.clinbiochem.2012.10.025PubMedCrossRef
83.
Jeong K, Kim S, Bandeira N: False discovery rates in spectral identification. BMC Bioinform 2012, 13(Suppl 16):S2. 10.1186/1471-2105-13-S16-S2CrossRef
84.
Kall L, Storey JD, MacCoss MJ, Noble WS: Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J Proteome Res 2008, 7: 29–34. 10.1021/pr700600nPubMedCrossRef
85.
Keller A, Nesvizhskii AI, Kolker E, Aebersold R: Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 2002, 74: 5383–5392. 10.1021/ac025747hPubMedCrossRef
86.
Ma K, Vitek O, Nesvizhskii AI: A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinform 2012, 13(Suppl 16):S1. 10.1186/1471-2105-13-S16-S1CrossRef
87.
Moore RE, Young MK, Lee TD: Qscore: an algorithm for evaluating SEQUEST database search results. J Am Soc Mass Spectrom 2002, 13: 378–386. 10.1016/S1044-0305(02)00352-5PubMedCrossRef
88.
Reiter L, Claassen M, Schrimpf SP, Jovanovic M, Schmidt A, Buhmann JM, Hengartner MO, Aebersold R: Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry. Mol Cell Proteomics 2009, 8: 2405–2417. 10.1074/mcp.M900317-MCP200PubMedCentralPubMedCrossRef
89.
Teng B, Huang T, He Z: Decoy-free protein-level false discovery rate estimation. Bioinformatics 2014, 30: 675–681. 10.1093/bioinformatics/btt431PubMedCrossRef
90.
Li YF, Radivojac P: Computational approaches to protein inference in shotgun proteomics. BMC Bioinform 2012, 13(Suppl 16):S4.
91.
Frantzi M, Metzger J, Banks RE, Husi H, Klein J, Dakna M, Mullen W, Cartledge JJ, Schanstra JP, Brand K, Kuczyk MA, Mischak H, Vlahou A, Theodorescu D, Merseburger AS: Discovery and validation of urinary biomarkers for detection of renal cell carcinoma. J Proteomics 2013, 98C: 44–58.
92.
Schiffer E, Vlahou A, Petrolekas A, Stravodimos K, Tauber R, Geschwend JE, Neuhaus J, Stolzenburg JU, Conaway MR, Mischak H, Theodorescu D: Prediction of muscle-invasive bladder cancer using urinary proteomics. Clin Cancer Res 2009, 15: 4935–4943. 10.1158/1078-0432.CCR-09-0226PubMedCrossRef
93.
Theodorescu D, Wittke S, Ross MM, Walden M, Conaway M, Just I, Mischak H, Frierson HF: Discovery and validation of new protein biomarkers for urothelial cancer: a prospective analysis. Lancet Oncol 2006, 7: 230–240. 10.1016/S1470-2045(06)70584-8PubMedCrossRef
94.
Stalmach A, Albalat A, Mullen W, Mischak H: Recent advances in capillary electrophoresis coupled to mass spectrometry for clinical proteomic applications. Electrophoresis 2013, 34: 1452–1464. 10.1002/elps.201200708PubMedCrossRef
95.
Latosinska A, Frantzi M, Vlahou A, Mischak H: Clinical applications of capillary electrophoresis coupled to mass spectrometry in biomarker discovery: focus on bladder cancer. Proteomics Clin Appl 2013, 7: 779–793. 10.1002/prca.201300038PubMedCrossRef
96.
Zuberovic A, Hanrieder J, Hellman U, Bergquist J, Wetterhall M: Proteome profiling of human cerebrospinal fluid: exploring the potential of capillary electrophoresis with surface modified capillaries for analysis of complex biological samples. Eur J Mass Spectrom 2008, 14: 249–260. 10.1255/ejms.929CrossRef
97.
Zurbig P, Renfrow MB, Schiffer E, Novak J, Walden M, Wittke S, Just I, Pelzing M, Neususs C, Theodorescu D, Root KE, Ross MM, Mischak H: Biomarker discovery by CE-MS enables sequence analysis via MS/MS with platform-independent separation. Electrophoresis 2006, 27: 2111–2125. 10.1002/elps.200500827PubMedCrossRef
98.
Mullen W, Albalat A, Gonzalez J, Zerefos P, Siwy J, Franke J, Mischak H: Performance of different separation methods interfaced in the same MS-reflection TOF detector: a comparison of performance between CE versus HPLC for biomarker analysis. Electrophoresis 2012, 33: 567–574. 10.1002/elps.201100415PubMedCrossRef
99.
