Objective

Despite substantial clinical efficacy of tumor necrosis factor-α (TNF)-antagonist therapy in patients with rheumatoid arthritis (RA), some patients respond poorly to such agents. Since an interferon (IFN) signature is variably expressed among RA patients, we investigated whether plasma type I IFN activity might predict response to TNF-antagonist therapy.

Methods

RA patients (n=35), the majority Hispanic, from a single center were evaluated prior to and following initiation of TNF-antagonist therapy. As controls, 12 RA patients from the same center who were not treated with a TNF antagonist were studied. Plasma type I IFN activity was measured using a reporter cell assay and disease status assessed using the Disease Activity Score (DAS28). IL-1 receptor antagonist (IL-1ra) levels were determined in baseline samples using a commercial ELISA. The clinical response was classified according to the European League against Rheumatism (EULAR) RA improvement criteria.

Results

Plasma type I IFN activity at baseline was significantly associated with clinical response (OR=1.36, CI: 1.05-1.76, p=0.02), with high baseline IFN activity associated with a good response. Changes in DAS28 scores were greater among patients with a baseline plasma IFNβ/α ratio >0.8 (indicating elevated plasma IFNβ levels). Consistent with the capacity of IFNβ to induce IL-1ra, elevated baseline IL-1ra levels were associated with better therapeutic outcomes (OR=1.82, CI: 1.1-3.29, p=0.027).

Conclusion

Plasma type I IFN activity, IFNβ/α ratio, and IL-1ra levels were predictive of therapeutic response in TNF-antagonist-treated RA patients, indicating that those parameters might define clinically meaningful subgroups of RA patients with distinct responses to therapeutic agents.

Study Subjects

The study population included three subject cohorts: 1) RA patients receiving their care at the Los Angeles County + University of Southern California Medical Center (LAC+USC MC) Rheumatology clinics (n = 38) who, based on the clinical judgment of their attending physicians, required the addition of a TNF antagonist (etanercept, n = 16; infliximab, n = 9; adalimumab, n = 13) to their therapeutic regimens and who agreed to such treatment; 2) RA patients receiving medical care at the same clinics (total n = 12) who either refused (n = 2) or were felt not to be candidates for TNF-antagonist therapy (n=10); and 3) healthy volunteers (n = 50) of age and sex distribution similar to that of the RA cohorts. Two of the 38 TNF-antagonist-treated patients were excluded from analysis based on less than 8 weeks monitoring on therapy, and one patient was excluded due to pregnancy during the course of the study, possibly confounding interpretation of disease activity. Each of the 3 excluded RA patients was on a distinct TNF-antagonist agent. Baseline characteristics of the RA cohorts have been previously described (14). Consistent with the LAC+USC MC patient population at-large, the majority of the study subjects were Hispanic (33 of 35). Healthy control subjects selected for comparison included a comparable proportion of Hispanic individuals.

Clinical Outcomes

Outcome was evaluated during a window of therapy greater than 3 months and less than 9 months, allowing for sufficient time for clinical response, and was categorized by the Disease Activity Score (DAS28), as defined by the EULAR RA improvement criteria (30,31), into three groups: non-response, moderate response, and good response, based on the absolute DAS28 at follow-up (mean 5.6±1.4 months after initiation of TNF-antagonist therapy for TNF-antagonist-treated patients; 8.8±2.9 months for non-treated control patients) and the change in DAS28 from baseline. To be considered a good responder, the patient must have experienced an improvement of ≥1.2 in DAS28 with an absolute DAS28 of ≤3.2. To be considered a non-responder, the patient must have experienced an improvement of <0.6 in DAS28 with an absolute DAS28 of >5.1. If the patient met criteria for neither good responder nor non-responder he/she was classified as a moderate responder.

Plasma type I IFN activity

Type I IFN activity was detected using a reporter cell assay (32-33). In brief, cells of the WISH epithelial cell line (ATCC #CCL-25, Manassas, VA) express the type I IFN receptor and are highly responsive to type I IFN. WISH cells were plated at a density of 5×10⁵ cells/ml in 96 well plates in Minimum Essential Media (Cellgro, Herndon, VA) with 10% fetal calf serum (FCS) and then were cultured with 50% patient plasma for 6 hours. Recombinant human IFNα (IFNaA, Biosource International, Camarillo, CA) and media only were used as positive and negative controls, respectively.

