Proteomic profiling by mass spectrometry has tremendous potential for identifying disease biomarkers. A key limitation of mass spectrometry is that the information provided on the abundance of the various peptides is only relative. Thus, normalization is typically employed. Several normalization methods have been proposed and implemented in the literature. However, it is not clear if there is any reason to prefer one method over another. The goal of this study was to investigate the effect of normalization strategy on the identification of putative biomarkers from MALDI-TOF and SELDI-TOF mass spectra. Our results demonstrate that many of the putative biomarkers identified by mass spectrometry will be the same regardless of which data normalization scheme is applied. However, there can be substantial differences in the m/z values identified as being most discriminatory based on choice of normalization method. As there is no consistent pattern as to which normalization method yields the most promising targets for follow up study, we recommend that investigators routinely repeat their analysis with multiple normalization methods and consider the top several candidates identified in each case.
Get full access to this article
View all access options for this article.
References
1.
AlfassiZ.B.2004. On the normalization of a mass spectrum for comparison of two spectra. J Am Soc Mass Spectrom, 15:385–387.
2.
BellmanR.E.1961. Adaptive Control Processes: A Guided Tour. Princeton Univesity Press: Princeton, NJ.
3.
BhattacharyyaS., SiegelE.R., PetersenG.M., ChariS.T., SuvaS.J., HaunR.S.2004. Diagnosis of pancreatic cancer using serum proteomic profiling. Neoplasia, 6:674–686.
4.
CairnsD.A., ThompsonD., PerkinsD.N., StanleyA.J., SelbyP.J., BanksR.E.2008. Proteomic profiling using mass spectrometry does normalising by total ion current potentially mask some biological differences?Proteomics, 8:21–27.
5.
CallisterS.J., BarryR.C., AdkinsJ.N., JohnsonE.T., QianW.J., Webb-RobertsonB.J.M.et al.2006. Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics. J Proteome Res, 5:277–286.
6.
ConradsT.P., ZhouM., PetricoinE.F.3rd, LiottaL., VeenstraT.D.2003. Cancer diagnosis using proteomic patterns. Expert Rev Mol Diag, 3:411–420.
7.
CoombesK.R., FritscheH.A.Jr., ClarkeC., ChenJ.N., BaggerlyK.A., MorrisJ.S.et al.2003. Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin Chem, 49:1615–1623.
8.
De NooM.E., TollenaarR., OzalpA., KuppenP.J.K., BladergroenM.R., EilersP.H.C.et al.2005. Reliability of human serum protein profiles generated with C8 magnetic beads assisted MALDI-TOF mass spectrometry. Anal Chem, 77:7232–7241.
9.
De NooM.E., TollenaarR., DeelderA.M., BouwmanL.H.2006. Current status and prospects of clinical proteomics studies on detection of colorectal cancer: hopes and fears. World J Gastroenterol, 12:6594–6601.
10.
DrahosL., VekeyK.1996. Quantification of isomeric differences in mass spectra. Rapid Commun Mass Spectrom, 10:1309–1315.
11.
Eckel-PassowJ.E., ObergA.L., TherneauT.M., BergenH.R.2009. An insight into high-resolution mass-spectrometry data. Biostatistics, 10:481–500.
12.
EfronB., TibshiraniR.J., EfronB.1994. An Introduction to the Bootstrap. Chapman & Hall/CRC: New York.
13.
El-AneedA., CohenA, BanoubJ.2009. Mass spectrometry, review of the basics: electrospray, MALDI, and commonly used mass analyzers. Appl Spectrosc Rev, 44:210–230.
14.
EngwegenJ., HelgasonH.H., CatsA., HarrisN., BonfrerJ.M.G., Schellens.J.H.M.et al.2006. Identification of serum proteins discriminating colorectal cancer patients and healthy controls using surface-enhanced laser desorption ionisation-time of flight mass spectrometry. World J Gastroenterol, 12:1536–1544.
15.
GrundyS.M., BenjaminI.J., BurkeG.L., ChaitA., ECKELR.H., HOWARDJ.V.B.et al.1999. Diabetes and cardiovascular disease—a statement for healthcare professionals from the American Heart Association. Circulation, 100:1134–1146.
16.
HoffmannE., StroobantV. 2002. Mass Spectrometry: Principles and Applications, 2nd. Wiley: New York.
17.
JankuF., KleiblZ., NovotnyJ., TesarovaP., PetruzelkaL., MatousB.2004. Mammaglobin A, a novel marker of minimal residual disease in early stages of breast cancer. Neoplasma, 51:204–208.
18.
JiangY., MetzC.E., NishikawaR.M.1996. A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology, 201:745–750.
19.
KornerM., StockliM., WaserB., ReubiJ.C.2007. GLP-1 receptor expression in human tumors and human normal tissues: potential for in vivo targeting. J Nuclear Med, 48:736–743.
