Sjögren’s Disease Study

Background

Sjögren’s disease is often associated with other auto-immune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE)³. There is a clear need to better stratify Sjögren’s patients into more clinically relevant subtypes, in order to develop a more personalized approach to treatment.

We analyzed a dataset consisting of 990 cases and 1,969 controls with genotype data on 547,197 SNPs. An age- and gender-matched control set was generated consisting of randomly selected individuals who did not have any reported eye disorders and were not diagnosed with any auto-immune disease.

The identified disease signatures were used to explore disease mechanisms and identify novel disease targets. The disease-associated SNPs were mapped to the human reference genome in order to identify disease-associated and clinically relevant target genes. A semantic knowledge graph derived from multiple public and private data sources was used to annotate the targets, testing the 5Rs criteria of early drug discovery⁴ and forming strong, testable hypotheses for their mechanism of action and impact on the disease phenotype.

Outcomes : Combinatorial Risk Signatures to Explain Disease Mechanisms

Our combinatorial analytics approach took less than two days to produce combinatorial risk signatures that can be used to accurately explain disease mechanisms and identify clinically relevant target genes for novel drug development and biomarker discovery.

The study identified more than 7,000 disease signatures representing different combinations of SNPs within the Sjögren’s patient population. We found 299 risk-associated genes with SNP variants in them that are strongly associated with Sjögren’s disease. Some of these gene targets have already been demonstrated to be biological drivers of Sjögren’s pathogenesis in the scientific literature, providing validation for our hypothesis-free approach to analyzing disease populations.

Many of the genes we identified represent novel targets that have a strong mechanistic link to auto-immunity, but have not yet been studied in the context of Sjögren’s. As patients appear to stratify according to several disease mechanisms, this could help explain the apparent heterogeneity of the patient population observed in the clinic. Furthermore, as several of the targets identified have known active chemistry, they could represent potential novel drug target opportunities.

Next Steps : Greater Disease Insights and Drug Repurposing Opportunities

In addition to gaining greater insights into the underlying disease processes in Sjögren’s syndrome, we can also use our platform to identify drug repurposing candidates for key disease-associated targets. We can map existing drug options onto the genes found in each community of the patient population, enabling us to rapidly identify candidates for subsets of the patient population. Among a range of novel repurposing leads we found several examples of targets with drugs that already been shown to protect against the development of auto-immune diseases in a range of mouse models. Although some of these genes are directly involved in T-cell-mediated auto-immunity and pro-inflammatory cytokine signalling, others are implicated in epithelial cell integrity and neurotransmission.

As additional data becomes available, more detailed stratification and analysis of the patient population will be possible using a combination of genomic and phenotypic features. This will enable development of more detailed insights and personalized medicine strategies for Sjögren’s syndrome patients.

References

1. Maciel, G., Crowson, C., Matteson, E., & Cornec, D. (2017). FRI0278 Prevalence of primary Sjögren’s syndrome in a population-based cohort in the United States. Annals of the Rheumatic Diseases,76, 591

2. Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., Cortes, A., Welsh, S., Young, A., Effingham, M., McVean, G., Leslie, S., Allen, N., Donnelly, P., & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z
   
   

3. Hammitt, K. M., Naegeli, A. N., van den Broek, R., & Birt, J. A. (2017). Patient burden of Sjögren’s: a comprehensive literature review revealing the range and heterogeneity of measures used in assessments of severity. RMD Open, 3(2), e000443. https://doi.org/10.1136/rmdopen-2017-000443

4. Morgan, P., Brown, D. G., Lennard, S., Anderton, M. J., Barrett, J. C., Eriksson, U., Fidock, M., Hamrén, B., Johnson, A., March, R. E., Matcham, J., Mettetal, J., Nicholls, D. J., Platz, S., Rees, S., Snowden, M. A., & Pangalos, M. N. (2018). Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nature Reviews. Drug Discovery, 17(3), 167–181. https://doi.org/10.1038/nrd.2017.244