Next Article in Journal
Cardiac Troponin I but Not N-Terminal Pro-B-Type Natriuretic Peptide Predicts Outcomes in Cardiogenic Shock
Previous Article in Journal
Intraoperative Fluorescent Ureter Visualization in Complex Laparoscopic or Robotic-Assisted Gynecologic Surgery
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 13
Issue 9
10.3390/jpm13091347
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Brief Report

TNFα rs1800629 Polymorphism and Response to Anti-TNFα Treatment in Behçet Syndrome: Data from an Italian Cohort Study

by
Maria Carmela Padula
1,2,*,
Angela Anna Padula
1,
Salvatore D’Angelo
1,
Nancy Lascaro
1,
Rosa Paola Radice
2,
Giuseppe Martelli
2 and
Pietro Leccese
1
1
Rheumatology Department of Lucania and Rheumatology Institute of Lucania (IReL), San Carlo Hospital of Potenza, 85100 Potenza, Italy
2
Department of Science, University of Basilicata, 85100 Potenza, Italy
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2023, 13(9), 1347; https://doi.org/10.3390/jpm13091347
Submission received: 2 August 2023 / Revised: 28 August 2023 / Accepted: 30 August 2023 / Published: 31 August 2023
(This article belongs to the Section Pharmacogenetics)
Review Reports Versions Notes

Abstract

:
Tumor Necrosis Factor-alpha (TNFα) rs1800629 (-308G>A) is a single nucleotide polymorphism (SNP) related to variable responses to anti-TNFα therapy. This therapy is efficient in severe and refractory manifestation of Behçet syndrome (BS), an auto-inflammatory systemic vasculitis. We investigated (1) the association between rs1800629 genotypes and responses to therapy and (2) the correlation between SNP and clinical patterns in a cohort of 74 BS Italian patients receiving anti-TNFα therapy with a follow-up of at least 12 months. The rs1800629 was genotyped through amplification, direct sequencing and bioinformatics analyses. The rs1800629 GG and GA genotypes were assessed as predictors of outcomes dividing the patients between therapy responders and non-responders. The rs1800629 GG and GA genotypes were found, respectively, in 59/74 (79.7%) and 15/74 BS patients (21.3%) (p < 0.05). We identified 16/74 (21.9%) non-responder patients, of which 9/16 (56.3%) showed the GG genotype and 7/16 (43.7%) the GA genotype. A total of 50/58 (86.2%) responder patients showed the GG genotype, and 8/58 (13.8%) the GA genotype (p < 0.05). The percentage of non-responder females (68.8%) was significantly higher than non-responder males (31.2%) (p < 0.05). No correlation between SNP and clinical patterns was observed. To successfully include rs1800629 as a predictive biomarker of TNFα inhibitor response, genome-wide association studies in larger, well-characterised cohorts are required.

1. Introduction

Tumor Necrosis Factor-alpha (TNFα) is a pleiotropic cytokine involved in the regulation of a wide spectrum of biological processes, including cell proliferation, differentiation, apoptosis, lipid metabolism, and coagulation. This cytokine has been implicated in a variety of diseases, including autoimmune diseases, insulin resistance, cancer, and also in the current pandemic coronavirus infectious disease (COVID-19) [1,2,3,4,5]. Its critical role has also been reported in the pathogenesis of several systemic rheumatic diseases, including Behçet syndrome (BS), a chronic vasculitis characterized by partially unknown etiology and wide clinical heterogeneity [2,6,7,8,9,10,11]. BS clinical signs range from mucocutaneous, articular, vascular, and gastrointestinal involvement, to ocular and neurologic involvement. These clinical manifestations may co-exist in the same patient, defining a broad spectrum of clinical phenotypes [6,7,8,9,10,11]. Both genetics and environmental factors are involved in disease etiopathogenesis [2,7,8,10,11,12,13,14]. Human leukocyte antigen (HLA)-B*51 was the most strongly associated genetic marker of BS [10,11,12,13,14]. Other non-HLA genetic loci were identified within genes mainly involved in the inflammation and immunity processes. In fact, the associations with BS susceptibility were demonstrated for several genes, such as Endoplasmic reticulum aminopeptidase 1 (ERAP1) and Interleukin 10 (IL10) and IL23R-IL12RB2, as well as TNFα [2,10,13,14].
The heterogeneity of treatments reflects the variability of clinical signs. The 2018 update of the EULAR recommendations for the management of BS provided to physicians evidence-based recommendations for patient care in single disease manifestations. Due to BS being characterized by a relapsing and remitting course, the treatment objective is to counter the inflammatory response and to prevent organ damage [15]. Colchicine, azathioprine (AZA), thalidomide, interferon-alpha (IFN α), cyclosporine-A, cyclophosphamide, glucocorticoids, apremilast, and the blockage of TNFα, IL-1, and IL-6 IL-17 are the main significant treatments reported [15]. The need for tailoring the treatments to patient phenotype (characterized by various clinical manifestations) rather than having a single involvement was summarized in the review by Bettiol and colleagues [9]. The authors reported the treatment for the three major BS phenotypes: patients with the “mucocutaneous and articular” phenotype should be treated with colchicine, alone or in combination with corticosteroids; AZA can be used in case of resistance or intolerance to colchicine; and anti-TNFα or IFNα should be considered for refractory or severe forms. In case of an “extra-parenchymal and peripheral vascular phenotype”, immunosuppressant drugs and additional anticoagulants in selected patients should be recommended. Traditional immunosuppressants (mainly AZA) should be the first-line treatment, while anti-TNFα therapy could be a second-line treatment. In the case of the “parenchymal neurological and ocular phenotype”, AZA is recommended as a first-line treatment after an induction therapy with high-dose steroids. In the case of severe manifestations or intolerance to AZA, anti-TNFα drugs should be employed [9].
The approach of targeting TNFα has considerably improved success in the treatment of BS, in particular for patients with refractory, severe BS, and in particular for ocular, central nervous system, and gastrointestinal manifestations, as well as vascular involvement [6,9,15,16,17,18]. Five different TNFα inhibitors are now used: infliximab, etanercept, adalimumab, golimumab, and certolizumab-pegol. Infliximab is the most commonly administered drug. The blockade of TNFα is characterized by a high clinical efficacy and rapid onset of action, but the lack of response is a common trait after repeated infusions [4,5,6]. Although biological treatments with anti-TNFα agents are effective in BS, not all patients are definite responders. Non-responder patterns could be due to alternative non-TNFα-related pathways of inflammation, the presence of anti-drug antibodies or development and polymorphic alleles of the TNFα gene [4,5,6,7,17,18,19,20,21,22,23,24,25]. TNFα (RefSeq NG_007462.1) is located quite close to the Major Histocompatibility Complex (MHC) at chromosome 6 (6p21.33), and encodes a 233-amino-acid type II transmembrane protein. TNFα is formed by 4 exons and about 3000 nucleotides, including both 5′UTR and 3′UTR regulatory regions. TNFα -308G>A (rs1800629; NG_007462.1:g.4682G>A; HGVS nomenclature) was classified as a drug-response polymorphism in several specific databases (for example dbSNP, ClinVar, ClinGen Allele Registry). We previously studied the genotype distribution of BS patients compared with healthy controls and we found a statistically significantly higher frequency of the TNFα rs1800629 GA genotype in patients than in controls. No significant association was recognized between the polymorphism and the clinical parameters, as well as between SNP and disease severity [2]. The association between the SNP and both susceptibility and clinical patterns of BS was investigated with conflicting data [4,5,6,7,17,18,19,20,21,22,23,24,25]. Poorer data are currently available regarding the association of this polymorphism and its responsiveness to TNFα blockers in BS patients, in particular in Italian populations. The aim of this study was to investigate (1) the association of rs1800629 with the response to anti-TNFα therapy (outcome) in a cohort of Italian patients with BS, and (2) the correlation between SNP and clinical manifestations.