20.
ListgartenJ., EmiliA.2005. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics, 4:419–434.
21.
MarkeyM.K., TourassiG.D., FloydC.E.Jr.2003. Decision tree classification of proteins identified by mass spectrometry of blood serum samples from people with and without lung cancer. Proteomics, 3:1678–1679.
22.
MercataliL., ValentiV., CalistriD., CalponaS., RostiG., FolliS.et al.2006. RT-PCR determination of maspin and mammaglobin B in peripheral blood of healthy donors and breast cancer patients. Annals of Oncology, 17:424–428.
23.
MeulemanW., EngwegenJ., GastM.C.W., BeijnenJ.W., ReindersM.J.T., WesselsL.F.A.2008. Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data. BMC Bioinformatics, 9.
24.
NevilleP., TanP., MannG., WolfingerR.2003. Generalizable mass spectrometry mining used to identify disease state biomarkers from blood serum. Proteomics, 3:1710–1715.
25.
NooM.E., DeelderA., WerffM., OzalpA., MertensB., TollenaarR.2006. Maldi-tof serum protein profiling for the detection of breast cancer. Onkologie, 29:501–506.
26.
OzciftA., GultenA.2008. Assessing effects of pre-processing mass spectrometry data on classification performance. Eur J Mass Spectrom, 14:267–273.
27.
PaniciP.B., ScambiaG., MassiddaB., ChessaP., TarquiniA., MancusoS.1986. Elevated plasma levels of beta-thromboglobulin in breast cancer. Oncology, 43:208–211.
28.
PetricoinE.F., ArdekaniA.M., HittB.A., LevineP.J., FusaroV.A., SteinbergS.M.et al.2002. Use of proteomic patterns in serum to identify ovarian cancer. Lancet, 359:572–577.
29.
PetricoinE.F., LiottaL.A.2004. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer. Curr Opin Biotechnol, 15:24–30.
30.
PusztaiL., GregoryB.W., BaggerlyK.A., PengB., KoomenJ., KuererH.M.et al.2004. Pharmacoproteomic analysis of prechemotherapy and postchemotherapy plasma samples from patients receiving neoadjuvant or adjuvant chemotherapy for breast carcinoma. Cancer, 100:1814–1822.
31.
RosnerB.2005. Fundamentals of Biostatistics. Duxbury Press: Belmont, CA.
32.
SaeysY., InzaI., LarranagaP.2007. A review of feature selection techniques in bioinformatics. Bioinformatics, 23:2507–2517.
33.
SauveA.C., SpeedT.P.2005. Normalization, baseline correction and alignment of high-throughput mass spectrometry data. Proc Gensips, 2004.
34.
SharmaS.1995. Applied Multivariate Techniques. John Wiley & Sons: New York.
35.
ShinH., MarkeyM.K.2006. A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples. J Biomed Inform, 39:227–248.
36.
ShinH., SampatM. P., BishS.F., KoomenJ., MarkeyM.K.2006. Statistical Characterization of Chemical Noise in MALDI TOF MS by Wavelet Analysis of Multiple Noise realizations. American Medical Informatics Association (AMIA) Annual Symposium: Washington, DC.
SolassolJ., JacotW., LhermitteL., BoulleN., MaudelondeT., MangeA.2006. Clinical proteomics and mass spectrometry profiling for cancer detection. Expert Rev Proteomics, 3:311–320.
39.
TatayJ.W., FengX., SobczakN., JiangH., ChenC., KirovaR.et al.2003. Multiple approaches to data-mining of proteomics data based on statistical and pattern classification methods. Proteomics, 3:1704–1709.
40.
TibshiraniR., HastieT., NarasimhanB., SoltysS., ShiG., KoongA.et al.2004. Sample classification from protein mass spectrometry, by “peak probability contrasts.”Bioinformatics, 20:3034–3044.
41.
VermaM., WrightG.L.Jr., HanashS.M., Gopal-SrivastavaR., SrivastavaS.2001. Proteomic approaches within the NCI early detection research network for the discovery and identification of cancer biomarkers. Ann N Y Acad Sci, 945:103–115.
42.
WagnerM., NaikD., PothenA.2003. Protocols for disease classification from mass spectrometry data. Proteomics, 3:1692–1698.
43.
WegdamW., MoerlandP.D., BuistM.R., Van ThemaatE.V.L., BleijlevensB., HoefslootH.C.J.et al.2009. Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical datasets. Proteome Sci, 7.
44.
ZhangX.W., WeiD., YapY., LiL., GuoS.Y., ChenF.2007. Mass spectrometry-based “omics” technologies in cancer diagnostics. Mass Spectrom Rev, 26:403–431.
