Novel mRNA Signature for Anti-TNF-α Therapy Primary Response in Patients With Ulcerative Colitis

Ulcerative colitis (UC), an idiopathic, chronic inflammatory disorder of the colonic mucosa, is commonly treated with antitumor necrosis factor α (anti-TNF-α) agents. However, only approximately two-thirds have an initial response to these therapies.We integrated gene expression profiling from 3 independent data sets of 79 UC patients before they began anti-TNF-α therapy and calculated the differentially expressed genes between patient response and nonresponse to anti-TNF-α therapy and developed a de novo response-associated transcription signature score (logOR_Score) to demonstrate the predictive capability of anti-TNF-α therapy for therapeutic efficacy. Furthermore, we performed association analysis of the logOR_Score and clinical features, such as disease activity and immune microenvironment.A total of 2522 responsive and 1824 nonresponsive genes were identified from the integrated data set. Responsive genes were significantly enriched in metabolism-related pathways, whereas nonresponsive ones were associated with immune response-related pathways. The logOR_Score enabled the accurate prediction of the therapeutic efficacy of anti-TNF-α in 4 independent patient cohorts and outperformed the predictions made based on 6 transcriptome-based signatures. In terms of clinical features, the logOR_Score correlated highly with the activity of UC. From an immune microenvironment perspective, logOR_Scores of CD8+IL-17+ T cells, follicular B cells, and innate lymphoid cells significantly decreased in inflamed UC tissue.The de novo response-associated transcription signature may provide novel insights into the personalized treatment of patients with UC. Comprehensive analyses of the response-related subtypes and the association between logOR_Score and clinical features and immune microenvironment may provide insights into the underlying UC pathogenesis.We developed a de novo response-associated transcription signature score (logOR_Score) to predict the response of patients with UC to anti-TNF-α agents prior to treatment and explored the different response mechanisms of UC.