Immunological profiling of paediatric inflammatory bowel disease using unsupervised machine learning
Immunological profiling of paediatric inflammatory bowel disease using unsupervised machine learning
Objectives:
The current classification of inflammatory bowel disease (IBD) is based on clinical phenotypes, which is blind to the molecular basis of the disease. The aim of this study was to stratify a treatment-naïve paediatric IBD cohort through specific innate immunity pathway profiling and application of unsupervised machine learning (UML).
Methods:
In order to test the molecular integrity of biological pathways implicated in IBD, innate immune responses were assessed at diagnosis in 22 paediatric patients and 10 age-matched controls. Peripheral blood mononuclear cells (PBMCs) were selectively stimulated for assessing the functionality of upstream activation receptors including NOD2, toll-like receptor (TLR) 1-2 and TLR4, and the downstream cytokine responses (IL-10, IL-1β, IL-6, and TNF-α) using multiplex assays. Cytokine data generated were subjected to hierarchical clustering to assess for patient stratification.
Results:
Combined immune responses in patients across 12 effector responses were significantly reduced compared with controls (P = 0.003) and driven primarily by “hypofunctional” TLR responses (P values 0.045, 0.010, and 0.018 for TLR4-mediated IL-10, IL-1β, and TNF-α, respectively; 0.018 and 0.015 for TLR1-2 -mediated IL-10 and IL-1β). Hierarchical clustering generated 3 distinct clusters of patients and a fourth group of “unclustered” individuals. No relationship was observed between the observed immune clusters and the clinical disease phenotype.
Conclusions:
Although a clinically useful outcome was not observed through hierarchical clustering, our study provides a rationale for using an UML approach to stratify patients. The study also highlights the predominance of hypo-inflammatory innate immune responses as a key mechanism in the pathogenesis of IBD.
833-840
Coelho, Tracy
a78b627c-ea78-41e1-9553-0390921e3c93
Mossotto, Enrico
a2a572db-3e95-41c6-94f6-f1b019594372
Gao, Yifang
3faffb99-162f-4cd6-aeb6-371ed15a58c2
Haggarty, Rachel
d79fd59d-2cdc-465a-93e4-b0aeb78583c6
Ashton, James J.
03369017-99b5-40ae-9a43-14c98516f37d
Batra, Akshay
822f891e-87ca-41d9-b68d-27c395e88809
Stafford, Imogen S.
50987dc1-3772-408f-9093-9124f3d6b2cd
Beattie, Robert M.
9a66af0b-f81c-485c-b01d-519403f0038a
Williams, Anthony P.
973ff46f-46f1-4d7c-b27d-0f53221e4c44
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
1 June 2020
Coelho, Tracy
a78b627c-ea78-41e1-9553-0390921e3c93
Mossotto, Enrico
a2a572db-3e95-41c6-94f6-f1b019594372
Gao, Yifang
3faffb99-162f-4cd6-aeb6-371ed15a58c2
Haggarty, Rachel
d79fd59d-2cdc-465a-93e4-b0aeb78583c6
Ashton, James J.
03369017-99b5-40ae-9a43-14c98516f37d
Batra, Akshay
822f891e-87ca-41d9-b68d-27c395e88809
Stafford, Imogen S.
50987dc1-3772-408f-9093-9124f3d6b2cd
Beattie, Robert M.
9a66af0b-f81c-485c-b01d-519403f0038a
Williams, Anthony P.
973ff46f-46f1-4d7c-b27d-0f53221e4c44
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Coelho, Tracy, Mossotto, Enrico, Gao, Yifang, Haggarty, Rachel, Ashton, James J., Batra, Akshay, Stafford, