Publication: IDENTIFICATION OF PREDICTIVE MARKERS FOR TREATMENT RESPONSE OF TOFACITINIB AMONG RHEUMATOID ARTHRITIS PATIENTS
| dc.contributor.author | SIVAKAMI JANAHIRAMAN | |
| dc.date.accessioned | 2025-11-08T03:20:39Z | |
| dc.date.available | 2025-11-08T03:20:39Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Rheumatoid arthritis (RA) is a chronic autoimmune condition manifested by synovial inflammation and joint destruction. Tofacitinib was introduced for RA patients who have failed treatment with conventional synthetic or biologic disease-modifying antirheumatic drug (cs/bDMARDs). However, the interindividual variations in response may have both clinical and genetic aetiology. Hence, precise risk stratification based on clinical and genetic predictive markers may aid in identifying patients who can reap the full benefit from treatment management. This project aimed to explore the clinical and genetic factors associated with response to tofacitinib in RA patients. A retrospective medical data collection and the very first comprehensive whole genome sequencing was performed. Two third of the patients have responded to tofacitinib therapy. There was an increased odd ratio for baseline C-reactive peptide level, anti-citrullinated peptide autoantibody negativity, absence of bone erosion and baseline biologics naive associated with tofacitinib responders. Genomic analysis revealed that five significant variants with p value less than 1x10-7 were identified for the outcome measure of tofacitinib response. The strongest evidence for association to good response were with rs12607965_T variant of RNF125 gene followed by rs12149039_C variant of CDH13 gene, rs13315685_C of SLC12A8 gene, rs5749279_A of EIF4ENIF1 gene and rs13418335_C variant of GCC2-AS1 gene. The SEMA3F, SSH1, RNF138, SLC12A8, ADA2, ABCC13, ITGA8 and SORT1 genes were replicated from previous studies and related to RA inflammatory and destructive process. Furthermore, other genetic variants associated with tofacitinib responders were identified, which have not been reported previously i.e., CDH13, PCNX2, FEZ2, XRCC5, ZNF736, ARHGEF12, SLC12A8, EIF4ENIF1, GRID1, DMAC1, DRG1, GCC2-AS1, LIMS1 and RNF125. Overall, the identified predictive markers hold the potential to a broader precision medicine framework for RA, enabling more accurate treatment stratification and fostering cost-effective treatment allocation. Nevertheless, these markers needed further validation in larger and independent cohort prior to transforming into RA management that will reduce healthcare cost due to ineffective treatments, improving quality of life for patients and enhancing overall therapeutic efficacy. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14377/37241 | |
| dc.language.iso | en | |
| dc.publisher | IMU University | |
| dc.subject | Rheumatoid Arthritis | |
| dc.subject | Tofacitinib | |
| dc.subject | Treatment Outcome | |
| dc.subject | Biomarkers | |
| dc.subject | Whole Genome | |
| dc.subject | Sequencing | |
| dc.subject | Genetic Variation | |
| dc.title | IDENTIFICATION OF PREDICTIVE MARKERS FOR TREATMENT RESPONSE OF TOFACITINIB AMONG RHEUMATOID ARTHRITIS PATIENTS | |
| dc.type | Thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | #PLACEHOLDER_PARENT_METADATA_VALUE# |