Treg Fitness As Biomarker For Disease Activity To Predict Treatment Response In JIA

Specialised white blood cells, called regulatory T cells, or Tregs, normally police the immune system to maintain tolerance and stop other immune cells attacking the body’s own tissue.

In autoimmune childhood arthritis, Juvenile idiopathic arthritis (JIA), unfit Tregs lead to rouge immune cells attacking the child’s joints, causing pain, inflammation and ultimately joint destruction and disability. 30-50% of children and young people with JIA experience flare-ups without warning or obvious trigger, even on treatment.

In contrast, some remain on treatment and are exposed to drug-side effects even though they feel better, because clinicians cannot tell who will continue to do well, who might flare, or whether they are on the best treatment for them.

We can look at Treg ‘fitness’ through our Treg gene signature – which genes are switched on or off that are important for Tregs – and protein spectral flow cytometry – how different Tregs look like, called their phenotype.

Treg fitness is different in children/young people with actively inflamed joints (active JIA) compared to healthy controls and children/young people with JIA but no inflamed joints at the time of sample (inactive JIA), with Tregs from the most inactive patients being close to healthy control Tregs. We also see some signs of JIA medicines changing Treg fitness.

Thus, we believe that Treg fitness can monitor and predict disease activity and treatment response, as well as that specific medication change Treg fitness differently.

We will test Treg fitness by Treg gene signature and spectral flow cytometry on blood samples from more children and young people with active and inactive JIA, where we have data on the medication they are on and how well they are doing in the 3-6 months after the sample was taken, and age-appropriate controls.

For some children and young people with JIA we will also look at a second blood sample, to see how Treg fitness changes over time.

We will use mathematics and machine learning to build and test biomarker algorithms, along with hypothesis driven “off-the-shelf” and unbiased analysis, to define how Treg fitness can track and predict disease activity, the likelihood of flare, treatment response and/or whether remission will be maintained.

We will also take steps to make it easier to translate these tests into clinical practise by mathematically extracting Treg fitness information from non-purified cells and by developing a Treg fitness flow cytometry panel that can be run on machines available in clinical laboratories.

In addition, we will look closer at how Treg fitness is different in inflamed tissue, synovial fluid and blood from active JIA plus how different JIA medications change Treg fitness.

Therefore, with the proposed work, we will learn more about Treg fitness across JIA sites, disease activity and treatment spectrum.

We will define Treg fitness-based biomarker tests to track and predict treatment response and disease activity, that is who is likely to flare and who has achieved sustained remission and could taper off medication safely, so that ultimately more children with JIA may achieve long-term drug-free remission.