Can molecular profiling predict the future in JIA?

Molecular signature characterisation of different inflammatory phenotypes of systemic juvenile idiopathic arthritis

Objectives

The International League of Associations for Rheumatology classification criteria define systemic juvenile idiopathic arthritis (SJIA) by the presence of fever, rash and chronic arthritis. Recent initiatives to revise current criteria recognise that a lack of arthritis complicates making the diagnosis early, while later a subgroup of patients develops aggressive joint disease. The proposed biphasic model of SJIA also implies a ‘window of opportunity’ to abrogate the development of chronic arthritis. We aimed to identify novel SJIA biomarkers during different disease phases.

Methods

Children with active SJIA were subgrouped clinically as systemic autoinflammatory disease with fever (SJIAsyst) or polyarticular disease (SJIApoly). A discovery cohort of n=10 patients per SJIA group, plus n=10 with infection, was subjected to unbiased label-free liquid chromatography mass spectrometry (LC-MS/MS) and immunoassay screens. In a separate verification cohort (SJIAsyst, n=45; SJIApoly, n=29; infection, n=32), candidate biomarkers were measured by multiple reaction monitoring MS (MRM-MS) and targeted immunoassays.

Results

Signatures differentiating the two phenotypes of SJIA could be identified. LC-MS/MS in the discovery cohort differentiated SJIAsyst from SJIApoly well, but less effectively from infection. Targeted MRM verified the discovery data and, combined with targeted immunoassays, correctly identified 91% (SJIAsyst vs SJIApoly) and 77% (SJIAsyst vs infection) of all cases.

Conclusions

Molecular signatures differentiating two phenotypes of SJIA were identified suggesting shifts in underlying immunological processes in this biphasic disease. Biomarker signatures separating SJIA in its initial autoinflammatory phase from the main differential diagnosis (ie, infection) could aid early-stage diagnostic decisions, while markers of a phenotype switch could inform treat-to-target strategies.

The biologic basis of clinical heterogeneity in juvenile idiopathic arthritis

Objective

Childhood arthritis encompasses a heterogeneous family of diseases. Significant variation in clinical presentation remains despite consensus-driven diagnostic classifications. Developments in data analysis provide powerful tools for interrogating large heterogeneous data sets. We report a novel approach to integrating biologic and clinical data toward a new classification for childhood arthritis, using computational biology for data-driven pattern recognition.

Methods

Probabilistic principal components analysis was used to transform a large set of data into 4 interpretable indicators or composite variables on which patients were grouped by cluster analysis. Sensitivity analysis was conducted to determine key variables in determining indicators and cluster assignment. Results were validated against an independent validation cohort.

Results

Meaningful biologic and clinical characteristics, including levels of proinflammatory cytokines and measures of disease activity, defined axes/indicators that identified homogeneous patient subgroups by cluster analysis. The new patient classifications resolved major differences between patient subpopulations better than International League of Associations for Rheumatology subtypes. Fourteen variables were identified by sensitivity analysis to crucially determine indicators and clusters. This new schema was conserved in an independent validation cohort.

Conclusion

Data-driven unsupervised machine learning is a powerful approach for interrogating clinical and biologic data toward disease classification, providing insight into the biology underlying clinical heterogeneity in childhood arthritis. Our analytical framework enabled the recovery of unique patterns from small cohorts and addresses a major challenge, patient numbers, in studying rare diseases.