Date: Sunday, November 7, 2021
Session Type: Poster Session B
Session Time: 8:30AM-10:30AM
Background/Purpose: Omic analyses of systemic sclerosis (SSc) skin biopsy datasets identified distinct sets of patients that respond differently to treatment. The goal of this study was to use the Connectivity Map (CMAP) library of gene expression profiles from drug treatments of cell lines to identify pathways and small molecules (perturbagens) that would normalize the aberrant gene expression profiles in SSc patients back to that of healthy controls.
Methods: CMAP 2.0 data was processed with RMA, quantile normalized, and fit to a multichip linear model. Probes were collapsed as average intensity and formatted for gene fold-change as the ratio of treatment to control intensities. DNA microarray data from SSc patient skin were obtained from Milano et al (GSE9285) and Pendergrass et al (GSE32413) then analyzed by Gene Set Variation Analysis (GSVA) in R for single-sample enrichment scores. Enrichment scores significant for each intrinsic subset of SSc patients were determined and perturbagens that have potential to regulate these pathways were chosen for further study. 3D skin-like tissues were constructed with dermal fibroblasts from ATCC, treated with selected perturbagens, and analyzed with H&E or Sirius Red for visualization.
Results: We focused on analyses on the inflammatory and fibroproliferative pathogenic subsets. Pathways specific to these two subsets were selected from publicly available gene expression data from SSc and control skin through single-sample GSVA. We identified 608 pathways enriched for upregulation in the inflammatory subset and 667 pathways enriched for upregulation in the fibroproliferative subset. Using single sample GSVA, we identified all CMAP perturbagens predicted to modulate the pathways in the inflammatory or fibroproliferative subsets. A parallel analysis using the BASE algorithm was also performed and perturbagens identified in both analyses were chosen for further study. EGFR inhibitors were shown to regulate gene set pathways from the inflammatory subset of SSc patients. We determined that PI3K inhibitors modulated gene set pathways from the fibroproliferative subset of SSc patients. Experimental validation of the PI3K inhibitor decreased collagen 1 expression of fibroblasts grown in 2D cell culture. Inhibitors were also analyzed in self-assembled (SA) 3D skin-like tissues containing only normal dermal fibroblasts, with and without TGFB stimulation. The PI3K inhibitor showed decreased extracellular matrix deposition, likely as a result of reduced collagen expression within the tissue environment. The EGFR inhibitor showed less significant changes in either tissue thickness or collagen deposition.
Conclusion: Multiple small molecule inhibitors have been identified that regulate gene set pathways that are specific to two molecular subsets of SSc patients determined by gene expression profiling. Testing these perturbagens within cell culture and 3D skin like tissues showed that PI3K inhibitors may inhibit multiple aspects of SSc disease progression in dermal fibroblasts.
To cite this abstract in AMA style:Popovich D, Abel T, Kosarek N, Espinoza M, Parvizi R, Garlick J, Whitfield M. Pathway-Driven Drug Repositioning in Systemic Sclerosis from Omics Data [abstract]. Arthritis Rheumatol. 2021; 73 (suppl 9). https://acrabstracts.org/abstract/pathway-driven-drug-repositioning-in-systemic-sclerosis-from-omics-data/. Accessed November 26, 2022.
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/pathway-driven-drug-repositioning-in-systemic-sclerosis-from-omics-data/