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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Immunol. Author manuscript; available in PMC Jun 15, 2011.
Published in final edited form as:
PMCID: PMC2882518
NIHMSID: NIHMS198291

Analysis of complex biomarkers for human immune-mediated disorders based on cytokine responsiveness of peripheral blood cells1,2,3

Abstract

The advent of improved biomarkers promises to enhance the clinical care for patients with rheumatoid arthritis (RA) and other immune-mediated disorders. We have developed an innovative approach to broadly assess the cytokine responsiveness of human PBMC using a multi-stimulant panel and multiplexed immunoassays. The objective of this study was to demonstrate this concept by determining whether cytokine profiles could discriminate RA patients according to disease stage (early vs. late) or severity. A 10-cytokine profile, consisting of IL-12, CCL4, TNFα, IL-4, and IL-10 release in response to stimulation with anti-CD3/anti-CD28, CXCL8 and IL-6 in response to CMV/EBV lysate, and IL-17A, GM-CSF, and CCL2 in response to HSP60, easily discriminated the early RA group from controls. These data were used to create an immune response score, which performed well in distinguishing the early RA patients from controls and also correlated with several markers of disease severity among the patients with late RA. In contrast, the same 10-cytokine profile assessed in serum was far less effective in discriminating the groups. Thus, our approach lays the foundation for the development of immunologic ‘signatures’ that could be useful in predicting disease course and monitoring the outcomes of therapy among patients with immune-mediated diseases.

INTRODUCTION

Among the greatest challenges in managing patients with rheumatoid arthritis (RA) and other immune-mediated disorders is how to determine whether an individual patient has attained an optimal response to therapy (13). This is important because patients with suboptimal control of inflammation may suffer chronic pain (4), impaired quality of life (5), disability (6), progressive joint damage (7), extra-articular complications and comorbidity (8), and increased mortality (9). Evaluating the adequacy of treatment response in RA is problematic because the conventional assessments of disease activity suffer from limited sensitivity to detect low levels of articular as well as systemic inflammation (10). Consequently, patients judged clinically to be in ‘remission’ over time can have structural joint deterioration due to subclinical disease activity (11). The limited capacity to detect systemic inflammation may explain in part the increased burden of atherosclerosis and cardiovascular disease suffered by patients with RA and other rheumatic diseases (12, 13). Thus, therapeutic decision-making for individuals with RA might be enhanced by the development of improved biomarkers, which could assist clinicians in assessing disease activity, the outcomes of therapy, and predicting long-term disease outcomes.

In an effort to identify a multivariate immunologic biomarker for use in managing RA, we have developed an approach to broadly assess the functional activity of peripheral blood mononuclear cells (PBMC) based on the responsiveness of cytokine production ex vivo. A previous study by de Jager et al. discussed the potential utility of simultaneously detecting the release of 15 cytokines by PBMC into culture supernatants after stimulation with antigen or mitogens to monitor cellular immune responses in patients with inflammatory diseases (14). In this study, we advance a sophisticated and comprehensive analytical approach, which makes several contributions to studying human immunology. First, the methodology involves a unique panel of stimuli designed to elicit diverse responses of the innate and adaptive immune systems, which may optimize the identification of discerning cytokine profiles. As suggested by de Jager et al., we use high-throughput technology to analyze ex vivo cytokine release with multiplexed immunoassays. Second, we developed a statistical methodology to account for variability due to extraneous patient and assay effects and to identify the most informative cytokine profile. Third, we integrate the resulting cytokine data to create an immune response score that may have enhanced discriminative and predictive power.

In this study, our objective was to demonstrate that multiplexed analysis of ex vivo cytokine responsiveness could identify an immunologic signature that discriminates patient groups according to clinical differences as well as theoretical constructs of disease severity (construct validity). Therefore, we tested the hypothesis that a distinct cytokine response profile can differentiate patients, both with early and late RA, from controls. We also tested the hypothesis that an immune response score can stratify a relatively diverse group of patients with longstanding RA according to clinical and laboratory indicators of disease severity.

MATERIALS AND METHODS

Study design and participants

Patients with rheumatoid arthritis (RA) as defined by the American College of Rheumatology classification criteria (15) were included from two prospective cross-sectional studies at our institution. All participants were recruited in parallel during the period August 2006 to June 2008. First, we included patients with recently diagnosed disease (early RA) from the outpatient clinic of the Division of Rheumatology. Second, we included patients with longstanding established RA (‘late’ RA) who had been recruited from the community of Olmsted County, Minnesota to study the relationships between cytokine response profiles and cardiac disease. Healthy volunteers with no history of inflammatory or autoimmune diseases were recruited by advertisement on campus bulletin boards. The procedures for blood sampling and transport were similar for all subjects. The study was approved by the Mayo Foundation institutional review board and was conducted according to the principles of the Declaration of Helsinki. All patients provided written informed consent prior to participating in this study.

PBMC isolation, cell culture, and stimulation panel

A single, experienced laboratory technician (M.S.) performed all experiments, which had identical procedures for all subjects. Venous blood samples were harvested and maintained at room temperature. Within 1 – 2 hours, fresh PBMC were isolated by Ficoll density gradient centrifugation. The PBMC were stimulated in tissue culture under 8 separate conditions using a panel of stimuli. Monoclonal antibodies to the CD3 receptor and the costimulatory molecule CD28 (αCD3/αCD28) (Dynabeads ® Human T-Activator, Invitrogen, Carlsbad, CA) as well as a plant lectin, PHA (Sigma, St. Louis, MO), were used to crosslink signaling receptors and thereby activate T cells under conditions not requiring antigen presentation. Staphylococcus enterotoxins A and B (Toxin Technology, Sarasota, FL) are bacterial superantigens capable of cross-linking MHC class I and class II molecules on antigen presenting cells to T cell receptors, activating naive and memory T cells independently of antigen (16). Combined cytomegalovirus and Epstein Barr virus lysates (CMV/EBV) (Advanced Biotechnologies, Columbia, MD) were selected to induce T cell responses in an antigen-dependent manner with strong cell-mediated and milder humoral responses (17, 18).

