**8. References**


*Omics*- this suffix, often used in modern biological research, refers to the lofty aim of observing, identifying and quantifying the totality of a particular class of molecules i.e.

*Orthogonal method*- describes the ideal of using alternate types of analyses to corroborate the

*Peptide-centric*- or *bottom-up,* proteomics is a common method used to identify proteins by

*Protein-centric***-** or *top-down,* proteomics is a method of intact protein identification e.g. an ion trapping mass spectrometer used to store an isolated protein ion for mass measurement and

*Power analysis*- to calculate the number of samples required for a study to reach statistically

*Predictive model*- in the context of medicine, is created or chosen to try to predict the

*Prognostic*- is a clinical test which can forecast the likely course or outcome of an illness. *Sensitivity*- measures the proportion of true positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). *Specificity*- measures the proportion of true negatives which are correctly identified (e.g. the percentage of healthy people who are correctly identified as not having the condition). *Throughput*- refers to the rate of analysis of samples by a particular method e.g. analysis of a

single protein by Western blotting is relatively low throughput compared to ELISA.

a *classifier* and begins to assess the sensitivity in expanded populations.

*Validation*- later stage in the biomarker pipeline is defined as the documented act of demonstrating that putative biomarker classifiers will consistently lead to the expected results i.e establish *sensitivity* and *specificity* performance in large populations and begin to

*Verification*- intermediate phase in biomarker pipeline bridging *discovery* and *validation*, which typically reduces the number of candidates, confirms specific protein isoforms within

DSG would like to acknowledge continued support from Arthritis Research UK in the form of a Travelling Fellowship (No. 19250). The UCD Conway Institute and the Proteome Research Centre are funded by the Programme for Research in Third Level Institutions, as administered by the Higher Education Authority of Ireland. SRP acknowledges support for equipment from Science Foundation Ireland. DSG and MR would like to acknowledge the funding of this research by Arthritis Research UK in the form of a Project Grant (No. 18748).

Alonso-Ruiz A., Pijoan J.I., Ansuategui E., Urkaregi A., Calabozo M. & Quintana A. Tumor

necrosis factor alpha drugs in rheumatoid arthritis: systematic review and metaanalysis of efficacy and safety. *BMC Musculoskelet. Disord.*, 9, (2008), pp 52. Anders H.J., Rihl M., Vielhauer V. & Schattenkirchner M. Assessment of renal function in

rheumatoid arthritis: validity of a new prediction method. *J. Clin. Rheumatol.*, 8(3),

proteolytic digestion of proteins prior to analysis by mass spectrometry.

probability of a clinical outcome by use of one or more *classifiers*.

genomics, proteomics.

sound conclusions.

original findings by independent means.

tandem mass spectrometry analysis.

optimize the assay for commercial use.

**7. Acknowledgement** 

**8. References** 

(2002), pp 130-3.


Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 157

Gibson D.S. & Rooney M.E. The human synovial fluid proteome: A key factor in the

Gibson, D.S. Blelock S., Curry J., Finnegan S., Healy A., Scaife C., McAllister C., Pennington

Haab B.B, Geierstanger B.H., Michailidis G., Vitzthum F., Forrester S., Okon R., Saviranta P.,

Hu, S., Loo, J.A. & Wong, D.T. Human body fluid proteome analysis. *Proteomics*, 6, (2006),

Hueber W., Kidd B.A., Tomooka B.H., Lee B.J., Bruce B., Fries J.F., Sønderstrup G., Monach

Hueber W., Tomooka B.H., Batliwalla F., Li W., Monach P.A., Tibshirani R.J., Van

Jenkins R.E., Kitteringham N.R., Hunter C.L., Webb S., Hunt T.J., Elsby R., Watson

Karp N.A. & Lilley K.S. Design and analysis issues in quantitative proteomics studies.

Kastbom A., Strandberg G., Lindroos A. & Skogh T. Anti-CCP antibody test predicts the

Kavanaugh A.F., Solomon D.H. & American College of Rheumatology Ad Hoc Committee

Keshishian H., Addona T., Burgess M., Kuhn E. & Carr S.A. Quantitative, multiplexed

stable isotope dilution. *Mol. Cell. Proteomics*, 6(12), (2007), pp 2212-2229. Kuhn E., Wu J., Karl J., Liao H., Zolg W. & Guild B. Quantification of C-reactive protein in

stage disease. *J. Proteomics*, 72, (2009), pp 656-676.

glycoprotein. *Glycoconj. J.*, 15(7), (1998), pp 723-729.

