Regulatory T cells as a biomarker for response to adalimumab in rheumatoid arthritis.

To the Editor:

Despite the success of anti-TNF therapy for patients with rheumatoid arthritis (RA), around one-third of patients fail to benefit from this treatment. There is an intensive search for biomarkers, mostly on an empirical basis, that will guide the use of anti-TNF therapy to those patients with RA most likely to respond. Ineffective treatments allow inflammation to persist, resulting in joint damage and disability. A method to predict response to anti-TNF therapy would be cost-effective and minimize delays in receiving an efficacious treatment, therefore representing a step toward precision medicine for patients with RA.

We have previously shown that CD4 regulatory T (Treg)-cell numbers and suppressor function were enhanced in the peripheral blood of patients with RA who responded to anti-TNF antibody therapy (adalimumab) in contrast to patients responding to the soluble TNF receptor etanercept. We developed an in vitro assay that led to the hypothesis that Treg-cell-monocyte interactions via TNF-TNFRII were pivotal to the immunomodulatory actions of anti-TNF antibody blockade in RA. We recruited a cohort of patients with RA about to commence treatment with adalimumab (see Table E1 in this article's Online Repository at www.jacionline.org) to determine whether this TNF inhibitor's ability to boost Treg cells in vitro using PBMCs before treatment would predict clinical response.

Table E1

Patient with RA disease activity and treatment from the longitudinal study

Responder (R) or nonresponder (NR)	Concomitant treatment	DAS28 baseline	DAS28 3 mo
R	Nil	5.64	3.89
R	Nil	6.08	2.11
R	Nil	4.13	2.55
R	Nil	7.2	4.52
R	Nil	5.17	1.63
R	Nil	5.18	2.62
R	LEF, HCQ	5.18	3.67
R	MTX	5.32	2.56
R	MTX	5.27	2.14
R	MTX, SSZ	5.92	3
R	MTX, Pred	7.15	4.12
R	MTX, SSZ	5.61	3.27
R	MTX, HCQ	5.37	3.98
R	MTX, HCQ, SSZ	6.45	5.19
NR	Nil	5.17	5.75
NR	LEF	5.62	4.89
NR	Pred, SSZ	6	5.19
NR	MTX	5.1	4.36
NR	MTX, Pred	5.16	5.83

Nil indicates that the patient was not taking any disease-modifying antirheumatic drug.

DAS28 = 0.56 × √(tender28) + 0.28 × √ (swollen28) + 0.70 × ln(CRP) + 0.014 × VASpatient. See https://www.das-score.nl/das28/en/introduction-menu.html. All patients who were taking MTX were on a constant dose for the last 5 months before the sample was taken.

CRP, C-reactive protein; HCQ, hydroxychloroquine; LEF, leflunomide; MTX, methotrexate; Pred, prednisolone; SSZ, sulphasalazine.

We first sought to identify any significant correlation between the anti-TNF antibody-induced Treg-cell changes in vivo after therapy with adalimumab and in vitro before therapy. The change in the percentage of CD4 Treg cells in vitro at baseline correlated with the shift in the frequency of circulating Treg cells in those patients at 3 months after anti-TNF antibody therapy (see Fig E1, A, in this article's Online Repository at www.jacionline.org).

Fig E1

A, Correlation between the change in CD4 Treg-cell frequency (proportion of CD4+ T cells) in PBMCs from patients at baseline cultured with adalimumab in vitro and the change in frequency of peripheral blood Treg cells in the same patients after 3 months of adalimumab therapy. B, Absolute number of Treg cells on day 3 from PBMCs from patients who responded (n = 14) or not to adalimumab therapy assessed at 3 months (n = 5) cultured with either anti-TNF agent. C, Percentage of Treg cells in PBMCs at baseline from patients who responded to therapy (n = 14) divided according to whether they were treated with methotrexate in combination with adalimumab. *P < .05, **P < .01 by paired t test.

