Genetic and clinical markers for predicting treatment responsiveness in rheumatoid arthritis
Xin Wu1, Xiaobao Sheng2,3, Rong Sheng1, Hongjuan Lu1, Huji Xu1,4,5()
1. Department of Rheumatology and Immunology, Shanghai Changzheng Hospital, the Second Military Medical University, Shanghai 200003, China 2. School of Economics and Management, Tongji University, Shanghai 200092, China 3. The Third Research Institute of the Ministry of Public Security, Shanghai 200031, China 4. Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 100084, China 5. Peking-Tsinghua Center for Life Sciences, Tsinghua University, Beijing 100084, China
Although many drugs and therapeutic strategies have been developed for rheumatoid arthritis (RA) treatment, numerous patients with RA fail to respond to currently available agents. In this review, we provide an overview of the complexity of this autoimmune disease by showing the rapidly increasing number of genes associated with RA. We then systematically review various factors that have a predictive value (predictors) for the response to different drugs in RA treatment, especially recent advances. These predictors include but are certainly not limited to genetic variations, clinical factors, and demographic factors. However, no clinical application is currently available. This review also describes the challenges in treating patients with RA and the need for personalized medicine. At the end of this review, we discuss possible strategies to enhance the prediction of drug responsiveness in patients with RA.
Serine/threonine/dual specificity protein kinase, catalytic domain
37
IPR020635
Tyrosine-protein kinase, catalytic domain
37
IPR013106
Immunoglobulin V-set domain
34
IPR001811
Chemokine interleukin-8-like domain
30
IPR003598
Immunoglobulin subtype 2
25
Tab.1
NCBI Entrez ID
No. of PubMed abstracts
HGNC gene symbol
Note
7124
574
TNF
Cytokine
3123
173
HLA-DRB1
Immunoglobulin
3569
157
IL6
Cytokine
3605
68
IL17A
Cytokine
26191
64
PTPN22
Kinase
7422
61
VEGFA
Cytokine
920
56
CD4
Immunoglobulin
4790
53
NFKB1
Transcription factor
8797
50
TNFRSF10A
TNF receptor
3586
47
IL10
Cytokine
Tab.2
NCBI Entrez ID
HGNC gene symbol
Associated variations
DMARD/TNF-blocking agent
Note (reference and statistics)
1544
CYP1A2
SNP
Leflunomide
CYP1A2*1F allele is associated with leflunomide toxicity [17]; CC vs. A allele: OR= 9.7 (95% CI= 2.276–41.403), P = 0.002
s1557
CYP2C19
SNP
Leflunomide
CYP2C19*2 allele influences leflunomide metabolite concentrations that are associated with treatment responses but not with leflunomide-induced toxicity [18]; leflunomide metabolite concentration was ~71% higher in carriers in the CYP2C19*2 allele than in noncarriers
2212
FCGR2A
Infliximab
Infliximab treatment in patients with RA is influenced by the FCGR2A and FCGR3A genotypes; this effect is observed at different times during follow-up (6 and 30 weeks, respectively) [19]; in patients with low-affinity homozygotes, FCGR2A and FCGR3A alleles could achieve better responses to infliximab (P<0.05 for both cases)
2214
FCGR3A
Infliximab
Stated in the comment above and also in the reference [19]
SNP rs396991
Infliximab
The wild-type allele is associated with better treatment responses, and the strength of the response depends on the type and stage of disease [20]; patients with homozygous V158F polymorphism achieved better response to infliximab (P<0.05)
3135
HLA-G
Indel
Methotrexate
A –14bp deletion in the 3′-unstranslated region (3′ UTR) of HLA-G was clinically advantageous for methotrexate treatment; however, the results were controversial among studies [21–23]; for example, one study showed that the –14/–14 bp deletion was enriched in the responder group (OR= 2.46 with 95% CI= 1.26–4.84, P = 0.009) [21], whereas another study reported the lack of significant results [23]
3569
IL6
SNP; −174
Rituximab
−174 CC genotype is associated with a lack of response to rituximab [24] (OR= 2.83; 95% CI= 1.10–7.27; P = 0.031)
3586
IL10
Etanercept
Promoter polymorphisms in IL10 are useful in predicting clinical response to etanercept treatment [25]
4524
MTHFR
SNP; C677T and A1298C
Methotrexate
C667T polymorphism is associated with responses to methotrexate; however, controversial results were recorded among different populations [26–29]
5243
ABCB1
SNP; C3435T
Methotrexate
More nonresponders to methotrexate were found in patients with the TT allele than the CC allele [30] (OR= 8.78, P = 0.038)
7124
TNF
SNP; −308
Adalimumab
Promoter SNP −308 is associated with treatment responses to adalimumab [31]; 88.2% of G/G versus 68.4% of G/A for the −308 polymorphism were responders (P = 0.05)
SNP; −308
Etanercept
Promoter SNP −308 is not associated with treatment responses to etanercept [32]
SNP; −238 and+489
Methotrexate
Promoter SNP −238 GG homozygosity is associated with severity and unresponsiveness, but the coding+489 polymorphism is not; the −238 AG genotype is absent in severe-unresponsive RA but present in mild-responsive RA subjects; thus, −238 GG homozygosity is associated with severity and unresponsiveness [33]
SNP; −308 and −238
Infliximab
Promoter SNP −238 is associated with treatment responses to infliximab, but the −308 SNP is not; A allele carrier state was significantly lower among responders (OR 0.344, 95% CI= 0.152–0.779, P = 0.01) [34]
7133
TNFRSF1B
SNP; M296R
Infliximab
The M196R SNP leads to lower responsiveness to infliximab [35]
7298
TYMS
Indel
Methotrexate
3′ UTR indel is associated with responses to methotrexate [27]; in patients with RA with the CC genotype, the OR (95% CI) for the risk of toxicity was 3.8 (2.29–6.33) for the CT genotype and 4.7 (2.40–9.04) for the TT genotype)
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