*Rheum.*, 52(9), (2005), pp 2645-2655.

Proteomics, 6(6), (2006), pp 1934-1947.

*Proteomics*, 7 Suppl 1, (2007), pp 42-50.

project). *Ann. Rheum. Dis*., 63(9), (2004), pp 1085-1089.

*Ther*., 11(3), (2009), pp R76.

pp 889-899.

pp 6326-6353.

pp 546-555.

pathology of joint disease. *PROTEOMICS - CLINICAL APPLICATIONS*, 1, (2007),

S., Dunn M. & Rooney M.. Comparative analysis of synovial fluid and plasma proteomes in juvenile arthritis--proteomic patterns of joint inflammation in early

Brinker A., Sorette M., Perlee L., Suresh S., Drwal G., Adkins J.N., Omenn G.S. Immunoassay and antibody microarray analysis of the HUPO Plasma Proteome Project reference specimens: systematic variation between sample types and calibration of mass spectrometry data. *Proteomics*, 5(13), (2005), pp 3278-3291. Havenaar E.C., Axford J.S., Brinkman-van der Linden E.C., Alavi A., Van Ommen E.C., van

het Hof B., Spector T., Mackiewicz A. & Van Dijk W.. Severe rheumatoid arthritis prohibits the pregnancy-induced decrease in alpha3-fucosylation of alpha1-acid

P., Drijfhout J.W., van Venrooij W.J., Utz P.J., Genovese M.C. & Robinson W.H. Antigen microarray profiling of autoantibodies in rheumatoid arthritis. *Arthritis* 

Vollenhoven R.F., Lampa J., Saito K., Tanaka Y., Genovese M.C., Klareskog L., Gregersen P.K. & Robinson W.H. Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis. *Arthritis Res.* 

R.B.,Williams D., Pennington S.R., Park B.K. Relative and absolute quantitative expression profiling of cytochromes P450 using isotope-coded affinity tags.

disease course during 3 years in early rheumatoid arthritis (the Swedish TIRA

on Immunologic Testing Guidelines. Guidelines for immunologic laboratory testing in the rheumatic diseases: anti-DNA antibody tests. *Arthritis Rheum*., 47(5), (2002),

assays for low abundance proteins in plasma by targeted mass spectrometry and

the serum of patients with rheumatoid arthritis using multiple reaction monitoring


Chait, B. T. Chemistry. Mass spectrometry: bottom-up or top-down? *Science*, 314, (2006), pp

Chen P.P., Robbins D.L., Jirik F.R., Kipps T.J. & Carson D.A. Isolation and characterization

Dai Y., Hu C., Wang L., Huang Y., Zhang L., Xiao X. & Tan Y. Serum peptidome patterns of

Davis M.T., Auger P.L. & Patterson S.D. Cancer biomarker discovery via low molecular

de Seny D., Fillet M., Ribbens C., Marée R., Meuwis M.A., Lutteri L., Chapelle J.P., Wehenkel

Dowsey, A.W., Morris, J.S., Gutstein, H.B. & Yang, G.Z. in Proteome Bioinformatics (eds

Duncan M.W., Aebersold R. & Caprioli R.M. The pros and cons of peptide-centric

Dupuy A. & Simon R.M. Critical review of published microarray studies for cancer outcome

Ercan, A. Cui J., Chatterton D.E., Deane K.D., Hazen M.M., Brintnell W., O'Donnell C.I.,

Eriksson C., Kokkonen H., Johansson M., Hallmans G., Wadell G. & Rantapaa-Dahlqvist S.

Fattal I., Shental N., Mevorach D., Anaya J.M., Livneh A., Langevitz P., Zandman-Goddard

Flower L., Ahuja R.H., Humphries S.E. & Mohamed-Ali V. Effects of sample handling on the

Fung E.T. A recipe for proteomics diagnostic test development: the OVA1 test, from biomarker discovery to FDA clearance. *Clin. Chem.*, 56(2), (2010), pp 327-329. Garcia G.G., Berger S.B., Sadighi Akha A.A. & Miller R.A.. Age-associated changes in

Hubbard, S. J. & Jones, A. R.) 239 (Humana Press, New York, 2009).

rheumatoid arthritis. *Arthritis Rheum*., 62, (2010), pp 2239-2248.

in arthritis. *Clin. Chem*., 54(6), (2008), pp 1066-1075.

proteomics. *Nat. Biotechnol*., 28(7), (2010), pp 659-64.