We next tested whether this in vitro Treg-cell assay could predict clinical response. Adalimumab boosted the proportion (Fig 1, A) and absolute number (Fig E1, B) of CD4 Treg cells by at least 40% in vitro in 12 of the 14 patients who went on to respond to this biologic therapy at 3 months but not in any of the patients who did not respond to this treatment. Those patients who responded continued to do so at 6 and 12 months although only the 3-month data set was complete because some of the patients had an intercurrent infection at 6 and 12 months and had to temporarily stop the adalimumab. All the nonresponders had stopped adalimumab by 6 months. There was a significant difference between responders and nonresponders (P = .016) with respect to the changes in Treg cells following in vitro stimulation with adalimumab (see Table E2 in this article's Online Repository at www.jacionline.org). Logistic regression analysis to assess predictive power with respect to clinical response yielded high sensitivity and specificity (area under the curve [AUC], 0.87) for the shift in CD4 Treg-cell frequency in the baseline PBMC sample stimulated by adalimumab in vitro (Fig 1, B; see Table E3 in this article's Online Repository at www.jacionline.org). We wondered whether the 2 patients who responded to adalimumab without a rise in their Treg-cell frequency in vitro did so because of the lack of combination treatment with methotrexate, which has been shown to have Treg-cell immunomodulatory properties. Once the cohort of responders was stratified according to the use of concomitant methotrexate therapy, both patients who responded without increasing their Treg-cell frequency in vitro were treated with adalimumab monotherapy (Fig E1, C). Patients who had greater than a 40% rise in CD4 Treg cells showed a significant reduction in C-reactive protein after 3 and 6 months of therapy (Fig 1, C).

Fig 1

Adalimumab-driven increase in CD4 Treg cells in PBMCs from patients with RA in vitro predicted subsequent clinical response to therapy. A, Representative FACS plot indicating the percentage of CD4+Foxp3+ Treg cells in PBMCs stimulated with adalimumab in vitro from a patient who subsequently responded and a patient who did not respond to adalimumab therapy assessed at 3 months. The right-hand panel shows that Foxp3+ CD4 T cells cosegregate with CD127loCD25hi CD4 T cells. The corresponding cumulative data of Treg-cell frequency in PBMCs from patients who responded (n = 14) or not (n = 5) to adalimumab therapy cultured with adalimumab or etanercept. B, Receiver-operating characteristic (ROC)-curve analysis of the percentage increase in Treg cells predicting clinical response (n = 19). C, Serum CRP before and after therapy in patients divided according to whether adalimumab increased Treg-cell frequency by more than 40% in the baseline sample in vitro (n = 19). CRP values for 2 responding patients who temporarily stopped their adalimumab at 6 months because of infection come from data collected between 6 and 9 months. CRP, C-reactive protein. *P < .05, ***P < .001 by paired t test.

Table E2

t tests comparing changes in CD4 Treg-cell and monocyte membrane TNF in response to anti-TNF stimulation

Variable	Anti-TNF	Anti-TNF—unstimulated
		Nonresponders (N = 5)			Responders (N = 14)			Responders – nonresponders
		Difference	95% CI	P value	Difference	95% CI	P value	Difference	95% CI	P value
Treg cell	Adalimumab	0.1	−1.4 to 1.5	.92	3.1	1.7 to 4.5	.0003	3.1	0.7 to 5.5	.016
Treg cell	Etanercept	−0.5	−1.3 to 0.3	.13	−0.1	−0.9 to 0.6	.68	0.4	−0.9 to 1.7	.52
mTNF	Adalimumab	35	−198 to 268	.69	303	114 to 492	.004	267	−62 to 597	.11
mTNF	Etanercept	−0.4	−360 to 364	.99	95	−126 to 235	.37	95	−304 to 494	.62

Paired t tests were performed for each anti-TNF agent to compare stimulated with anti-TNF and unstimulated values for responders and nonresponders separately. Two sample t tests were used to compare the anti-TNF agent stimulated-unstimulated differences in responders and nonresponders. P values <.05 are highlighted in bold.

mTNF, MembraneTNF.

Table E3

Logistic regression predicting clinical response to therapy

Variable	Predictor	N	AUC	AUC 95% CI	Cutoff	% Sensitivity	% Specificity
Treg cell	Adalimumab: unstimulated	19	0.87	0.70-1.00	40% increase	71.4	80.0
Treg cell	Etanercept: unstimulated	19	0.56	0.27-0.84	40% increase	14.3	100.0
mTNF cell	Adalimumab: unstimulated	19	0.69	0.34-1.00	40% increase	21.4	100.0
mTNF cell	Etanercept: unstimulated	19	0.61	0.26-0.97	40% increase	14.3	80.0
mTNF cell	Ex vivo expression	19	0.87	0.70-1.00	420	78.6	80.0
Adalimumab	Ex vivo binding	18	0.91	0.71-1.00	14	92.3	80.0

