Machine learning identifies clusters of longitudinal autoantibody profiles predictive of systemic lupus erythematosus disease outcomes.

Objectives: A novel longitudinal clustering technique was applied to comprehensive autoantibody data from a large, well-characterised, multinational inception systemic lupus erythematosus (SLE) cohort to determine profiles predictive of clinical outcomes.
Methods: Demographic, clinical and serological data from 805 patients with SLE obtained within 15 months of diagnosis and at 3-year and 5-year follow-up were included. For each visit, sera were assessed for 29 antinuclear antibodies (ANA) immunofluorescence patterns and 20 autoantibodies. K-means clustering on principal component analysis-transformed longitudinal autoantibody profiles identified discrete phenotypic clusters. One-way analysis of variance compared cluster enrolment demographics and clinical outcomes at 10-year follow-up. Cox proportional hazards model estimated the HR for survival adjusting for age of disease onset.
Results: Cluster 1 (n=137, high frequency of anti-Smith, anti-U1RNP, AC-5 (large nuclear speckled pattern) and high ANA titres) had the highest cumulative disease activity and immunosuppressants/biologics use at year 10. Cluster 2 (n=376, low anti-double stranded DNA (dsDNA) and ANA titres) had the lowest disease activity, frequency of lupus nephritis and immunosuppressants/biologics use. Cluster 3 (n=80, highest frequency of all five antiphospholipid antibodies) had the highest frequency of seizures and hypocomplementaemia. Cluster 4 (n=212) also had high disease activity and was characterised by multiple autoantibody reactivity including to antihistone, anti-dsDNA, antiribosomal P, anti-Sjögren syndrome antigen A or Ro60, anti-Sjögren syndrome antigen B or La, anti-Ro52/Tripartite Motif Protein 21, antiproliferating cell nuclear antigen and anticentromere B). Clusters 1 (adjusted HR 2.60 (95% CI 1.12 to 6.05), p=0.03) and 3 (adjusted HR 2.87 (95% CI 1.22 to 6.74), p=0.02) had lower survival compared with cluster 2.
Conclusion: Four discrete SLE patient longitudinal autoantibody clusters were predictive of long-term disease activity, organ involvement, treatment requirements and mortality risk.
Competing Interests: Competing interests: MYC has received consulting fees from Janssen, AstraZeneca, Mallinckrodt Pharmaceuticals and MitogenDx. AEC has received consulting fees, speaking fees and/or honoraria from AstraZeneca, Bristol Myers Squibb and GlaxoSmithKline (<US$10 000 each) and research support from GlaxoSmithKline. MJF is Director of Mitogen Diagnostics (Calgary, Alberta, Canada) and a consultant to Werfen International (San Diego, California, USA; Barcelona, Spain), Aesku Group (Wendelsheim, Germany) and Alexion Canada (<US$10 000). CG has received consulting fees, speaking fees and/or honoraria from Eli Lilly, UCB, GlaxoSmithKline, Merck Serono and BMS (<US$10 000 each) and grants from UCB. Grants from UCB were not to CG but to Sandwell and West Birmingham Hospitals NHS Trust. DDG received consulting fees, speaking fees and/or honoraria from GlaxoSmithKline (<US$10 000). INB has received consulting fees, speaking fees and/or honoraria from Eli Lilly, UCB, Roche, Merck Serono, MedImmune (<US$10 000 each) and grants from UCB, Genzyme Sanofi and GlaxoSmithKline. EMG has paid consultation with investment analysts Guidepoint Global Gerson Lerman Group. KK has received grants from UCB, Human Genome Sciences/GlaxoSmithKline, Takeda, Ablynx, Bristol Myers Squibb, Pfizer and Kyowa Hakko Kirin, and has received consulting fees from Exagen Diagnostics, Genentech, Eli Lilly, Bristol Myers Squibb and Anthera (<US$10 000 each). KHC has consulted for or collaborated on research projects with Janssen, GlaxoSmithKline, Gilead, Exagen Diagnostics, Lilly, Merck, AstraZeneca, Amgen and Neutrolis (<US$10 000 each). The remainder of the authors have no disclosures.
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