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Theme: Pharmacogenomic, Proteomic & Metabolomic Applications - Review

Label-free mass spectrometry-based proteomics for biomarker discovery and validation

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Pages 343-359 | Published online: 09 Jan 2014

References

  • Boja ES, Rodriguez H. The path to clinical proteomics research: integration of proteomics, genomics, clinical laboratory and regulatory science. Korean J. Lab. Med.31(2), 61–71 (2011).
  • Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat. Biotechnol.24(8), 971–983 (2006).
  • Zhang Q, Faca V, Hanash S. Mining the plasma proteome for disease applications across seven logs of protein abundance. J. Proteome Res.10(1), 46–50 (2011).
  • Simpson RJ, Bernhard OK, Greening DW, Moritz RL. Proteomics-driven cancer biomarker discovery: looking to the future. Curr. Opin. Chem. Biol.12(1), 72–77 (2008).
  • Rajcevic U, Niclou SP, Jimenez CR. Proteomics strategies for target identification and biomarker discovery in cancer. Front. Biosci.14, 3292–3303 (2009).
  • Surinova S, Schiess R, Huttenhain R, Cerciello F, Wollscheid B, Aebersold R. On the development of plasma protein biomarkers. J. Proteome Res.10(1), 5–16 (2011).
  • Cox J, Mann M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu. Rev. Biochem.80, 273–299 (2011).
  • Gerszten RE, Asnani A, Carr SA. Status and prospects for discovery and verification of new biomarkers of cardiovascular disease by proteomics. Circ. Res.109(4), 463–474 (2011).
  • Wisniewski JR, Ostasiewicz P, Mann M. High recovery FASP applied to the proteomic analysis of microdissected formalin fixed paraffin embedded cancer tissues retrieves known colon cancer markers. J. Proteome Res.10(7), 3040–3049 (2011).
  • Hartwell L, Mankoff D, Paulovich A, Ramsey S, Swisher E. Cancer biomarkers: a systems approach. Nat. Biotechnol.24(8), 905–908 (2006).
  • Ransohoff DF, Gourlay ML. Sources of bias in specimens for research about molecular markers for cancer. J. Clin. Oncol.28(4), 698–704 (2010).
  • Whiteaker JR, Lin C, Kennedy J et al. A targeted proteomics-based pipeline for verification of biomarkers in plasma. Nat. Biotechnol.29(7), 625–634 (2011).
  • Jimenez CR, Piersma S, Pham TV. High-throughput and targeted in-depth mass spectrometry-based approaches for biofluid profiling and biomarker discovery. Biomark. Med.1(4), 541–565 (2007).
  • Neilson KA, Ali NA, Muralidharan S et al. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics11(4), 535–553 (2011).
  • Ong SE, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol.1(5), 252–262 (2005).
  • Bantscheff M, Schirle M, Sweetman G, Rick J, Kuster B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem.389(4), 1017–1031 (2007).
  • Ong SE, Blagoev B, Kratchmarova I et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics1(5), 376–386 (2002).
  • Ross PL, Huang YN, Marchese JN et al. Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol. Cell. Proteomics3(12), 1154–1169 (2004).
  • Thompson A, Schafer J, Kuhn K et al. Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal. Chem.75(8), 1895–1904 (2003).
  • Hsu JL, Huang SY, Chow NH, Chen SH. Stable-isotope dimethyl labeling for quantitative proteomics. Anal. Chem.75(24), 6843–6852 (2003).
  • Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc. Natl Acad. Sci. USA100(12), 6940–6945 (2003).
  • Beynon RJ, Doherty MK, Pratt JM, Gaskell SJ. Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides. Nat. Methods2(8), 587–589 (2005).
  • Levin Y, Schwarz E, Wang L, Leweke FM, Bahn S. Label-free LC–MS/MS quantitative proteomics for large-scale biomarker discovery in complex samples. J. Sep. Sci.30(14), 2198–2203 (2007).
  • Piersma SR, Fiedler U, Span S et al. Workflow comparison for label-free, quantitative secretome proteomics for cancer biomarker discovery: method evaluation, differential analysis, and verification in serum. J. Proteome Res.9(4), 1913–1922 (2010).