Sweden. *Arthritis Res. Ther*.,. 13(1), (2011), pp R30.

of a light chain variable region gene for human rheumatoid factors. *J. Exp. Med*.,

human systemic lupus erythematosus based on magnetic bead separation and MALDI-TOF mass spectrometry analysis. *Scand. J. Rheumatol.*, 39(3), (2010), pp 240-

weight serum profiling--are we following circular paths? *Clin. Chem.*, 2010, 56(2),

L., Louis E., Merville M.P. & Malaise M. Monomeric calgranulins measured by SELDI-TOF mass spectrometry and calprotectin measured by ELISA as biomarkers

and guidelines on statistical analysis and reporting. *J. Natl. Cancer Inst*., 99(2),

Derber L.A., Weinblatt M.E., Shadick N.A., Bell D.A., Cairns E., Solomon D.H., Holers V.M., Rudd P.M. & Lee D.M. Aberrant IgG galactosylation precedes disease onset, correlates with disease activity, and is prevalent in autoantibodies in

Autoantibodies predate the onset of Systemic Lupus Erythematosus in northern

G., Pauzner R., Lerner M., Blank M., Hincapie M.E., Gafter U., Naparstek Y., Shoenfeld Y., Domany E. & Cohen I.R. An antibody profile of systemic lupus erythematosus detected by antigen microarray. *Immunology*, 130(3), (2010), pp 337-

stability of interleukin 6, tumour necrosis factor-alpha and leptin. *Cytokine*. 12(11),

glycosylation of CD43 and CD45 on mouse CD4 T cells. *Eur. J. Immunol*., 35(2),

65-66.

246.

pp 244-247.

(2007), pp 147-157.

343.

(2000), pp 1712-1716.

(2005), pp 622-31.

166(6), (1987), pp 1900-1905.


mass spectrometry and 13C-labeled peptide standards. *Proteomics*, 4(4), (2004), pp 1175-86.

Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 159

Nakamura RM. Progress in the use of biochemical and biological markers for evaluation of

Nielen M.M., van Schaardenburg D., Reesink H.W., van de Stadt R.J., van der Horst-

Papini, A.M. The use of post-translationally modified peptides for detection of biomarkers

Pepe M.S., Feng Z., Janes H., Bossuyt P.M. & Potter J.D. Pivotal evaluation of the accuracy of

Pepys M.B. & Hirschfield G.M. C-reactive protein: a critical update. *J. Clin. Invest*., 111(12),

Petricoin, E.F. Ardekani A.M., Hitt B.A., Levine P.J., Fusaro V.A., Steinberg S.M., Mills G.B.,

Quintana F.J., Farez M.F., Viglietta V., Iglesias A.H., Merbl Y., Izquierdo G., Lucas M., Basso

Rai A.J., Gelfand C.A., Haywood B.C., Warunek D.J., Yi J., Schuchard M.D., Mehigh R.J.,

Ramachandran N., Hainsworth E., Bhullar B., Eisenstein S., Rosen B., Lau A.Y., Walter J.C. & LaBaer J. Self-assembling protein microarrays. *Science*, 305(5680), (2004), pp 86-90.

A.S., Khoury S.J., Lucchinetti C.F., Cohen I.R. & Weiner H.L. Antigen microarrays identify unique serum autoantibody signatures in clinical and pathologic subtypes of multiple sclerosis. *Proc. Natl. Acad. Sci. USA*, 105(48), (2008), pp 18889-18894. Quintana F.J., Hagedorn P.H., Elizur G., Merbl Y., Domany E. & Cohen I.R. Functional

immunomics: microarray analysis of IgG autoantibody repertoires predicts the future response of mice to induced diabetes. *Proc. Natl. Acad. Sci. USA*, 101 Suppl. 2,

Cockrill S.L., Scott G.B., Tammen H., Schulz-Knappe P., Speicher D.W., Vitzthum F., Haab B.B., Siest G. & Chan D.W. HUPO Plasma Proteome Project specimen collection and handling: towards the standardization of parameters for plasma

rheumatoid arthritis. *J. Clin. Lab. Anal*., 14(6), (2000), pp 305-313.

of immune-mediated diseases. *J. Pept. Sci.*, 15, (2009), pp 621-628.

applications. *J. Proteome Res*., 8(2), (2009), pp 787-797.

pathways. *Ann. Rheum. Dis.*, 69(7), (2010), pp 1315-1320.

proteome samples. *Proteomics*, 5(13), (2005), pp 3262-3277.

Poste G. Bring on the Biomarkers. *Nature*, 469, (2011), pp 156-157.

*Cancer Inst*., 100(20), (2008), pp 1432-1438.

(2003), pp 1805-1812.

(2004), pp 14615-14621.

nonresponse to therapy. Arthritis Res. Ther., 7(4), (2005), pp R746-755. Morgan R., Gao G., Pawling J., Dennis J.W., Demetriou M. & Li B. N-

7208.

systemic-onset juvenile idiopathic arthritis differentiates response versus

acetylglucosaminyltransferase V (Mgat5)-mediated N-glycosylation negatively regulates Th1 cytokine production by T cells. *J. Immunol*., 173(12), (2004), pp 7200-

Bruinsma I.E., de Koning M.H., Habibuw M.R., Vandenbroucke J.P. & Dijkmans B.A. Specific autoantibodies precede the symptoms of rheumatoid arthritis: a study of serial measurements in blood donors. *Arthritis Rheum*., 50(2), (2004), pp 380-6. Pan S., Aebersold R., Chen R., Rush J., Goodlett D.R., McIntosh M.W., Zhang J., Brentnall

T.A. Mass spectrometry based targeted protein quantification: methods and

a biomarker used for classification or prediction: standards for study design. *J. Natl.* 

Simone C., Fishman D.A., Kohn E.C. & Liotta L.A. Use of proteomic patterns in serum to identify ovarian cancer. *Lancet*, 359, (2002), pp 572-577. Potter C., Cordell H.J., Barton A., Daly A.K., Hyrich K.L., Mann D.A., Morgan A.W., Wilson A.G., the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) & Isaacs J.D.. Association between anti-tumour necrosis factor treatment response and genetic variants within the TLR and NF{kappa}B signalling


Kurreeman F.A., Padyukov L., Marques R.B., Schrodi S.J., Seddighzadeh M., Stoeken-

Kuzyk M.A., Smith D., Yang J., Cross T.J., Jackson A.M., Hardie D.B., Anderson N.L. &

Lange V., Picotti P., Domon B.& Aebersold R. Selected reaction monitoring for quantitative

Li Q.Z., Xie C., Wu T., Mackay M., Aranow C., Putterman C. & Mohan C. Identification of

Liao H., Wu J., Kuhn E., Chin W., Chang B., Jones M.D., O'Neil S., Clauser K.R., Karl J.,

Liu Q., Sung A.H., Chen Z., Liu J., Huang X. & Deng Y. Feature selection and classification

Long L., Li R., Li Y., Hu C. & Li Z. Pattern-based diagnosis and screening of differentially

MacBeath G. & Schreiber S.L. Printing proteins as microarrays for high-throughput function

Marengo E., Robotti E., Bobba M., Liparota M.C., Rustichelli C., Zamò A., Chilosi M. &

McLaren M,. Waring A., Galarraga B., Rudd A., Morley K. & Belch J.J. Investigation of

Meyer O., Labarre C., Dougados M., Goupille P., Cantagrel A., Dubois A., Nicaise-Roland

Miyamae T., Malehorn D.E., Lemster B., Mori M., Imagawa T., Yokota S., Bigbee W.L.,Welsh

determination. *Science*, 289(5485), (2000), pp 1760-1763.

electrophoresis. *Electrophoresis*, 27(2), (2006), pp 484-494.

rheumatoid arthritis. *Scand. J. Rheumatol*., 34, (2005), pp 437-440.

with rheumatoid arthritis. *Arthritis Rheum.*, 50(12), (2004), pp 3792-803. Lim S.Y., Raftery M.J., Goyette J., Hsu K. & Geczy C.L. Oxidative modifications of S100 proteins: functional regulation by redox. *J. Leukoc. Biol*., 86(3), 2009, pp 577-587. Lindqvist E., Eberhardt K., Bendtzen K., Heinegard D. & Saxne T. Prognostic laboratory

rheumatoid arthritis. *PLoS Med*., 4(9), (2007), pp e278.

proteomics: a tutorial. *Mol .Syst. Biol.*, 4, (2008), pp 222.

proteome arrays. *J. Clin. Invest.*, 115(12), (2005), pp 3428-39.