Bramwell D: An introduction to statistical process control in research proteomics. J Proteomics 2013, 95: 3–21.PubMedCrossRef
100.
Dakna M, Harris K, Kalousis A, Carpentier S, Kolch W, Schanstra JP, Haubitz M, Vlahou A, Mischak H, Girolami M: Addressing the challenge of defining valid proteomic biomarkers and classifiers. BMC Bioinform 2010, 11: 594. 10.1186/1471-2105-11-594CrossRef
101.
102.
Pham TV, Piersma SR, Oudgenoeg G, Jimenez CR: Label-free mass spectrometry-based proteomics for biomarker discovery and validation. Expert Rev Mol Diagn 2012, 12: 343–359. 10.1586/erm.12.31PubMedCrossRef
103.
Stoevesandt O, Taussig MJ: Affinity proteomics: the role of specific binding reagents in human proteome analysis. Expert Rev Proteomics 2012, 9: 401–414. 10.1586/epr.12.34PubMedCrossRef
104.
Brennan DJ, O’Connor DP, Rexhepaj E, Ponten F, Gallagher WM: Antibody-based proteomics: fast-tracking molecular diagnostics in oncology. Nat Rev Cancer 2010, 10: 605–617. 10.1038/nrc2902PubMedCrossRef
105.
Caiazzo RJ Jr, O’Rourke DJ, Barder TJ, Nelson BP, Liu BC: Native antigen fractionation protein microarrays for biomarker discovery. Methods Mol Biol 2011, 723: 129–148. 10.1007/978-1-61779-043-0_9PubMedCrossRef
106.
Ozer JS, Dieterle F, Troth S, Perentes E, Cordier A, Verdes P, Staedtler F, Mahl A, Grenet O, Roth DR, Wahl D, Legay F, Holder D, Erdos Z, Vlasakova K, Jin H, Yu Y, Muniappa N, Forest T, Clouse HK, Reynolds S, Bailey WJ, Thudium DT, Topper MJ, Skopek TR, Sina JF, Glaab WE, Vonderscher J, Maurer G, Chibout SD, et al.: A panel of urinary biomarkers to monitor reversibility of renal injury and a serum marker with improved potential to assess renal function. Nat Biotechnol 2010, 28: 486–494. 10.1038/nbt.1627PubMedCrossRef
107.
Li H, Bergeron S, Juncker D: Microarray-to-microarray transfer of reagents by snapping of two chips for cross-reactivity-free multiplex immunoassays. Anal Chem 2012, 84: 4776–4783. 10.1021/ac3003177PubMedCrossRef
108.
Li H, Leulmi RF, Juncker D: Hydrogel droplet microarrays with trapped antibody-functionalized beads for multiplexed protein analysis. Lab Chip 2011, 11: 528–534. 10.1039/c0lc00291gPubMedCrossRef
109.
Pla-Roca M, Leulmi RF, Tourekhanova S, Bergeron S, Laforte V, Moreau E, Gosline SJ, Bertos N, Hallett M, Park M, Juncker D: Antibody colocalization microarray: a scalable technology for multiplex protein analysis in complex samples. Mol Cell Proteomics 2012, 11: M111. 011460 011460PubMedCentralPubMedCrossRef
110.
Shi T, Su D, Liu T, Tang K, Camp DG II, Qian WJ, Smith RD: Advancing the sensitivity of selected reaction monitoring-based targeted quantitative proteomics. Proteomics 2012, 12: 1074–1092. 10.1002/pmic.201100436PubMedCentralPubMedCrossRef
111.
Yocum AK, Chinnaiyan AM: Current affairs in quantitative targeted proteomics: multiple reaction monitoring-mass spectrometry. Brief Funct Genom Proteomic 2009, 8: 145–157. 10.1093/bfgp/eln056CrossRef
112.
Ebhardt HA: Selected reaction monitoring mass spectrometry: a methodology overview. Methods Mol Biol 2014, 1072: 209–222. 10.1007/978-1-62703-631-3_16PubMedCrossRef
113.
Kitteringham NR, Jenkins RE, Lane CS, Elliott VL, Park BK: Multiple reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 2009, 877: 1229–1239. 10.1016/j.jchromb.2008.11.013PubMedCrossRef
114.
Elschenbroich S, Kislinger T: Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery. Mol Biosyst 2011, 7: 292–303. 10.1039/c0mb00159gPubMedCrossRef
115.