Total cellular mRNA was purified from stimulated cells at the end of the culture period using the Qiagen Turbocapture oligo-dT coated 96 well plate system as per the manufacturer’s protocol (Qiagen, Valencia, CA). Total cellular mRNA was reverse-transcribed to cDNA immediately following purification using the TaqMan® Reverse Transcription Reagents (Applied Biosystems, Foster City, CA).

Quantitative real-time polymerase chain reaction (PCR) was used to quantify specific cDNAs using the Bio-Rad SYBR Green intercalating fluorophore system with a Bio-Rad I-cycler thermocycler and fluorescence detector (Bio-Rad, Hercules, CA). Primers for genes that are highly induced by type I IFN signaling were used in the PCR reaction on the WISH cell-derived cDNAs (Operon, Huntsville, AL): interferon induced with tetratricopeptide repeats 1 (IFIT-1, Forward CTCCTTGGGTTCGTCTATAAATTG; Reverse AGTCAGCAGCCAGTCTCAG), double-stranded RNA-dependent protein kinase (PKR) (Forward CTTCCATCTGACTCAGGTTT; Reverse TGCTTCTGACGGTATGTATTA) and myxovirus resistance 1 (MX-1) (Forward TACCAGGACTACGAGATTG; Reverse TGCCAGGAAGGTCTATTAG). The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH, Forward CAACGGATTTGGTCGTATT; Reverse GATGGCAACAATATCCACTT) was also quantified in the cDNA samples to control for background gene expression. Threshold values were recorded for each sample at the logarithmic portion of the amplification. Melt curve analysis was used to ensure the specificity of the PCR product. The type I IFN-induced genes were compared with housekeeping gene expression to determine relative expression. The relative expression was then normalized to the relative expression of the respective genes in unstimulated cells from the same cell preparation. RA samples were compared to the mean and SD of a pool of healthy donors in the same assay, and the sum of the number of SD above healthy donors for each of the three genes was calculated for each sample (34).

Determination of IFNβ/α ratio

To determine whether the capacity of RA plasma to stimulate expression of type I IFN-inducible genes is predominantly due to the presence of IFNα or IFNβ, samples from TNF-antagonist-treated RA patients with detectable type I IFN activity at baseline were incubated with monoclonal anti-IFNα or anti-IFNβ antibody (10μg/ml) (Chemicon, Temecula, CA) prior to addition to WISH cells. These antibodies have previously been shown to be specific for their designated IFNs (data not shown). An IFNβ/α ratio was calculated based on the ratio of % inhibition of type I IFN activity by anti-IFNβ to the % inhibition of type I IFN activity by anti-IFNα antibody.

IL-1 receptor antagonist (IL-1ra) ELISA

Determination of IL-1ra levels was performed by a solid-phase ELISA according to manufacturer’s instructions (Quantikine HS, R&D Systems, Minneapolis, US). This assay employs the quantitative “sandwich” enzyme immunoassay technique.

Statistical Analysis

Comparison between continuous variables was performed by the paired t-test for matched data and by the Mann-Whitney test for non-matched data. Comparisons between categorical variables were performed by Fisher’s exact two sided tests. Comparisons between three groups were performed using the one-way ANOVA test and Kruskal-Wallis test. Correlations between continuous variables were determined using the nonparametric Spearman’s test. Type I IFN and IL-1ra baseline levels were analyzed using both univariate and multivariate ordinal logistic regression to model the probability of achieving a higher response category in the presence of each predictor variable. A logistic multivariate model was constructed to identify independent predictors that could be associated with different therapeutic outcomes. Age, which was shown to be statistically significantly different between non-, moderate, and good responder groups (p<0.05) at baseline, was included in a multivariate model. Results have been presented as the odds ratio (OR) with corresponding 95% confidence intervals (CI).

Demographics

The baseline characteristics of the TNF-antagonist-treated RA patients according to response to therapy are presented inTable 1. While no statistically significant differences were revealed among the three groups in sex, race, medications, disease activity, disease duration, or prevalence of RF or anti-CCP positivity, the moderate responders were significantly older than the good responders (p<0.05).