1175-86.

1860-1877.

196-201.

*PLoS One*, 4(12), (2009), pp e8250.

*Dis*., 62(2), (2003), pp 120-126.

*Rheumatol. Int*., (2010).

mass spectrometry and 13C-labeled peptide standards. *Proteomics*, 4(4), (2004), pp

Rijsbergen G., van der Helm-van Mil A.H., Allaart C.F., Verduyn W., Houwing-Duistermaat J., Alfredsson L., Begovich A.B., Klareskog L., Huizinga T.W. & Toes R.E. A candidate gene approach identifies the TRAF1/C5 region as a risk factor for

Borchers C.H. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. *Mol. Cell. Proteomics*, 8(8), (2009), pp

autoantibody clusters that best predict lupus disease activity using glomerular

Hasler F., Roubenoff R., Zolg W. & Guild B.C. Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients

markers of joint damage in rheumatoid arthritis. *Ann. Rheum. Dis*., 64(2), (2005), pp

of MAQC-II breast cancer and multiple myeloma microarray gene expression data.

expressed serum proteins for rheumatoid arthritis by proteomic fingerprinting.

Righetti P.G. Multivariate statistical tools applied to the characterization of the proteomic profiles of two human lymphoma cell lines by two-dimensional gel

platelet glycoprotein IIIa polymorphism using flow cytometry in patients with

P., Sibilia J. & Combe B.Anticitrullinated protein/peptide antibody assays in early rheumatoid arthritis for predicting five year radiographic damage. *Ann. Rheum.* 

M., Klarskov K., Nishomoto N., Vallejo A.N. & Hirsch R. Serum protein profile in

systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy. Arthritis Res. Ther., 7(4), (2005), pp R746-755.


Validation of Protein Biomarkers to Advance the Management of Autoimmune Disorders 161

Tammen H., Schulte I., Hess R., Menzel C., Kellmann M. & Schulz-Knappe P. Prerequisites

Taylor C.F., Paton N.W., Lilley K.S., Binz P.A., Julian R.K. Jr, Jones A.R., Zhu W., Apweiler

proteomics experiment (MIAPE). *Nat. Biotechnol*., 25(8), (2007), pp 887-93. Tilleman K., Deforce D. & Elewaut D. Rheumatology: a close encounter with proteomics.

van der Horst-Bruinsma I.E., Visser H., Hazes J.M., Breedveld F.C., Verduyn W., Schreuder

van Venrooij W.J., van Beers J.J. & Pruijn G.J. Anti-CCP Antibody, a Marker for the Early Detection of Rheumatoid Arthritis. *Ann. N. Y. Acad. Sci.*, 1143, (2008), pp 268-285. Vang T., Congia M., Macis M.D., Musumeci L., Orrú V., Zavattari P., Nika K., Tautz L.,

Vencovsky J., Machacek S., Sedova L., Kafková J., Gatterová J., Pesáková V. & Růzicková S.

Wagner U, Kaltenhauser S, Sauer H, Arnold S, Seidel W, Häntzschel H, Kalden JR,

Washburn M.P., Wolters D. & Yates J.R.,3rd. Large-scale analysis of the yeast proteome by

Wegner N., Lundberg K., Kinloch A., Fisher B., Malmström V., Feldmann M. & Venables P.J.

Weiss N.G., Jarvis J.W., Nelson R.W. & Hayes M.A. Examining serum amyloid P component

Whiteaker J.R., Zhao L., Anderson L. & Paulovich A.G. An automated and multiplexed

Wiik A.S. Anti-nuclear autoantibodies: clinical utility for diagnosis, prognosis, monitoring,

rheumatoid arthritis. *Ann. Rheum. Dis*., 62(5), (2003), pp 427-30.

rheumatoid arthritis. Arthritis Rheum., 40(2), (1997), pp 341-351.

of rheumatoid arthritis. *Immunol. Rev.*, 233, (2010), pp 34-54.

*Mol. Cell. Proteomics*, 9(1), (2010), pp 184-196.

Scand. *J. Rheumatol*., 34(4), (2005), pp 260-268.

*Throughput Screen*., 8(8), (2005), pp 725-733.