Yin HR, Zhang L, Xie LQ, Huang LY, Xu Y, Cai SJ, Yang PY, Lu HJ: Hyperplex-MRM: a hybrid multiple reaction monitoring method using mTRAQ/iTRAQ labeling for multiplex absolute quantification of human colorectal cancer biomarker. J Proteome Res 2013, 12: 3912–3919. 10.1021/pr4005025PubMedCrossRef
116.
Anderson NL, Jackson A, Smith D, Hardie D, Borchers C, Pearson TW: SISCAPA peptide enrichment on magnetic beads using an in-line bead trap device. Mol Cell Proteomics 2009, 8: 995–1005. 10.1074/mcp.M800446-MCP200PubMedCentralPubMedCrossRef
117.
Whiteaker JR, Lin C, Kennedy J, Hou L, Trute M, Sokal I, Yan P, Schoenherr RM, Zhao L, Voytovich UJ, Kelly-Spratt KS, Krasnoselsky A, Gafken PR, Hogan JM, Jones LA, Wang P, Amon L, Chodosh LA, Nelson PS, McIntosh MW, Kemp CJ, Paulovich AG: A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat Biotechnol 2011, 29: 625–634. 10.1038/nbt.1900PubMedCentralPubMedCrossRef
118.
Whiteaker JR, Zhao L, Zhang HY, Feng LC, Piening BD, Anderson L, Paulovich AG: Antibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkers. Anal Biochem 2007, 362: 44–54. 10.1016/j.ab.2006.12.023PubMedCentralPubMedCrossRef
119.
Kim K, Kim SJ, Yu HG, Yu J, Park KS, Jang IJ, Kim Y: Verification of biomarkers for diabetic retinopathy by multiple reaction monitoring. J Proteome Res 2010, 9: 689–699. 10.1021/pr901013dPubMedCrossRef
120.
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: 212–222. 10.1373/clinchem.2009.127019PubMedCrossRef
121.
Kuhn E, Wu J, Karl J, Liao H, Zolg W, Guild B: 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 2004, 4: 1175–1186. 10.1002/pmic.200300670PubMedCrossRef
122.
Yang Z, Hayes M, Fang X, Daley MP, Ettenberg S, Tse FL: LC-MS/MS approach for quantification of therapeutic proteins in plasma using a protein internal standard and 2D-solid-phase extraction cleanup. Anal Chem 2007, 79: 9294–9301. 10.1021/ac0712502PubMedCrossRef
123.
Keshishian H, Addona T, Burgess M, Kuhn E, Carr SA: Quantitative, multiplexed assays for low abundance proteins in plasma by targeted mass spectrometry and stable isotope dilution. Mol Cell Proteomics 2007, 6: 2212–2229. 10.1074/mcp.M700354-MCP200PubMedCentralPubMedCrossRef
124.
Kuhn E, Addona T, Keshishian H, Burgess M, Mani DR, Lee RT, Sabatine MS, Gerszten RE, Carr SA: Developing multiplexed assays for troponin I and interleukin-33 in plasma by peptide immunoaffinity enrichment and targeted mass spectrometry. Clin Chem 2009, 55: 1108–1117. 10.1373/clinchem.2009.123935PubMedCentralPubMedCrossRef
125.
Kuhn E, Whiteaker JR, Mani DR, Jackson AM, Zhao L, Pope ME, Smith D, Rivera KD, Anderson NL, Skates SJ, Pearson TW, Paulovich AG, Carr SA: Interlaboratory evaluation of automated, multiplexed peptide immunoaffinity enrichment coupled to multiple reaction monitoring mass spectrometry for quantifying proteins in plasma. Mol Cell Proteomics 2011, 11: M111. 013854 013854PubMedCentralPubMed
126.
Navarro P, Trevisan-Herraz M, Bonzon-Kulichenko E, Nunez E, Martinez-Acedo P, Perez-Hernandez D, Jorge I, Mesa R, Calvo E, Carrascal M, Hernáez ML, García F, Bárcena JA, Ashman K, Abian J, Gil C, Redondo JM, Vázquez J: General statistical framework for quantitative proteomics by stable isotope labeling. J Proteome Res 2014, 13: 1234–1247. 10.1021/pr4006958PubMedCrossRef
127.
Tucker G, Loh PR, Berger B: A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis. BMC Bioinform 2013, 14: 299. 10.1186/1471-2105-14-299CrossRef
128.
Jantos-Siwy J, Schiffer E, Brand K, Schumann G, Rossing K, Delles C, Mischak H, Metzger J: Quantitative urinary proteome analysis for biomarker evaluation in chronic kidney disease. J Proteome Res 2009, 8: 268–281. 10.1021/pr800401mPubMedCrossRef
129.