  • Albrethsen J, Knol JC, Piersma SR et al. Subnuclear proteomics in colorectal cancer: identification of proteins enriched in the nuclear matrix fraction and regulation in adenoma to carcinoma progression. Mol. Cell. Proteomics9(5), 988–1005 (2010).
  • Fratantoni SA, Piersma SR, Jimenez CR. Comparison of the performance of two affinity depletion spin filters for quantitative proteomics of CSF: evaluation of sensitivity and reproducibility of CSF analysis using GeLC–MS/MS and spectral counting. Proteomics Clin. Appl.4(6–7), 613–617 (2010).
  • Collier TS, Sarkar P, Franck WL, Rao BM, Dean RA, Muddiman DC. Direct comparison of stable isotope labeling by amino acids in cell culture and spectral counting for quantitative proteomics. Anal. Chem.82(20), 8696–8702 (2010).
  • Higgs RE, Knierman MD, Gelfanova V, Butler JP, Hale JE. Comprehensive label-free method for the relative quantification of proteins from biological samples. J. Proteome Res.4(4), 1442–1450 (2005).
  • Liu H, Sadygov RG, Yates JR 3rd. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem.76(14), 4193–4201 (2004).
  • Old WM, Meyer-Arendt K, Aveline-Wolf L et al. Comparison of label-free methods for quantifying human proteins by shotgun proteomics. Mol. Cell. Proteomics4(10), 1487–1502 (2005).
  • Collier TS, Randall SM, Sarkar P, Rao BM, Dean RA, Muddiman DC. Comparison of stable-isotope labeling with amino acids in cell culture and spectral counting for relative quantification of protein expression. Rapid Commun. Mass Spectrom.25(17), 2524–2532 (2011).
  • Asara JM, Christofk HR, Freimark LM, Cantley LC. A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen. Proteomics8(5), 994–999 (2008).
  • Griffin NM, Yu J, Long F et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol.28(1), 83–89 (2010).
  • Bellew M, Coram M, Fitzgibbon M et al. A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC–MS. Bioinformatics22(15), 1902–1909 (2006).
  • Voss B, Hanselmann M, Renard BY et al. SIMA: simultaneous multiple alignment of LC/MS peak lists. Bioinformatics27(7), 987–993 (2011).
  • Jaffe JD, Mani DR, Leptos KC, Church GM, Gillette MA, Carr SA. PEPPeR, a platform for experimental proteomic pattern recognition. Mol. Cell. Proteomics5(10), 1927–1941 (2006).
  • Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol.26(12), 1367–1372 (2008).
  • Khan Z, Bloom JS, Garcia BA, Singh M, Kruglyak L. Protein quantification across hundreds of experimental conditions. Proc. Natl Acad. Sci. USA106(37), 15544–15548 (2009).
  • Tsou CC, Tsai CF, Tsui YH et al. IDEAL-Q, an automated tool for label-free quantitation analysis using an efficient peptide alignment approach and spectral data validation. Mol. Cell. Proteomics9(1), 131–144 (2010).
  • Hoehenwarter W, Wienkoop S. Spectral counting robust on high mass accuracy mass spectrometers. Rapid Commun. Mass Spectrom.24(24), 3609–3614 (2010).
  • Paoletti AC, Parmely TJ, Tomomori-Sato C et al. Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc. Natl Acad. Sci. USA103(50), 18928–18933 (2006).
  • Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J. Proteome Res.5(9), 2339–2347 (2006).
  • Zhang Y, Wen Z, Washburn MP, Florens L. Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. Anal. Chem.82(6), 2272–2281 (2010).
  • Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B57, 289–300 (1995).
  • Quackenbush J. Computational analysis of microarray data. Nat. Rev. Genet.2(6), 418–427 (2001).
  • Pham TV, Piersma SR, Warmoes M, Jimenez CR. On the β-binomial model for analysis of spectral count data in label-free tandem mass spectrometry-based proteomics. Bioinformatics26(3), 363–369 (2010).
  • Booth JG, Eilertson KE, Olinares PD, Yu H. A bayesian mixture model for comparative spectral count data in shotgun proteomics. Mol. Cell. Proteomics10(8), M110.007203 (2011).
  • Choi H, Fermin D, Nesvizhskii AI. Significance analysis of spectral count data in label-free shotgun proteomics. Mol. Cell. Proteomics7(12), 2373–2385 (2008).