*Rheumatology (Oxford)*, 44, (2005), pp 1217-1226.

(1999), pp 152-158.

1317-1319.

242-247.

11(1), (2011), pp 106-113.

for peptidomic analysis of blood samples: I. Evaluation of blood specimen qualities and determination of technical performance characteristics. *Comb. Chem. High* 

R., Aebersold R., Deutsch E.W., Dunn M.J., Heck A.J., Leitner A., Macht M., Mann M., Martens L., Neubert T.A., Patterson S.D., Ping P., Seymour S.L., Souda P., Tsugita A.,Vandekerckhove J., Vondriska T.M., Whitelegge J.P., Wilkins M.R., Xenarios I., Yates J.R. 3rd & Hermjakob H. The minimum information about a

G.M., de Vries R.R., Zanelli E. HLA-DQ-associated predisposition to and dominant HLA-DR-associated protection against rheumatoid arthritis. Hum. Immunol., 60(2),

Taskén K., Cucca F., Mustelin T. & Bottini N. Autoimmune-associated lymphoid tyrosine phosphatase is a gain-of-function variant. Nat. Genet., 37(12), (2005), pp

Autoantibodies can be prognostic markers of an erosive disease in early

Wassmuth R. HLA markers and prediction of clinical course and outcome in

multidimensional protein identification technology. *Nat. Biotechnol.* 19, (2001), pp

Autoimmunity to specific citrullinated proteins gives the first clues to the etiology

microheterogeneity using capillary isoelectric focusing and MALDI-MS. *Proteomics*,

method for high throughput peptide immunoaffinity enrichment and multiple reaction monitoring mass spectrometry-based quantification of protein biomarkers.

and planning of treatment strategy in systemic immunoinflammatory diseases.


Ramachandran N., Raphael J.V., Hainsworth E., Demirkan G., Fuentes M.G., Rolfs A., Hu Y.

Ransohoff D.F. & Gourlay M.L. Sources of bias in specimens for research about molecular

Ransohoff D.F. Promises and limitations of biomarkers. *Recent Results Cancer Res.* 181, (2009),

Ransohoff D.F. Proteomics research to discover markers: what can we learn from Netflix?

Rantapää-Dahlqvist S., de Jong B.A., Berglin E., Hallmans G., Wadell G., Stenlund H.,

Rehder D.S., Nelson R.W. & Borges C.R. Glycosylation status of vitamin D binding protein

Reid J.D., Holmes D.T., Mason D.R., Shah B. & Borchers C.H. Towards the development of

Reiner A., Yekutieli D. & Benjamini Y. Identifying differentially expressed genes using false discovery rate controlling procedures. *Bioinformatics*, 19(3), (2003), pp 368-375. Robinson W.H., DiGennaro C., Hueber W., Haab B.B., Kamachi M., Dean E.J., Fournel S.,

Robinson W.H., Fontoura P., Lee B.J,. de Vegvar H.E., Tom J., Pedotti R., DiGennaro C.D.,

Schiess R., Wollscheid B. & Aebersold R. Targeted proteomic strategy for clinical biomarker

Smith M.P., Banks R.E., Wood S.L., Lewington A.J. & Selby P.J. Application of proteomic analysis to the study of renal diseases. *Nat. Rev. Nephrol*., 5(12), (2009), pp 701-712. Somers K., Govarts C., Stinissen P. & Somers V. Multiplexing approaches for autoantibody profiling in multiple sclerosis. *Autoimmun. Rev*., 8(7), (2009), pp 573-579. Song Q., Liu G., Hu S., Zhang Y., Tao Y., Han Y., Zeng H., Huang W., Li F., Chen P., Zhu J.,

Storey J.D. & Tibshirani R. Statistical significance for genomewide studies. *Proc. Natl. Acad.* 

Strimmer K. A unified approach to false discovery rate estimation. *BMC Bioinformatics*, 9,

Sundin U. & van Venrooij WJ. Antibodies against cyclic citrullinated peptide and IgA rheumatoid factor predict the development of rheumatoid arthritis. *Arthritis* 

an immuno MALDI (iMALDI) mass spectrometry assay for the diagnosis of

Fong D., Genovese M.C., de Vegvar H.E., Skriner K., Hirschberg D.L., Morris R.I., Muller S., Pruijn G.J., van Venrooij W.J., Smolen J.S., Brown P.O., Steinman L. & Utz P.J. Autoantigen microarrays for multiplex characterization of autoantibody