Chang CY, Picotti P, Huttenhain R, Heinzelmann-Schwarz V, Jovanovic M, Aebersold R, Vitek O: Protein significance analysis in selected reaction monitoring (SRM) measurements. Mol Cell Proteomics 2012, 11: M111. 014662 014662CrossRef
130.
Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007, 8: 118–127. 10.1093/biostatistics/kxj037PubMedCrossRef
131.
Loman N, Watson M: So you want to be a computational biologist? Nat Biotechnol 2013, 31: 996–998. 10.1038/nbt.2740PubMedCrossRef
132.
Mayer P, Mayer B, Mayer G: Systems biology: building a useful model from multiple markers and profiles. Nephrol Dial Transplant 2012, 27: 3995–4002. 10.1093/ndt/gfs489PubMedCrossRef
133.
134.
Luscombe NM, Greenbaum D, Gerstein M: What is bioinformatics? A proposed definition and overview of the field. Methods Inf Med 2001, 40: 346–358.PubMed
135.
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A: NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 2013, 41: D991-D995. 10.1093/nar/gks1193PubMedCentralPubMedCrossRef
136.
Rustici G, Kolesnikov N, Brandizi M, Burdett T, Dylag M, Emam I, Farne A, Hastings E, Ison J, Keays M, Kurbatova N, Malone J, Mani R, Mupo A, Pedro Pereira R, Pilicheva E, Rung J, Sharma A, Tang YA, Ternent T, Tikhonov A, Welter D, Williams E, Brazma A, Parkinson H, Sarkans U: ArrayExpress update–trends in database growth and links to data analysis tools. Nucleic Acids Res 2013, 41: D987-D990. 10.1093/nar/gks1174PubMedCentralPubMedCrossRef
137.
Zhang F, Drabier R: IPAD: the integrated pathway analysis database for systematic enrichment analysis. BMC Bioinform 2012, 13(Suppl 15):S7. 10.1186/1471-2105-13-S15-S7CrossRef
138.
139.
Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M: The KEGG resource for deciphering the genome. Nucleic Acids Res 2004, 32: D277-D280. 10.1093/nar/gkh063PubMedCentralPubMedCrossRef
140.
Joshi-Tope G, Gillespie M, Vastrik I, D’Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath GR, Wu GR, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 2005, 33: D428-D432.PubMedCentralPubMedCrossRef
141.
van Iersel MP, Kelder T, Pico AR, Hanspers K, Coort S, Conklin BR, Evelo C: Presenting and exploring biological pathways with PathVisio. BMC Bioinform 2008, 9: 399. 10.1186/1471-2105-9-399CrossRef
142.
Paley SM, Latendresse M, Karp PD: Regulatory network operations in the pathway tools software. BMC Bioinform 2012, 13: 243. 10.1186/1471-2105-13-243CrossRef
143.
Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pages F, Trajanoski Z, Galon J: ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 2009, 25: 1091–1093. 10.1093/bioinformatics/btp101PubMedCentralPubMedCrossRef
144.
Maere S, Heymans K, Kuiper M: BiNGO: a cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2005, 21: 3448–3449. 10.1093/bioinformatics/bti551PubMedCrossRef
145.
Berriz GF, Beaver JE, Cenik C, Tasan M, Roth FP: Next generation software for functional trend analysis. Bioinformatics 2009, 25: 3043–3044. 10.1093/bioinformatics/btp498PubMedCentralPubMedCrossRef
146.
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010, 38: W214-W220. 10.1093/nar/gkq537PubMedCentralPubMedCrossRef
147.
Bauer-Mehren A, Rautschka M, Sanz F, Furlong LI: DisGeNET: a cytoscape plugin to visualize, integrate, search and analyze gene-disease networks. Bioinformatics 2010, 26: 2924–2926. 10.1093/bioinformatics/btq538PubMedCrossRef
148.
Merico D, Isserlin R, Stueker O, Emili A, Bader GD: Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PloS One 2010, 5: e13984. 10.1371/journal.pone.0013984PubMedCentralPubMedCrossRef
149.
Yang L, Walker JR, Hogenesch JB, Thomas RS: NetAtlas: a cytoscape plugin to examine signaling networks based on tissue gene expression. In Silico Biol 2008, 8: 47–52.PubMed
150.
Doncheva NT, Assenov Y, Domingues FS, Albrecht M: Topological analysis and interactive visualization of biological networks and protein structures. Nat Protoc 2012, 7: 670–685. 10.1038/nprot.2012.004PubMedCrossRef
151.
