Children with juvenile idiopathic arthritis (JIA) are more likely to respond to methotrexate therapy if they have higher baseline expression of interferon (IFN)-stimulated genes, UK research finds.
Methotrexate has been the first-line treatment for most forms of non-systemic JIA, however only half of patients adequately respond to the therapy, which also has unpleasant side effects, the study authors wrote in the journal Annals of the Rheumatic Diseases.
Non-responders to methotrexate would likely benefit from earlier intervention with biologic agents if they could be reliably identified, however, there were no validated biomarker tests to predict which children needed early escalation.
In this latest study from the UK’s CLUSTER Consortium, researchers identified and validated gene expression biomarkers in the peripheral blood of 97 children with JIA before they started methotrexate treatment and examined their response after six months of therapy.
They found genes in the IFN alpha (type-I) and gamma (type-II) response pathways were associated with response to methotrexate at six months.
The findings were replicated in two validation cohorts of JIA, the researchers said, and the association was confirmed using an independent score of five IFN-driven genes (IGS5).
Importantly, they found the association between IFN-driven pathways and response differed between children with JIA and a cohort of adults with rheumatoid arthritis (RA), emphasising the need for age-specific research in inflammatory arthritis.
A precision-based approach in JIA
‘Given that parents and patients report uncertainty about treatment response as a major burden, and that methotrexate intolerance is very prevalent in JIA, the clinical value of a biomarker measured in a small blood sample that could predict response to methotrexate and aid clinical choices of medication would be high,’ they wrote.
‘Our study provides proof of principle that carefully designed analyses can yield hope for a more precision-based approach to treatment in the future for children and families living with arthritis.’
The CLUSTER Consortium, which includes researchers from Great Ormond Street Hospital (GOSH) and University College London (UCL), is a large multidisciplinary group of researchers working in JIA who have come together to find ways to improve treatment.
CLUSTER lead investigator Professor Lucy Wedderburn, consultant in paediatric rheumatology at GOSH and UCL Great Ormond Street Institute of Child Health, explained it was difficult to choose the right treatment for a particular child with JIA.
‘Therefore, it is important to find indicators or ‘biomarkers’ that can tell the doctors and healthcare team which drug treatment is most likely to work for each child depending on their disease status,’ she said.
‘Our findings mean that in the future, a simple blood test could help doctors know early if methotrexate will work. This means that children could get the right treatment faster, avoid side effects and stay healthier.’
Methotrexate and machine learning
In an interview with Hospital Healthcare Europe last year, Professor Wedderburn said CLUSTER, which represented about 5,000 families, was about moving to a point where there is true precision medicine.
‘Many of my patients are in these studies, if they’re willing, and most people want to be involved because we explain it’s the way to get real-world data,’ she said.
‘This consortium brought together childhood data from huge cohorts – absolutely fabulous for such a rare disease.’
Last year the team reported results from a study of four cohorts of children who began their methotrexate treatment before January 2018, which used machine learning to find patterns in treatment outcomes and determine how effective methotrexate was on different elements of JIA.
The researchers noted that machine learning would play an essential role in improving understanding of treatment outcomes, minimising children’s exposure to unnecessary treatments, optimising treatment selection and ultimately improving the quality of care for children with JIA.
