I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment

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.

Sputum macrophage diversity and activation in asthma: Role of severity and inflammatory phenotype

“…Gene set variation analysis is a gene set enrichment method that estimates the variation in a pathway activity across a sample population in an unsupervised manner. It is used to detect changes in pathway or gene signature activity over a sample population and indicates differences within populations and between groups of subjects 13,14 . GSVA calculates sample‐wise enrichment scores (ES) across the whole data set with a range from −1 to +1.…”

Transcriptional Effects of Ozone and Impact on Airway Inflammation

“…Further analysis of the transcriptomic data from this model using gene set variation analysis (GSVA) (98, 99) and the online freeware R Bioconductor (https://www.bioconductor.org/) (Figure 2). GSVA is a non-parametric, unsupervised method for estimating the variation of sets of genes or pathways across a dataset and indicates that there is enrichment of gene signatures associated with fibrosis in this model that are reversed by co-treatment with N-acetyl cysteine (NAC).…”

<p>Integrated Gene Expression Profiling Analysis Reveals Probable Molecular Mechanism and Candidate Biomarker in Anti-TNFα Non-Response IBD Patients</p>

“…8,10 Thus, a comprehensive comparison via genetics, gene expression data, and microbe provides an excellent to investigate the molecular mechanisms of resistance to anti-TNFα agents and predictive biomarkers. [11][12][13][14][15][16][17] So far, published studies using gene expression data from intestinal or blood samples of IBD patients collected before anti-TNFα therapy have identified several signature patterns of non-response patients. [11][12][13][14][15][16] However, it is still difficult to predict the response of anti-TNFα therapy.…”

“…[11][12][13][14][15][16][17] So far, published studies using gene expression data from intestinal or blood samples of IBD patients collected before anti-TNFα therapy have identified several signature patterns of non-response patients. [11][12][13][14][15][16] However, it is still difficult to predict the response of anti-TNFα therapy. 1,6 We hypothesized that shared baseline molecular patterns may be associated with the clinical efficacy of anti-TNFα agents in IBD patients.…”