novel multiplex technology for diagnostic characterization...

RESEARCH ARTICLE Open Access

Novel multiplex technology for diagnosticcharacterization of rheumatoid arthritisPiyanka E Chandra1,2, Jeremy Sokolove1,2, Berthold G Hipp3, Tamsin M Lindstrom1,2, James T Elder4,John D Reveille5, Heike Eberl3, Ursula Klause3 and William H Robinson1,2*

Abstract

Introduction: The aim of this study was to develop a clinical-grade, automated, multiplex system for thedifferential diagnosis and molecular stratification of rheumatoid arthritis (RA).

Methods: We profiled autoantibodies, cytokines, and bone-turnover products in sera from 120 patients with adiagnosis of RA of < 6 months’ duration, as well as in sera from 27 patients with ankylosing spondylitis, 28 patientswith psoriatic arthritis, and 25 healthy individuals. We used a commercial bead assay to measure cytokine levelsand developed an array assay based on novel multiplex technology (Immunological Multi-Parameter ChipTechnology) to evaluate autoantibody reactivities and bone-turnover markers. Data were analyzed by SignificanceAnalysis of Microarrays and hierarchical clustering software.

Results: We developed a highly reproducible, automated, multiplex biomarker assay that can reliably distinguishbetween RA patients and healthy individuals or patients with other inflammatory arthritides. Identification ofdistinct biomarker signatures enabled molecular stratification of early-stage RA into clinically relevant subtypes. Inthis initial study, multiplex measurement of a subset of the differentiating biomarkers provided high sensitivity andspecificity in the diagnostic discrimination of RA: Use of 3 biomarkers yielded a sensitivity of 84.2% and a specificityof 93.8%, and use of 4 biomarkers a sensitivity of 59.2% and a specificity of 96.3%.

Conclusions: The multiplex biomarker assay described herein has the potential to diagnose RA with greatersensitivity and specificity than do current clinical tests. Its ability to stratify RA patients in an automated andreproducible manner paves the way for the development of assays that can guide RA therapy.

IntroductionRheumatoid arthritis (RA) is a systemic inflammatorycondition characterized by polyarthritis of presumedautoimmune etiology. Although the production of auto-antibodies against synovial antigens and an increase incytokine levels are known to be associated with RA[1,2], the molecular basis of the disease remains unclear.Insight into the pathogenesis of RA – and hence effec-tive treatment of RA – has been impeded by the hetero-geneity of the disease. Not only can the disease courserange from mild and self-limiting to severe and progres-sive, but also some patients respond well to early thera-peutic intervention whereas others do not [3].Therefore, there is a need for tests that can diagnose

early-stage RA, as well as tests that can predict whichRA patients will require and respond to anti-rheumatictherapies.Diagnostic tests currently used in the management of

early-stage RA are not sufficiently accurate, largelybecause they are based on detection of single biomar-kers that are either not specific to RA, e.g. rheumatoidfactor (RF) and C-reactive protein (CRP), or are presentin only a subset of RA patients, e.g. autoantibodies thatrecognize cyclic citrullinated peptides (CCP). Even whenthey correctly diagnose RA, current tests cannot ade-quately predict the course of the disease or the responseto therapy because detection of a single biomarker can-not differentiate between the multiple, distinct subtypesof RA. Simultaneous analysis of multiple biomarkersmay be more informative, yielding ‘biomarker signatures’of RA subtypes. Indeed, we previously demonstratedthat multiplex analysis of biomarkers in early-stage RA

* Correspondence: [email protected] of Immunology and Rheumatology, Department of Medicine,Stanford University School of Medicine, Stanford, CA 94305, USAFull list of author information is available at the end of the article

Chandra et al. Arthritis Research & Therapy 2011, 13:R102http://arthritis-research.com/content/13/3/R102

© 2011 Chandra et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative CommonsAttribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction inany medium, provided the original work is properly cited

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could define molecular subtypes of RA that correlatedwith clinically identifiable RA subtypes [1,2]. Notably,the presence of autoantibodies targeting citrullinatedproteins correlated with an increase in expression ofproinflammatory cytokines [2]. In addition, we recentlyidentified a biomarker signature of autoantibody specifi-cities and cytokine levels that could distinguish betweenRA patients who will respond to anti-TNF treatmentand those who will not [4].Translation of these multiplex biomarkers onto a

highly reproducible, automated platform is necessary fortheir use in robust validation studies and, ultimately,clinical practice. In this study, we developed such ahighly reproducible, automated, multiplex biomarkerassay and tested its performance in the diagnosis of RAand in the molecular stratification of RA patients intoclinically relevant subtypes.