Three molecules containing pathogen-associated molecular patterns were selected to induce innate cytokine responses via Toll-like receptor (TLR) signaling pathways. CMV/EBV lysates contain ligands for TLR2, TLR3, and TLR9 (19, 20), and bacterial CpG oligonucleotides are ligands for TLR9 (21); these molecules activate cytokine production in B cells and innate immune effectors (i.e., monocytes, dendritic cells). Human heat shock protein 60 (HSP60) (Stressgen, Victoria BC, Canada) is an endogenous ligand for TLR2 and TLR4 that is released by damaged tissues into the extracellular microenvironment (2224). HSP60 modulates innate and adaptive immunity through both proinflammatory and anti-inflammatory (i.e. regulatory T cell) responses (25, 26). Finally, phorbol myristate acetate with ionomycin (Sigma, St. Louis, MO) was included as a stimulus to activate diverse cell types via induction of protein kinases in mitogenic pathways. For comparison, we evaluated the release of cytokines by PBMC cultured in media alone without the use of additional stimulants.

For each stimulation condition, 4 × 105 PBMC were cultured in 200 μl of medium (RPMI-1640 + 10% FBS + 1% penicillin/streptomycin/glutamine) containing the stimulant (or media alone) in quadruplicate wells of a micro-titer plate. The final concentrations of each stimulant in cell culture were based on our established protocols and published work as follows: αCD3/αCD28, 0.5 ×106 beads per culture well (1:1 ratio of beads to PBMC per manufacturer instructions); PHA, 5 μg/ml (27); Staphylococcus enterotoxin A, 10 ng/ml, with Staphylococcus enterotoxin B, 10 ng/ml (28); CMV, 1 μg/ml, with EBV, 1 μg/ml (29); CpG, 10 μg/ml (30); HSP60, 1 μg/ml (24); and PMA, 1 μg/ml, with ionomycin, 700 ng/ml (27). The PBMC were incubated at 37°C in 5% CO2 for 48 hours; the supernatants were subsequently harvested, transferred to a storage plate, and frozen at −80°C for later analysis.

Differential leukocyte counts

The results of differential leukocyte counts were collected from electronic laboratory records when available. These results were included if the blood counts were drawn within 14 days of the blood draws for the cytokine profiles. Data were available for a sample of the RA patients but none of the controls.

Multiplexed cytokine immunoassays

A panel of 17 cytokines and chemokines was analyzed using a multiplexed approach with commercially available human 17-plex kits. The following cytokines were assessed: IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, CXCL8 (IL-8), IL-10, IL-12, IL-13, IL-17A, IFNγ, TNFα, CCL2 (MCP-1), CCL4 (MIP1β), G-CSF and GM-CSF. For serum samples, we used the Bio-Plex 17-plex kit (Bio-Rad Laboratories, Hercules, CA) and determined the cytokine concentrations using the Bio-Plex 200 reader (Bio-Rad Laboratories). For the PBMC culture supernatants, we used a customized platform obtained from Meso Scale Discovery (Gaithersburg, MD) and determined the cytokine concentrations using the Sector 2400 instrument (Meso Scale Discovery, Gaithersburg, MD). This technology was chosen because it performed better than the Bio-Plex at values near the upper limit of detection (data not shown). Cytokine concentrations were determined based on a standard curve generated on each plate using the manufacturer-supplied reagents. For each of the cytokines, the inter-assay coefficient of variation (CV) was 9–15% except for IL-7, which had a CV of 22%.

Statistical analysis

The distributions of the patient characteristics were analyzed descriptively using the mean ± SD for continuous variables or numbers and percentages for categorical variables. To test for differences between the groups, we used the t-test for continuous variables or the chi square test for categorical variables. Differences in the peripheral blood leukocyte counts between the patients with early and late disease were tested for statistical significance using the Wilcoxon rank sum test.

The analytic methods were required to explicitly account for the multiplicity and inter-relatedness of the cytokine data, along with the blocking induced by multiple patients and assays per reaction plate. All cytokine concentrations were log transformed. Mixed effects models were used to normalize the data and to estimate and test for differences between the groups. The analyses resulted in fold differences (95% confidence intervals) in the geometric mean cytokine concentrations between the groups; p-values for differences between the early RA, late RA and controls subjects were determined using 2 degree of freedom tests. The models included fixed effects for age, sex and stimulation and random effects for subject and plate; the values as presented are therefore adjusted for age, sex, and assay effects. In order to identify biomarker profiles that reliably discriminated patient groups while controlling for the likelihood of spurious findings, we selected cytokines into the profile based on the magnitude and significance of differences among the groups, and more importantly, on potential immunological mechanisms of cytokine production.

A multivariate technique was developed to construct an immune response score with characteristics suitable for class prediction. Standard stepwise regression methods, which remove “non-significant” variables from the model, were not used because deletion of non-significant variables from the final score may increase prediction error and decrease generalizability (31). Although the individual coefficients may be more variable when collinear cytokines are included, the overall score is more stable. To create the immune response score, the selected cytokines were converted to Z-scores (each cytokine value subtracted by the mean of the control group and divided by the SD of the control group), and the Z-scores were added (or subtracted depending on the direction of differences between the control and early RA groups). For ease of interpretation, the score was rescaled so the minimum score was 0 and the optimal cutoff value for discriminating the groups was 50. The score was then dichotomized, and differences between the resulting patient groups were tested as appropriate.

