Tu110 - Utilizing High-dimensionality Mass Cytometry to Investigate Peripheral Immune Landscape in Progressive Interstitial Lung Disease in Systemic Sclerosis with Potential Theragnostic Applications

Abstract Text: Background
Systemic sclerosis (SSc) is a heterogenous disease characterised by autoimmunity, vasculopathy, and fibrosis. The leading causes of mortality in SSc is interstitial lung disease (ILD). This study aimed to characterize the peripheral immunome associated with progressive ILD in SSc.

Methods
Peripheral blood mononuclear cells were collected from SSc patients (n=10 progressive ILD, n=10 stable ILD). Single-cell proteomic data was acquired using mass cytometry with 77 unique markers; data was analysed using the Extended Polydimensional Immunome Characterization platform, an unsupervised machine learning algorithm. Using the identified cell subset frequencies, we built multiple logistic regression models to classify progressive ILD. Correlation network between nodes was constructed by performing pairwise correlations between each node pair.

Results
Dysregulated peripheral immunome was observed in progressive ILD in SSc. CCR6+ naïve B cells and immature B cells were increased in progressive ILD, while CLA+HLA-DR+ lineage negative cells expressing pro-fibrotic cytokines and CD8 T cell subsets expressing immune checkpoints were decreased in progressive ILD, compared to stable ILD. Using the identified immune cell subsets to evaluate progressive and stable ILD, receiver operating characteristic curve analyses showed an area under the curve of 0.91. Immune cell network from stable ILD showed more negatively connected edges and lower modularity compared to network from progressive ILD.

Conclusions
We identified dysregulated immune cell subsets and network in progressive ILD. Future studies to investigate their functional significance would shed light into the disease pathogenesis. The identified immune signatures could potentially be used as theragnostic tools to evaluate ILD status in SSc.