Defining epigenetic biomarkers in whole blood DNA samples to predict response to biological therapies in inflammatory bowel disease

Summary

Biological therapies (therapeutic monoclonal antibodies) are increasingly frequently used to induce and maintain remission in patients with inflammatory Bowel Disease (IBD). There are no specific tests available to assess whether a patient will respond to a specific therapeutic antibody for IBD. Treatment algorithms currently rely on a “trial and error” approach, and thus a proportion of patients run the risk of receiving ineffective therapy for many months, with associated risks. Further, these drugs are costly, and more targeted use would yield significant financial benefits to the health service. Our project investigates whether biomarkers (changes to the genetic material, DNA) within patient’s whole blood are able to predict if an individual will respond to treatment with either adalimumab and ustekinumab, both of which are licensed biological therapies in the management of Crohn’s disease (CD) and ulcerative colitis (UC). 

In the first stage of investigation, we will assess these markers in patients recruited into UK IBD Bioresource who have been documented to have responded to these treatments and compare the results with patients who did not respond from a cohort of patients for each drug. This will allow us to derive a panel of biomarkers that predict response or non-response to each drug, both in CD and in UC. The findings based on the IBD Bioresource patients will then be further validated in independent cohorts. We will utilise machine learning techniques on our cohorts to determine the predictive signature of response for the two treatments. Our algorithm will provide a point of care test with a precision medicine approach to deliver healthcare in a tailored manner which will result in a patient responding to treatment. 

Participation: The BioResource will provide samples from 400 participants from the IBD BioResource.

Organisation: This study is organised by Professor Jack Satsangi from the University of Oxford.