RESULTS

Patient characteristics

We recruited 25 patients with early RA, 60 with late RA, and 15 healthy volunteers as controls (Table 1). The patients with early RA, who were nearly a decade younger on average than the patients with late RA, were newly diagnosed (mean disease duration = 0.2 years) and beginning initial disease-modifying therapy. The early patients had highly active disease as demonstrated by high values for C-reactive protein, the Health Assessment Questionnaire disability index, and pain scores. In contrast, the patients with late RA were more diverse but, on average, had lower disease activity, pain, and disability and had normal acute phase reactants. The patients with early RA had a higher frequency of anti-citrullinated protein antibodies and a similar frequency of erosive disease (as determined by radiologist reports of available x-rays) as compared to the patients with late disease.

Table 1
Characteristics of the patients with early or late RA and controls*

Selection of the cytokine response profile

We tested for differences in 136 values (8 stimulation conditions * 17 cytokines) between the groups. The analyses showed statistically significant differences for 58 of 136 (43%) of the stimulated cytokine values between the 3 groups at a significance level of 0.05 (data not shown). The large number of statistically significant differences indicated that these profiles easily distinguished the RA groups, both early and late, from controls.

The results of the analyses revealed clear, recurring profiles of immune response most characteristic of either T cell responses or myeloid lineage responses (Table 2). The cytokine profiles showed consistency in the direction of effect for cytokines within canonical classes. For example, the profiles of IFN-γ, IL-4, IL-10, and TNFα release by PBMC in response to stimulation with αCD3/αCD28 were all significantly reduced in the early RA group as compared to controls (with the exception of IFN-γ, which was non-significant at p=0.2 and hence not included in the final profile). Considerable redundancy was present in the profiles of several cytokines among the different stimuli; for example, the responses of PBMC to αCD3/αCD28 and SEA/SEB, or CpG and HSP60, were often similar.

Table 2
Distributions of cytokine concentrations for the selected ex vivo cytokine response profile among patients with early or late RA as compared to controls*

Our strategy was to incorporate several cytokines in the context of a particular stimulation to describe the immunologic responsiveness of a theoretical immune compartment. Then, cytokine groups for particular stimulants were assimilated to create the cytokine response profile. Ultimately, we selected IL-12, CCL4, TNFα, IL-4, and IL-10 with αCD3/αCD28 to assess T cell-mediated responses; IL-6 and CXCL8 with CMV/EBV to assess adaptive and innate immune responses; and IL-17A, GM-CSF, and CCL2 with HSP60 to assess IL-17A producing cell and innate immune responses (Table 2).

Cytokine response profiles in early or late RA as compared to controls

Next we investigated the fold differences in the 10-cytokine profiles for patients with early RA as compared to controls (Fig. 1A). In response to stimulation with αCD3/αCD28, the production of IL-12, CCL4, TNFα, IL-4 and IL-10 were significantly reduced in the early RA group as compared to controls, each by more than 50%. In response to CMV/EBV, the production of CXCL8 was significantly decreased while the production of IL-6 was significantly increased as compared to controls. In response to stimulation with HSP60, a different pattern was evident, with significantly elevated production of IL-17A and GM-CSF and marginally elevated CCL2 as compared to controls.

Figure 1
Multiplexed analysis of ex vivo cytokine production by stimulated PBMC discriminated patients with both early and late RA from controls. We measured the profiles of 10 cytokines released into the culture supernatants by patient PBMC in response to stimulation ...

The 10-cytokine profiles were significantly different for the patients with late RA (Fig. 1B) compared to controls. In response to stimulation with αCD3/αCD28, the release of IL-12 and IL-10 were significantly decreased in the group with late RA, but the release of CCL4, IL-4, and TNFα were not significantly different from controls. As compared to the early RA group (Fig. 1A), the average impairments in immune responsiveness to αCD3/αCD28 stimulation appeared to be ameliorated in the late RA group. In response to stimulation with CMV/EBV, the production of CXCL8 was significantly decreased yet the production of IL-6 was significantly increased as compared to controls (Fig. 1B). These responses were almost identical to those of the early RA group (Fig. 1A). In response to stimulation with HSP60, the release of IL-17A and GM-CSF were significantly increased to 12.9- and 9.6-fold higher, respectively, in the patients with late RA as compared to controls, and the release of CCL2 was also significantly elevated (Fig. 1B). As compared to the group with early RA (Fig. 1A), these HSP60 responses appeared to be higher in the group with late RA.

‘Unstimulated’ cytokine profiles of PBMC in patients with early or late RA as compared to controls

To assess the value of the multi-stimulant panel, we next evaluated the cytokine profiles of PBMC in media alone without any additional stimulants (Fig. 2). The profiles of the early RA group (Fig. 2A) and the late RA group (Fig. 2B) were nearly identical. In the group with early RA, the basal production of GM-CSF and CCL2 was significantly increased as compared to controls (Fig. 2A). In the group with late RA, the only differences from early RA were the significantly increased responses of CCL4 and IL-17A as compared to controls (Fig. 2B). Thus, because most of the cytokine responses in both patient groups were not significantly different from controls, the ‘unstimulated’ profiles were less useful in differentiating the patient groups.

Figure 2
Multiplexed analysis of ex vivo cytokine production by ‘unstimulated’ PBMC was less effective in discriminating patients with early or late RA from controls as compared to the stimulated profiles. Here we assessed the profiles of 10 cytokines ...