Mitchell D.J., Fong D., Ho P.P., Ruiz P.J., Maverakis E., Stevens D.B., Bernard C.C., Martin R., Kuchroo V.K., van Noort J.M., Genain C.P., Amor S., Olsson T., Utz P.J., Garren H. & Steinman L. Protein microarrays guide tolerizing DNA vaccine treatment of autoimmune encephalomyelitis. *Nat. Biotechnol*., 21(9), (2003), pp 1033-

Hu C., Zhang S., Li Y., Zhu H. & Wu L. Novel autoimmune hepatitis-specific autoantigens identified using protein microarray technology. *J. Proteome Res*., 9(1),

markers for cancer. *J. Clin. Oncol*., 28(4), (2010), pp 698-704.

in cancer patients. *Protein. Sci*., 18(10), 2009, pp 2036-2042.

hypertension. *J. Am. Soc. Mass Spectrom*., 21(10), (2010), pp 1680-6.

Nat. Methods, 5(6), (2008), pp 535-538.

*Clin. Chem.* 56, (2010), pp 172-176.

*Rheum*., 48(10), (2003), pp 2741-2749.

responses. Nat. Med., 8(3), (2002), pp 295-301.

discovery. *Mol. Oncol*., 3(1), (2009), pp 33-44.

*Sci. USA*, 100(16), (2003), pp 9440-9445.

pp 55-59.

9.

(2010), pp 30-39.

(2008), pp 303.

& LaBaer J. Next-generation high-density self-assembling functional protein arrays.


Wilson C., Schulz S. & Waldman S.A. Biomarker development, commercialization, and regulation: individualization of medicine lost in translation. *Clin. Pharmacol. Ther*., 81(2), (2007), pp 153-155.

**9** 

**Relevance of Autoantibodies for the** 

Simone Mader1, Benjamin Obholzer2 and Markus Reindl1 *1Clinical Department of Neurology, Innsbruck Medical University* 

In the last decade research on autoantibodies in neurological diseases of the central nervous system (CNS) has been very successful. An increasing number of autoantibodies and their target antigens have been detected, supporting stratification of patients and enabling specific treatment. The detection of autoantibodies depends on the assay used, as antibody binding requires the native conformation of the antigen. Although cumulative data is suggesting an important role of B cells and antibodies in Multiple Sclerosis (MS), numerous studies failed to identify specific biomarkers for MS (Figure 1). Even though several clinical, immunological and radiological studies tried to discover risk factors for disease progression, it remains an open issue to predict the individual disease course. However, recently autoantibodies have been discovered in some rare CNS demyelinating disease closely resembling MS (Table 1). Particularly Neuromyelitis Optica (NMO) gained enormous interest due to the discovery of autoantibodies targeting the water channel protein aquaporin-4 (AQP4) (Lennon et al., 2004; Lennon et al., 2005), which is expressed on astrocytic endfeet at the blood brain barrier (Nicchia et al., 2004) (Figure 1). This inflammatory demyelinating disease represents itself with optic neuritis and longitudinally extensive transverse myelitis (Wingerchuk et al., 1999) and was long considered as a severe variant of MS. Due to the detection and validation of this highly sensitive and specific biomarker, NMO is now regarded as a separate disease entity to MS. Consequently, the anti-AQP4 antibody serostatus was included into the diagnostic criteria of NMO (Jarius et al., 2007; Wingerchuk et al., 2007). Compared to MS, patients with NMO have a worse prognosis and require different treatment strategies according to the dominant humoral immunopathogenesis. With the advent of anti-AQP4 antibodies as biomarkers in NMO spectrum disorders (NMOSD), different NMO antibody assays have been developed, whereby cell based assays using the M23 isoform of AQP4 yield highest sensitivity (Takahashi et al., 2007; Waters & Vincent, 2008; Mader et al., 2010). Despite the high percentage of anti-AQP4 IgG positive NMO patients, various studies described a lack of these autoantibodies in a cohort of NMO patients, which we will critically discuss in this chapter. It remains an open question whether these patients form their own subgroup of NMO patients or if the antibodies are not detected due to a sensitivity problem of the applied assays. Moreover, we will address

**1. Introduction** 

**Classification and Pathogenesis** 

**of Neurological Diseases** 

*2University of Innsbruck* 

*Austria* 