Moulos P, Klein J, Jupp S, Stevens R, Bascands JL, Schanstra JP: The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases. BMC Bioinform 2013, 14: 235. 10.1186/1471-2105-14-235CrossRef
152.
Razick S, Mora A, Michalickova K, Boddie P, Donaldson IM: iRefScape: a cytoscape plug-in for visualization and data mining of protein interaction data from iRefIndex. BMC Bioinform 2011, 12: 388. 10.1186/1471-2105-12-388CrossRef
153.
Gao J, Ade AS, Tarcea VG, Weymouth TE, Mirel BR, Jagadish HV, States DJ: Integrating and annotating the interactome using the MiMI plugin for cytoscape. Bioinformatics 2009, 25: 137–138. 10.1093/bioinformatics/btn501PubMedCentralPubMedCrossRef
154.
Avila-Campillo I, Drew K, Lin J, Reiss DJ, Bonneau R: BioNetBuilder: automatic integration of biological networks. Bioinformatics 2007, 23: 392–393. 10.1093/bioinformatics/btl604PubMedCrossRef
155.
Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, Bringas R: BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinform 2010, 11: 91. 10.1186/1471-2105-11-91CrossRef
156.
Creek DJ, Jankevics A, Burgess KE, Breitling R, Barrett MP: IDEOM: an excel interface for analysis of LC-MS-based metabolomics data. Bioinformatics 2012, 28: 1048–1049. 10.1093/bioinformatics/bts069PubMedCrossRef
157.
Scheltema RA, Jankevics A, Jansen RC, Swertz MA, Breitling R: PeakML/mzMatch: a file format, Java library, R library, and tool-chain for mass spectrometry data analysis. Anal Chem 2011, 83: 2786–2793. 10.1021/ac2000994PubMedCrossRef
158.
Barabasi AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat RevGenetics 2011, 12: 56–68.CrossRef
159.
Chowdhury SA, Koyuturk M: Identification of coordinately dysregulated subnetworks in complex phenotypes. In Pacific Symposium on Biocomputing 2010. Edited by: Altman RB, Dunker AK, Hunter L, Murray TA, Klein TE. Hackensack: World Scientific Publishing Co; 2011:133–144.
160.
Chowdhury SA, Nibbe RK, Chance MR, Koyuturk M: Subnetwork state functions define dysregulated subnetworks in cancer. J Comput Biol 2011, 18: 263–281. 10.1089/cmb.2010.0269PubMedCentralPubMedCrossRef
161.
Dao P, Colak R, Salari R, Moser F, Davicioni E, Schonhuth A, Ester M: Inferring cancer subnetwork markers using density-constrained biclustering. Bioinformatics 2010, 26: i625-i631. 10.1093/bioinformatics/btq393PubMedCentralPubMedCrossRef
162.
Dao P, Wang K, Collins C, Ester M, Lapuk A, Sahinalp SC: Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 2011, 27: i205-i213. 10.1093/bioinformatics/btr245PubMedCentralPubMedCrossRef
163.
Lee E, Chuang HY, Kim JW, Ideker T, Lee D: Inferring pathway activity toward precise disease classification. PLoS Comput Biol 2008, 4: e1000217. 10.1371/journal.pcbi.1000217PubMedCentralPubMedCrossRef
164.
Muller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, Lu C, Park IH, Rao MS, Shamir R, Schwartz PH, Schmidt NO, Loring JF: Regulatory networks define phenotypic classes of human stem cell lines. Nature 2008, 455: 401–405. 10.1038/nature07213PubMedCentralPubMedCrossRef
165.
Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ: Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 2010, 6: e1000662. 10.1371/journal.pcbi.1000662PubMedCentralPubMedCrossRef
166.
Chu LH, Chen BS: Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst Biol 2008, 2: 56. 10.1186/1752-0509-2-56PubMedCentralPubMedCrossRef
167.
Baker SG: The central role of receiver operating characteristic (ROC) curves in evaluating tests for the early detection of cancer. J Natl Cancer Inst 2003, 95: 511–515. 10.1093/jnci/95.7.511PubMedCrossRef
168.
Peres J: Risks of PSA screening now better understood. J Natl Cancer Inst 2013, 105: 1590–1592. 10.1093/jnci/djt328PubMedCrossRef
169.
Andriole GL, Crawford ED, Grubb RL III, Buys SS, Chia D, Church TR, Fouad MN, Isaacs C, Kvale PA, Reding DJ, Weissfeld JL, Yokochi LA, O’Brien B, Ragard LR, Clapp JD, Rathmell JM, Riley TL, Hsing AW, Izmirlian G, Pinsky PF, Kramer BS, Miller AB, Gohagan JK, Prorok PC: Prostate cancer screening in the randomized prostate, lung, colorectal, and ovarian cancer screening trial: mortality results after 13 years of follow-up. J Natl Cancer Inst 2012, 104: 125–132. 10.1093/jnci/djr500PubMedCentralPubMedCrossRef
170.