Materials and methodsRoche multiplex automated assayRoche Professional Diagnostics (Roche DiagnosticsGmbH, Penzberg, Germany) is developing a multiplexplatform called IMPACT (Immunological Multi-Para-meter Chip Technology) that is based on a small poly-styrene chip, as previously described [5]. Duringmanufacturing, the chip is coated with a streptavidinlayer onto which biotinylated markers – antibodies, pro-teins, or peptides – are spotted in vertical rows for theduplicate analysis of samples (Figure 1). Each chip con-tains up to 10 different markers, and each marker isarrayed on the chip as a vertical row of 10 to 12 spots; aminimum of five spots is required for determination ofthe level of a specific analyte in a sample. During theassay, the arrayed markers are probed with a smallvolume of sample and with a digoxigenylated secondarymonoclonal antibody. The secondary antibody is thendetected by the addition of an anti-digoxigenin antibodyconjugated to a fluorescent latex label. This labelenables sensitive detection of less than 10 individualbinding events in a single spot, down to fmol/L concen-trations (Roche Diagnostics, Penzberg, Germany; pro-prietary data on file). After this final incubation withanti-digoxigenin antibody, chips are transferred to adetection unit where a charge-coupled device cameracreates an image that is converted to signal intensities,and fluorescence intensity of the array features is quan-tified by image analysis. The IMPACT platform cur-rently enables multiplex analysis of up to 10 analytes ina sandwich or indirect antibody assay format, requiresonly microliter quantities of serum samples, and ishighly sensitive. The throughput of the prototype is 40determinations per hour. One run is intended to com-prise 100 single determinations, including standards andcontrols.

The chips and markers used in the present study arelisted in Table 1; the sequences of the peptides spottedonto the chips are listed in Table S1 in Additional File1. Autoantibody reactivities were measured in an indir-ect immunoassay in which candidate RA antigens werespotted onto the chips. Levels of analytes (e.g. inflamma-tory and bone-turnover markers) were measured in asandwich immunoassay in which primary, capture anti-bodies were spotted onto the chips. All antigens andantibody pairs on these chronic inflammatory disease(CID) chips were developed by Roche Diagnostics. Formeasurement of RF, human anti-IgA and anti-IgM anti-bodies were spotted onto the chip as capture antibodies,and the RF they bound was then detected using biotiny-lated polymerized human IgG. Antigens on the synovialchips [see Table S1 in Additional File 1 were selectedthrough screens performed in the laboratory of Robin-son et al. [1] or our collaborators’ laboratory [6]; theywere then synthesized and spotted onto IMPACT chipsby Roche Diagnostics. Using the appropriate chip-speci-fic dilution buffers, we diluted the serum samples 1:10for use in the synovial antigen 1 and 2, CID 3, and CID4 chips, and 1:100 for use in the CID 1 chips. In theassays using the synovial antigen 1 and 2, CID 1, CID 3,or CID 4 chips, the arrayed antigens or antibodies wereprobed with 40 μl of diluted serum sample, washed, andthen probed with 40 μl of digoxigenylated secondarymonoclonal antibody. In assays using the chips contain-ing markers of bone turnover (bone chips), the arrayedantibodies were probed with 40 μl of serum at a 1:2dilution and then 20 μl of digoxigenylated monoclonalantibody. Standards specific to each type of chip wereincluded in the assays using the CID 1, CID 3, CID 4,and bone chips, and levels of each analyte were calcu-lated on the basis of the standard curves generated.Results for the synovial antigen 1 and 2 chips (for whichstandards have not yet been generated) were reportedand analyzed as signal intensities. We minimized non-specific binding by using fragments (Fab, Fab’, or Fab’2)as capture antibodies and by using proprietary bufferreagents (in addition to the standard casein, BSA, anddetergents) to minimize non-specific binding to thesolid phase. For the indirect immunoassays (CCP andsynovial chips), a proprietary detection antibody wasused that has been optimized to ensure minimal non-specific binding. Extensive evaluation revealed that dilut-ing the sample does not significantly influence non-spe-cific binding (data not shown).

Multiplex cytokine assayTo measure cytokine or chemokine levels in sera, weused the Milliplex Map Human cytokine/chemokine kit(Millipore, Billerica, MA, USA) run on the Luminex 200platform coupled with BioRad Bio-Plex software


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(BioRad, Hercules, CA, USA), according to the manufac-turers’ protocols. The cytokines and chemokines mea-sured were eotaxin, fibroblast growth factor 2,granulocyte macrophage colony-stimulating factor, IL-1a, IL-1b, IL-6, IL-12 (p40), IL-12 (p70), IL-15, IL-17,IP-10, monocyte chemoattractant protein 1 (MCP-1),and TNF. To prevent RF from bridging capture anddetection antibodies in the immunoassays, we addedHeteroblock (Omega Biologicals, Bozeman, MT, USA)to the sera at a final concentration of 3 μg/ml (we haveshown that this concentration of Heteroblock eliminatesfalse augmentation of the readout by heterophilic anti-bodies [2]). Calibration controls and recombinant stan-dards were used as specified by the manufacturer.

Single automated assaysRoche Tina-Quant assays run on a fully automated plat-form (Roche/Hitachi COBRAS C system) were used forthe individual, automated measurement of CRP and RF

levels in patient sera. In the CRP assay, latex particlescoated with monoclonal anti-CRP antibodies agglutinatewith human CRP. In the RF assay, latex-bound, heat-inactivated IgG reacts with RF to form antigen-antibodycomplexes. Both assays use turbidimetry to determinelatex agglutination, which occurs in cases of positive testresults.