Comparison with cytokine profiles in the serum of patients with early or late RA as compared to controls

Because serum multi-cytokine profiles have been shown to discriminate patients with RA and controls by other investigators, we compared the profiles of the same 10 cytokines that we previously assessed in PBMC culture supernatants with the profiles in serum samples (Fig. 3). Patients with early RA had significantly elevated serum levels of IL-12, TNFα, IL-10, IL-6, and IL-17A, between 2- and 5-fold in magnitude, as compared to controls (Fig. 3A). In contrast, patients with late RA, who had lower average disease activity, had no significant differences compared to controls (Fig. 3B). These data suggested that serum cytokine profiles are substantially less sensitive to low disease activity states than ex vivo profiles.

Figure 3
Serum profiles demonstrated increased cytokine levels in the early disease patients but failed to differentiate the patients with late RA from controls. We assessed the serum profiles of the same 10 cytokines and chemokines that were evaluated in PBMC ...

Comparison of peripheral leukocyte differentials among the patients with early or late RA

Next, we compared the peripheral blood leukocyte differential counts between the patients with early and late disease, in order to evaluate the possibility that changes in the distribution of peripheral immune cell types might explain the differences in the cytokine response profiles between the groups (Fig. 4). We observed a small but statistically significant increase in total leukocytes in the patients with early RA as compared to late RA (p=0.019). There was a non-significant trend toward mild increases in basophils in the early as compared to late RA group (p=0.055). There were no significant differences in the distributions of lymphocytes (p=0.44), monocytes (p=0.13), or eosinophils (p=0.84) between the groups. These results differed from the profiles of ex vivo cytokine production, which suggested significant differences in lymphoid and myeloid derived cytokines. Thus, the blood leukocyte differential counts could not explain the variation in cytokine profiles among the patient groups.

Figure 4
The distributions of leukocyte types in the peripheral blood did not explain the variation in the ex vivo cytokine profiles observed among the groups with early or late RA. We compared differential leukocyte counts, which were measured within 14 days ...

Development of an immune response score

Finally, we created an immune response score, which integrated the variation for each individual cytokine of our selected cytokine response profile into a continuous index of immune response (Fig. 5). The cutoff of ≥50 accurately classified 17 of 19 early RA patients and 12 of 13 controls, indicating that the immune response score performed very well in discriminating the early RA group from controls. Among the 60 patients with late RA group, 35 had an immune response score of ≥50, and 25 had a score of <50, showing greater heterogeneity among the late RA group, with some individuals having immune profiles more similar to early RA patients and others having profiles more similar to controls.

Figure 5
Multivariate analysis revealed a multi-cytokine immune response score. A cutoff of ≥50 easily distinguished the patients with early, highly active RA from controls. The scores among the patients with late RA showed that a subgroup had profiles ...

We assessed the construct validity of the immune response score by testing whether subgroups of the patients with late RA defined by dichotomous levels of the immune response score differed in several clinical indicators of disease severity (Table 3). The groups had similar distributions for age, sex, and disease duration. However, the group with higher immune response scores had higher levels of disability as defined using the Health Assessment Questionnaire (p=0.05) and higher proportions of rheumatoid factor (p=0.048), erosive disease (p=0.025), and methotrexate use (p<0.001). The group with higher immune response scores tended to have higher C-reactive protein (p=0.09) and to be taking TNF blockers (p=0.08). Notably, there was no association between prednisone use and the score.

Table 3
A multi-cytokine immune response score among subjects with late RA distinguishes subgroups with different disease severity. *

In a sensitivity analysis, we assembled another ex vivo cytokine profile that evaluated only those 10 cytokines with the most statistically significant differences (all p<0.01) among the patient groups and controls, without considering potential mechanisms. This profile included release of IL-12, CCL4, TNF-α, IL-4, and IL-10 in response to αCD3/αCD28; CXCL8 release in response to CMV/EBV; GM-CSF production in media alone; G-CSF release in response to HSP60; and IL-7 release in response to PMA/ION. The immune response score based on this profile performed as well as the former score in discriminating the early RA group from controls but correlated poorly with markers of disease severity among the late RA group (data not shown).

DISCUSSION

RA is a systemic inflammatory autoimmune disease orchestrated by diverse cellular mediators of the innate and adaptive immune systems, producing a myriad of cytokines and chemokines to initiate and amplify inflammation in diarthrodial joints, leading ultimately to destruction of articular bone and cartilage (32, 33). In this study, we have developed an innovative strategy of biomarker discovery for immune-mediated disorders based on the premise that the responsiveness of the various canonical subsets of the immune system is an important determinant of disease severity and therapeutic outcomes. We have demonstrated that multiplexed analysis of ex vivo cytokine production by stimulated PBMC with the development of a multi-cytokine immune response score effectively discriminated the patients with RA, particularly the early RA group, from controls. Among the more diverse group with late RA—with wider variation in disease activity and severity—we demonstrated evidence for construct validity by showing significant correlations of the immune response score with clinical indicators of disease severity. The findings underscore the potential clinical utility of our approach in identifying immunologic ‘signatures’ for predicting disease course and monitoring the outcomes of therapy in patients with immune-mediated inflammatory diseases.