2. Materials and Methods

This is a retrospective, monocentric cohort study conducted at the Rheumatology Institute of Lucania/San Carlo Hospital (Potenza, Italy). The Regional Ethics Committee approved this study (Permit Number: 705/2017).

2.1. Patient Enrolment

Patients with BS were identified from our large database. Inclusion criteria were BS patients fulfilling the ISG criteria [26] who received anti-TNFα therapy with a follow-up of at least 12 months after drug initiation, who had available medical records (with information on treatment duration and efficacy), and who consented to participate. Prior to enrolling, all subjects provided their written, informed consent. Demographic and clinical data of enrolled patients were collected from medical records and analysed. The rs1800629 genotype was assessed as a predictor of outcomes, dividing the patients in two groups: therapy responders and non-responders. We set a period of 12 months as the time interval useful for evaluating clinical response and we distinguished the primary (patients who do not respond to the induction therapy) and secondary loss of response (patients who respond to the therapy after an induction regimen, but subsequently lose response during maintenance treatment). Potential confounders could be age, gender, ethnicity, and clinical manifestations.

2.2. Genotyping

Genomic DNA was extracted from whole blood by using a commercial kit (Nuclear Laser Medicine Srl, Settala, Italy) according to the manufacturer’s recommendations. After isolation, DNA was quantified using the NanoDrop™ 1000 spectrophotometer (NanoDrop Technologies, Inc., Wilmington, Delaware) to assess the quality and concentration of the nucleic acid for downstream application. In vitro PCR was performed using home-made specific primer pairs for rs1800629 coverage (primer design by NCBI Primer-Blast tool) (forward: 5′TTCCCTCCAACCCCGTTTTC3′, reverse: 5′CTGCACCTTCTGTCTCGGTT3′). PCR amplification was carried out using Q5 Hot Start High-Fidelity DNA Polymerase (New England BioLabs Inc., Ipswich, USA), according to the manufacturer’s recommendations: 5 uL of 5X Reaction Buffer was mixed with 0.5 uL of 10 mM dNTPs, 1.25 uL of each primer, and 0.25 uL of High-Fidelity DNA polymerase, and added to each sample for a final volume of 25 uL. The amplification program was (1) 98 °C for 5 min (initial denaturation); (2) 94 °C for 1 min, 58 °C for 1 min, 72 °C for 2 min (thermocycling, repeated 35 times); and (3) 72 °C for 7 min (final extension). A negative control was also used in PCR reactions. Amplification products were analysed using gel electrophoresis (1.5% agarose gel) and sequenced with the Microsynth sequencing service (Sanger method). In silico analysis was performed downstream using the NCBI-BlastN similarity search tool and Mutation Surveyor 3.9 software.

2.3. Statistics

Descriptive and analytical statistics were carried out using SPSS Statistic version 17 for Windows (SPSS Inc., Chicago, IL, USA). Demographic and genotype frequencies and clinical manifestations were compared using a chi-square goodness of fit test or Fisher’s exact test (for 2 × 2 tables). A p-value less than 0.05 was considered statistically significant.