Serum samplesAll patient serum samples were used after obtaininginformed consent from the patients and under humansubjects protocols approved by the Stanford UniversityInstitutional Review Board. Samples from RA patientswere obtained from ARAMIS (Arthritis, Rheumatismand Aging Medical Information System), which includesa biobank of serum samples from 793 Caucasian RApatients who were recruited by a consortium of 161practising rheumatologists throughout the USA [1,2,7,8].All patients met the 1987 Arthritis College of

19.9

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Biglycan (247-266)

Histone 2B/e (1-20)

Fibromodulin (246-265)


Vimentin (58-77) (Cit 64, 69, 71)

Acetyl-calpastatin (184-210)

Fibrinogen A (616-635) (Cit 621, 627, 630)

Clusterin (170-188)

Fibrinogen A (31-50) (Cit 35, 38, 42)

Profilaggrin (293-310) (Cit 301, 302)

Figure 1 Chips used for biomarker profiling on the IMPACT platform. (a) Images of an IMPACT synovial antigen chip 1 probed with seraderived from a patient with RA. Fluoresence was captured with a charge-coupled device camera and quantified by software analysis. Theimages are false color representations of the fluorescence signals detected. Blue represents low, green intermediate, yellow high, and white thehighest levels of fluorescence. The upper chip image is enhanced in the lower image by conversion of the lowest 5% of signals to black and thetop 5% of signals to white, with the color scale adjusted accordingly. The rheumatoid arthritis sample analyzed exhibits very high levels ofautoantibody reactivity to fibrinogen A (616-635) (Cit 621, 627, 630), vimentin (58-77) (Cit 64, 69, 71), and profilaggrin (293-310) (Cit 301, 302)),and low levels of antibody reactivity to fibrinogen A (31-50) (Cit 35, 38, 42), biglycan (247-266), and histone 2B/e (1-20). (b) List of chips and theircomponents.


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Rheumatology criteria [9] and had RA of less than sixmonths’ duration. We used a randomisation algorithmto select serum samples from 120 patients in the ARA-MIS cohort. The baseline characteristics of this sub-group of patients with early RA were assessed and

found to be comparable with those of the whole cohortof patients [7]. Psoriatic arthritis (PsA) samples wereprovided by James T. Elder and represent a mixture ofdifferent subtypes of PsA (25% RA-like, 25% mutilans,and 50% distal interphalangeal predominant disease).

Table 1 Chips and markers used on the IMPACT platform*

Chip name Chip components

Antigens Capture antibodies

Synovial antigen chip 1 Histone 2B/e (1-20)

Biglycan (247-266)


Vimentin (58-77) (Cit 64, 69, 71)

Acetyl-calpastatin (184-210)



Clusterin (170-188)

Fibrinogen A (31-50) (Cit 35, 38, 42)

Fibrinogen A (616-635) (Cit 621, 627, 630)

Synovial antigen chip 2 Histone 2A (95-114)


HSP60 (287-297)

Serine protease 11 (433-452)

Osteoglycin (177-196)

Apolipoprotein E (277-296) (Cit 278, 292)

Clusterin (334-353) (Cit 336, 339)

COMP (453-472)

CID 1 anti-CRP

anti-IgA (for RF measurement)

anti-IgM (for RF measurement)

CID 3 chip 1 Cit peptide 1

Cit peptide 2

Cit peptide 3

Cit peptide 4

CID 3 chip 2 Cit peptide 5

Cit peptide 6

Cit peptide 7

Cit peptide 8

Cit peptide 9

Cit peptide 10

Cit peptide 11

CID 4 anti-MMP 3

anti-IL-6

anti-S100 protein A8/A9

anti-E-Selectin

anti-HABP

Bone anti-PTH

anti-bCrosslapsanti-Osteocalcin

anti-P1NP

*Candidate rheumatoid arthritis antigens were spotted on the chip for measurement of autoantibody reactivities. Primary antibodies were spotted on the chipfor measurement of analyte (e.g. inflammatory mediators and products of bone turnover) levels.

Cit, citrullinated; HSP 60, heat shock protein 60; COMP, cartilage oligomeric matrix protein; CRP, C-reactive protein; MMP3, matrix metalloproteinase 3; IL-6,interleukin-6; HABP, hyaluronic acid binding protein; PTH, parathyroid hormone; P1NP, procollagen type 1 amino-terminal propeptide.


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Ankylosing spondylitis (AS) samples were provided byJohn Reveille and represent a cohort of patients withactive axial and/or uveal disease. Serum samples fromhealthy individuals were obtained from Bioreclamation,Inc (Hicksville, NY, USA). All serum samples wereshipped on dry ice, stored at -80°C, and subjected toone freeze-thaw cycle before being analyzed.In assessing the analytical precision of the IMPACT

assay, we used serum samples from the REFLEX study,a phase III trial on the efficacy of rituximab on a back-ground of methotrexate in RA refractory to anti-TNFtherapy [10]. We used only samples obtained at baseline.

Statistical analysisValues for each marker were divided by six times themean value obtained for that marker in the healthy controlsamples and then log transformed. These normalizedvalues were analyzed by SAM (Significance Analysis ofMicroarrays) [11,12]. Output was sorted based on falsediscovery rates (FDRs) in order to identify antigens withthe greatest differences in autoantibody reactivity, or cyto-kines with the greatest differences in concentrations,between patients with RA, patients with other inflamma-tory arthritides, and healthy individuals. Most of our com-parisons involved high-dimensional data, and we thereforeused FDR for our exploratory analyses, an analyticalmethod that obviates the need for multiple correctionswhen using high-dimensional data [11]. We then usedhierarchical clustering software (Cluster® 3.0, developedby Michael Eisen at Stanford University, Stanford, Califor-nia) to arrange the SAM results according to similaritiesamong patient samples in autoantibody specificities orcytokine levels, and Java Treeview® (Java Treeview 1.1.3,developed by Alok J. Saldanha at Stanford University,Stanford, California) to graphically display the results.To evaluate the IMPACT assay’s diagnostic sensitivity

