Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients

Abstract

Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients.3

Personalized medicine in rheumatology

“…Genetic/epigenetic predisposition is probably one of the most important factors that play a role in response to the efficacy and toxicity of the drug. These biomarkers may be useful in daily practice because they do not vary with time and analysis can be carried out using samples derived from patients’ blood [18]. In addition, the knowledge about the molecular basis of the disease affects the clinical strategies, which are no longer a linear process, and the integrated system combining molecular and pharmacogenetic data as well as clinical information from one patient in the system, called the “knowledge management system”.…”

“…Although several SNPs have been detected to be associated with drug response in patients with RA, the majority of these findings are still inconclusive and inconsistent as well as limited to known genes involved in the DMARDs’ and biologic agents’ cellular pathways [21, 23]. Several factors may contribute to the inconsistency: size of samples (usually too small, n < 1000), number of SNPs in drug target genes, differences in ethnic populations, population stratification, genetic background for tested populations, differences in clinical characteristics of patients, differences in disease stages, previous drug history, and finally differences in study design [18, 23]. …”

“…GWAS scan hundreds of thousands or even millions of polymorphisms across the whole genome per individual; they have proven to be a powerful hypothesis-free method to identify common disease-associated polymorphisms that are present in the general population [18, 25]. The success of GWAS has opened a wide new perspective for exploration and highlighted the complicated genomic architecture susceptibility [19].…”

“…The success of GWAS has opened a wide new perspective for exploration and highlighted the complicated genomic architecture susceptibility [19]. As the GWAS do not identify the association between a gene and phenotype of the disease, findings from GWAS as well as the biological and clinical interactions between the specific loci and diseases should be further explored by traditional candidate gene studies such as allelic discrimination by TaqMan real-time PCR [18, 25]. In addition to GWAS, new techniques such as next-generation sequencing (NGS) are not limited to gene chips and thereby enable identification of common and rare variants determining the response to drugs or adverse drugs reactions [18].…”

“…As the GWAS do not identify the association between a gene and phenotype of the disease, findings from GWAS as well as the biological and clinical interactions between the specific loci and diseases should be further explored by traditional candidate gene studies such as allelic discrimination by TaqMan real-time PCR [18, 25]. In addition to GWAS, new techniques such as next-generation sequencing (NGS) are not limited to gene chips and thereby enable identification of common and rare variants determining the response to drugs or adverse drugs reactions [18]. NGS technology, with high sensitivity and high-bandwidth properties, results in the provision of more convincing data, which cannot be obtained using GWAS.…”