  • Dudoit S, Shaffer JP, Boldrick JC. Multiple hypothesis testing in microarray experiments. Stat. Sci.18, 71–103 (2003).
  • Oberg AL, Vitek O. Statistical design of quantitative mass spectrometry-based proteomic experiments. J. Proteome Res.8(5), 2144–2156 (2009).
  • Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol.4, 222 (2008).
  • Anderson L, Hunter CL. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics5(4), 573–588 (2006).
  • 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. Proteomics6(12), 2212–2229 (2007).
  • Addona TA, Abbatiello SE, Schilling B et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat. Biotechnol.27(7), 633–641 (2009).
  • Zhi W, Wang M, She JX. Selected reaction monitoring (SRM) mass spectrometry without isotope labeling can be used for rapid protein quantification. Rapid Commun. Mass Spectrom.25(11), 1583–1588 (2011).
  • Balasubramaniam D, Eissler CL, Stauffacher CV, Hall MC. Use of selected reaction monitoring data for label-free quantification of protein modification stoichiometry. Proteomics10(23), 4301–4305 (2010).
  • Elschenbroich S, Kislinger T. Targeted proteomics by selected reaction monitoring mass spectrometry: applications to systems biology and biomarker discovery. Mol. Biosyst.7(2), 292–303 (2011).
  • Huttenhain R, Malmstrom J, Picotti P, Aebersold R. Perspectives of targeted mass spectrometry for protein biomarker verification. Curr. Opin. Chem. Biol.13(5–6), 518–525 (2009).
  • Maclean B, Tomazela DM, Shulman N et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics26(7), 966–968 (2010).
  • Stergachis AB, Maclean B, Lee K, Stamatoyannopoulos JA, Maccoss MJ. Rapid empirical discovery of optimal peptides for targeted proteomics. Nat. Methods8(12), 1041–1043 (2011).
  • Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell138(4), 795–806 (2009).
  • Warmoes M, Jaspers JE, Pham TV et al. Proteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with diagnostic and prognostic value in human breast cancer. Mol. Cell. Proteomics doi:10.1074/mcp.M111.013334 (2012) (Epub ahead of print).
  • Addona TA, Shi X, Keshishian H et al. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat. Biotechnol.29(7), 635–643 (2011).
  • Tabata T, Sato T, Kuromitsu J, Oda Y. Pseudo internal standard approach for label-free quantitative proteomics. Anal. Chem.79(22), 8440–8445 (2007).
  • Bluemlein K, Ralser M. Monitoring protein expression in whole-cell extracts by targeted label- and standard-free LC–MS/MS. Nat. Protoc.6(6), 859–869 (2011).
  • Choi S, Kim J, Yea K, Suh PG, Kim J, Ryu SH. Targeted label-free quantitative analysis of secretory proteins from adipocytes in response to oxidative stress. Anal. Biochem.401(2), 196–202 (2010).
  • Zhang H, Liu Q, Zimmerman LJ et al. Methods for peptide and protein quantitation by liquid chromatography–multiple reaction monitoring mass spectrometry. Mol. Cell. Proteomics10(6), M110.006593 (2011).
  • Park J, Cha DH, Lee SJ, Kim YN, Kim YH, Kim KP. Discovery of the serum biomarker proteins in severe preeclampsia by proteomic analysis. Exp. Mol. Med.43(7), 427–435 (2011).
  • Turtoi A, Mazzucchelli GD, De PE. Isotope coded protein label quantification of serum proteins – comparison with the label-free LC–MS and validation using the MRM approach. Talanta80(4), 1487–1495 (2010).
  • Cima I, Schiess R, Wild P et al. Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer. Proc. Natl Acad. Sci. USA108(8), 3342–3347 (2011).
  • Fugmann T, Borgia B, Revesz C et al. Proteomic identification of vanin-1 as a marker of kidney damage in a rat model of type 1 diabetic nephropathy. Kidney Int.80(3), 272–281 (2011).
  • Zhou JY, Afjehi-Sadat L, Asress S et al. Galectin-3 is a candidate biomarker for amyotrophic lateral sclerosis: discovery by a proteomics approach. J. Proteome Res.9(10), 5133–5141 (2010).
  • De Wit M, Jimenez CR, Carvalho B et al. Cell surface proteomics identifies glucose transporter type 1 and prion protein as candidate biomarkers for colorectal adenoma-to-carcinoma progression. Gut doi:10.1136/gutjnl-2011-300511 (2011) (Epub ahead of print).
  • Alldridge L, Metodieva G, Greenwood C et al. Proteome profiling of breast tumors by gel electrophoresis and nanoscale electrospray ionization mass spectrometry. J. Proteome Res.7(4), 1458–1469 (2008).
  • May D, Pan S, Crispin DA et al. Investigating neoplastic progression of ulcerative colitis with label-free comparative proteomics. J. Proteome Res.10(1), 200–209 (2011).
  • Gromov P, Gromova I, Bunkenborg J et al. Up-regulated proteins in the fluid bathing the tumour cell microenvironment as potential serological markers for early detection of cancer of the breast. Mol. Oncol.4(1), 65–89 (2010).
  • Han CL, Chen JS, Chan EC et al. An informatics-assisted label-free approach for personalized tissue membrane proteomics: case study on colorectal cancer. Mol. Cell. Proteomics.10(4), M110.003087 (2011).
  • Conrotto P, Roesli C, Rybak J et al. Identification of new accessible tumor antigens in human colon cancer by ex vivo protein biotinylation and comparative mass spectrometry analysis. Int. J. Cancer123(12), 2856–2864 (2008).
  • Hyung SW, Lee MY, Yu JH et al. A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers. Mol. Cell. Proteomics10(10), M111.011023 (2011).
  • Rikova K, Guo A, Zeng Q et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell131(6), 1190–1203 (2007).
  • Han CL, Chien CW, Chen WC et al. A multiplexed quantitative strategy for membrane proteomics: opportunities for mining therapeutic targets for autosomal dominant polycystic kidney disease. Mol. Cell. Proteomics7(10), 1983–1997 (2008).
  • Pan S, Chen R, Aebersold R, Brentnall TA. Mass spectrometry based glycoproteomics–from a proteomics perspective. Mol. Cell. Proteomics10(1), R110.003251 (2011).
  • Strassberger V, Fugmann T, Neri D, Roesli C. Chemical proteomic and bioinformatic strategies for the identification and quantification of vascular antigens in cancer. J. Proteomics73(10), 1954–1973 (2010).
  • Rush J, Moritz A, Lee KA et al. Immunoaffinity profiling of tyrosine phosphorylation in cancer cells. Nat. Biotechnol.23(1), 94–101 (2005).
  • Gu TL, Deng X, Huang F et al. Survey of tyrosine kinase signaling reveals ROS kinase fusions in human cholangiocarcinoma. PLoS One6(1), e15640 (2011).
  • Gutstein HB, Morris JS. Laser capture sampling and analytical issues in proteomics. Expert Rev. Proteomics4(5), 627–637 (2007).
  • Xu BJ. Combining laser capture microdissection and proteomics: methodologies and clinical applications. Proteomics Clin. Appl.4(2), 116–123 (2010).
  • Thakur D, Rejtar T, Wang D et al. Microproteomic analysis of 10,000 laser captured microdissected breast tumor cells using short-range sodium dodecyl sulfate-polyacrylamide gel electrophoresis and porous layer open tubular liquid chromatography tandem mass spectrometry. J. Chromatogr. A1218(45), 8168–8174 (2011).
  • Hill JJ, Tremblay TL, Pen A et al. Identification of vascular breast tumor markers by laser capture microdissection and label-free LC–MS. J. Proteome Res.10(5), 2479–2493 (2011).
  • Braakman RB, Tilanus-Linthorst MM, Liu NQ et al. Optimized nLC–MS workflow for laser capture microdissected breast cancer tissue. J. Proteomics doi:10.1016/j.jprot.2012.01.022 (2012) (Epub ahead of print).
  • Tanca A, Pagnozzi D, Addis MF. Setting proteins free: Progresses and achievements in proteomics of formalin-fixed, paraffin-embedded tissues. Proteomics Clin. Appl.6(1–2), 7–21 (2011).
  • Ostasiewicz P, Zielinska DF, Mann M, Wisniewski JR. Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry. J. Proteome Res.9(7), 3688–3700 (2010).
