OP0231 MASS CYTOMETRY DATA RECLASSIFY SYSTEMIC AUTOIMMUNE DISEASE PATIENTS IN PHENOTYPICALLY DISTINCTIVE GROUPS

Background

Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSC), Sjögren’s syndrome (SJS), mixed connective tissue disease (MCTD), primary antiphospholipid syndrome (PAPS) and undifferentiated connective tissue disease (UCTD) are classified as systemic autoimmune diseases (SADs). They are diagnosed based on different clinical and laboratory criteria. Due to their high internal heterogeneity and overlapping symptoms, SADs are difficult to diagnose. Therefore, molecular and cellular-based studies need to be undertaken to precisely classify the patients. Mass cytometry is a single-cell proteomics technology that measures approximately 50 markers per cell, thus it is a suitable tool to perform deep-phenotyping studies in SADs.

Objectives

Explore differences and similarities between SADs and build reclassification framework using high-dimensional cytometry data.

Methods

The whole blood samples collected from 129 individuals, including patients and controls were stained with a 39-plex antibody panel and acquired in 9 batches on a CyTOF (HELIOS) instrument. Data were cleaned, and normalized for batch effects using semi-automated cytof analysis pipeline. Cell frequencies and median signal intensities (MSI) for each population were extracted using FlowSOM for mononuclear cells (PBMC) and Phenograph for granulocytes. Secretion of 44 cytokines and chemokines were analyzed using a multiplexed luminex assay. Diseases were compared by Kruskal-Wallis analysis and hierarchical clustering and reclassification was done using unsupervised k-means clustering. Cytokine analysis across clusters was performed using Kruskal-Wallis test.

Results

Differently expressed features were observed between patient groups, regarding frequency of classical monocytes, B and T cells subpopulations, mature and immature granulocytes and intensities of CD38, HLA-DR and CD95 across various populations. However, none of them were disease specific. K-means clustering identified four patient clusters, which were composed by a mixture of different diagnosis. Cluster C1 was characterized by increased levels of circulating cells from PBMC compartment, and lower activation of different populations of the T cell compartment. It presented lower frequency in multiple granulocyte populations and the highest expression of CD95 and CD38. This cluster was also associated with antimalarial and steroid treatment. Clusters C1 and C2 were exactly opposite to each other, cluster C3 was characterized by intermediate features between C1 and C2 and cluster C4 could be considered as undifferentiated, mixed group. Higher production of TNFα, IL-10 and IP-10 were found in patients from C1 compared to C2, suggesting more active phenotype in C1 and physiological one in C2. The cytokine levels were independent of the treatment.

Conclusion

We constructed a patient reclassification framework using cell frequencies and expression levels of functional markers. To our knowledge this is the first time when 7 different SADs were compared using mass cytometry. In agreement with other reports we did not detect any disease-specific cellular markers. Distribution of diagnosis across different clusters confirms diseases heterogeneity. Patients can be classified into phenotypically similar groups, that could potentially benefit from the same line of treatment.

Acknowledgements

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 831434 (3TR) and The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. Also from No 115565 PRECISESADS.

Disclosure of Interests

None declared

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Phenotyping asthma, rhinitis and eczema in MeDALL population-based birth cohorts: an allergic comorbidity cluster

BACKGROUND

Asthma, rhinitis and eczema often co-occur in children, but their interrelationships at the population level have been poorly addressed. We assessed co-occurrence of childhood asthma, rhinitis and eczema using unsupervised statistical techniques.

METHODS

We included 17 209 children at 4 years and 14 585 at 8 years from seven European population-based birth cohorts (MeDALL project). At each age period, children were grouped, using partitioning cluster analysis, according to the distribution of 23 variables covering symptoms 'ever' and 'in the last 12 months', doctor diagnosis, age of onset and treatments of asthma, rhinitis and eczema; immunoglobulin E sensitization; weight; and height. We tested the sensitivity of our estimates to subject and variable selections, and to different statistical approaches, including latent class analysis and self-organizing maps.

RESULTS

Two groups were identified as the optimal way to cluster the data at both age periods and in all sensitivity analyses. The first (reference) group at 4 and 8 years (including 70% and 79% of children, respectively) was characterized by a low prevalence of symptoms and sensitization, whereas the second (symptomatic) group exhibited more frequent symptoms and sensitization. Ninety-nine percentage of children with comorbidities (co-occurrence of asthma, rhinitis and/or eczema) were included in the symptomatic group at both ages. The children's characteristics in both groups were consistent in all sensitivity analyses.

CONCLUSION

At 4 and 8 years, at the population level, asthma, rhinitis and eczema can be classified together as an allergic comorbidity cluster. Future research including time-repeated assessments and biological data will help understanding the interrelationships between these diseases.