3. Results

A total of 74 BS patients (44 males, 30 females; mean age: 43.1 ± 11.3 years) meeting the inclusion criteria were enrolled. Patients’ predominant lesions were oral aphthous (100%), skin lesions (82.4%), eye involvement (75.7%), and genital ulcers (50.0%). HLA-B*51 was found in the 67.6% of cases.

Response to TNFα Therapies and Pharmacogenetics

We compared the patients for therapy responses to anti-TNFα drugs, and we found 58/74 (78.4%) therapy responders and 16/74 (21.6%) non-responder patients. Within the non-responder group, 3/16 (18.75%) patients were primary non-responders and 13/16 (81.25%) were characterized by losing drug efficacy during the treatment (secondary loss of response). No statistically significant differences were found when the responder and non-responder groups were analysed for the presence/absence of each clinical manifestation (p > 0.05) (Table 1).
For genetic characterization, the overall analysis showed that the TNFα rs1800629 wild-type GG genotype was recognized in 59/74 (79.7%) BS patients; the heterozygous genotype (GA) was identified in 15/74 (21.3%) patients (p < 0.05). We identified that 50/58 (86.2%) responders showed the GG genotype and 8/58 (13.8%) responder patients showed the GA genotype, while 9/16 (56.25%) non-responder patients showed the GG genotype and 7/16 (43.75%) the GA genotype (p = 0.008; OR: 4.86 (1.41–16.76) (Table 2).
The two groups were also analysed for gender differences and stratified for anti-TNFα agent (Table 3). The percentage of non-responder females (68.8%; 11/16 patients) was significantly higher than non-responder males (31.2%; 5/16 patients) (p = 0.009, OR: 0.22 (0.07–0.83). We found that 60/74 (81.8%) patients were treated with Infliximab, 12/74 (16.2%) with Adalimumab, 1/74 (1.35%) with Certiluzumab Pergol and 1/74 (1.35%) with Golimumab. We also performed a sub-analysis of responses based on each anti-TNFα drug. We found 45/58 (77.6%) responders treated with Infliximab and 15/16 (93.75%) non-responder patients receiving the same therapy (p = 0.1439, OR: 0.23 (0.03–1.92)). A total of 11/58 (19.0%) patients treated with Adalimumab were responder patients, while 1/16 (6.25%) was a non-responder (p > 0.05). No differences were found when responders and non-responders were compared for Certulizumab Pergol and Golimumab (p > 0.05).

4. Discussion

We studied the association between TNFα rs1800629 genotypes and anti-TNFα therapy responses, as well as between the same SNP and the clinical response in BS. We found that the frequency of the rs1800629 wild-type GG genotype was statistically significantly higher in the case of therapy response compared to the GA genotype, suggesting a possible role of the SNP-containing genotype in affecting the drug response. We also recognized a higher frequency of non-responder females compared with males. No significant differences were found when responders and non-responders were stratified for all anti-TNFα drugs and compared.
BS is an auto-inflammatory systemic vasculitis characterized by a relapsing-remitting course. A wide spectrum of pharmaceutical agents is now available for treating clinical manifestations of the disease; the therapy goal is to prevent symptom worsening triggered and supported by different molecular and cellular mechanisms, such as inflammation [4,5,6,9,15,27]. Although BS etiopathogenesis remains partially unknown, it has been reported that hexogen triggers (such as infective agents and unbalanced microbiomes) are responsible for immune activation resulting in inflammatory symptoms in genetically predisposed subjects [2,4,5,6,7,8,9,10,11,12,13,14,15,27]. A dysregulated Th1/Th2 and Th17/Treg cells balance and a hyper-activation of pro-inflammatory cytokines (in particular IL-1, IL-6, and TNFα) are associated with chronic inflammation and typical BS clinical phenotypes [4,5,6,9,27]. In this scenario, TNFα inhibitors are significant effective therapeutic tools, due to anti-TNFα targeting being a good way to achieve disease remission. Not all patients have long-term remission, so the discontinuation of the TNFα inhibitor therapy has become an area of interest, due to obvious economic and risk-benefit concerns. These aspects underline the need to have useful biomarkers for predicting the response to therapy [4,5,6,9,16,18,27,28]. Due to advances in DNA genotyping and sequencing approaches, genetic variations become an interesting source of markers able to predict treatment responses and to obtain useful information within the field of personalized medicine. Literature data about TNFα rs1800629 have shown a consistent amount of data about the role of SNP in BS susceptibility, while limited and conflicting data are available on the ability to predict anti-TNFα treatment responses in BS based on gene polymorphisms. The association between TNFα rs1800629 SNP and BS susceptibility is a common literature finding in several ethnic groups [7,8,17,18,19,20,21,22,23,24,25], as well as in Italian populations, based on our recently published data [2]. The ability of SNP to affect TNFα expression and the inflammation pathway was also reported in relation to the SNP localization; that is, within the gene regulatory region and, in particular, within the gene promoter [7,17,19,20,21,22,23,24,25].
In the present investigation, the distribution of rs1800629 genotypes showed a higher percentage of non-responder patients carrying the A allele (GA genotype) in agreement with previous data about the SNP role in influencing drug response. These studies reported that patients with inflammatory diseases having the AA genotype may be less likely to achieve improvements in clinical manifestations compared with patients carrying the GG genotype when treated with anti-TNFα drugs [4,20,24,29,30,31,32,33,34,35,36,37,38,39,40].
Gender differences in anti-TNFα therapy responses have not previously been investigated. We found a higher frequency of non-responder females compared with non-responder males, and this difference was statistically significant, suggesting that genetics could be one of the possible contributors modulating therapy responses in the sexes, together with anatomical, physiological, neuronal, hormonal, psychological and social factors. A previous study by our group investigated gender differences in BS clinical manifestations and found that BS tends to be less aggressive in female patients [41].
In this study, we also performed an explorative analysis on the distribution of non-responder patients when our cohort was stratified for anti-TNFα drugs, and we found a lower response in the case of Infliximab, also depending on the higher number of patients treated with this drug. The role of genetic factors for the lack of therapeutic response to anti-TNFα agents was previously suggested in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA), ankylosing spondylitis (AS), juvenile idiopathic arthritis (JIA), and Sjögren’s syndrome, as well as in inflammatory bowel disease, in particular Crohn’s disease (CD), but also in Wegener’s granulomatosis and sarcoidosis [4,29,30,31,32,33,34,35,36,37,38,39,40].
The drugs showed significant differences in structure and in clinical efficacy, as well as in their mechanisms of action. Firstly, TNF-α antagonists could be divided into two main types of agents: monoclonal antibodies and soluble receptors. Infliximab, Adalimumab, and Golimumab are biological drugs against human TNFα, while Etanercept is direct against human TNFα receptors. Infliximab is a chimeric human-mouse anti-TNFα monoclonal antibody, while Adalimumab and Golimumab are fully humanized anti-TNFα antibodies. Etanercept is formed from the extracellular portion of the two human TNF-R2 linked to the Fc portion of human IgG1, while Certolizumab is a fragment of an anti-TNF-α IgG1 monoclonal antibody. Infliximab, Adalimumab, and Etanercept are more effective for the treatment of RA, PsA, and AS. This variability response could be related to differences in pharmacokinetics, tissue distribution and functional properties of each anti-TNFα agent [1,9,16,18,27,29,40,42,43,44,45,46,47,48,49,50,51,52].
Although different mechanisms of action have been suggested, such as pro-inflammatory cytokine down-regulation, apoptosis induction, complement-dependent cytotoxicity, and antibody-dependent cell-mediated cytotoxicity, the cellular and molecular mechanisms of action of the anti-TNFα antibodies remain partially unknown, and the mechanisms of the lack of drug response are also partially unclear [1,4,5,9,16,18,27,29,40,42,43,44,45,46,47,48,49,50,51,52]. In both contexts, pharmacogenetics and pharmacogenomics represent a good way to compose the puzzle by finding the missing tiles.