and specificity, we used a subpanel of markers from the ori-ginal array results – markers identified by univariate analy-sis as ones that differentiate between patients with RA andpatients with other arthritides. A fluorescent value threetimes the mean value of that obtained in healthy controlsamples was defined as positive because this cutoff yieldedgreater specificity than a cutoff of three standard deviationsabove the mean. Similarly, because we had fewer healthycontrols than RA cases, this method provided greater speci-ficity than did Z-normalization. We excluded RF valuesfrom the analysis when comparing RF-positive and RF-negative subgroups, and CCP values when comparing anti-CCP-positive and anti-CCP-negative subgroups.

ResultsAnalytical precision of IMPACT assaysTo develop a system for the multiplex analysis of differ-ent types of biomarkers in the sera of RA patients, we

used a bead-based commercial assay (Millipore/Lumi-nex) to evaluate cytokine levels, and an array-basedassay in development (IMPACT) to evaluate autoanti-body reactivities and bone turnover. To determine theintra-assay reproducibility achieved with the IMPACTplatform, we performed 21 replicate measurements ofeach of nine markers within one run on the IMPACTplatform. The intra-assay coefficients of variance (CV)ranged from 1.5 to 9.0% (Figure 2a). To determineinter-assay reproducibility, we compared measurementsobtained from 5 to 15 independent runs of the samesample at low, medium, and high dilutions; this wasdone for eight of the markers present on the IMPACTplatform. Analysis demonstrated inter-assay CVs rangingfrom 1.1 to 14.9% (Figure 2a). Notably, these resultscompare favorably with CVs obtained with current com-mercial ELISA tests for RF (which yield intra-assay CVsof 6% and inter-assay CVs of 8%) [13] and CCP (whichyield intra-assay CVs of 4.8 to 13% and inter-assay CVsof 9 to 17%) [14].To assess the correlation between IMPACT multiplex

assays and single automated assays, we used both theIMPACT and the Roche/Hitachi cobas c platforms tomeasure RF and CRP in baseline serum samples fromsubjects enrolled in the REFLEX study [10]. Linearregression analysis demonstrated that the correlationbetween the results from the multiplex assay and thosefrom the single assay was good, with correlation coeffi-cients of 0.92 for RF and 0.97 for CRP (Figures 2b and2c). Analysis of the bone-turnover markers withIMPACT was previously described, the results of whichcorrelated well with those of corresponding single auto-mated assays [5].

Biomarker signatures define distinct arthritides andarthritis subtypesTo identify molecular signatures of arthritis subtypes, weused antigen-containing chips on the IMPACT platformto measure autoantibody reactivities and bone-turnovermarkers [5], and bead-based assays on the Luminexplatform to measure cytokines, in serum samples from120 patients with RA, 27 patients with AS, 28 patientswith PsA, and 25 healthy individuals. Values were nor-malized as described in the methods, subjected to hier-archical clustering, and displayed as a software-generated heat map (Figure 3). As expected, autoanti-body levels were significantly higher in RA patients thanin AS patients, PsA patients, or healthy controls. How-ever, within the pool of RA patients were subgroupswith distinct patterns of autoantibody specificities,including a subgroup with minimal autoantibody reac-tivity. Elevations in cytokine levels clearly distinguishedcertain subsets of patients with RA, AS, or PsA fromhealthy individuals. Certain subsets of arthritis patients


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had lower cytokine levels than did other patients withthe same diagnosis. As autoantibody production is nottypically a feature of PsA, the detection of autoantibo-dies in several patients diagnosed with PsA (Figure 3)raises the possibility that evaluation of a larger panel ofautoantibodies than that measured by the commerciallyavailable assays may be able to correct misdiagnosis.In contrast to previous findings [15,16] we did not

find an association between RA and markers of boneturnover. This is perhaps not surprising given that ouranalysis was done using a cohort of patients with early-stage RA, and erosion of bone occurs in established andadvanced RA. In contrast, an association between ASand elevated levels of markers of bone turnover – speci-fically, beta crosslaps, and osteocalcin – was revealed inthe course of the biomarker analysis (Figure 4),

suggesting that activation of bone-turnover pathways,exceeding that seen in RA or PsA, occurs in AS. Alsointriguing was the increase in levels of the bone-markerparathyroid hormone. However, because levels of para-thyroid hormone are heavily influenced by vitamin Dstatus [17] (a variable not accounted for in our study),firm conclusions about associations between parathyroidhormone and AS cannot be drawn from our presentdata. Levels of proinflammatory cytokines were also sig-nificantly higher in AS patients than in healthy indivi-duals, in line with previous findings [18,19].

Association of biomarker signatures with parameterspredictive of severe RAUsing research-grade platforms, we previously demon-strated an association between specific biomarker

Intraassay CV% Interassay CV%CID1 Rf-IgM 2.5-4.3 1.1-4.0

Rf-IgA 4.7-7.7 1.9-6.3CRP 5.2-5.4 5.3-12.6SAA 4.6-8.7 2.7-4.8

CID4 MMP3 3.9-4.6 8.6-14.6IL6 2.9-6.5 11.6-12.0E-Selec�n 3.9-9.0 7.2-8.8S100A8/A9 1.5-5.5 5.0-14.9Hyaluronic Acid 2.3-4.6 n.t.

