Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 https://doi.org/10.1186/s12891-021-04248-y RESEARCH ARTICLE Open Access Interactions between rheumatoid arthritis antibodies are associated with the response to anti-tumor necrosis factor therapy Antonio Julià1* , María López-Lasanta1, Francisco Blanco2 , Antonio Gómez1 , Isabel Haro3, Antonio Juan Mas4, Alba Erra1, Ma Luz García Vivar5, Jordi Monfort6, Simón Sánchez-Fernández7, Isidoro González8, Mercedes Alperi9, Raúl Castellanos-Moreira10, Antonio Fernández-Nebro11, César Díaz-Torné12, Núria Palau1, Raquel Lastra1, Jordi Lladós1, Raimon Sanmartí10 and Sara Marsal1 Abstract Background: Blocking of the Tumor Necrosis Factor (TNF) activity is a successful therapeutic approach for 50–60% of rheumatoid arthritis (RA) patients. However, there are yet no biomarkers to stratify patients for anti-TNF therapy. Rheumatoid factor (RF) and anti-cyclic-citrullinated antibodies (anti-CCP) have been evaluated as biomarkers of response but the results have shown limited consistency. Anti-carbamylated protein (anti-CarP) and anti-peptidylarginine deiminase type 4 (anti-PAD4) antibodies have been much less studied. Despite being linked to common immune processes, the interaction between these markers has not been evaluated yet. Our aim was to analyze the interaction between these four antibodies in relation to the response to anti-TNF therapy. Methods: For this objective, a prospective cohort of n = 80 RA patients starting anti-TNF therapy was recruited. Serum determinations at baseline were performed for RF, anti-CCP, anti-CarP and anti-PAD4 antibodies using enzyme-linked immunosorbent assays (ELISA). The clinical response to anti-TNF therapy was determined at week 12 using the change in DAS28 score. Association was performed using multivariate linear regression adjusting for baseline DAS28, sex and age. Results: The interaction between pairs of antibodies was tested by the addition of an interaction term. We found two highly significant antibody interactions associated with treatment response: anti-CarP with anti-PAD4 (p = 0.0062), and anti-CCP with RF (p = 0.00068). The latter antibody interaction was replicated in an independent retrospective cohort of RA patients (n = 199, p = 0.04). Conclusions: The results of this study suggest that antibody interaction effects are important factors in the response to anti-TNF therapy in RA. Keywords: Rheumatoid arthritis, Treatment response, Anti-TNF therapy, Autoantibodies * Correspondence: toni.julia@vhir.org 1 Rheumatology Research Group, Vall d’Hebron University Hospital Research Institute, 08035 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 Page 2 of 7 Background mechanisms. Recent experimental evidence supports the Rheumatoid arthritis (RA) is the most common chronic presence of this type of pathogenic interactions between inflammatory arthritis, with a Worldwide prevalence of RA autoantibodies [12]. From a clinical perspective, the 0.5–1%. Tumor necrosis factor (TNF) is a proinflamma- presence of synergic effects between antibodies could tory cytokine that is central to the inflammatory process translate into stronger responses to therapy. In the of RA. Systemic blocking of this cytokine has proven to present study we have addressed this question and ana- be a highly efficacious approach to control the disease lyzed, for the first time, the association of antibody inter- activity [1]. Despite this major therapeutic advance, up actions with the response to anti-TNF therapy. to 40% of RA patients treated with a TNF blocking agent don’t show a significant clinical improvement. Very little Methods knowledge exists on the factors that determine this Patients and samples unfavorable response, and biomarkers have yet to be A prospective cohort of n = 80 RA patients was recruited identified. from 11 university hospitals from Spain. Enrolled pa- RA is characterized by the expression of antibodies tients fulfilled the ACR/EULAR 2010 classification cri- against self-antigens, and consequently they have been teria for RA [2] and were starting an anti-TNF therapy. among the first biomarkers to be evaluated for associ- All patients had an active disease at baseline, described ation with treatment response. Antibodies against the Fc as a 28-joint Disease Activity Score (DAS28) ≥ 3.2. The portion of immunoglobulin G –rheumatoid factor (RF)- same day of treatment initiation, blood samples were ob- and against cyclic citrullinated peptides (anti-CCP) are tained and the plasma fraction separated and stored at currently the two most relevant diagnostic tests for RA − 80 °C until analysis. [2]. Both autoantibodies have been clearly associated to The validation dataset consisted on a retrospective co- unfavorable prognosis [3]. However, their association to hort of n = 199 RA patients that were recruited by the the response to anti-TNF therapy is much less clear. IMID Consortium, a network of rheumatology depart- Previous studies have shown inconclusive or conflicting ments from n = 15 university hospitals in Spain [13]. All results [4–6]. Consequently, interest has shifted in ana- patients fulfilled the ACR/EULAR 2010 classification cri- lyzing more recently discovered antibodies as potential teria for RA, and the primary response to anti-TNF ther- biomarkers for treatment response. apy at week 12 was collected. Plasma samples were Anti-peptidylarginine deiminase type 4 (anti-PAD4) processed following the same procedure. antibodies [7] and anti-carbamylated protein (anti-CarP) antibodies [8] are recent markers in RA. Anti-PAD4 Antibody measurements antibodies, although not specific for RA, have been asso- The four antibodies were analyzed using enzyme-linked ciated to a more severe disease phenotype [9]. There is immunosorbent assays (ELISA). RF and anti-CCP were yet scarce data on the association of anti-PAD4 in anti- measured using the IBL International and the Euro TNF response. A first small study on 40 patients found Diagnostica anti-CCP2 ELISA kits, respectively. Positiv- that patients positive for this antibody had a worse re- ity for anti-CCP and RF were defined according to the sponse to therapy [10]. A more recent study involving manufacturer’s protocols (≥ 18 U/ml and ≥ 25 U/ml for triple DMARD and anti-TNF therapy treated patients, RF and anti-CCP, respectively). Anti-PAD4 titers were suggested that anti-PAD4 positive patients had instead a measured using the PAD4 autoantibody ELISA kit (Cay- more favorable response [11]. However, the association man Chemical). Anti-carbamylated proteins IgG auto- was not tested individually for each drug arm, so the antibodies (anti-CarP) were determined using a home- specific association to TNF blocking is unclear. Anti- made ELISA test using as antigen carbamylated fetal calf CarP antibodies occur in up to 40% of RA patients and, serum (FCS). A non-carbamylated version of the FCS like anti-CCPs, they can appear several years before the was used to control for homocitrulline specificity. Re- onset of the disease. To date, anti-CarP antibodies have activity to non-modified FCS was subtracted from the not been tested for association with the response to reactivity to carbamylated FCS and a standard curve of anti-TNF therapy in RA. serial dilutions of a pool of four positive samples was The presence of previous conflicting results could be used to convert optical density values to arbitrary units an indication that a more complex relationship exists be- (AU). Compared to IgM-RF and anti-CCP2, there is no tween antibodies and the response to anti-TNF therapy established threshold for positivity for anti-PAD4 or in RA. From a biological perspective, anti-CarP, anti- anti-CarP biomarkers, and antibody titers were directly PAD4, RF and anti-CCP target proteins involved in used to test for association with anti-TNF response. closely related biological processes. The simultaneous In the validation cohort, anti-CCP was measured using expression of two or more of these antibodies could an electrochemiluminescence assay (Cobas, Roche) and therefore represent a higher load of specific pathogenic the RF was determined using an immunoturbidimetric Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 Page 3 of 7 method (Cobas, Roche). Positivity thresholds were de- in the DAS28 score was observed for the global cohort, fined using the manufacturer’s protocol (≥ 17 U/ml which is consistent with previous studies [15]. and ≥ 14 U/ml for anti-CCP and RF, respectively). Association of antibodies with anti-TNF response Multivariate linear regression was used to simultaneously Statistical analysis test for association between anti-CCP, RF, anti-CarP and The primary outcome measure was the change in the anti-PAD4 antibodies and treatment response. At the DAS28 score (ΔDAS28) between baseline and 12 single-marker level, none of the autoantibodies were weeks of anti-TNF therapy [14]. The association be- found to be significantly associated with an improvement tween anti-CarP, PAD4, RF and anti-CCP antibodies in the DAS28 (Supplementary Table S1). We next tested and treatment response was determined using multi- for the presence of interaction effects between all six pair- variate linear regression. The multivariate model in- wise antibody combinations in relation to treatment re- cluded sex, age and the baseline DAS28 measure, as sponse. We found a highly significant antibody interaction described previously [4]. between anti-CarP and anti-PAD4 (P = 0.0062), and be- Interaction testing was performed by including an add- tween anti-CCP and RF (P = 0.00068) with anti-TNF re- itional interaction term in the multivariate regression sponse (Table 2). anti-CarP:anti-PAD4 interaction was model. All pairwise interaction models between the four associated with a worse response to therapy (interaction biomarkers were tested (n = 6). Multiple testing signifi- coefficient β < 0) and anti-CCP:RF interaction with a bet- cance correction was performed using Bonferroni’s ter response to TNF blocking (β > 0). The remaining four adjustment. pairwise antibody interactions were not significant (P > 0.05). After adjusting for multiple testing, anti-CarP:anti- Results PAD4 and anti-CCP:RF interactions remained statistically Patient characteristics significant (P = 0.0041 and P = 0.037, respectively). Baseline characteristics of the prospective patient cohort Using an independent cohort of 199 RA patients, we are summarized in Table 1. Clinical measures are com- replicated the interaction between anti-CCP and RF parable to previous RA cohorts. The four autoantibody (P = 0.044, Fig. 1). Like in the prospective cohort, the titers were determined in 100% of the patients. 65% of interaction was also found to be positive, indicating that the RA patients were positive for RF and 72% of for the simultaneous presence of both antibodies is associ- anti-CCP. An average reduction of 1.96 (+/− 1.33) points ated with a better response to anti-TNF therapy. Table 1 Baseline characteristics of the prospective RA patient cohort Baseline variable Total Responders (n = 67) Non-Responders (n = 13) (n = 80) Age, years (mean ± SD) 54.2 ± 11.93 53.12 ± 11.65 59.7 ± 12.32 Gender (Female,n %) 66 (82.5) 54 (80.6) 12 (92) Previous csDMARDs (mean ± SD) 1.85 ± 1.28 1.82 ± 1.24 2 ± 1.53 Disease duration, years (median/IQR) 9.74 (9.25) 9.24 (9.5) 12.31 (11.41) ESR, mm/h (median/IQR) 35.1 (28.75) 36.52 (34) 27.7 (21.17) CRP, mg/dL (median/IQR) 1.63 (1.27) 1.63 (1.23) 1.62 (1.47) MTX dosage (mean mg/week) 18.46 18.25 19.64 Prednisone use (n, %) 60 (75) 50 (74.6) 10 (77) Smoking (n, %) Never 54 (67.5) 44 (65.7) 10 (77) Past 10 (12.5) 6 (9) 3 (23) Current 16 (20) 17 (25.3) 0 (0) Adalimumab 16 (20) 15 (22.4) 1 (8) Certolizumab 24 (30) 21 (31.3) 3 (23) Etanercept 19 (23.75) 16 (23.4) 3 (23) Golimumab 21 (26.25) 15 (22.4) 6 (46) Clinical and epidemiological characteristics of the prospective cohort. Patients are shown globally and split according to the EULAR response at week 12 (Good and Moderate responders aggregated into a unique Responder group). MTX methotrexate; csDMARDs conventional synthetic DMARDs; IQR interquartile range; SD standard deviation Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 Page 4 of 7 Table 2 Association results for RA antibody interactions with anti-TNF response Regression coefficient (95%CI), P-value Antibody pair Interaction effect Antibody #1 main effect Antibody #2 main effect #1: Anti-CCP 2.74 (1.20,4.26), −1.21 (−2.14, −0.28), −2.24 (−3.57, − 0.91), #2: RF P = 0.00068 P = 0.012 P = 0.0013 #1: RF − 2.7e-4 (−7.5e-4,1.9e-4), 0.17 (− 0.89,1.21), 1.9e-4 (− 2.1e-4,5.9e-4), #2: Anti-PAD4 P = 0.25 P = 0.75 P = 0.34 #1: Anti-PAD4 −1.3e-6 (− 2.20–6,3.8e-6), 2.2e-4 (− 3.6e-5,4.8e-4), 1.3e-3 (9.6e-5,2.5e-3), #2: Anti-CarP P = 0.0062 P = 0.090 P = 0.035 #1: Anti-CCP −2.5e-4 (− 7.5e-4,2.5e-4), 0.12 (− 0.94,1.18), 1.8e-4 (− 2.6e-4,6.2e-4), #2: Anti-PAD4 P = 0.33 P = 0.83 P = 0.41 #1: Anti-CCP 8.9e-4 (−8.9e-4,2.7e-3), −0.45 (− 1.38,0.48), −8.4e-4 (− 2.5e-3,7.9e-4), #2: Anti-CarP P = 0.32 P = 0.34 P = 0.31 #1: RF −5.39e-4 (− 3.1e-3,1.9e-3), − 0.17 (− 0.99,0.65), 3.87e-3 (− 2e-3,2.8e-3), #2: Anti-CarP P = 0.67 P = 0.67 P = 0.75 Each row shows the association results for each of the six possible pairwise interactions between the four RA antibodies and anti-TNF treatment response, adjusting for baseline DAS28, sex and age. Regression coefficients (β value) with 95% confidence intervals (CI) and P values for association are shown for the interaction term (first column) and for the independent effect of each antibody (second and third columns). In bold, interaction P-values that are significant after correcting for multiple testing. A highly significant interaction was found for anti-CCP:RF and anti-PAD4:anti-CarP interactions with anti-TNF response. None of the remaining four antibody interactions showed a significant association, even at the nominal (P < 0.05) level Discussion inconsistency could be due to the presence of interaction The identification of factors associated with the response effects between the autoantibodies. In the present study to anti-TNF therapy is a major objective for treatment we have tested this hypothesis for the first time. Using a personalization in RA. Diagnostic autoantibodies, like prospective cohort of RA patients starting anti-TNF rheumatoid factor and anti-CCP, are appealing for this therapy, we have found that the interaction between task since they are already integrated into the standard anti-CCP and RF and the interaction between anti-CarP clinical routine. However, conflicting results have been and anti-PAD4 antibodies are both strongly associated reported and their association to treatment response to with the clinical response at week 12. The present re- anti-TNF is yet not clear. We hypothesized that this sults suggest that interactions between antibodies are Fig. 1 Validation study of the anti-CCP and RF interaction and anti-TNF response in RA. Forest plot showing the regression coefficients and 95% confidence intervals of the variables in the linear model testing the association of the two antibody combination with the response to anti-TNF therapy at week 12. Like in the prospective patient cohort, the interaction between anti-CCP and RF is statistically significant and positively associated with anti-TNF response (Beta:1.06 (0.03 to 2.10); P < 0.05). Anti-CCP:RF: regression coefficient capturing the interaction effect Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 Page 5 of 7 important in the response to anti-TNF therapy, and pro- antibodies therefore suggests a more predominant role vide an explanation for the previous conflicting of neutrophils in RA pathology. According to our re- evidence. sults, patients with a strong neutrophil-mediated inflam- Our study shows that the presence of both anti-CCP mation are less sensitive to therapeutic TNF blocking. and RF antibodies is needed for a favorable response to This is in line with recent experimental evidence show- anti-TNF therapy. Several previous studies have analyzed ing that neutrophil activation and TNF have independ- the association of either antibody in relation with the re- ent effects in RA pathology [19]. New therapies that sponse to anti-TNF drugs. The results, however, have directly affect neutrophil activation like, granulocyte- been largely inconsistent or inconclusive [16]. In those macrophage colony-stimulating factor blocking, are cur- few studies where both antibodies were determined, the rently under way in RA [20]. Our results suggest that presence of interactions was not evaluated. Here we the simultaneous analysis of anti-PAD4 and anti-CarP show that, when the interaction is considered, a strong antibodies could be a useful biomarker of response in and positive association between these two classic anti- this new therapeutic approach. bodies and the clinical response emerges. From a statis- The present study has limitations. Despite most tical perspective, when interaction effects are present previous studies analyzing the association of anti- and are strong, not taking them into account in the as- bodies to anti-TNF response have used similar or sociation model can lead to inconsistent findings [17]. smaller sample sizes, the number of patients used in Failing to take this into account could therefore explain our prospective study is relatively modest. Having a the lack of reproducibility of previous studies with anti- larger patient cohort would have enabled a more pre- CCP and RF and treatment response. cise estimation of the interaction effects, with nar- The interaction association identified between anti- rower confidence intervals. To this regard, while the CCP and RF with the response to anti-TNF therapy is in quantitative nature of the ΔDAS28 can help increase accordance with recent findings at the functional level. the power to identify biomarkers of drug efficacy, In a recent study, macrophages -the main producers of small improvements in DAS28 might not have a TNF in the RA joint- have been shown to secrete much translation into clinically meaningful responses. More higher TNF cytokine levels when stimulated with both patient data on the four antibodies will help to better anti-CCP and RF antibodies than when stimulated with define this boundary. Finally, the comparison of the anti-CCP alone [12]. According to these results, disease interaction association between different types of anti- activity in RA patients that express both anti-CCP and TNF drugs could not be explored. There is evidence RF might be partially due to the overexpression of TNF that TNF blocking agents work through biological by macrophages reacting strongly to the combination of mechanisms that are not entirely overlapping [21, 22]. the two antibodies. Instead, in patients expressing only An individual analysis of each drug type might reveal one of the antibodies or in seronegative patients, this stronger interactions and better biomarker utility. For synergic production of TNF by the synovial macrophage this aspect to be adequately tested, larger drug- will not occur, and other inflammatory pathways will specific prospective patient cohorts will need to be therefore have a more predominant role in disease activ- analyzed. ity. Our results show that, although still effective in some patients, anti-TNF therapy has a much less pro- Conclusion nounced therapeutic effect in patients with only one In summary, in the present study we have found that RA antibody compared to patients positive for both anti- antibodies show significant interaction effects with the CCP and RF. response to anti-TNF therapy. The observed interactions In our study we also found that the interaction be- are in line with pathogenic mechanisms recently de- tween anti-CarP and anti-PAD4 is associated to anti- scribed in RA. Our findings also provide an explanation TNF response. In this case, we found that the higher the for the lack of consistency observed in previous studies, expression of both antibodies, the worse the patients in which antibodies were analyzed independently. These responded to TNF blocking. Compared to anti-CCP and results leverage the potential of antibodies as biomarkers RF, these two antibodies do not physically interact at the for anti-TNF response in RA. molecular level. However, the two antibodies share a strong association to neutrophil activity. PAD4 is re- Abbreviations sponsible for most of the citrullinated epitopes in RA TNF: Tumor Necrosis Factor; RA: Rheumatoid Arthritis; DAS28: Disease Activity [18] and is specifically expressed in neutrophils. Protein Score for 28 joints; RF: Rheumatoid factor; anti-CCP: Anti-cyclic-citrullinated carbamylation is caused by an increase in tissue cyanate antibodies; anti-CarP: Anti-carbamylated protein antibodies; anti-PAD4: Anti- peptidylarginine deiminase type 4 antibodies; ELISA: Enzyme-linked due to the activity of neutrophil myeloperoxidase during immunosorbent assay; ACR: American College of Rheumatology; inflammation in RA. A higher abundance of both EULAR: European League Against Rheumatism Julià et al. BMC Musculoskeletal Disorders (2021) 22:372 Page 6 of 7 Supplementary Information 4. Potter C, Hyrich K, Tracey A, Lunt M, Plant D, Symmons D, et al. The online version contains supplementary material available at https://doi. Association of rheumatoid factor and anti-cyclic citrullinated peptide org/10.1186/s12891-021-04248-y. positivity, but not carriage of shared epitope or PTPN22 susceptibility variants, with anti-tumour necrosis factor response in rheumatoid arthritis. Ann Rheum Dis. 2009;68(1):69–74. https://doi.org/10.1136/ard.2 Additional file 1: Supplementary Table S1. Association results for the 007.084715. four antibodies and anti-TNF response at week 12 in the prospective co- 5. Klaasen R, Cantaert T, Wijbrandts CA, Teitsma C, Gerlag DM, Out TA, et al. hort (independent effects). The value of rheumatoid factor and anti-citrullinated protein antibodies as predictors of response to infliximab in rheumatoid arthritis: an exploratory Acknowledgements study. Rheumatol. 2011;50(8):1487–93. https://doi.org/10.1093/rheuma Not Applicable. tology/ker010. 6. Martin-Mola E, Balsa A, García-Vicuna R, Gómez-Reino J, González-Gay MA, Authors’ contributions Sanmartí R, et al. Anti-citrullinated peptide antibodies and their value for All authors were involved in drafting the article and all authors approved the predicting responses to biologic agents: a review. Rheumatol Int. 2016;36(8): final version to be published. Study conception and design. AJ, RS, SM. 1043–63. https://doi.org/10.1007/s00296-016-3506-3. Acquisition of data. MLL, FB, IH, AJM, AE, MLGV, JM, SSF, IG, MA, RCM, AFN, CDT, 7. Harris ML, Darrah E, Lam GK, Bartlett SJ, Giles JT, Grant AV, et al. Association RS, NP, RL, SM. Analysis and interpretation of data. AJ, MLL, AG, JLL, RS, SM. of autoimmunity to peptidyl arginine deiminase type 4 with genotype and disease severity in rheumatoid arthritis. Arthritis Rheum. 2008;58(7):1958–67. https://doi.org/10.1002/art.23596. Funding 8. Shi J, van de Stadt LA, Levarht EN, Huizinga TW, Toes RE, Trouw LA, et al. This project was supported by UCB Pharma. The funders had no role in the Brief report: anti–carbamylated protein antibodies are present in arthralgia study design, data analysis, data interpretation or writing the manuscript. patients and predict the development of rheumatoid arthritis. Arthritis Rheum. 2013;65(4):911–5. https://doi.org/10.1002/art.37830. Availability of data and materials 9. Halvorsen EH, Pollmann S, Gilboe I-M, van der Heijde D, Landewé R, The datasets used and/or analysed during the current study available from Ødegård S, et al. Serum IgG antibodies to peptidylarginine deiminase 4 in the corresponding author on reasonable request. rheumatoid arthritis and associations with disease severity. Ann Rheum Dis. 2008;67(3):414–7. https://doi.org/10.1136/ard.2007.080267. Declarations 10. Halvorsen E, Haavardsholm E, Pollmann S, Boonen A, van der Heijde D, Kvien T, et al. 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