Increased plasma type I IFN activity in some RA patients

The WISH epithelial cell assay was used to determine the capacity of plasma to induce expression of IFN-regulated genes. As shown inFigure 1A, high type I IFN levels (defined as levels above the mean + 2 SD of a pool of healthy controls tested in the same assay) were detected in 29.7% (14/47) of all RA patients at baseline compared to 6.3% (3/47) of healthy controls (p<0.0001) of comparable age, gender, and ethnicity. These results represent a lower proportion of patients and a lower mean IFN score compared to results observed in SLE patients (47%; >10 mean IFN activity score among SLE patients meeting 4 classification criteria) (34). No baseline differences in type I IFN activity were detected between anti-TNF and non anti-TNF treated RA groups (p=0.82) (Figure 1B).

Association between baseline plasma type I IFN activity and clinical response among RA patients treated with TNF antagonists

To investigate whether type I IFN plasma activity levels prior to initiation of TNF-antagonist therapy might be associated with response to therapy, we compared baseline plasma activities in RA patients who were categorized by non-, moderate or good response at follow-up, based on the EULAR response criteria. As shown inFigure 2A, there was a graded relationship between type I IFN activity at baseline and response to TNF-antagonist therapy (mean ± SD: 0.4±1.9 versus 1.7±2.0 versus 3.4±4.9 for RA patients with non-, moderate, and good response, respectively; p=0.08). When the moderate and good responders were clustered together and compared to the non-response group, a statistically significant difference in baseline type I IFN levels emerged (p=0.04,Figure 2B). Regression analysis demonstrated that patients with higher baseline type I IFN scores were more likely to respond [OR 1.36 (95% CI 1.05– 1.76), p=0.02] per unit increase in type I IFN activity (Table 2). The association remained significant when the OR was adjusted for age (adjusted OR=1.35, CI: 1.04-1.77, p=0.027,Table 2). No statistically significant differences in baseline type I IFN activity between non-, moderate, and good responders were observed in the non-TNF-antagonist-treated group, although the number of subjects is likely too small to draw definite conclusions (data not shown).

Association of increased IFNβ/α ratio with favorable response to TNF-antagonist treatment

The assay for type I IFN activity does not distinguish between IFNα and IFNβ. Although type I IFN activity in SLE is predominantly mediated by IFNα (34), there may be a greater contribution from IFNβ in RA, given the documented expression of IFNβ in synovial membranes of RA patients and in animal models of inflammatory arthritis (24-29). Accordingly, the percent inhibition of plasma type I IFN activity after addition of neutralizing anti-IFNα or anti-IFNβ antibody to the plasma of those patients with elevated type I IFN activity was determined, and the results were converted to IFNβ/α ratios. In contrast to our results in SLE, plasma type I IFN activity from most RA patients was partially neutralized by anti-IFNβ as well as anti-IFNα antibodies (Figure 3A), demonstrating that IFNβ and IFNα each contribute to the type I IFN activity in RA plasma.

Indeed, when RA patients with elevated baseline plasma type I IFN activity were subdivided into two groups according to their baseline IFNβ/α ratio (patients were categorized as having ratios greater than or less than 0.8, the median value of the distribution), those patients with higher IFNβ/α ratios (≥0.8) had greater changes in DAS28 scores at follow-up in comparison to those with lower IFNβ/α ratio (<0.8) [(2.8±1.2 vs 1.5±1.4, p=0.04);Figure 3B], suggesting that IFNβ might represent an important contributor to the increased control of inflammation and therapeutic responses among TNF-antagonist-treated RA patients. No other differences in demographic variables or treatment modalities were detected between the two groups (data not shown).

Association between baseline plasma IL-1ra levels and clinical response among RA patients treated with TNF antagonists

Since IL-1ra can be induced in PBMC and synoviocytes by IFNβ (18,19), we postulated that IL-1ra might represent an IFNβ-induced mediator that contributes to the superior therapeutic response to TNF-antagonist therapy. We observed significantly higher baseline IL-1ra levels in plasmas from good responders compared to non-/moderate responders (Figure 4). The magnitude of the association between higher IL-1ra levels and response to anti-TNF treatment was further determined by ordinal logistic regression (OR=1.82, CI: 1.1-3.29, p=0.027,Table 2). The association remained significant when OR was adjusted for age (adjusted OR=1.79, CI: 1.02-3.16, p=0.044,Table 2).

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