Figure 2 Analytical precision of selected IMPACT assays and comparison with standard single assays. (a) Analytical precision. Intra-assaycoefficients of variance (CV) were generated by performing 21 replicate measurements of each of nine markers in one sample within one runon the IMPACT platform. Inter-assay CVs were calculated based on results from 5 to 15 independent runs of the same sample on the IMPACTplatform. The range of the CV for each marker corresponds to that of three independent pools of sample analyzed at low, medium, and highconcentrations. (b) Correlation of values obtained with the Roche IMPACT platform with those obtained with the standard Roche Tina Quant(latex aggregation) assay. IgM autoantibody reactivity to rheumatoid factor (IgM-RF) in 1,312 RA serum samples was measured with the IMPACTplatform and with Tina Quant assay. C-reactive protein (CRP) levels in 1,198 RA serum samples were measured with the IMPACT platform andwith Tina Quant assay. Linear regression was used to determine the correlation between the multiplex chip assay (IMPACT) and the standardsingle assay (Tina Quant). IL-6, interleukin-6; MMP3, matrix metalloproteinase 3; SAA, serum amyloid A.


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Figure 3 Proteomic characterization of serum samples from patients with rheumatoid arthritis, psoriatic arthritis, or ankylosingspondylitits. Autoantibody reactivities and levels of bone-turnover products in serum samples from 120 patients with rheumatoid arthritis (RA),27 patients with ankylosing spondylitits (AS), 28 patients with psoriatic arthritis (PSA), and 25 healthy individuals were measured on the IMPACTplatform. Cytokine levels were measured with a bead-based assay (Millipore) run on the Luminex platform. Values were normalized as describedin the methods and subjected to hierarchical clustering; individual patients are listed above the heat map and the individual cytokines andantigens are listed to the right of the heat map. Cytokine levels and autoantibody reactivities are displayed, with blue representing a decreaserelative to the mean value obtained in samples from healthy individuals, yellow no change, and red an increase. Cit, citrullinated; COMP,cartilage oligomeric matrix protein; CRP, C-reactive protein; FGF-2, fibroblast growth factor 2; GM-CSF, granulocyte macrophage colony-stimulating factor; HABP, hyaluronic acid binding protein; HSP 60, heat shock protein 60; IL, interleukin; MCP-1, monocyte chemoattractantprotein 1; MMP3, matrix metalloproteinase 3; P1NP, procollagen type 1 amino-terminal propeptide; PTH, parathyroid hormone; RF, rheumatoidfactor; TNFa, tumor necrosis factor a.


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signatures and the presence of RF, anti-CCP antibodies,or shared-epitope (SE) alleles [1,2], each of which pre-dicts progression to severe RA [20]. To determinewhether the automated IMPACT platform could recapi-tulate this finding, we used the IMPACT platform inconjunction with bead-based multiplex assays to charac-terize serum samples from 120 RA patients, of which 73had anti-CCP antibodies (as assessed by the IMPACTassay), 78 had RF (as assessed by the IMPACT assay),and 74 had one or two SE alleles. We performed ouranalysis using a subset of the antigen markers we usedpreviously [1,2,4], as well as an additional set of analyteassays previously developed for use on the IMPACTplatform (Figure 1). Data from the CCP-containingchips used to determine anti-CCP-antibody status of thepatient samples (i.e., CID 3 chips 1 and 2) wereexcluded from analyses comparing patients on the basisof presence or absence of anti-CCP antibodies.

We again demonstrate a clear association between thepresence of anti-CCP (Figure 5) or RF (Figure 6) antibo-dies and increased targeting of RA-associated autoanti-gens – most citrullinated, but some native. Notably,distinct but overlapping sets of antigens were targetedin RF-positive patients compared with anti-CCP-anti-body-positive patients. Likewise, the pattern of increasesin cytokine levels showed both differences and similari-ties between RF-positive patients and anti-CCP-anti-body-positive patients. Despite the strong associationbetween seropositivity (the presence of RF and/or anti-CCP antibodies) and elevation of serum cytokines, asubset of seronegative patients had significantly elevatedserum cytokines, possibly reflecting a subpopulationmore clinically and immunologically similar to thosewho can be defined as seropositive. When we sought toidentify differences on the basis of the presence orabsence of SE alleles, we found that the presence of SE

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Figure 4 Increased markers of bone metabolism in ankylosing spondylitis. Autoantibody reactivity and bone-turnover products werecharacterized on the IMPACT platform in 27 ankylosing spondylitis (AS) patients and 25 healthy individuals. Cytokine levels in the same sampleswere measured using a bead-based assay run on the Luminex platform. Values were normalized as described in the methods. SignificanceAnalysis of Microarrays (SAM) followed by a hierarchical clustering algorithm were used for determination of cluster relations that group patientsamples (top dendrogram) and antigen reactivities (right dendrogram) based on similarities in patient autoantibodies and cytokines (falsediscovery rate < 1). Dendrogram branch lengths and distances between nodes illustrate the extent of similarities in antigen reactivity andcytokine levels, with blue representing a decrease relative to the mean value obtained in samples from healthy individuals, yellow no change,and red an increase. Bone-turnover markers are in red text. GM-CSF, granulocyte macrophage colony-stimulating factor; IL, interleukin; PTH,parathyroid hormone; TNFa, tumor necrosis factor a.


