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

Title: I_MDS: an inflammatory bowel disease molecular activity score to classify patients with differing disease-driving pathways and therapeutic response to anti-TNF treatment
Authors: Pavlidis, S
Monast, C
Loza, MJ
Branigan, P
Chung, KF
Adcock, IM
Guo, Y
Rowe, A
Baribaud, F
Item Type: Journal Article
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.
Issue Date: 30-Apr-2019
Date of Acceptance: 13-Mar-2019
URI: http://hdl.handle.net/10044/1/69530
DOI: https://dx.doi.org/10.1371/journal.pcbi.1006951
ISSN: 1553-734X
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS Computational Biology
Volume: 15
Issue: 4
Copyright Statement: © 2019 Pavlidis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 115010
Keywords: 06 Biological Sciences
08 Information and Computing Sciences
01 Mathematical Sciences
Bioinformatics
Publication Status: Published online
Conference Place: United States
Article Number: e1006951
Appears in Collections:Computing
National Heart and Lung Institute
Airway Disease
Faculty of Medicine



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