Abstract
Crohn’s disease and ulcerative colitis are driven by both common and distinct underlying mechanisms of pathobiology. Both diseases, exhibit heterogeneity underscored by the variable clinical responses to therapeutic interventions.We aimed to identify disease-driving pathways and classify individuals into subpopulations that differ in their pathobiology and response to treatment.We applied hierarchical clustering of enrichment scores derived from gene set variation analysis of signatures representative of various immunological processes and activated cell types, to a colonic biopsy dataset that included healthy volunteers, Crohn’s disease and ulcerative colitis patients. Patient stratification at baseline or after anti-TNF treatment in clinical responders and non-responders was queried. Signatures with significantly different enrichment scores were identified using a general linear model. Comparisons to healthy controls were made at baseline in all participants and then separately in responders and non-responders. Fifty-nine percent of the signatures were commonly enriched in both conditions at baseline, supporting the notion of a disease continuum within ulcerative colitis and Crohn’s disease. Signatures included T cells, macrophages, neutrophil activation and poly:IC signatures, representing acute inflammation and a complex mix of potential disease-driving biology. Collectively, identification of significantly enriched signatures allowed establishment of an inflammatory bowel disease molecular activity score which uses biopsy transcriptomics as a surrogate marker to accurately track disease severity. This score separated diseased from healthy samples, enabled discrimination of clinical responders and non-responders at baseline with 100% specificity and 78.8% sensitivity, and was validated in an independent data set that showed comparable classification. Comparing responders and non-responders separately at baseline to controls, 43% and 70% of signatures were enriched, respectively, suggesting greater molecular dysregulation in TNF non-responders at baseline. This methodological approach could facilitate better targeted design of clinical studies to test therapeutics, concentrating on patient subsets sharing similar underlying pathobiology, therefore increasing the likelihood of clinical response.
Highlights
Inflammatory bowel disease (IBD) is a phenotypically and molecularly heterogeneous condition characterized by chronic inflammation of the gut [1, 2, 3, 4]
We sought to take a different approach and analyze previously published IBD data using gene expression signatures. Use of these signatures reduces the complexity of a gene expression dataset from over 50 thousand probe sets to 103 defined units of biology represented by each signature
The gene signature library we assembled from gene expression omnibus (GEO) datasets representing various pathways or cell types (S1 and S2 Tables) covering a broad range of immunological processes that could quantify IBD disease biology
Summary
Inflammatory bowel disease (IBD) is a phenotypically and molecularly heterogeneous condition characterized by chronic inflammation of the gut [1, 2, 3, 4]. While controlling inflammation with relevant therapeutics has been shown to improve quality of life and clinical outcomes [5, 6], biomarkers to guide the choice of therapeutics are currently limited to CRP and fecal calprotectin, and patients must often be treated for an extended period to determine if the chosen drug is efficacious [7]. Availability of data is not the main issue hampering personalized medicine in IBD. Personalized medicine, especially in Crohn’s disease (CD), is challenged by the lack of accuracy in defining a responsive phenotype and lack of agreement in the field on the types of molecular features that should be used to predict patient response [8, 9]
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