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alleles was associated with increased targeting of RA-associated autoantigens; however, unlike the presence ofRF or anti-CCP antibodies, the presence of SE allelesalone was not associated with elevations in serum cyto-kines (Figure 7). There was no significant differencebetween carrying one versus two copies of the SE allele(data not shown).

Autoantibody and cytokine signatures as sensitive andspecific diagnostics of RAUsing univariate analysis, we determined which of thebiomarkers (out of 31 autoantigens, 4 bone markers, 5inflammatory mediators, and 14 cytokines) distinguishRA patients from a pool of 120 patients with early-stageRA, 27 patients with AS, 28 patients with PSA, and 25healthy individuals. We found that a panel of six auto-antigens distinguished RA. We then used the sameserum samples to evaluate the diagnostic sensitivity andspecificity of different combinations of the individualautoantigens in this differentiating panel of six biomar-kers. The sensitivity and specificity of these subpanels in

the differential diagnosis of RA were similar to that ofanti-CCP status [21] and better than that of RF status[22] (Table 2).

DiscussionWe report the development of a highly reproducible,automated, multiplex biomarker assay that can reliablydistinguish RA patients from healthy individuals orpatients with other inflammatory arthritides. Multiplexmeasurement of a subset of the differentiating biomar-kers provided high sensitivity and specificity in the diag-nostic discrimination of RA. Furthermore, thebiomarker profiles we identified enabled stratification ofRA patients into distinct, clinically relevant subtypes.Current clinical tests fall short of being accurate and

all-encompassing diagnostics of RA because RF is notspecific to RA and anti-CCP antibodies are not pro-duced in all cases of RA. Compared with single-biomar-ker detection, multiplex-biomarker detection – bycasting the net wider – provides greater sensitivity andspecificity of diagnosis. Although they remain to be

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RF-IgM RF-IgA Fibrinogen A (31-50) (Cit 35, 38, 42) IL-1α FGF-2 IL-15 IL-1β COMP (453-472) Acetyl-calpastatin (184-210) Vimentin (58-77) (Cit 64, 69, 71) Fibrinogen A (616-635) (Cit 621, 627, 630) Profilaggrin (293-310) (Cit 301, 302) Clusterin (334-353) (Cit 336, 339) Profilaggrin (293-310) (Cit 301, 305) TNFα GM-CSF IL-12(p40)

CCP+ RACCP- RA

-0.5 -1 -1.5 -2 -2.5 <-3

0

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Figure 5 Autoantibodies and cytokine levels stratified according to anti-CCP seropositivity. Autoantibody and cytokine levels are higherin anti- cyclic citrullinated peptide (CCP)-antibody-positive than in anti-CCP-antibody-negative RA. Serum samples from 73 patients with anti-CCP-antibody-positive RA and from 47 patients with anti-CCP-antibody-negative RA were analyzed. Chips containing CCP were excluded fromthis analysis. Autoantibody reactivity was assessed on the IMPACT platform and cytokine levels were measured in a bead-based assay run on theLuminex platform. For assays run on the IMPACT platform, values were normalized as described in the methods. Significance Analysis ofMicroarrays (SAM) followed by a hierarchical clustering algorithm were used to determine cluster relations that group patient samples (topdendrogram) and antigen reactivities (right dendrogram) on the basis of similarities in patient autoantibody and cytokine profiles (false discoveryrate < 1). Dendrogram branch lengths and distances between nodes illustrate the extent of similarities in antigen reactivity and cytokine levels,with blue representing a decrease relative to the mean value obtained in samples from healthy individuals, yellow no change, and red anincrease. Cit, citrullinated; COMP, cartilage oligomeric matrix protein; FGF-2, fibroblast growth factor 2; GM-CSF, granulocyte macrophage colony-stimulating factor; IL, interleukin; RF, rheumatoid factor; TNFa, tumor necrosis factor a.


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472 730 366 894 377 191 133 829 175 812 170 165 134 782 367 412 375 742 126 173 174 152 379 99 113 158 105 172 138 334 409 371 449 378 327 477 751 117 329 365 324 803 183 92 701 102 750 659 154 394 331 876 773 431 127 131 446 373 160 425 813 145 760 380 907 179 182 658 391 665 772 122 168 338 96F 106 354 103 110 731 364 159 157 123 460 197 887 136 811 741 167 119 111 148 762 97 181 178 156 352 325 802 384 323 151 723 434 918 792 114 470 361 395 132 120 382 140 370 155 400

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Figure 6 Autoantibodies and cytokine levels stratified according to RF seropositivity. Autoantibody and cytokine levels are higher inrheumatoid factor (RF)-positive RA than in RF-negative RA. Serum samples from 78 patients with RF-positive RA and from 42 patients with RF-negative RA were analyzed. Autoantibody reactivity was assessed on the IMPACT platform and cytokine levels were measured in a bead-basedassay run on the Luminex platform. For assays run on the IMPACT platform, values were normalized as described in the methods. SignificanceAnalysis of Microarrays (SAM) followed by a hierarchical clustering algorithm were used to determine cluster relations that group patient samples(top dendrogram) and antigen reactivities (right dendrogram) on the basis of similarities in patient autoantibody and cytokine profiles (falsediscovery rate < 1). Dendrogram branch lengths and distances between nodes illustrate the extent of similarities in antigen reactivity andcytokine levels, with blue representing a decrease relative to the mean value obtained in samples from healthy individuals, yellow no change,and red an increase. Cit, citrullinated; COMP, cartilage oligomeric matrix protein; FGF-2, fibroblast growth factor 2; GM-CSF, granulocytemacrophage colony-stimulating factor; IL, interleukin; RF, rheumatoid factor.


