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While methotrexate is currently the first-line drug given for juvenile idiopathic arthritis, its effectiveness and tolerability are in fact limited in some patients. With the development of stratified medicine their top priority, Dr Stephanie Shoop-Worrall PhD, Professor Lucy Wedderburn and the CLUSTER consortium set out to discover whether machine learning could transform treatment pathways for children with this debilitating condition.
Children with a diagnosis of juvenile idiopathic arthritis (JIA) often face a long journey to get the right medications, leading to unnecessary pain, uncontrolled symptoms and risking joint damage. Currently, methotrexate is the first-line drug given for JIA, but it is only effective or tolerated in just under half of the children who are treated with it.
In a complex and varied disease like JIA, what does ‘effective’ mean? With signs and symptoms ranging from swollen joints to skin rashes, debilitating pain to symptomless, sight-risking eye inflammation, what does ‘response to treatment’ look like? And how can it mean the same thing for every child?
Researchers have started to look beyond a response versus non-response paradigm and are seeking to understand whether different elements of disease change in different ways following a new treatment, and so require different approaches to disease management. These kinds of investigations are only made possible using new methods of machine learning.
By studying large data sets from thousands of children with JIA, it is hoped that machine learning can facilitate stratified treatment, ultimately reducing pain and suffering in children, aiding clinicians with treatment pathway decisions, and saving money for the NHS and other health systems around the world.
The goal of stratified medicine
Professor Wedderburn is a professor of paediatric rheumatology based at Great Ormond Street Hospital and University College London (UCL). Mixing clinical and research work, she leads the large UK consortium CLUSTER – a multidisciplinary group of researchers working in JIA who have come together to find ways to improve treatment.
She describes the approach in paediatric rheumatology as ‘holistic’, bringing expertise from psychology, nursing, physiotherapy, occupational therapy and many more specialities.
‘Paediatric rheumatology is an incredibly collaborative field. We do a lot of work with the patients and families. We’re relatively small compared to the adult RA [rheumatoid arthritis] research community, but we’re very linked up, and I think that is a huge benefit,’ Professor Wedderburn says.
However, Professor Wedderburn remains ‘frustrated’ by drug treatments available for children with JIA. Despite the increase in the number of medications available, the drugs are licensed without guidance on when and how to use them in children. What’s more, with a rare and complex disease such as JIA, a child’s predicted response to drugs such as methotrexate varies significantly across different disease features.
As such, the researchers want to move away from a one-size-fits-all system and provide a scientific and biological basis for medication pathways based on the predicted outcomes shown in their data.
Professor Wedderburn says: ‘That’s really what CLUSTER is all about. How can we move to a point where you have true precision medicine or stratified 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. This consortium brought together childhood data from huge cohorts – absolutely fabulous for such a rare disease.’
Machine learning and methotrexate
Professor Wedderburn’s colleague and the lead author of their recent publication, Dr Stephanie Shoop-Worrall PhD, is a research fellow at the University of Manchester. She specialises in epidemiology and data science and analyses the CLUSTER data using machine learning.
CLUSTER represents about 5,000 families, which make up approximately half the number of cases of JIA in the UK. Looking at four cohorts of these children who began their treatment before January 2018, Dr Shoop-Worrall and the team were able to find patterns in treatment outcomes which determined how effective methotrexate was on different elements of JIA for groups of children.
‘We’ve got this window of opportunity; we need to treat early on to get better outcomes,’ Dr Shoop-Worrall says. ‘This trial-and-error approach [to medicines] is just wasting people’s time and could lead to much worse outcomes, prolonging chronic pain in children, the potential for disability in the longer term, and massively impacting their lives. So, we really do need to get the right drug first.’
When a child is diagnosed with JIA, they will be diagnosed with one of seven types of the disease. Some diagnoses mirror those seen in adults, while others are unique to children. Describing the current approach as ‘a bit contentious’, Dr Shoop-Worrall has shown through machine learning that the traditional diagnosis groups do not necessarily predict how effective methotrexate will be.
The research team analysed data from when the children started taking methotrexate and followed them over the next year, looking at four outcomes: active joint count, both clinician and patient progress scores, and blood biomarker data. The aim was to capture a spectrum of objective clinical and patient-reported measurements to give an overview of the disease and determine which parts of JIA might be affected by methotrexate.
Methotrexate response groups
Dr Shoop-Worrall identified six different groups of children, each describing a different response to methotrexate. The first group, known as the ‘fast responders’, comprised about one in 10 children, all of whose disease responded well to the medication. By six months, this group had no swollen joints, normal bloodwork, and both looked better clinically and felt completely better. The next group, termed ‘slow improvers’, was made up of children who took about a year to achieve the same outcome.
Two more groups showed only partial improvement of JIA with methotrexate. In approximately 8% of children, their joints got better, and the children felt better, but the clinicians reported evidence of JIA, and the children remained on methotrexate. Another 13% of children showed partial improvement and looked better clinically in terms of having no swollen joints and normal blood work, but their symptoms, including pain, persisted.
A small group of about 7% of children, known as the ‘improve-relapse’ group, showed improved symptoms after six months, followed by a relapse. And in the final group of about 44% of children, methotrexate did not impact most of their disease. Small improvements in swollen joints were observed in some cases as the drug is designed to tackle inflammation, but the overall clinical picture did not improve.
Dr Shoop-Worrall says: ‘Once we’d found the clusters, we looked to see if existing subtypes of JIA match up with the patterns. And the answer is no.’
Professor Wedderburn adds: ‘It’s a rather depressing message for families to discover that the name of the condition doesn’t help us know whether they’re going to get better on methotrexate or what the next drug should be. It really isn’t a stratifier. It’s just a label based on what we see in the first few months in the clinic – a nice descriptor.’
Their findings also challenge the traditional binary classification of patients into ‘responders’ and ‘non-responders’ seen in standard clinical trials. For Professor Wedderburn, the groups described by machine learning resonate strongly with what she sees in clinic. She adds: ‘[It’s] really important to get that message across to our community that just dichotomising response doesn’t bring the real lived experience out the way this dissecting of the response can start to do.’
Stratified medicine and achieving remission
Machine learning has opened the door to the possibility of predicting which aspects of a child’s disease would be helped by methotrexate and which children should start other therapies either alongside, or instead of, methotrexate as first line.
‘We want to get kids into remission quickly; that’s the overall aim of stratified medicine in this disease,’ Dr Shoop-Worrall says. ‘This paper is the first step towards that. If we can figure out who should be on this drug and what kind of response they’ll get, that really pushes forward the aim of stratified treatment.’
The next steps will see the researchers bringing more biological data, such as gene expression and protein data, into their analyses and integrating their findings across different disciplines. They will also investigate the impact of the sociological and psychological elements of the illness, in both cases, working with global cohorts of JIA patients.
Dr Shoop-Worrall concludes: ‘Being able to get a panel of biomarkers or a prediction model that we can integrate into practice to say, right, okay, now we know the different types of response, and this is exactly the group you fit into. That would be a huge step forward for drug selection right from diagnosis.’
The researchers believe machine learning will 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.