5. Conclusions

Pharmacogenetics and pharmacogenomics represent the new frontiers for the discovery of potential genetic markers of biological responses to TNFα inhibitors. The identification of robust and validated SNP-based biomarker panels is one of the most significant goals of Predictive, Preventive and Personalised Medicine (PPPM). In particular, insights into how the therapy response is genetics-related could lead to the development of strategies for predicting the non-response to anti-TNFα therapy prior to treatment initiation and for implementing a stratified and tailored approach of clinical care. This paper could provide a first step in this direction in a cohort of Italian patients with BS. Since our sample size is relatively small, analyses of a larger cohort of patients are needed to confirm the study findings and to explain the SNP role as an outcome predictor; also, in randomized clinical trials, it is useful to include gene variation in clinical decision-making and drug management.

Author Contributions

M.C.P., G.M., S.D., P.L. and A.A.P. conceived and designed the study. M.C.P. and R.P.R. performed the experiments. P.L., N.L. and A.A.P. contributed to the acquisition of patient data. All authors participated in the interpretation of the data. M.C.P. wrote the first paper draft. All authors were involved in drafting, reading, and revising the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, and approved by the Regional Ethics Committee (CEUR Basilicata) (protocol code 705/2017).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

Thanks to Olivieri.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kłak, A.; Paradowska-Gorycka, A.; Kwiatkowska, B.; Raciborski, F. Personalized medicine in rheumatology. Reumatologia 2016, 54, 177–186. [Google Scholar] [CrossRef] [PubMed]
  2. Padula, M.C.; Leccese, P.; Lascaro, N.; Radice, R.P.; Limongi, A.R.; Sorrento, G.G.; Carbone, T.; Padula, A.A.; Martelli, G.; D’Angelo, S. Correlation of Tumor Necrosis Factor-α-308G>A Polymorphism with Susceptibility, Clinical Manifestations, and Severity in Behçet Syndrome: Evidences from an Italian Genetic Case-Control Study. DNA Cell Biol. 2020, 39, 1104–1110. [Google Scholar] [CrossRef] [PubMed]
  3. Pedersen, S.F.; Ho, Y.C. SARS-CoV-2: A storm is raging. J. Clin. Investig. 2020, 130, 2202–2205. [Google Scholar] [CrossRef] [PubMed]
  4. Jang, D.-I.; Lee, A.-H.; Shin, H.-Y.; Song, H.-R.; Park, J.-H.; Kang, T.-B.; Lee, S.-R.; Yang, S.-H. The Role of Tumor Necrosis Factor Alpha (TNF-α) in Autoimmune Disease and Current TNF-α Inhibitors in Therapeutics. Int. J. Mol. Sci. 2021, 22, 2719. [Google Scholar] [CrossRef]
  5. Salomon, B.L. Insights into the biology and therapeutic implications of TNF and regulatory T cells. Nat. Rev. Rheumatol. 2021, 17, 487–504. [Google Scholar] [CrossRef]
  6. Jarrot, P.A.; Kaplanski, G. Anti-TNF-alpha therapy and systemic vasculitis. Mediat. Inflamm. 2014, 2014, 493593. [Google Scholar] [CrossRef]
  7. Lee, E.B.; Kim, J.Y.; Lee, Y.J.; Park, M.H.; Song, Y.W. TNF and TNF receptor polymorphisms in Korean Behcet’s disease patients. Hum. Immunol. 2003, 64, 614–620. [Google Scholar] [CrossRef]
  8. Liang, Y.; Xu, W.D.; Zhang, M.; Qiu, L.J.; Ni, J.; Wang, X.S.; Wen, P.F.; Cen, H.; Leng, R.X.; Pan, H.F.; et al. Meta-analysis of association between cytokine gene polymorphisms and Behcet’s disease risk. Int. J. Rheum. Dis. 2013, 16, 616–624. [Google Scholar] [CrossRef]
  9. Bettiol, A.; Hatemi, G.; Vannozzi, L.; Barilaro, A.; Prisco, D.; Emmi, G. Treating the Different Phenotypes of Behçet’s Syndrome. Front. Immunol. 2019, 16, 2830. [Google Scholar] [CrossRef]
  10. Padula, M.C.; Leccese, P.; Lascaro, N.; Padula, A.A.; Carbone, T.; Martelli, G.; D’Angelo, S. A First Step for the Molecular Characterization of Neurological Involvement of Behçet Syndrome: An Italian Pivotal Study. J. Mol. Neurosci. 2021, 71, 1284–1289. [Google Scholar] [CrossRef]