Logistic regression analysis was performed to predict response (yes/no). A number of alternative models were investigated. The receiver-operating characteristic (ROC) curve for each model plots sensitivity against 1 − specificity using each observed value of the predictor to define a positive test result. Summary measures are the AUC and its 95% CI. The AUC has a maximum value of 1 and a minimum value of 0; an AUC of 0.5 would indicate a predictor with no discriminating ability. For percentage change variables, a greater than 40% increase was used to define a positive test result. For each cutoff, the corresponding values of the sensitivity and specificity are presented. The 3 models with the highest values for the AUC have been highlighted in bold. For data using adalimumab in the in vitro assay, a cutoff of a 40% increase was used to define a positive test result when calculating the sensitivity and specificity presented in the table. For ex vivo analysis, the table presents the sensitivity and cutoff corresponding to a specificity of 80%.

mTNF, MembraneTNF.

We hypothesized that elevated expression of baseline monocyte membrane TNF, to which adalimumab binds, would be associated with increased Treg-cell frequency in vitro and predict clinical response. Indeed, there was a significant correlation between the pretreatment monocyte membrane TNF expression and the change in the percentage of CD4 Treg cells stimulated by adalimumab in vitro (see Fig E2, A, in this article's Online Repository at www.jacionline.org). The baseline expression of monocyte membrane TNF in PBMCs was significantly higher in patients responding to adalimumab compared with those whose disease activity did not improve (Fig E2, B). Stratifying the responding patients according to concurrent treatment with methotrexate revealed that monocyte membrane TNF expression was elevated at baseline in those patients receiving methotrexate compared with those not on this treatment (Fig E2, C). Moreover, methotrexate directly enhanced membrane TNF expression in vitro in PBMCs from patients with RA before treatment with this conventional disease-modifying antirheumatic drug (Fig E2, D).

Fig E2

Increased monocyte membrane TNF expression and adalimumab binding predicted therapeutic response. A, Correlation between the change in the percentage of Treg in vitro stimulated by adalimumab and baseline monocyte membrane TNF expression in patients before adalimumab treatment (n = 19). B, Membrane TNF expression on CD14+ monocytes isolated before adalimumab treatment from responders (n = 14) and nonresponders to therapy (n = 5). C, Monocyte membrane TNF expression at baseline from patients who responded to adalimumab therapy (n = 14), divided according to concurrent methotrexate therapy. D,In vitro effect of methotrexate on monocyte membrane TNF expression in PBMCs from untreated patients with RA (n = 12). E, ROC-curve analysis of the utility of baseline membrane TNF expression before therapy to predict response to adalimumab (n = 19). F, Adalimumab binding to monocytes from patients before treatment divided according to their clinical response to adalimumab (n = 18). G, ROC-curve analysis of the percentage binding of adalimumab to monocytes to predict response to adalimumab (n = 18). H, Monocyte membrane expression after culture with adalimumab or etanercept using PBMCs taken before adalimumab therapy, divided according to subsequent response (n = 14) or not (n = 5) to adalimumab. ROC, Receiver-operating characteristic. *P < .05; **P < .01; ***P < .001 by paired t test or Mann-Whitney test.

Logistic regression analysis to assess the predictive power of baseline membrane TNF expression with respect to clinical response yielded high sensitivity and specificity (AUC, 0.87) (Fig E2, E; see Table E3). These results translated into differential binding of adalimumab to monocytes in the baseline sample between subsequent responders and nonresponders (Fig E2, F). Adalimumab binding was also strongly predictive of clinical response (AUC, 0.91) (Fig E2, G; Table E3). Adalimumab not only bound to membrane TNF but also increased its expression after 3 days in culture in responding patients (Fig E2, H). There were no significant differences between the AUC for the 3 prediction models (increase in Treg cells, monocyte membrane TNF expression, and adalimumab binding).

We next sought to elucidate the underlying signaling mechanism triggered within monocytes by adalimumab. We investigated the role of p38 signaling in modulating monocyte membrane TNF expression by adalimumab because of its role in reverse signaling via membrane TNF. In vitro, adalimumab, but not etanercept, enhanced p38 MAP kinase phosphorylation in monocytes from patients treated with methotrexate, which was associated with an increase in Treg cells (see Fig E3, A, in this article's Online Repository at www.jacionline.org). Mirroring the in vitro results, monocytes from patients responding to adalimumab therapy displayed enhanced p38 phosphorylation (Fig E3, B) compared with patients treated with methotrexate. Adalimumab did not alter total p38 expression either in vitro (see Fig E4, C, in this article's Online Repository at www.jacionline.org) or in vivo (Fig E4, D). The expression of membrane TNF correlated with p38 phosphorylation in monocytes stimulated by adalimumab (Fig E3, C). There was also a significant correlation between monocyte membrane TNF and p38 phosphorylation in vivo in patients responding to adalimumab treatment but not in patients responding to methotrexate (Fig E3, D).

Fig E3

Adalimumab enhanced monocyte membrane TNF expression and increased Treg-cell frequency through an IL-10/p38-dependent mechanism. A, Monocyte p-p38 expression in methotrexate-treated RA PBMCs after stimulation with adalimumab in vitro. The results were divided according to whether the frequency of CD4+Foxp3+ Treg cells increased by more than 40% (n = 14) or not (n = 5) in response to adalimumab. B,Ex vivo p-p38 in CD14+ monocytes from patients responding to methotrexate (n = 14), adalimumab (n = 9), or etanercept (n = 5). C, Correlation between monocyte membrane TNF and p-p38 expression in PBMCs from patients treated with methotrexate cultured with adalimumab (n = 20). D,Ex vivo correlation between monocyte membrane TNF and p-p38 expression in monocytes from patients responding to methotrexate (n = 14) or to adalimumab therapy (n = 9). E, Representative FACS plot and cumulative data of IL-10 production by monocytes stimulated with adalimumab ± a p38 inhibitor (inh), or stimulated with etanercept (n = 9). F,Ex vivo IL-10 production by monocytes from patients responding to DMARD (n = 15), to adalimumab (n = 7), or to etanercept (n = 3). The effects of p38 inhibition and/or IL-10 blockade on (G) monocyte membrane TNF expression and (H) Treg-cell frequency from patients who responded to adalimumab (n = 7). DMARD, Disease-modifying antirheumatic drug. *P < .05; **P < .01; ***P < .001 by paired t test or Mann-Whitney test.

Fig E4

Flow cytometry gating strategy for CD4+FoxP3+ Treg cells and total p38 expression within monocytes. A, Gating strategy to reveal CD4+Foxp3+ Treg cells following in vitro stimulation with adalimumab. B, Representative histograms of CD4+ T cells stained with LIVE/DEAD fixable dead cell stain, from an RA PBMC sample cultured with adalimumab, etanercept, or without stimulation for 3 days. C, Histogram and cumulative data of total p38 expression in CD14+ monocytes in PBMCs from patients with RA treated with methotrexate following stimulation with adalimumab, where Treg cells increased by more than 40% or etanercept in vitro (n = 8). D, Total p38 expression from PBMCs isolated ex vivo from different patient groups (methotrexate, n = 14; adalimumab, n = 6; etanercept, n = 5). FSC, Forward scatter; SSC, side scatter.

As p38 MAP Kinase can regulate IL-10 production in monocytes, we examined whether adalimumab triggered the production of this immunoregulatory cytokine by monocytes. Adalimumab, but not etanercept, stimulated IL-10 production by monocytes in vitro, which was dependent on p38 phosphorylation (Fig E3, E). Similarly, IL-10 production from monocytes was increased in vivo in patients responding to adalimumab therapy (Fig E3, F). Blockade of p38 phosphorylation or IL-10 abrogated the adalimumab-driven increase in membrane TNF (Fig E3, G) and the associated rise in Treg-cell frequency (Fig E3, H). Very little IL-10 production was detected by CD4 Treg cells following stimulation with adalimumab (data not shown).

Our findings highlight the value in understanding the mechanism of action of biologic therapies, which facilitates the rational development of clinical response biomarkers. These results indicate that monocyte membrane TNF expression at baseline determines a patient's response to adalimumab therapy, which may be applicable to other therapeutic anti-TNF antibodies. Monocyte membrane TNF expression, its binding to adalimumab, which in turn increased the CD4 Treg-cell frequency in vitro, all predicted the subsequent response of patients with RA to adalimumab. The almost identical AUC for these 3 biomarkers suggests that they are likely modulated along the same pathway by adalimumab to improve clinical outcome. The increase in Treg cells was more consistently associated with a clinical response if patients were also treated with methotrexate, which is well established to have a synergistic benefit with adalimumab. Indeed, methotrexate directly enhanced monocyte membrane TNF expression, thereby priming patients to respond to adalimumab. Further investigations are required to confirm these results in larger cohorts but raise the prospect of developing a biomarker, or set of biomarkers, which would direct therapy to those most likely to respond, thereby aligning with the principles of personalized medicine. Moreover, these data validate Treg cells as a therapeutic target in RA, and identify novel therapeutic approaches, such as directly boosting membrane TNF expression.