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validated in independent cohorts of RA patients, ourpreliminary results suggest that our biomarker assay hasthe potential to provide greater diagnostic sensitivityand specificity than that provided by current clinicaltests. Including in our analysis a larger number of con-trol patients with non-RA inflammatory diseases shouldallow us to further increase the sensitivity and specificityof our biomarker assay. Whereas the commercial anti-CCP-antibody assay relies on the measurement of anti-body reactivity against a mixture of different citrulli-nated peptides, our multiplex biomarker assay allowsmeasurement of antibody reactivity against each of sev-eral different citrullinated peptides independently, thusenabling more-precise diagnostic characterization.Moreover, the integrated evaluation of multiple addi-tional biomarkers (i.e. autoantibody specificities, cyto-kine levels, and bone-turnover products) enables the

stratification of RA into disease subtypes and providesfurther insight into disease pathogenesis at the indivi-dual level. For instance, our biomarker assay identified asubset of seronegative RA patients who had elevationsin serum cytokines suggestive of more aggressive dis-ease, an association that would have gone undetectedwith current clinical tests.As RA is such a heterogeneous disease, diagnosis must

be accompanied by prognosis in order to identify whichpatients with early-stage RA are in need of aggressivetherapeutic intervention. The presence of serum RF oranti-CCP antibodies is associated with progression tosevere RA [23-25]. When combined, these two biomar-kers offer a somewhat improved prognostic capability[26]. Although we did not observe an association ofbone-turnover markers with early-stage RA in thisstudy, elevations in markers of bone and cartilage

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Vimentin ( 58-77) (Cit 64, 69, 71) Profilagrin (293-310) (Cit 301, 302) Fibrinogen A (616-635) (Cit 621, 627, 630) Clusterin (334-353) (Cit 336, 339) Profilagrin (293-310) (Cit 301, 305) RF-IgM Cit peptide 7 Cit peptide 4 Cit peptide 11 Cit peptide 2 Cit peptide 5 Cit peptide 6 Cit peptide 1 Cit peptide 3 Cit peptide 8 Fibrinogen A (31-50) (Cit 35, 38, 42) Cit peptide 9 Cit peptide 10

Shared epitope:1,20

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Figure 7 Autoantibodies and cytokine levels stratified according to presence of a shared epitope allele. Autoantibody levels are higherin RA patients with one or two shared-epitope alleles than in those with no shared-epitope alleles. Serum samples from 74 RA patients witheither one or two copies of the shared epitope and from 46 RA patients with no shared epitope were characterized with the IMPACT platform.Autoantibody reactivity was assessed on the IMPACT platform and cytokine levels were measured in a bead-based assay run on the Luminexplatform. For assays run on the IMPACT platform, values were normalized as described in the methods. Significance Analysis of Microarrays (SAM)followed by a hierarchical clustering algorithm were used to determine cluster relations that group patient samples (top dendrogram) andantigen reactivities (right dendrogram) on the basis of similarities in patient autoantibody and cytokine profiles (false discovery rate < 1).Dendrogram branch lengths and distances between nodes illustrate the extent of similarities in antigen reactivity and cytokine levels, with bluerepresenting a decrease relative to the mean value obtained in samples from healthy individuals, yellow no change, and red an increase. Cit,citrullinated; RF, rheumatoid factor.


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turnover [27] also have been proposed to predict a moredestructive course of RA, as have elevations in acute-phase reactants [28]. Multiplex biomarker detectionshould be more accurate and informative than single-biomarker detection in RA prognosis, as it is in diagno-sis. We show here that multiplex biomarker detection inearly-stage RA can identify biomarker signatures thatare associated with immunological (presence of anti-CCP-antibodies or RF) and genetic (possession of SEalleles) parameters predictive of more severe RA. Unfor-tunately, information on the degree of radiographic jointdamage at the time of diagnosis, a powerful predictor ofdisease outcome, was not available for the cohort weanalyzed. In addition, the greater use of disease-modify-ing anti-rheumatic drugs in patients with RF-positiveRA confounded attempts to correlate our biomarker sig-natures with disability at diagnosis (as assessed by theStanford Health Assessment Questionaire), a good pre-dictor of later functional impairment [29]. In addition tovalidating out present findings in an independent cohortof patients, we aim to evaluate the prognostic utility ofour assay. Given that our biomarker panel enables dis-ease stratification and yields detailed molecular informa-tion, we expect that it will provide more preciseprognosis than that achieved in the clinic at present.Although not an overt objective of the present study,

our biomarker analysis revealed that AS is associated withelevated levels of bone-turnover markers and cytokines, inline with previous findings [18,30]. The small number ofAS patients included in this study precludes any firm con-clusions, but this observation suggests that our multiplexplatform may be useful in developing a diagnostic or prog-nostic test for AS – a major unmet clinical need.This exploratory study has several limitations. Given

that we were unable to adjust for treatment-relatedeffects on the studied biomarkers, it is possible that useof immunosuppressant therapy could affect levels ofserum cytokines and thereby confound interpretation ofour data. In addition, the ARAMIS RA cohort studiedrepresents a Caucasian, American, early-RA cohort, and

therefore it is possible that our findings cannot be extra-polated to all RA patients; our findings remain to bevalidated in independent and more diverse cohorts. Inaddition, the fact that RA and control patients were notmatched by demographics or by handling of their serumsamples could bias our results.

