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


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
04 2019
Historique:
received: 21 09 2018
accepted: 13 03 2019
revised: 10 05 2019
pubmed: 1 5 2019
medline: 13 11 2019
entrez: 1 5 2019
Statut: epublish

Résumé

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.

Identifiants

pubmed: 31039157
doi: 10.1371/journal.pcbi.1006951
pii: PCOMPBIOL-D-18-01634
pmc: PMC6510457
doi:

Substances chimiques

Gastrointestinal Agents 0
Tumor Necrosis Factor-alpha 0
Infliximab B72HH48FLU

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1006951

Déclaration de conflit d'intérêts

S.P., C.M., M.J.L, P.B., A.R., and F.B. are employees of Janssen R&D and shareholders of Johnson and Johnson. The remaining authors have declared that no competing interests exist.

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Auteurs

Stelios Pavlidis (S)

Janssen Research & Development Ltd, High Wycombe, United Kingdom.
National Heart and Lung Institute, Imperial College & Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom.
Data Science Institute, Imperial College London, London, United Kingdom.

Calixte Monast (C)

Janssen Research & Development LLC, United States of America.

Matthew J Loza (MJ)

Janssen Research & Development LLC, United States of America.

Patrick Branigan (P)

Janssen Research & Development LLC, United States of America.

Kiang F Chung (KF)

National Heart and Lung Institute, Imperial College & Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom.

Ian M Adcock (IM)

National Heart and Lung Institute, Imperial College & Biomedical Research Unit, Royal Brompton & Harefield NHS Trust, London, United Kingdom.

Yike Guo (Y)

Data Science Institute, Imperial College London, London, United Kingdom.

Anthony Rowe (A)

Janssen Research & Development LLC, United States of America.

Frédéric Baribaud (F)

Janssen Research & Development LLC, United States of America.

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Classifications MeSH