Biologics such as TNF antagonists are a new class of drugs that have greatly improved Rheumatoid Arthritis (RA) treatment. However, for unknown reasons, individual patients with RA respond to one of these drugs but not to others even those targeting the same molecule. Methods to predict response are sorely needed because these drugs are currently selected by trial and error, what is very inefficient and prejudicial for the patient and the healthcare system. Here, we have explored the discovery of protein biomarkers in serum from patients treated with infliximab, one of the major anti-TNF drugs. The study was based in a quantitative proteomics approach using 8-plex iTRAQ labeling. It combined depletion of the most abundant serum proteins, two-dimensional LC fractionation, protein identification and relative quantification with a hybrid Orbitrap mass spectrometer. This approach allowed the identification of 315 proteins of which 237 were confidently quantified with two or more peptides. The detection range covered up to 6 orders of magnitude including multiple proteins at the ng/mL level. A new set of putative biomarkers was identified comprising 14 proteins significantly more abundant in the non-responder patients. The differential proteins were enriched in apolipoproteins, components of the complement system and acute phase reactants. These results show the feasibility of this approach and provide a set of candidates for validation as biomarkers for the classification of RA patients before the beginning of treatment, so that anticipated non-responders could be treated with an alternative drug. Rheumatoid Arthritis (RA) treatment. However, for unknown reasons, individual 3 patients with RA respond to one of these drugs but not to others even those targeting the 4 same molecule. Methods to predict response are sorely needed because these drugs are 5 currently selected by trial and error, what is very inefficient and prejudicial for the 6 patient and the healthcare system. Here, we have explored the discovery of protein 7 biomarkers in serum from patients treated with infliximab, one of the major anti-TNF 8 drugs. The study was based in a quantitative proteomics approach using 8-plex iTRAQ 9 labeling. It combined depletion of the most abundant serum proteins, two-dimensional 10 LC fractionation, protein identification and relative quantification with a hybrid 11Orbitrap mass spectrometer. This approach allowed the identification of 315 proteins of 12 which 237 were confidently quantified with two or more peptides. The detection range 13 covered up to 6 orders of magnitude including multiple proteins at the ng/mL level. A 14 new set of putative biomarkers was identified comprising 14 proteins significantly more 15 abundant in the non-responder patients. The differential proteins were enriched in 16 apolipoproteins, components of the complement system and acute phase reactants. 17These results show the feasibility of this approach and provide a set of candidates for 18 validation as biomarkers for the class...
SLC8A3 was identified as a new risk locus for ACPA-positive RA. This study demonstrates the advantage of analysing relevant subsets of RA patients to identify new genetic risk variants.
Background:
Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA.
Methods:
We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses.
Results:
A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (
P
< 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (
P
= 0.0015) and infliximab (
P
= 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (
P
= 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (
P
= 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (
P
= 0.041).
Conclusions:
These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.
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