[Fellowship] Using core genes and pathways to stratify rheumatoid arthritis and predict outcomes in Rheumatoid Arthritis
Ente: Medical Research Council
Scadenza: 2026-08-30
Paese: GB
Descrizione
Rheumatoid arthritis is a long-term condition in which the immune system attacks the joints. It affects 1 in 100 people in the United Kingdom. This inflammation causes pain and, if uncontrolled, can lead to joint damage and disability. The earlier we can control the inflammation, the better the long-term outcomes. The development of biologic therapies means that we now have more options than ever before to treat rheumatoid arthritis. There are a number of types of these drugs, all affecting different immune pathways to try and reduce inflammation. We have no reliable way of predicting which type of biologic treatment will be most effective for an individual. Currently, selection is done by a process of trial and error with each new drug trialled for a period of time. During this time, if the drug does not work, a person may develop irreversible joint damage which could lead to permanent disability.
The aim of this study is to see whether genetic and protein markers, identified using a blood sample, can be used to predict whether a person with rheumatoid arthritis is likely to respond to a certain treatment and inform us about the likely severity of their disease. These are known as biomarkers. This project will analyse data collected in MATURA (Maximising Therapeutic Utility in Rheumatoid Arthritis), a nationwide consortium of academics, doctors and industry groups developing personalised approaches to treatment of rheumatoid arthritis.
When researching biomarkers of treatment response, it is really important to first establish what we consider a good response to be. One of the methods health professionals use in clinical practice is a scoring system known as the DAS-28 score. This score is made up of four components: number of tender joints, number of swollen joints, the patient global visual analogue score (a self-reported score from 0-100 of a patient's overall health) and CRP (C-reactive protein, a blood test which measures inflammation). Lots of factors can affect the tender joint count and patient global visual analogue score, such as presence of other joint conditions. This can result in a high DAS-28 score even though inflammation is well-controlled. As biologic drugs work by targeting inflammation, a switch in therapy would not provide additional benefit if there is no inflammation present. Research has shown that only "swollen joint count" and CRP are linked to levels of inflammation in the joints. For these reasons, a 2-component DAS-28 score would provide a better indication if these drugs are working.
Adherence to therapy refers to whether patients take their medication as prescribed. For lots of reasons, including side effects, forgetting and having to stop therapy due to infections or planned operations, a patient may not take their biologic treatment as prescribed. We know that this impacts on the likelihood of treatment response. However, none of the studies to date looking for genetic and protein predictors has tak
Rheumatoid arthritis (RA) is a multisystem disease characterised by dysregulation of immunological pathways leading to pain, progressive joint damage and disability. Biological therapies targeting different molecular pathways have revolutionised the treatment landscape of RA. However, each drug type fails to induce adequate control of inflammation in up to 40% of patients. There is an unmet need for biomarkers that can inform therapy selection to optimise prescribing the right treatments to the right patients. Identification of such biomarkers from biomarker panels using traditional approaches has been challenging because the number of variables (dimensionality) is large compared with the number of observations. I aim to overcome this problem by using cutting edge statistical and bioinformatic techniques to reduce dimensionality by identifying core genes and pathways that stratify RA by underlying mechanisms of disease.
Using the GENOSCOREs platform, preliminary work has identified putative core genes associated with plasma levels of proteins and risk of RA in UKBiobank. I will validate these findings in a large case control study of RA. I will test for association of trans-scores with radiographic outcome and with treatment response to TNFi. This will be followed by identification of proteins associated with response to TNF-inhibitors, from direct measurements using a proteomics platform.
This research could help to determine why up to 40% of patients do not respond to a b
Settori: School of Biological Sciences
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