Brenner DE, Normolle DP: Biomarkers for cancer risk, early detection, and prognosis: the validation conundrum. Cancer Epidemiol Biomarkers Prev 2007, 16: 1918–1920. 10.1158/1055-9965.EPI-07-2619PubMedCrossRef
171.
Borrebaeck CA, Wingren C: Antibody array generation and use. Methods Mol Biol 2014, 1131: 563–571. 10.1007/978-1-62703-992-5_36PubMedCrossRef
172.
Kozak KR, Wang J, Lye M, Chuh J, Takkar R, Kim N, Lee H, Jeon NL, Lin K, Zhang C, Wong WL, DeForge LE: Micro-volume wall-less immunoassays using patterned planar plates. Lab Chip 2013, 13: 1342–1350. 10.1039/c3lc40973bPubMedCrossRef
173.
Ostendorff HP, Awad A, Braunschweiger KI, Liu Z, Wan Z, Rothschild KJ, Lim MJ: Multiplexed VeraCode bead-based serological immunoassay for colorectal cancer. J Immunol Methods 2013, 400–401: 58–69.PubMedCrossRef
174.
Tekin HC, Gijs MA: Ultrasensitive protein detection: a case for microfluidic magnetic bead-based assays. Lab Chip 2013, 13: 4711–4739. 10.1039/c3lc50477hPubMedCrossRef
175.
176.
Cho M, Soo Oh S, Nie J, Stewart R, Eisenstein M, Chambers J, Marth JD, Walker F, Thomson JA, Soh HT: Quantitative selection and parallel characterization of aptamers. Proc Natl Acad Sci USA 2013, 110: 18460–18465. 10.1073/pnas.1315866110PubMedCentralPubMedCrossRef
177.
Martinez O, Bellard E, Golzio M, Mechiche-Alami S, Rols MP, Teissie J, Ecochard V, Paquereau L: Direct validation of aptamers as powerful tools to image solid tumor. Nucleic Acid Ther in press in press
178.
Chung H, Kim B, Jung SH, Won KJ, Jiang X, Lee CK, Lim SD, Yang SK, Song KH, Kim HS: Does phosphorylation of cofilin affect the progression of human bladder cancer? BMC Cancer 2013, 13: 45. 10.1186/1471-2407-13-45PubMedCentralPubMedCrossRef
179.
Frantzi M, Zoidakis J, Papadopoulos T, Zurbig P, Katafigiotis I, Stravodimos K, Lazaris A, Giannopoulou I, Ploumidis A, Mischak H, Mullen W, Vlahou A: IMAC fractionation in combination with LC-MS reveals H2B and NIF-1 peptides as potential bladder cancer biomarkers. J Proteome Res 2013, 12: 3969–3979. 10.1021/pr400255hPubMedCrossRef
180.
Kato M, Wei M, Yamano S, Kakehashi A, Tamada S, Nakatani T, Wanibuchi H: DDX39 acts as a suppressor of invasion for bladder cancer. Cancer Sci 2012, 103: 1363–1369. 10.1111/j.1349-7006.2012.02298.xPubMedCrossRef
181.
Orenes-Pinero E, Corton M, Gonzalez-Peramato P, Algaba F, Casal I, Serrano A, Sanchez-Carbayo M: Searching urinary tumor markers for bladder cancer using a two-dimensional differential gel electrophoresis (2D-DIGE) approach. J Proteome Res 2007, 6: 4440–4448. 10.1021/pr070368wPubMedCrossRef
182.
Zoidakis J, Makridakis M, Zerefos PG, Bitsika V, Esteban S, Frantzi M, Stravodimos K, Anagnou NP, Roubelakis MG, Sanchez-Carbayo M, Vlahou A: Profilin 1 is a potential biomarker for bladder cancer aggressiveness. Mol Cell Proteomics 2012, 11: M111. 009449 009449PubMedCentralPubMedCrossRef
183.
Lee JY, Kim JY, Cheon MH, Park GW, Ahn YH, Moon MH, Yoo JS: MRM validation of targeted nonglycosylated peptides from N-glycoprotein biomarkers using direct trypsin digestion of undepleted human plasma. J Proteomics 2014, 98C: 206–217.CrossRef
184.