Several points illuminate the potential significance of this approach. Circulating PBMC experience the unique microenvironments in many body tissues of an individual. The particular milieu present likely influences the responsiveness of cytokine production by interacting with genetic variation and affecting epigenetic control of immunoregulatory pathways. In RA, environmental factors such as tobacco smoking are known to interact with a patient’s genotype to influence disease pathogenesis and severity (34). In view of this it is noteworthy that the level of ex vivo cytokine production by peripheral blood immune cells can be affected by multiple environmental factors, such as infectious exposures and place of residence (35). Thus, ex vivo cytokine profiling may capture information on relevant but unknown environmental factors, which could impact disease phenotype. Our approach also assesses the systemic immune reactions to local, articular inflammation. The phenotype of the systemic immune compartment can be polarized as compared to that of the primary disease site, for example, with low Th1 cell activity in the peripheral blood of RA patients as compared to high Th1 activity in their affected joints (36). The systemic immune phenotype may therefore be closely associated, if divergently, with the underlying immunopathology. The developed approach involves mixed populations of cells, and while precluding elucidation of the cellular mechanisms involved, allows cell-cell interactions as well as autocrine and paracrine signaling to influence the cytokine responses, potentially creating biomarker assays with greater discriminative power. The use of a panel to induce ‘dynamic’ cellular responses may offer higher sensitivity to detect abnormal immune function as compared to the use of only a few stimuli since differences in a particular cytokine can often be detected using one stimulant but not another (37). This method may be useful not only to assess current disease status, including systemic disease activity, but also to assess the potential for future disease progression. Finally, multivariate analyses can be used to construct a prediction model, such as the immune response score reported herein, to classify the likelihood of particular outcomes. Such a classifier could provide higher predictive accuracy as compared to any single cytokine response as has been suggested in RA and other immune-mediated diseases (3841).

While previous studies of RA patients have reported abnormalities for cytokines that we have evaluated individually, ours is among the first to assimilate these immune abnormalities into a multivariable biomarker. We noted decreased production of IL-12, CCL4, IL-4, IL-10 and TNFα following stimulation of the PBMC with anti-CD3/anti-CD28, suggesting changes in the peripheral T cell compartment. Previous studies have reported abnormalities of CD4+ T cell function in patients with early RA, including defective Th1 (i.e., IFNγ) and Th2 (i.e., IL-4) immune responses (4245). Further, we observed among patients with RA significantly increased release of IL-6 with CMV/EBV and of GM-CSF with HSP60, suggesting changes in the myeloid compartment such as increased activity of a proinflammatory subset of monocytes or dendritic cells. Our results extend the previous observation in RA that ex vivo responsiveness to stimulation with TLR ligands (including HSP60) is increased, as indicated by proliferative capacity (46), IL-6 release (47), or TNFα release (48, 49). Also consistent with our data, the expression of IL-17A mRNA is reported to be increased in PBMC of patients with RA as compared to osteoarthritis (50). Prior work supports the use of ex vivo immune biomarkers to monitor treatment responses with the evidence that disease-modifying therapy may lead to recovery of peripheral T cell and innate immune responses during follow-up (37, 48, 51, 52). Finally, previous studies have shown that pre-treatment cytokine responsiveness of stimulated PBMC may be useful in predicting outcomes of antirheumatic therapy (48, 52).

Our approach appears to have several advantages over existing biomarker approaches for rheumatic diseases. As we have shown, serum biomarker profiles are prone to high variability, likely related to the complex matrix of the serum samples, including the potential for non-specific binding by heterophilic antibodies (53). In addition, many cytokines have diurnal variation, so the time of blood sampling can significantly impact the cytokine levels in serum. The degree of ‘leakage’ of particular cytokines from the joints into the bloodstream may determine how useful they are as biomarkers (3). These factors should impact ex vivo PBMC based assays much less. Not surprisingly, the serum profiles we observed differed significantly from the ex vivo profiles. For example, severe RA is correlated with higher serum levels of TNFα, IFN-γ, IL-4, and IL-10 (54, 55) whereas the production of these cytokines by stimulated PBMC was significantly reduced in our study. We showed that the serum cytokine profiles were less discriminative than the ex vivo cytokine profiles, suggesting that PBMC based assays are more useful as biomarkers. Additionally, the differences in routine blood leukocyte counts could not explain the differences in cytokine production observed among the groups. In future studies, we will perform immunophenotyping to determine whether differences in the distributions of lymphoid or myeloid subtypes can explain our findings. An important insight from our data is that ex vivo cytokine profiles offer unique and clinically relevant information that is not available from routine laboratory techniques.

In conclusion, we have developed an innovative approach to identify complex biomarkers for human immune-mediated disorders by analyzing the responsiveness of cytokine production by peripheral blood cells. We have demonstrated this concept with a 10-cytokine immune response score that may identify patients with more severe or less treatment responsive RA. However, this score is currently not useful as a biomarker, and additional steps are necessary to refine the approach and to establish the clinical applicability of the findings. By design, this study does not have systematically collected data for joint counts, global assessments, or composite disease activity scores, so correlative analyses of the cytokine assays with validated measures of disease activity are required. Prospective studies should determine if the developed approach adds value beyond current clinical or serological assessments in predicting treatment outcomes for patients with RA or other inflammatory diseases. While the assays appear to have adequate reproducibility overall, further studies should test alternative methods of blood cell isolation and stimulation to facilitate the development of practical assays with the level of standardization necessary for clinical translation. Future clinical trials of RA and other immune-mediated disorders should endeavor to collect and store PBMC in a manner conducive to the development of similar strategies of immune monitoring.

Acknowledgments

The authors wish to thank Sherry L. Kallies and Jennifer J. M. Gall for administrative support as well as Jane M. Jaquith and Cynthia J. Stoppel for coordinating this study. The authors are grateful for the contributions of Larry R. Pease, PhD, who helped conceptualize the scientific approach in the early phase of study design. The authors are indebted to the generosity and inspiration of Lawrence E. and Ruth M. Eaton, who know firsthand how this disease can impact individuals and families, for supporting this study as well as the early career development of the first author.

Footnotes

DISCLOSURES

Drs. Davis, Knutson, and Gabriel have filed a provisional patent application pertaining to the technology described in this study.