  • Negishi A, Masuda M, Ono M et al. Quantitative proteomics using formalin-fixed paraffin-embedded tissues of oral squamous cell carcinoma. Cancer Sci.100(9), 1605–1611 (2009).
  • Pan S, Chen R, Stevens T et al. Proteomics portrait of archival lesions of chronic pancreatitis. PLoS One6(11), e27574 (2011).
  • Bell LN, Saxena R, Mattar SG, You J, Wang M, Chalasani N. Utility of formalin-fixed, paraffin-embedded liver biopsy specimens for global proteomic analysis in nonalcoholic steatohepatitis. Proteomics Clin. Appl.5(7–8), 397–404 (2011).
  • Nirmalan NJ, Hughes C, Peng J et al. Initial development and validation of a novel extraction method for quantitative mining of the formalin-fixed, paraffin-embedded tissue proteome for biomarker investigations. J. Proteome Res.10(2), 896–906 (2011).
  • Tanca A, Addis MF, Pagnozzi D et al. Proteomic analysis of formalin-fixed, paraffin-embedded lung neuroendocrine tumor samples from hospital archives. J. Proteomics74(3), 359–370 (2011).
  • Sprung RW, Brock JW, Tanksley JP et al. Equivalence of protein inventories obtained from formalin-fixed paraffin-embedded and frozen tissue in multidimensional liquid chromatography-tandem mass spectrometry shotgun proteomic analysis. Mol. Cell. Proteomics8(8), 1988–1998 (2009).
  • Tanca A, Pagnozzi D, Falchi G et al. Impact of fixation time on GeLC–MS/MS proteomic profiling of formalin-fixed, paraffin-embedded tissues. J. Proteomics74(7), 1015–1021 (2011).
  • Teng PN, Bateman NW, Hood BL, Conrads TP. Advances in proximal fluid proteomics for disease biomarker discovery. J. Proteome Res.9(12), 6091–6100 (2010).
  • Kim MJ, Frankel AH, Tam FW. Urine proteomics and biomarkers in renal disease. Nephron Exp. Nephrol.119(1), E1–E7 (2011).
  • Filiou MD, Turck CW, Martins-de-Souza D. Quantitative proteomics for investigating psychiatric disorders. Proteomics Clin. Appl.5(1–2), 38–49 (2011).
  • Liang CR, Tan S, Tan HT et al. Proteomic analysis of human gastric juice: a shotgun approach. Proteomics10(21), 3928–3931 (2010).
  • Soltermann A, Ossola R, Kilgus-Hawelski S et al.N-glycoprotein profiling of lung adenocarcinoma pleural effusions by shotgun proteomics. Cancer114(2), 124–133 (2008).
  • Yu CJ, Wang CL, Wang CI et al. Comprehensive proteome analysis of malignant pleural effusion for lung cancer biomarker discovery by using multidimensional protein identification technology. J. Proteome Res.10(10), 4671–4682 (2011).
  • Haslene-Hox H, Oveland E, Berg KC et al. A new method for isolation of interstitial fluid from human solid tumors applied to proteomic analysis of ovarian carcinoma tissue. PLoS One6(4), e19217 (2011).
  • Kosanam H, Makawita S, Judd B, Newman A, Diamandis EP. Mining the malignant ascites proteome for pancreatic cancer biomarkers. Proteomics11(23), 4551–4558 (2011).
  • Farid SG, Craven RA, Peng J et al. Shotgun proteomics of human bile in hilar cholangiocarcinoma. Proteomics11(10), 2134–2138 (2011).
  • Drake RR, Elschenbroich S, Lopez-Perez O et al. In-depth proteomic analyses of direct expressed prostatic secretions. J. Proteome Res.9(5), 2109–2116 (2010).
  • Tan S, Liang CR, Yeoh KG, So J, Hew CL, Chung MC. Gastrointestinal fluids proteomics. Proteomics Clin. Appl.1(8), 820–833 (2007).
  • Hitti J, Lapidus JA, Lu X et al. Noninvasive diagnosis of intraamniotic infection: proteomic biomarkers in vaginal fluid. Am. J. Obstet. Gynecol.203(1), 32–38 (2010).