ConclusionsIn the diagnosis and prognosis of RA, measurement of asingle biomarker is not sufficiently sensitive or accurate,and individual measurement of multiple biomarkers islabor intensive and therefore expensive. Automated multi-plex biomarker analyses can help to reduce the laboratoryworkload involved in the analysis of multiple biomarkersand can provide greater sensitivity and specificity. How-ever, their use in clinical trials has been hampered by theirlimited reproducibility between and within multiplex plat-forms. The multiplex system we developed in this study isideally suited to the simultaneous analysis of multiple bio-markers because it uses a standardized assay platform andis highly automated, allowing high-throughput reproduci-bility across clinical laboratories. Here we demonstrate theeffectiveness of this multiplex biomarker assay in stratify-ing RA into clinically relevant subtypes. The ability to clas-sify RA patients in an automated and reproducible mannerpaves the way for further studies aimed at attaining perso-nalized medicine for RA.

Additional material

Additional file 1: Sequences of peptides spotted on synovialantigen chip 1 and 2. Supplementary table showing the amino acidsequences of the peptides spotted onto synovial antigen chips 1 and 2.

AbbreviationsAS: ankylosing spondylitis; CCP: cyclic citrullinated peptide; CID: chronicinflammatory disease; COMP: cartilage oligomeric matrix protein; CRP: C-reactive protein; CV: coefficient of variance; FDR: false discovery rate; IL:interleukin; IMPACT: Immunological Multi-Parameter Chip Technology; MCP:monocyte chemoattractant protein; PsA: psoriatic arthritis; RA: rheumatoidarthritis; RF: rheumatoid factor; SAM: significance analysis of microarrays; SE:shared epitope; TNF: tumor necrosis factor.

AcknowledgementsWe thank members of the Robinson Laboratory for their scientificdiscussions and input. This study was funded by NIH NIAMS R21 AI069160,NIH 1RC1AR058713-01, an American College of Rheumatology Research andEducation Foundation Within-Our-Reach Award and Veterans Affairs HealthCare System funding to WHR. Roche Diagnostics GmbH provided theIMPACT assay kits used in this study. COBAS C and TINA-QUANT aretrademarks of Roche.

Author details1Division of Immunology and Rheumatology, Department of Medicine,Stanford University School of Medicine, Stanford, CA 94305, USA. 2GeriatricResearch Education and Clinical Centers, Palo Alto VA Health Care System,3801 Miranda Avenue, Palo Alto, CA 94304, USA. 3Roche Diagnostics GmbH,Nonnenwald 2, 82377 Penzberg, Germany. 4Department of Dermatology,University of Michigan, Ann Arbor, MI 48109, USA. 5Division of

Table 2 Performance characteristics of multiplex-assayedautoantibody profiles in the diagnosis of rheumatoidarthritis

Number of positivebiomarkers*

PPV NPV Sensitivity Specificity

1+ markers 79.5% 92.6% 96.7% 62.5%

2+ markers 91.8% 89.7% 93.3% 87.5%

3+ markers 95.3% 79.8% 84.2% 93.8%

4+ markers 95.9% 61.1% 59.2% 96.3%

*Biomarker panel: histone 2B/e (1-20), vimentin (58-77) (Cit 64, 69, 71),fibrinogenA (616-635) (Cit 621, 627, 630), COMP (453-472), profilaggrin (293-310) (Cit 301, 305), RF-IgA. The cut-off value for positivity was defined as threetimes the mean value obtained in samples from healthy individuals. PPV,positive predictive value; NPV, negative predictive value.


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http://www.biomedcentral.com/content/supplementary/ar3383-S1.PDF

Rheumatology, The University of Texas Health Science Center at Houston,Houston, TX 77030, USA.

Authors’ contributionsPEC, JS, BGH, HE, UK, and WHR designed the studies, performed the studies,analyzed the data, and interpreted the data. JTE and JDR provided thepsoriatic arthritis and ankylosing spondylitis patient samples, respectively.TML contributed to data interpretation and provided editorial input. Allauthors read and approved the final manuscript.

Competing interestsBGH, HE, and UK are employees of Roche diagnostics. WHR has served on ascientific advisory board of Roche Diagnostics and has received research andreagent support from Roche Diagnostics for the study of RA biomarkers.Roche Diagnostics owns patents relating to the IMPACT platform. StanfordUniversity has applied for patents related to RA biomarker work performedprior to this study.

Received: 18 December 2010 Revised: 27 April 2011Accepted: 24 June 2011 Published: 24 June 2011

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doi:10.1186/ar3383Cite this article as: Chandra et al.: Novel multiplex technology fordiagnostic characterization of rheumatoid arthritis. Arthritis Research &Therapy 2011 13:R102.


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