3List of nonstandard abbreviations: ACPA = anti-citrullinated protein antibodies; αCD3/αCD28 = immobilized anti-CD3 and anti-CD28 monoclonal antibodies; CMV/EBV = cytomegalovirus and Epstein Barr virus lysates; CRP = C-reactive protein; DMARDs = disease-modifying antirheumatic drugs; HAQ = Health Assessment Questionnaire; HSP60 = human heat shock protein 60; MTX = methotrexate; RA = rheumatoid arthritis; RF = rheumatoid factor; VAS = visual analogue scale

Disclaimer: This is an author-produced version of a manuscript accepted for publication in The Journal of Immunology (The JI). The American Association of Immunologists, Inc. (AAI), publisher of The JI, holds the copyright to this manuscript. This version of the manuscript has not yet been copyedited or subjected to editorial proofreading by The JI; hence, it may differ from the final version published in The JI (online and in print). AAI (The JI) is not liable for errors or omissions in this author-produced version of the manuscript or in any version derived from it by the U.S. National Institutes of Health or any other third party. The final, citable version of record can be found at www.jimmunol.org.

1This work was supported by grant R01 R46849 to Dr. Gabriel from the National Institute for Arthritis, Musculoskeletal, and Skin Diseases; the Mayo Foundation; and a generous gift of Lawrence E. and Ruth M. Eaton. Dr. Davis is supported by grant 1 KL2 RR024151 from the National Center for Research Resources (NCRR) and a New Investigator Award from the Arthritis Foundation North Central Chapter. NCRR is a component of the National Institutes of Health (NIH) and the NIH Roadmap for Medical Research. The contents are solely the responsibility of the authors and do not necessarily represent the official view of NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/. Information on Reengineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/.

