A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis

Abstract

Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA. Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses. Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (P = 0.0015) and infliximab (P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (P = 0.041). Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.

Keywords: anti-TNF therapy; genomics; multi-omics association analysis; rheumatoid arthritis; transcriptomics.

Figure 1

Gene coexpression modules identified in the RA inflamed synovium. Dendrogram showing the 149 gene coexpression modules identified with the unsupervised hierarchical clustering approach in the genome-wide coexpression analysis. Each dendrogram branch corresponds to a gene coexpression module, as shown in the color bar below. The height of the dendrogram represents the co-expression distance among genes. The heatmap represents the adjacency matrix that was built using the pairwise gene expression correlation raised to a soft thresholding power of β = 14. The heatmap is colored according to the absolute value of the pairwise gene expression correlation, ranging from yellow (i.e., weak correlation) to red (i.e., strong correlation). The x and y axis represent the total of 18,524 genes that were included in the genome-wide coexpression analysis.

Figure 2

Enrichment of genetic variation from the adalimumab-associated module in H3K4me3 histone marks in specific cell types. Bar plot showing the statistical significance of the adalimumab-associated genetic module and H3K4me3 histone marks from the 34 cell types analyzed. Cell types with H3K4me3 histone marks significantly enriched in adalimumab-associated variants are represented with an asterisk (P < 0.05).