Lemoine J, Fortin T, Salvador A, Jaffuel A, Charrier JP, Choquet-Kastylevsky G: The current status of clinical proteomics and the use of MRM and MRM(3) for biomarker validation. Expert Rev Mol Diagn 2012, 12: 333–342. 10.1586/erm.12.32PubMedCrossRef
185.
Yang N, Feng S, Shedden K, Xie X, Liu Y, Rosser CJ, Lubman DM, Goodison S: Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification. Clin Cancer Res 2011, 17: 3349–3359. 10.1158/1078-0432.CCR-10-3121PubMedCentralPubMedCrossRef
186.
Raab SS, Grzybicki DM, Vrbin CM, Geisinger KR: Urine cytology discrepancies: frequency, causes, and outcomes. Am J Clin Pathol 2007, 127: 946–953. 10.1309/XUVXFXMFPL7TELCEPubMedCrossRef
187.
Chang YH, Wu CH, Lee YL, Huang PH, Kao YL, Shiau MY: Evaluation of nuclear matrix protein-22 as a clinical diagnostic marker for bladder cancer. Urology 2004, 64: 687–692. 10.1016/j.urology.2004.05.038PubMedCrossRef
188.
Halling KC, King W, Sokolova IA, Karnes RJ, Meyer RG, Powell EL, Sebo TJ, Cheville JC, Clayton AC, Krajnik KL, Ebert TA, Nelson RE, Burkhardt HM, Ramakumar S, Stewart CS, Pankratz VS, Lieber MM, Blute ML, Zincke H, Seelig SA, Jenkins RB, O’Kane DJ: A comparison of BTA stat, hemoglobin dipstick, telomerase and Vysis UroVysion assays for the detection of urothelial carcinoma in urine. J Urol 2002, 167: 2001–2006. 10.1016/S0022-5347(05)65072-0PubMedCrossRef
189.
Sullivan PS, Nooraie F, Sanchez H, Hirschowitz S, Levin M, Rao PN, Rao J: Comparison of ImmunoCyt, UroVysion, and urine cytology in detection of recurrent urothelial carcinoma: a “split-sample” study. Cancer 2009, 117: 167–173.PubMed
190.
Cheng L, Davison DD, Adams J, Lopez-Beltran A, Wang L, Montironi R, Zhang S: Biomarkers in bladder cancer: translational and clinical implications. Crit Rev Oncol Hematol in press in press
191.
Kamat AM, Hegarty PK, Gee JR, Clark PE, Svatek RS, Hegarty N, Shariat SF, Xylinas E, Schmitz-Drager BJ, Lotan Y, Jenkins LC, Droller M, van Rhijn BW, Karakiewicz PI: ICUD-EAU International Consultation on Bladder Cancer 2012: screening, diagnosis, and molecular markers. Eur Urol 2013, 63: 4–15. 10.1016/j.eururo.2012.09.057PubMedCrossRef
192.
Cho WC: Proteomics and translational medicine: molecular biomarkers for cancer diagnosis, prognosis and prediction of therapy outcome. Expert Rev Proteomics 2011, 8: 1–4. 10.1586/epr.10.108PubMedCrossRef
193.
Chen CL, Lin TS, Tsai CH, Wu CC, Chung T, Chien KY, Wu M, Chang YS, Yu JS, Chen YT: Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics. J Proteomics 2013, 85: 28–43.PubMedCrossRef
194.
Grau L, Luque-Garcia JL, Gonzalez-Peramato P, Theodorescu D, Palou J, Fernandez-Gomez JM, Sanchez-Carbayo M: A quantitative proteomic analysis uncovers the relevance of CUL3 in bladder cancer aggressiveness. PloS One 2013, 8: e53328. 10.1371/journal.pone.0053328PubMedCentralPubMedCrossRef
195.
Shimwell NJ, Bryan RT, Wei W, James ND, Cheng KK, Zeegers MP, Johnson PJ, Martin A, Ward DG: Combined proteome and transcriptome analyses for the discovery of urinary biomarkers for urothelial carcinoma. Br J Cancer 2013, 108: 1854–1861. 10.1038/bjc.2013.157PubMedCentralPubMedCrossRef
196.
Urquidi V, Rosser CJ, Goodison S: Molecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis. Curr Med Chem 2012, 19: 3653–3663. 10.2174/092986712801661103PubMedCentralPubMedCrossRef
197.