References

1. Cohen SB, Cohen MD, Cush JJ, Fleischmann RM, Mease PJ, Schiff MH, Simon LS, Weaver AL. Unresolved issues in identifying and overcoming inadequate response in rheumatoid arthritis: weighing the evidence. J Rheumatol Suppl. 2008;81:4–30. quiz 31–34. [PubMed]
2. Bykerk V. Unmet needs in rheumatoid arthritis. J Rheumatol Suppl. 2009;82:42–46. [PubMed]
3. Smolen JS, Aletaha D, Grisar J, Redlich K, Steiner G, Wagner O. The need for prognosticators in rheumatoid arthritis. Biological and clinical markers: where are we now? Arthritis Res Ther. 2008;10:208. [PMC free article] [PubMed]
4. Wolfe F, Michaud K. Assessment of pain in rheumatoid arthritis: minimal clinically significant difference, predictors, and the effect of anti-tumor necrosis factor therapy. J Rheumatol. 2007;34:1674–1683. [PubMed]
5. Wiles NJ, Scott DG, Barrett EM, Merry P, Arie E, Gaffney K, Silman AJ, Symmons DP. Benchmarking: the five year outcome of rheumatoid arthritis assessed using a pain score, the Health Assessment Questionnaire, and the Short Form-36 (SF-36) in a community and a clinic based sample. Ann Rheum Dis. 2001;60:956–961. [PMC free article] [PubMed]
6. Wolfe F, Cathey MA. The assessment and prediction of functional disability in rheumatoid arthritis. J Rheumatol. 1991;18:1298–1306. [PubMed]
7. Smolen JS, Han C, van der Heijde DM, Emery P, Bathon JM, Keystone E, Maini RN, Kalden JR, Aletaha D, Baker D, Han J, Bala M, St Clair EW. Radiographic changes in rheumatoid arthritis patients attaining different disease activity states with methotrexate monotherapy and infliximab plus methotrexate: the impacts of remission and tumour necrosis factor blockade. Ann Rheum Dis. 2009;68:823–827. [PubMed]
8. Turesson C, O’Fallon WM, Crowson CS, Gabriel SE, Matteson EL. Extra-articular disease manifestations in rheumatoid arthritis: incidence trends and risk factors over 46 years. Ann Rheum Dis. 2003;62:722–727. [PMC free article] [PubMed]
9. Gabriel SE, Crowson CS, Kremers HM, Doran MF, Turesson C, O’Fallon WM, Matteson EL. Survival in rheumatoid arthritis: a population-based analysis of trends over 40 years. Arthritis Rheum. 2003;48:54–58. [PubMed]
10. Wolfe F, Rasker JJ, Boers M, Wells GA, Michaud K. Minimal disease activity, remission, and the long-term outcomes of rheumatoid arthritis. Arthritis Rheum. 2007;57:935–942. [PubMed]
11. Brown AK, Conaghan PG, Karim Z, Quinn MA, Ikeda K, Peterfy CG, Hensor E, Wakefield RJ, O’Connor PJ, Emery P. An explanation for the apparent dissociation between clinical remission and continued structural deterioration in rheumatoid arthritis. Arthritis Rheum. 2008;58:2958–2967. [PubMed]
12. Sattar N, McCarey DW, Capell H, McInnes IB. Explaining how “high-grade” systemic inflammation accelerates vascular risk in rheumatoid arthritis. Circulation. 2003;108:2957–2963. [PubMed]
13. Ku IA, Imboden JB, Hsue PY, Ganz P. Rheumatoid arthritis: model of systemic inflammation driving atherosclerosis. Circ J. 2009;73:977–985. [PubMed]
14. de Jager W, te Velthuis H, Prakken BJ, Kuis W, Rijkers GT. Simultaneous detection of 15 human cytokines in a single sample of stimulated peripheral blood mononuclear cells. Clin Diagn Lab Immunol. 2003;10:133–139. [PMC free article] [PubMed]
15. Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, Healey LA, Kaplan SR, Liang MH, Luthra HS, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum. 1988;31:315–324. [PubMed]
16. Cameron SB, Nawijn MC, Kum WW, Savelkoul HF, Chow AW. Regulation of helper T cell responses to staphylococcal superantigens. Eur Cytokine Netw. 2001;12:210–222. [PubMed]
17. Sinclair E, BD, ECL, et al. CMV antigen-specific CD4+ and CD8+ T cell IFNgamma expression and proliferation responses in healthy CMV-seropositive individuals. Viral Immunol. 2004;17:445–454. [PMC free article] [PubMed]
18. Wang YL, Zhang YY, Zhou YL, Zhu ZJ, Tang ZQ, Jiang Y, Peng L, Li G, Zhang XH. T-helper and T-cytotoxic cell subsets monitoring during active cytomegalovirus infection in liver transplantation. Transplant Proc. 2004;36:1498–1499. [PubMed]
19. Compton T, Kurt-Jones EA, Boehme KW, Belko J, Latz E, Golenbock DT, Finberg RW. Human cytomegalovirus activates inflammatory cytokine responses via CD14 and Toll-like receptor 2. J Virol. 2003;77:4588–4596. [PMC free article] [PubMed]
20. Tabeta K, Georgel P, Janssen E, Du X, Hoebe K, Crozat K, Mudd S, Shamel L, Sovath S, Goode J, Alexopoulou L, Flavell RA, Beutler B. Toll-like receptors 9 and 3 as essential components of innate immune defense against mouse cytomegalovirus infection. Proc Natl Acad Sci U S A. 2004;101:3516–3521. [PMC free article] [PubMed]
21. Rutz M, Metzger J, Gellert T, Luppa P, Lipford GB, Wagner H, Bauer S. Toll-like receptor 9 binds single-stranded CpG-DNA in a sequence- and pH-dependent manner. Eur J Immunol. 2004;34:2541–2550. [PubMed]
22. Calderwood SK, Mambula SS, Gray PJ, Jr, Theriault JR. Extracellular heat shock proteins in cell signaling. FEBS Lett. 2007;581:3689–3694. [PubMed]
23. Cohen-Sfady M, Nussbaum G, Pevsner-Fischer M, Mor F, Carmi P, Zanin-Zhorov A, Lider O, Cohen IR. Heat shock protein 60 activates B cells via the TLR4-MyD88 pathway. J Immunol. 2005;175:3594–3602. [PubMed]
24. Vabulas RM, Ahmad-Nejad P, da Costa C, Miethke T, Kirschning CJ, Hacker H, Wagner H. Endocytosed HSP60s use toll-like receptor 2 (TLR2) and TLR4 to activate the toll/interleukin-1 receptor signaling pathway in innate immune cells. J Biol Chem. 2001;276:31332–31339. [PubMed]
25. Zanin-Zhorov A, Cahalon L, Tal G, Margalit R, Lider O, Cohen IR. Heat shock protein 60 enhances CD4+ CD25+ regulatory T cell function via innate TLR2 signaling. J Clin Invest. 2006;116:2022–2032. [PMC free article] [PubMed]
26. Rajaiah R, Moudgil KD. Heat-shock proteins can promote as well as regulate autoimmunity. Autoimmun Rev. 2008;8:388–393. [PMC free article] [PubMed]
27. Kruisbeek AM, Shevach E, Thornton AM. Curr Protoc Immunol. Wiley; New York: 2004. Proliferative assays for T cell function; pp. 3.12.11–13.12.20.
28. Godoy-Ramirez K, Franck K, Mahdavifar S, Andersson L, Gaines H. Optimum culture conditions for specific and nonspecific activation of whole blood and PBMC for intracellular cytokine assessment by flow cytometry. J Immunol Methods. 2004;292:1–15. [PubMed]