  11. Leccese, P.; Padula, M.C.; Santospirito, E.V.; Colucci, R.; Lascaro, N.; D’angelo, S. HLA-B*51 subtypes molecular analysis in a series of Italian patients with Behçet’s syndrome. Jt. Bone Spine 2019, 86, 807–808. [Google Scholar] [CrossRef]
  12. Ohno, S.; Ohguchi, M.; Hirose, S.; Matsuda, H.; Wakisaka, A.; Aizawa, M. Close association of HLA-Bw51 with Behçet’s disease. Arch. Ophthalmol. 1982, 100, 1455–1458. [Google Scholar] [CrossRef]
  13. Padula, M.C.; Leccese, P.; Pellizzieri, E.; Padula, A.A.; Gilio, M.; Carbone, T.; Lascaro, N.; Tramontano, G.; Martelli, G.; D’angelo, S. Distribution of rs17482078 and rs27044 ERAP1 polymorphisms in a group of Italian Behçet’s syndrome patients: A preliminary case-control study. Intern. Emerg. Med. 2019, 14, 713–718. [Google Scholar] [CrossRef] [PubMed]
  14. Padula, M.C.; Leccese, P.; Lascaro, N.; Carbone, T.; Limongi, A.R.; Radice, R.P. From structure to function for the characterization of ERAP1 active site in Behçet syndrome. A novel polymorphism associated with known gene variations. Mol. Immunol. 2020, 117, 155–159. [Google Scholar] [CrossRef] [PubMed]
  15. Hatemi, G.; Christensen, R.; Bang, D.; Bodaghi, B.; Celik, A.F.; Fortune, F.; Gaudric, J.; Gul, A.; Kötter, I.; Leccese, P.; et al. 2018 update of the EULAR recommendations for the management of Behçet’s syndrome. Ann. Rheum. Dis. 2018, 77, 808–818. [Google Scholar] [CrossRef]
  16. Aksoy, A.; Yazici, A.; Omma, A.; Cefle, A.; Onen, F.; Tasdemir, U.; Ergun, T.; Direskeneli, H.; Alibaz-Oner, F. Efficacy of TNFα inhibitors for refractory vascular Behçet’s disease: A multicenter observational study of 27 patients and a review of the literature. Int. J. Rheum. Dis. 2020, 23, 256–261. [Google Scholar] [CrossRef] [PubMed]
  17. Touma, Z.; Farra, C.; Hamdan, A.; Shamseddeen, W.; Uthman, I.; Hourani, H.; Arayssi, T. TNF polymorphisms in patients with Behçet disease: A meta-analysis. Arch. Med. Res. 2010, 41, 142–146. [Google Scholar] [CrossRef]
  18. Zhan, S.; Liu, C.; Li, N.; Li, T.; Tian, Z.; Zhao, M.; Wu, D.; Chen, M.; Zeng, Z.; Zhuang, X. Anti-TNF-α agents for refractory intestinal Behçet’s disease: Case series and meta-analysis. Ther. Adv. Gastroenterol. 2022, 15, 17562848221116666. [Google Scholar] [CrossRef] [PubMed]
  19. Abdolmohammadi, R.; Bonyadi, M. Polymorphisms of Promoter Region of TNF-α Gene in Iranian Azeri Turkish Patients with Behçet’s Disease. J. Korean Med. Sci. 2017, 32, 33–37. [Google Scholar] [CrossRef]
  20. Al-Okaily, F.; Arfin, M.; Al-Rashidi, S.; Al-Balawi, M.; Al-Asmari, A. Inflammation-related cytokine gene polymorphisms in Behçet’s disease. J. Inflamm. Res. 2015, 8, 173–180. [Google Scholar]
  21. Ateş, A.; Kinikli, G.; Düzgün, N.; Duman, M. Lack of association of tumor necrosis factor-alpha gene polymorphisms with disease susceptibility and severity in Behçet’s disease. Rheumatol. Int. 2006, 26, 348–353. [Google Scholar] [CrossRef] [PubMed]
  22. Bonyadi, M.; Abdolmohammadi, R.; Jahanafrooz, Z.; Somy, M.H.; Khoshbaten, M. TNF-alpha gene polymorphisms in Iranian Azari Turkish patients with inflammatory bowel diseases. Saudi J. Gastroenterol. 2014, 20, 108–112. [Google Scholar] [CrossRef] [PubMed]
  23. Duymaz-Tozkir, J.; Gül, A.; Uyar, F.A.; Ozbek, U.; Saruhan-Direskeneli, G. Tumour necrosis factor-alpha gene promoter region -308 and -376 G-->A polymorphisms in Behçet’s disease. Clin. Exp. Rheumatol. 2003, 21, S15–S18. [Google Scholar] [PubMed]
  24. Kamoun, M.; Chelbi, H.; Houman, M.H.; Lacheb, J.; Hamzaoui, K. Tumor necrosis factor gene polymorphisms in Tunisian patients with Behcet’s disease. Hum. Immunol. 2007, 68, 201–205. [Google Scholar] [CrossRef]
