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Predicting response to anti-TNF therapy based on serum cytokine and gene profile

52 months
Approved budget:
Professor Lisa Stamp
Health issue:
Proposal type:
Lay summary
The introduction of ""biological"" disease modifying anti-rheumatic drugs (bDMARDs) has been a major advance in the treatment of rheumatoid arthritis (RA). When conventional therapy fails, bDMARD therapy can be life changing. bDMARDs specifically target key components in the pathways of inflammation causing RA, such as the pro-inflammatory cytokine tumour necrosis factor (TNF). Consequently, knowledge of the inflammatory pathways active in individual patients is required for efficient targeting of bDMARDs. We have established a classification of joint synovial tissues, based on the expression of interleukin (IL) 17-A and CD21L genes, reflecting different inflammatory states. Our objective is to determine if we can predict response to anti-TNF therapy based on this system and concentrations of IL-17-related cytokines. The ability to predict response to treatment will improve outcomes for patients with RA and provide cost savings by ensuring that those patients most likely to respond receive these highly effective but expensive drugs.