29. Nascimbeni M, Shin EC, Chiriboga L, Kleiner DE, Rehermann B. Peripheral CD4(+)CD8(+) T cells are differentiated effector memory cells with antiviral functions. Blood. 2004;104:478–486. [PubMed]
30. Krieg AM. CpG motifs in bacterial DNA and their immune effects. Ann Rev Immunol. 2002;20:709–760. [PubMed]
31. Harrell FE. In: Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Harrell Frank E., Jr, editor. Springer; New York: 2001.
32. Firestein GS. Evolving concepts of rheumatoid arthritis. Nature. 2003;423:356–361. [PubMed]
33. Brennan FM, I, McInnes B. Evidence that cytokines play a role in rheumatoid arthritis. J Clin Invest. 2008;118:3537–3545. [PMC free article] [PubMed]
34. Klareskog L, Stolt P, Lundberg K, Kallberg H, Bengtsson C, Grunewald J, Ronnelid J, Harris HE, Ulfgren AK, Rantapaa-Dahlqvist S, Eklund A, Padyukov L, Alfredsson L. A new model for an etiology of rheumatoid arthritis: smoking may trigger HLA-DR (shared epitope)-restricted immune reactions to autoantigens modified by citrullination. Arthritis Rheum. 2006;54:38–46. [PubMed]
35. Djuardi Y, Wibowo H, Supali T, Ariawan I, Bredius RG, Yazdanbakhsh M, Rodrigues LC, Sartono E. Determinants of the relationship between cytokine production in pregnant women and their infants. PLoS ONE. 2009;4:e7711. [PMC free article] [PubMed]
36. Kawashima M, Miossec P. Decreased response to IL-12 and IL-18 of peripheral blood cells in rheumatoid arthritis. Arthritis Res Ther. 2004;6:R39–R45. [PMC free article] [PubMed]
37. Berg L, Lampa J, Rogberg S, van Vollenhoven R, Klareskog L. Increased peripheral T cell reactivity to microbial antigens and collagen type II in rheumatoid arthritis after treatment with soluble TNFalpha receptors. Ann Rheum Dis. 2001;60:133–139. [PMC free article] [PubMed]
38. Alex P, Szodoray P, Knowlton N, Dozmorov IM, Turner M, Frank MB, Arthur RE, Willis L, Flinn D, Hynd RF, Carson C, Kumar A, El-Gabalawy HS, Centola M. Multiplex serum cytokine monitoring as a prognostic tool in rheumatoid arthritis. Clinical Exp Rheumatol. 2007;25:584–592. [PubMed]
39. Alex P, Zachos NC, Nguyen T, Gonzales L, Chen TE, Conklin LS, Centola M, Li X. Distinct cytokine patterns identified from multiplex profiles of murine DSS and TNBS-induced colitis. Inflamm Bowel Dis. 2009;15:341–352. [PMC free article] [PubMed]
40. Linkov F, Ferris RL, Yurkovetsky Z, Marrangoni A, Velikokhatnaya L, Gooding W, Nolan B, Winans M, Siegel ER, Lokshin A, Stack BC. Multiplex analysis of cytokines as biomarkers that differentiate benign and malignant thyroid diseases. Proteomics Clin Appl. 2008;2:1575–1585. [PMC free article] [PubMed]
41. Nolen BM, Marks JR, Ta’san S, Rand A, Luong TM, Wang Y, Blackwell K, Lokshin AE. Serum biomarker profiles and response to neoadjuvant chemotherapy for locally advanced breast cancer. Breast Cancer Res. 2008;10:R45. [PMC free article] [PubMed]
42. Dolhain RJ, van der Heiden AN, ter Haar NT, Breedveld FC, Miltenburg AM. Shift toward T lymphocytes with a T helper 1 cytokine-secretion profile in the joints of patients with rheumatoid arthritis. Arthritis Rheum. 1996;39:1961–1969. [PubMed]
43. Allen ME, Young SP, Michell RH, Bacon PA. Altered T lymphocyte signaling in rheumatoid arthritis. Eur J Immunol. 1995;25:1547–1554. [PubMed]
44. Kusaba M, Honda J, Fukuda T, Oizumi K. Analysis of type 1 and type 2 T cells in synovial fluid and peripheral blood of patients with rheumatoid arthritis. J Rheumatol. 1998;25:1466–1471. [PubMed]
45. Berner B, Akca D, Jung T, Muller GA, Reuss-Borst MA. Analysis of Th1 and Th2 cytokines expressing CD4+ and CD8+ T cells in rheumatoid arthritis by flow cytometry. J Rheumatol. 2000;27:1128–1135. [PubMed]
46. Macht LM, Elson CJ, Kirwan JR, Gaston JS, Lamont AG, Thompson JM, Thompson SJ. Relationship between disease severity and responses by blood mononuclear cells from patients with rheumatoid arthritis to human heat-shock protein 60. Immunology. 2000;99:208–214. [PMC free article] [PubMed]
47. Kowalski ML, Wolska A, Grzegorczyk J, Hilt J, Jarzebska M, Drobniewski M, Synder M, Kurowski M. Increased responsiveness to toll-like receptor 4 stimulation in peripheral blood mononuclear cells from patients with recent onset rheumatoid arthritis. Mediators Inflamm. 2008;2008:132732. [PMC free article] [PubMed]
48. Leirisalo-Repo M, Paimela L, Jaattela M, Koskimies S, Repo H. Production of TNF by monocytes of patients with early rheumatoid arthritis is increased. Scand J Rheumatol. 1995;24:366–371. [PubMed]
49. Fabris M, Tolusso B, Di Poi E, Tomietto P, Sacco S, Gremese E, Ferraccioli G. Mononuclear cell response to lipopolysaccharide in patients with rheumatoid arthritis: relationship with tristetraprolin expression. J Rheumatol. 2005;32:998–1005. [PubMed]
50. Kohno M, Tsutsumi A, Matsui H, Sugihara M, Suzuki T, Mamura M, Goto D, Matsumoto I, Ito S, Suguro T, Sumida T. Interleukin-17 gene expression in patients with rheumatoid arthritis. Mod Rheumatol. 2008;18:15–22. [PubMed]
51. Kawashima M, Miossec P. Effect of treatment of rheumatoid arthritis with infliximab on IFN gamma, IL4, T-bet, and GATA-3 expression: link with improvement of systemic inflammation and disease activity. Ann Rheum Dis. 2005;64:415–418. [PMC free article] [PubMed]
52. Seitz M, Zwicker M, Wider B. Enhanced in vitro induced production of interleukin 10 by peripheral blood mononuclear cells in rheumatoid arthritis is associated with clinical response to methotrexate treatment. J Rheumatol. 2001;28:496–501. [PubMed]
53. de Jager W, Prakken BJ, Bijlsma JW, Kuis W, Rijkers GT. Improved multiplex immunoassay performance in human plasma and synovial fluid following removal of interfering heterophilic antibodies. J Immunol Methods. 2005;300:124–135. [PubMed]
54. Hitchon CA, Alex P, Erdile LB, Frank MB, Dozmorov I, Tang Y, Wong K, Centola M, El-Gabalawy HS. A distinct multicytokine profile is associated with anti-cyclical citrullinated peptide antibodies in patients with early untreated inflammatory arthritis. J Rheumatol. 2004;31:2336–2346. [PubMed]
55. Hueber W, Tomooka BH, Zhao X, Kidd BA, Drijfhout JW, Fries JF, van Venrooij WJ, Metzger AL, Genovese MC, Robinson WH. Proteomic analysis of secreted proteins in early rheumatoid arthritis: anti-citrulline autoreactivity is associated with up regulation of proinflammatory cytokines. Ann Rheum Dis. 2007;66:712–719. [PMC free article] [PubMed]
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