  25. Radouane, A.; Oudghiri, M.; Chakib, A.; Bennani, S.; Touitou, I.; Barat-Houari, M. SNPs in the TNF-α gene promoter associated with Behcet’s disease in Moroccan patients. Rheumatology 2012, 51, 1595–1599. [Google Scholar] [CrossRef]
  26. International Study Group for Behcet’s Disease. Criteria for diagnosis of Behcet’s disease. Lancet 1990, 335, 1078–1080. [Google Scholar]
  27. Van der Houwen, T.B.; van Hagen, P.M.; van Laar, J.A.M. Immunopathogenesis of Behçet’s disease and treatment modalities. Semin. Arthritis Rheum. 2022, 52, 151956. [Google Scholar] [CrossRef]
  28. Vallet, H.; Riviere, S.; Sanna, A.; Deroux, A.; Moulis, G.; Addimanda, O.; Salvarani, C.; Lambert, M.; Bielefeld, P.; Seve, P.; et al. Efficacy of anti-TNF alpha in severe and/or refractory Behçet’s disease: Multicenter study of 124 patients. J. Autoimmun. 2015, 62, 67–74. [Google Scholar] [CrossRef]
  29. Hüffmeier, U.; Mössner, R. Complex role of TNF variants in psoriatic arthritis and treatment response to anti-TNF therapy: Evidence and concepts. J. Investig. Dermatol. 2014, 134, 2483–2485. [Google Scholar] [CrossRef]
  30. Murdaca, G.; Gulli, R.; Spanò, F.; Lantieri, F.; Burlando, M.; Parodi, A.; Mandich, P.; Puppo, F. TNF-α gene polymorphisms: Association with disease susceptibility and response to anti-TNF-α treatment in psoriatic arthritis. J. Investig. Dermatol. 2014, 134, 2503–2509. [Google Scholar] [CrossRef]
  31. Ovejero-Benito, M.C.; Muñoz-Aceituno, E.; Reolid, A.; Saiz-Rodríguez, M.; Abad-Santos, F.; Daudén, E. Pharmacogenetics and Pharmacogenomics in Moderate-to-Severe Psoriasis. Am. J. Clin. Dermatol. 2018, 19, 209–222. [Google Scholar] [CrossRef] [PubMed]
  32. Prieto-Pérez, R.; Cabaleiro, T.; Daudén, E.; Abad-Santos, F. Gene polymorphisms that can predict response to anti-TNF therapy in patients with psoriasis and related autoimmune diseases. Pharmacogenom. J. 2013, 13, 297–305. [Google Scholar] [CrossRef]
  33. Wcisło-Dziadecka, D.; Zbiciak-Nylec, M.; Brzezińska-Wcisło, L.; Mazurek, U. TNF-α in a molecularly targeted therapy of psoriasis and psoriatic arthritis. Postgrad. Med. J. 2016, 92, 172–178. [Google Scholar] [CrossRef] [PubMed]
  34. Zeng, Z.; Duan, Z.; Zhang, T.; Wang, S.; Li, G.; Gao, J.; Ye, D.; Xu, S.; Xu, J.; Zhang, L.; et al. Association between tumor necrosis factor-α (TNF-α) promoter −308 G/A and response to TNF-α blockers in rheumatoid arthritis: A meta-analysis. Mod. Rheumatol. 2013, 23, 489–495. [Google Scholar] [CrossRef] [PubMed]
  35. Aflatoonian, M.; Moghimi, M.; Akbarian-Bafghi, M.J.; Morovati-Sharifabad, M.; Jarahzadeh, M.H.; Neamatzadeh, H. Association of TNFα -308G>A polymorphism with susceptibility to celiac disease: A systematic review and meta-analysis. Arq. Gastroenterol. 2019, 56, 88–94. [Google Scholar] [CrossRef] [PubMed]
  36. Marotte, H.; Miossec, P. Prevention of bone mineral density loss in patients with rheumatoid arthritis treated with anti-TNFalpha therapy. Biologics 2008, 2, 663–669. [Google Scholar] [PubMed]
  37. O’Rielly, D.D.; Roslin, N.M.; Beyene, J.; Pope, A.; Rahman, P. TNF-alpha-308 G/A polymorphism and responsiveness to TNF-alpha blockade therapy in moderate to severe rheumatoid arthritis: A systematic review and meta-analysis. Pharmacogenom. J. 2009, 9, 161–167. [Google Scholar] [CrossRef]
  38. Pavy, S.; Toonen, E.J.; Miceli-Richard, C.; Barrera, P.; van Riel, P.L.; Criswell, L.A.; Mariette, X.; Coenen, M.J. Tumour necrosis factor alpha −308G->A polymorphism is not associated with response to TNFalpha blockers in Caucasian patients with rheumatoid arthritis: Systematic review and meta-analysis. Ann. Rheum. Dis. 2010, 69, 1022–1028. [Google Scholar] [CrossRef]
  39. Qin, B.; Wang, J.; Liang, Y.; Yang, Z.; Zhong, R. The association between TNF-α, IL-10 gene poly-morphisms and primary Sjögren’s syndrome: A meta-analysis and systemic review. PLoS ONE 2013, 8, e63401. [Google Scholar]
