Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity.

OBJECTIVE

The objective of this study was to stratify patients with MCTD, based on their immunophenotype.

METHODS

We analysed the immunophenotype and transcriptome of 24 immune cell subsets [from patients with MCTD, SLE, idiopathic inflammatory myopathy (IIM) and SSc] from our functional genome database, ImmuNexUT (https://www.immunexut.org/). MCTD patients were stratified by employing machine-learning models, including Random Forest, trained by immunophenotyping data from SLE, IIM and SSc patients. The transcriptomes were analysed with gene set variation analysis (GSVA), and the clinical features of the MCTD subgroups were compared.

RESULTS

This study included 215 patients, including 22 patients with MCTD. Machine-learning models, constructed to classify SLE, IIM and SSc patients, based on immunophenotyping, were applied to MCTD patients, resulting in 16 patients being classified as having an SLEimmunophenotype and 6 as having a non-SLE immunophenotype. Among the MCTD patients, patients with the SLE immunophenotype had higher proportions of Th1 cells {2.85% [interquartile range (IQR) 1.54-3.91] vs 1.33% (IQR 0.99-1.74) P = 0.027} and plasmablasts [6.35% (IQR 4.17-17.49) vs 2.00% (IQR 1.20-2.80) P = 0.010]. Notably, the number of SLE-related symptoms was higher in patients with the SLE immunophenotype [2.0 (IQR 1.0-2.0) vs 1.0 (IQR 1.0-1.0) P = 0.038]. Moreover, the GSVA scores of interferon-α and -γ responses were significantly higher in patients with the SLE immunophenotype in central memory CD8+ T cells, while hedgehog signalling was higher in patients with the non-SLE immunophenotype, in five-cell subsets.

CONCLUSION

This study describes the stratification of MCTD patients, based on immunophenotyping, suggesting the presence of distinct immunological processes behind the clinical subtypes of MCTD.