Genetic Polymorphisms Underlying Treatment Response Variations in RA - Rheumatology Advisor

Genetic Polymorphisms Underlying Treatment Response Variations in RA

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  • The genetic and environmental associations of rheumatoid arthritis (RA) help to explain the variation in treatment response from one patient to the next [1] and offers an opportunity to modify individual risk factors, and thereby influence patient outcomes.

    Genetic Polymorphisms and Rheumatoid Arthritis

    The genetic and environmental associations of rheumatoid arthritis (RA) help to explain the variation in treatment response from one patient to the next [1] and offers an opportunity to modify individual risk factors, and thereby influence patient outcomes.

  • The clustering of RA among family members suggests a strong genetic link, supported by the identification of specific alleles within defined loci in the human leukocyte antigen (HLA) system that are associated with increased risk for the development of anticitrullinated protein antibody-positive and anticitrullinated protein antibody-negative RA. [3]

    Specific Human Leukocyte Antigen Alleles Associated With RA

    The clustering of RA among family members suggests a strong genetic link, supported by the identification of specific alleles within defined loci in the human leukocyte antigen (HLA) system that are associated with increased risk for the development of anticitrullinated protein antibody-positive and anticitrullinated protein antibody-negative RA. [3]

  • Family history is one of the strongest risk factors for RA, conferring a twofold to fourfold increased risk among first-degree relatives. [2] The heritability of RA has been estimated at approximately 60%. [2]

    Family History: A Strong Determinant of RA Risk

    Family history is one of the strongest risk factors for RA, conferring a twofold to fourfold increased risk among first-degree relatives. [2] The heritability of RA has been estimated at approximately 60%. [2]

  • The presence of the shared epitope alleles at HLA-DRB1 and detectable levels of RA-related autoantibodies (including rheumatoid factor and anticyclic citrullinated peptide antibodies) in the serum prior to symptom onset elevates the risk for RA. [2]

    Shared Epitope Alleles and RA-Related Autoantibodies

    The presence of the shared epitope alleles at HLA-DRB1 and detectable levels of RA-related autoantibodies (including rheumatoid factor and anticyclic citrullinated peptide antibodies) in the serum prior to symptom onset elevates the risk for RA. [2]

  • The contribution of the HLA gene alone to RA heritability has been estimated to be 11% to 37%. [1] Other genes, such as PTPN22, STAT4, CTLA4, TRAF1, PADI4, IRF5, FCRL3, TNFIP3, TNF-α, miRNAs, CD28, CD40, and TYK2 have been associated with susceptibility to, severity of, activity of, and treatment response to RA. [1]

    Human Leukocyte Antigen and the Genetic Predisposition to Developing RA

    The contribution of the HLA gene alone to RA heritability has been estimated to be 11% to 37%. [1] Other genes, such as PTPN22, STAT4, CTLA4, TRAF1, PADI4, IRF5, FCRL3, TNFIP3, TNF-α, miRNAs, CD28, CD40, and TYK2 have been associated with susceptibility to, severity of, activity of, and treatment response to RA. [1]

  • Nongenetic biomarkers, such as gene-expression signatures, can show very strong correlation with clinical response to drug treatment, and combining genetic and nongenetic measurements is a promising path forward in the effort to develop predictive biomarkers. This may provide the best predictive signature of treatment outcome.[6]

    The Use of Nongenetic Biomarkers in RA

    Nongenetic biomarkers, such as gene-expression signatures, can show very strong correlation with clinical response to drug treatment, and combining genetic and nongenetic measurements is a promising path forward in the effort to develop predictive biomarkers. This may provide the best predictive signature of treatment outcome.[6]

  • The genetic association of RA can help explain the variation in treatment response from one patient to the next. Despite highly effective DMARDs and a treat-to-target strategy, not all patients respond optimally. Many continue to experience pain, disability, and joint destruction, even after treatment with the widely used and generally effective methotrexate and targeted biologic agents. [4]

    How Genetics Can Help Explain Variability in Treatment Responses

    The genetic association of RA can help explain the variation in treatment response from one patient to the next. Despite highly effective DMARDs and a treat-to-target strategy, not all patients respond optimally. Many continue to experience pain, disability, and joint destruction, even after treatment with the widely used and generally effective methotrexate and targeted biologic agents. [4]

  • Single nucleotide polymorphism has been identified as predictive of methotrexate response; nonresponse was associated with genetic polymorphism among SLC22A11 and ABCC1 carriers. [5] Similar variations in response associated with genetic polymorphism have been reported for other RA therapies, including variation in toxicity to azathioprine, allopurinol hypersensitivity, and variability of tacrolimus pharmacokinetics.

    Genetic Polymorphisms Predictive of Treatment Response

    Single nucleotide polymorphism has been identified as predictive of methotrexate response; nonresponse was associated with genetic polymorphism among SLC22A11 and ABCC1 carriers. [5] Similar variations in response associated with genetic polymorphism have been reported for other RA therapies, including variation in toxicity to azathioprine, allopurinol hypersensitivity, and variability of tacrolimus pharmacokinetics.

  • Personalized Medicine and Genetic Testing to Predict Treatment Response

    Personalized Medicine and Genetic Testing to Predict Treatment Response

    The use of genetic polymorphism as a predictive tool in clinical practice offers an important strategy to identify those who are likely to benefit from treatment. It has important economic and health outcomes implications as well, such as avoiding exposure to unnecessary adverse events or expensive treatment in those who are unlikely to respond. Indeed, variability in response to expensive anti-TNF-α agents has prompted efforts to identify biomarkers that are predictive of response. [6]

  • The potential of germ-line genetic variation as a biomarker of treatment response offers distinct advantages over conventional biomarkers; germ-line genetic variation is stable throughout a person’s lifespan such that genetic variants can be measured well ahead of clinical need using relatively inexpensive analytic assays. [6]

    Are Biomarkers of Treatment Response the Future of RA Treatment?

    The potential of germ-line genetic variation as a biomarker of treatment response offers distinct advantages over conventional biomarkers; germ-line genetic variation is stable throughout a person’s lifespan such that genetic variants can be measured well ahead of clinical need using relatively inexpensive analytic assays. [6]

Compelling evidence suggests a strong genetic contribution to the risk of developing rheumatoid arthritis (RA). [1]  A personalized risk assessment and patient education may encourage modification of risk factors to improve outcomes among individuals with a genetic predisposition to RA.  Genetic polymorphisms have been used to explain the variation in treatment responses in RA. [1] Genotyping patients according to their genetic markers may have important clinical application in pre-treatment predictions about treatment outcomes. 

References

  1. Felson DT, Klareskog L. The genetics of rheumatoid arthritis: new insights and implications. JAMA. 2015;313(16):1623-1624
  2. Frisell T, Hellgren K, Alfredsson L, Raychaudhuri S, Klareskog L, Askling J. Familial aggregation of arthritis-related diseases in seropositive and seronegative rheumatoid arthritis: a register-based case-control study in Sweden. Ann Rheum Dis. 2016;75(1):183-189.
  3. Rantapää-Dahlqvist S, de Jong BA, Berglin E, et al. Antibodies against cyclic citrullinated peptide and IgA rheumatoid factor predict the development of rheumatoid arthritis. Arthritis Rheum. 2003;48(10):2741-2749.
  4. Jiang X, Frisell T, Askling J, et al. To what extent is the familial risk of rheumatoid arthritis explained by established rheumatoid arthritis risk factors? Arthritis Rheumatol. 2015;67(2):352-362.
  5. Lima A, Bernardes M, Azevedo R, Medeiros R, Seabra V. Pharmacogenomics of methotrexate membrane transport pathway: can clinical response to methotrexate in rheumatoid arthritis be predicted? Int J Mol Sci. 2015;16(6):13760-13780.
  6. Maranville JC, Di Rienzo A. Combining genetic and nongenetic biomarkers to realize the promise of pharmacogenomics for inflammatory diseases. Pharmacogenomics. 2014;15(15):1931-1940.
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