  40. Bek, S.; Bojesen, A.B.; Nielsen, J.V.; Sode, J.; Bank, S.; Vogel, U.; Andersen, V. Systematic review and meta-analysis: Pharmacogenetics of anti-TNF treatment response in rheumatoid arthritis. Pharmacogenom. J. 2017, 17, 403–411. [Google Scholar] [CrossRef]
  41. Leccese, P.; Padula, M.C.; Lascaro, N.; Padula, A.A.; D’Angelo, S. Clinical phenotypes of Behçet’s syndrome in a large cohort of Italian patients: Focus on gender differences. Scand J. Rheumatol. 2021, 50, 475–478. [Google Scholar] [CrossRef] [PubMed]
  42. Liu, J.; Dong, Z.; Zhu, Q.; He, D.; Ma, Y.; Du, A.; He, F.; Zhao, D.; Xu, X.; Zhang, H.; et al. TNF-α Promoter Polymorphisms Predict the Response to Etanercept More Powerfully than that to Infliximab/Adalimumab in Spondyloarthritis. Sci. Rep. 2016, 6, 32202. [Google Scholar] [CrossRef] [PubMed]
  43. Mohammed, S.; Zalzala, M.; Gorial, F. Association of tumor necrosis factor-alpha promoter region gene polymorphism at positions −308G/A, −857C/T, and −863C/A with etanercept response in Iraqi rheumatoid arthritis patients. Arch. Rheumatol. 2022, 37, 613–625. [Google Scholar] [CrossRef] [PubMed]
  44. Murdaca, G.; Negrini, S.; Magnani, O.; Penza, E.; Pellecchio, M.; Puppo, F. Impact of pharmacogenomics upon the therapeutic response to etanercept in psoriasis and psoriatic arthritis. Expert. Opin. Drug Saf. 2017, 16, 1173–1179. [Google Scholar] [CrossRef]
  45. Schmeling, H.; Horneff, G. Tumour necrosis factor alpha promoter polymorphisms and etanercept therapy in juvenile idiopathic arthritis. Rheumatol. Int. 2007, 27, 383–386. [Google Scholar] [CrossRef]
  46. Seitz, M.; Wirthmüller, U.; Möller, B.; Villiger, P.M. The −308 tumour necrosis factor-alpha gene polymorphism predicts therapeutic response to TNFalpha-blockers in rheumatoid arthritis and spondyloarthritis patients. Rheumatology 2007, 46, 93–96. [Google Scholar] [CrossRef]
  47. Curry, P.D.K.; Morris, A.P.; Barton, A.; Bluett, J. Do genetics contribute to TNF inhibitor response prediction in Psoriatic Arthritis? Pharmacogenom. J. 2023, 23, 1–7. [Google Scholar] [CrossRef]
  48. De Simone, C.; Farina, M.; Maiorino, A.; Fanali, C.; Perino, F.; Flamini, A.; Caldarola, G.; Sgambato, A. TNF-alpha gene polymorphisms can help to predict response to etanercept in psoriatic patients. J. Eur. Acad. Dermatol. Venereol. 2015, 29, 1786–1790. [Google Scholar] [CrossRef]
  49. Horiuchi, T.; Mitoma, H.; Harashima, S.; Tsukamoto, H.; Shimoda, T. Transmembrane TNF-alpha: Structure, function and interaction with anti-TNF agents. Rheumatology 2010, 49, 1215–1228. [Google Scholar] [CrossRef]
  50. Atiqi, S.; Hooijberg, F.; Loeff, F.C.; Rispens, T.; Wolbink, G.J. Immunogenicity of TNF-Inhibitors. Front. Immunol. 2020, 11, 312. [Google Scholar] [CrossRef]
  51. Chen, A.Y.; Wolchok, J.D.; Bass, A.R. TNF in the era of immune checkpoint inhibitors: Friend or foe? Nat. Rev. Rheumatol. 2021, 17, 213–223. [Google Scholar] [CrossRef] [PubMed]
  52. Minnema, L.A.; Giezen, T.J.; Hoekman, J.; Egberts, T.C.; Leufkens, H.G.; Gardarsdottir, H. Regulatory Safety Learning Driven by the Mechanism of Action: The Case of TNF-α Inhibitors. Clin. Pharmacol. Ther. 2021, 110, 123–131. [Google Scholar] [CrossRef] [PubMed]
Table 1. Clinical manifestations and demographic characteristics of BS patients divided into responders and non-responders.
Table 1. Clinical manifestations and demographic characteristics of BS patients divided into responders and non-responders.
Responders
(n = 58)		Non-Responders
(n = 16)		p-ValueOR95% CI
Clinical Manifestations
Oral ulcers
            with58 (100.0%)16 (100.0%)0.75853.630.00–NA
            without0 (0.0%)0 (0.0%)
Genital ulcers
            with (n = 37)26 (44.8%)11 (68.8%)0.09020.370.11–1.20