  • Cutillas PR, Chalkley RJ, Hansen KC et al. The urinary proteome in Fanconi syndrome implies specificity in the reabsorption of proteins by renal proximal tubule cells. Am. J. Physiol. Renal Physiol.287(3), F353–F364 (2004).
  • Morales DM, Townsend RR, Malone JP et al. Alterations in protein regulators of neurodevelopment in the cerebrospinal fluid of infants with post-hemorrhagic hydrocephalus of prematurity. Mol. Cell. ProteomicsM111.011973 (2011).
  • Schutzer SE, Angel TE, Liu T et al. Distinct cerebrospinal fluid proteomes differentiate post-treatment lyme disease from chronic fatigue syndrome. PLoS One6(2), e17287 (2011).
  • Zoidakis J, Makridakis M, Zerefos PG et al. Profilin 1 is a potential biomarker for bladder cancer aggressiveness. Mol. Cell. Proteomics11(4), M111.009449 (2011) (Epub ahead of print).
  • Rao PV, Reddy AP, Lu X et al. Proteomic identification of salivary biomarkers of type-2 diabetes. J. Proteome Res.8(1), 239–245 (2009).
  • Amon LM, Law W, Fitzgibbon MP et al. Integrative proteomic analysis of serum and peritoneal fluids helps identify proteins that are up-regulated in serum of women with ovarian cancer. PLoS One5(6), e11137 (2010).
  • Pan J, Chen HQ, Sun YH, Zhang JH, Luo XY. Comparative proteomic analysis of non-small-cell lung cancer and normal controls using serum label-free quantitative shotgun technology. Lung186(4), 255–261 (2008).
  • Hu X, Zhang Y, Zhang A et al. Comparative serum proteome analysis of human lymph node negative/positive invasive ductal carcinoma of the breast and benign breast disease controls via label-free semiquantitative shotgun technology. OMICS13(4), 291–300 (2009).
  • Beer LA, Tang HY, Barnhart KT, Speicher DW. Plasma biomarker discovery using 3D protein profiling coupled with label-free quantitation. Methods Mol. Biol.728, 3–27 (2011).
  • Beer LA, Tang HY, Sriswasdi S, Barnhart KT, Speicher DW. Systematic discovery of ectopic pregnancy serum biomarkers using 3-D protein profiling coupled with label-free quantitation. J. Proteome Res.10(3), 1126–1138 (2011).
  • Tang HY, Beer LA, Chang-Wong T et al. A xenograft mouse model coupled with in-depth plasma proteome analysis facilitates identification of novel serum biomarkers for human ovarian cancer. J. Proteome Res.11(2), 678–691 (2012).
  • Fatima N, Chelius D, Luke BT et al. Label-free global serum proteomic profiling reveals novel celecoxib-modulated proteins in familial adenomatous polyposis patients. Cancer Genomics Proteomics6(1), 41–49 (2009).
  • Metodieva G, Greenwood C, Alldridge L, Sauven P, Metodiev M. A peptide-centric approach to breast cancer biomarker discovery utilizing label-free multiple reaction monitoring mass spectrometry. Proteomics Clin. Appl.3(1), 78–82 (2009).
  • Ueda K, Saichi N, Takami S et al. A comprehensive peptidome profiling technology for the identification of early detection biomarkers for lung adenocarcinoma. PLoS One6(4), e18567 (2011).
  • Elschenbroich S, Ignatchenko V, Clarke B et al. In-depth proteomics of ovarian cancer ascites: combining shotgun proteomics and selected reaction monitoring mass spectrometry. J. Proteome Res.10(5), 2286–2299 (2011).
  • Cho CK, Drabovich AP, Batruch I, Diamandis EP. Verification of a biomarker discovery approach for detection of Down syndrome in amniotic fluid via multiplex selected reaction monitoring (SRM) assay. J. Proteomics74(10), 2052–2059 (2011).
  • Drabovich AP, Jarvi K, Diamandis EP. Verification of male infertility biomarkers in seminal plasma by multiplex selected reaction monitoring assay. Mol. Cell. Proteomics10(12), M110.004127 (2011).
  • Ludwig C, Claassen M, Schmidt A, Aebersold R. Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Mol. Cell. Proteomics11(3), M111.013987 (2011).

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