Mischak H, Ioannidis JP, Argiles A, Attwood TK, Bongcam-Rudloff E, Broenstrup M, Charonis A, Chrousos GP, Delles C, Dominiczak A, Dylag T, Ehrich J, Egido J, Findeisen P, Jankowski J, Johnson RW, Julien BA, Lankisch T, Leung HY, Maahs D, Magni F, Manns MP, Manolis E, Mayer G, Navis G, Novak J, Ortiz A, Persson F, Peter K, Riese HH, et al.: Implementation of proteomic biomarkers: making it work. Eur J Clin Invest 2012, 42: 1027–1036. 10.1111/j.1365-2362.2012.02674.xPubMedCentralPubMedCrossRef
198.
Majewski T, Spiess PE, Bondaruk J, Black P, Clarke C, Benedict W, Dinney CP, Grossman HB, Tang KS, Czerniak B: Detection of bladder cancer using proteomic profiling of urine sediments. PloS One 2012, 7: e42452. 10.1371/journal.pone.0042452PubMedCentralPubMedCrossRef
199.
Good DM, Zurbig P, Argiles A, Bauer HW, Behrens G, Coon JJ, Dakna M, Decramer S, Delles C, Dominiczak AF, Ehrich JH, Eitner F, Fliser D, Frommberger M, Ganser A, Girolami MA, Golovko I, Gwinner W, Haubitz M, Herget-Rosenthal S, Jankowski J, Jahn H, Jerums G, Julian BA, Kellmann M, Kliem V, Kolch W, Krolewski AS, Luppi M, Massy Z, et al.: Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics 2010, 9: 2424–2437. 10.1074/mcp.M110.001917PubMedCentralPubMedCrossRef
200.
Molin L, Seraglia R, Lapolla A, Ragazzi E, Gonzalez J, Vlahou A, Schanstra JP, Albalat A, Dakna M, Siwy J, Jankowski J, Bitsika V, Mischak H, Zürbig P, Traldi P: A comparison between MALDI-MS and CE-MS data for biomarker assessment in chronic kidney diseases. J Proteomics 2012, 75: 5888–5897. 10.1016/j.jprot.2012.07.024PubMedCrossRef
201.
Zurbig P, Jerums G, Hovind P, Macisaac RJ, Mischak H, Nielsen SE, Panagiotopoulos S, Persson F, Rossing P: Urinary proteomics for early diagnosis in diabetic nephropathy. Diabetes 2012, 61: 3304–3313. 10.2337/db12-0348PubMedCentralPubMedCrossRef
202.
Andersen S, Mischak H, Zurbig P, Parving HH, Rossing P: Urinary proteome analysis enables assessment of renoprotective treatment in type 2 diabetic patients with microalbuminuria. BMC Nephrol 2010, 11: 29. 10.1186/1471-2369-11-29PubMedCentralPubMedCrossRef
203.
Lankisch TO, Metzger J, Negm AA, Vosskuhl K, Schiffer E, Siwy J, Weismuller TJ, Schneider AS, Thedieck K, Baumeister R, Zürbig P, Weissinger EM, Manns MP, Mischak H, Wedemeyer J: Bile proteomic profiles differentiate cholangiocarcinoma from primary sclerosing cholangitis and choledocholithiasis. Hepatology 2011, 53: 875–884. 10.1002/hep.24103PubMedCrossRef
204.
Metzger J, Negm AA, Plentz RR, Weismuller TJ, Wedemeyer J, Karlsen TH, Dakna M, Mullen W, Mischak H, Manns MP, Lankisch TO: Urine proteomic analysis differentiates cholangiocarcinoma from primary sclerosing cholangitis and other benign biliary disorders. Gut 2013, 62: 122–130. 10.1136/gutjnl-2012-302047PubMedCrossRef
Metadata
Title
Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development
Authors
Maria Frantzi
Akshay Bhat
Agnieszka Latosinska
Publication date
01-12-2014
Publisher
Springer Berlin Heidelberg
Published in
Clinical and Translational Medicine / Issue 1/2014
Electronic ISSN: 2001-1326
DOI
https://doi.org/10.1186/2001-1326-3-7

Other articles of this Issue 1/2014

Clinical and Translational Medicine 1/2014 Go to the issue

Review

Biomechanics of TGFβ-induced epithelial-mesenchymal transition: implications for fibrosis and cancer

Review

Clinical potentials of human pluripotent stem cells in lung diseases

Review

Novel clinical therapeutics targeting the epithelial to mesenchymal transition

Review

A review of research on salivary biomarkers for oral cancer detection

Review

Cellular reprogramming for pancreatic β-cell regeneration: clinical potential of small molecule control

Research

Lung flute improves symptoms and health status in COPD with chronic bronchitis: A 26 week randomized controlled trial