            without (n = 37)32 (55.2%)5 (31.2%)
Papulopustular lesions
            with (n = 51)42 (72.4%)9 (56.3%)0.21622.040.65–6.41
            without (n = 23)16 (27.6%)7 (43.7%)
Erythema nodosum
            with23 (39.7%)5 (31.2%)0.53941.450.44–4.71
            without35 (60.3%)11 (68.8%)
Follicolitis
            with8 (13.8%)2 (12.5%)0.89341.120.21–5.88
            without50 (86.2%)14 (87.5%)
Anterior uveitis
            with20 (34.5%)6 (37.5%)0.82290.880.28–2.76
            without38 (65.5%)10 (62.5%)
Posterior uveitis
            with34 (58.6%)8 (50.0%)0.53781.420.47–4.30
            without24 (41.4%)8 (50.0%)
Arthritis
            with14 (24.1%)2 (12.5%)0.31682.230.45–11.2
            without44 (75.9%)14 (87.5%)
CNS involvement
            with15 (25.9%)4 (25.0%)0.94431.050.29–3.75
            without43 (74.1%)12 (75.0%)
Superficial venous thrombosis
            with6 (10.3%)2 (12.5%)0.80580.810.15–4.45
            without52 (89.7%)14 (87.5%)
Deep venous thrombosis
            with4 (6.9%)2 (12.5%)0.46720.520.09–3.13
            without54 (93.1%)14 (87.5%)
GI involvement
            with14 (24.1%)2 (12.5%)0.94330.950.27–3.44
            without44 (75.9%)14 (87.5%)
Abbreviations: OR, odds ratio; CI, confidence interval.
Table 2. Genotype of rs1800629 for responders and non-responders in BS patients.
Table 2. Genotype of rs1800629 for responders and non-responders in BS patients.
GenotypesResponders (n = 58)Non-Responders
(n = 16)		p-ValueOR (95% CI)
GG (n = 59)50 (86.2%)9 (56.3%)0.008 *4.86 (1.41–16.76)
GA (n = 15)8 (13.8%)7 (43.7%)
Abbreviations: OR, odds ratio; CI, confidence interval; * statistically significant.
Table 3. Demographic features of BS patients and responses to all anti-TNFα drugs in responder and non-responder groups.
Table 3. Demographic features of BS patients and responses to all anti-TNFα drugs in responder and non-responder groups.
Total
(n = 74)		Responders
(n = 58)		Non-Responders
(n = 16)		p-Value
Demographics
         Female30 (40.5%)19 (32.8%)11 (68.8%)0.009 *
         Male44 (59.5%)39 (67.2%)5 (31.2%)
         Age43.1 ± 11.342.8 ± 12.345.6 ± 10.2
Anti-TNFα drugs
         Infliximab60 (81.8%)45 (77.6%)15 (93.75%)0.1439
         Adalimumab12 (16.2%)11 (19.0%)1 (6.25%)0.1957
         Certiluzumab Pergol1 (1.35%)1 (1.7%)0 (0.0%)0.3494
         Golimumab1 (1.35%)1 (1.7%)0 (0.0%)0.3494
* statistically significant.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Padula, M.C.; Padula, A.A.; D’Angelo, S.; Lascaro, N.; Radice, R.P.; Martelli, G.; Leccese, P. TNFα rs1800629 Polymorphism and Response to Anti-TNFα Treatment in Behçet Syndrome: Data from an Italian Cohort Study. J. Pers. Med. 2023, 13, 1347. https://doi.org/10.3390/jpm13091347

AMA Style

Padula MC, Padula AA, D’Angelo S, Lascaro N, Radice RP, Martelli G, Leccese P. TNFα rs1800629 Polymorphism and Response to Anti-TNFα Treatment in Behçet Syndrome: Data from an Italian Cohort Study. Journal of Personalized Medicine. 2023; 13(9):1347. https://doi.org/10.3390/jpm13091347

Chicago/Turabian Style

Padula, Maria Carmela, Angela Anna Padula, Salvatore D’Angelo, Nancy Lascaro, Rosa Paola Radice, Giuseppe Martelli, and Pietro Leccese. 2023. "TNFα rs1800629 Polymorphism and Response to Anti-TNFα Treatment in Behçet Syndrome: Data from an Italian Cohort Study" Journal of Personalized Medicine 13, no. 9: 1347. https://doi.org/10.3390/jpm13091347

APA Style

Padula, M. C., Padula, A. A., D’Angelo, S., Lascaro, N., Radice, R. P., Martelli, G., & Leccese, P. (2023). TNFα rs1800629 Polymorphism and Response to Anti-TNFα Treatment in Behçet Syndrome: Data from an Italian Cohort Study. Journal of Personalized Medicine, 13(9), 1347. https://doi.org/10.3390/jpm13091347

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop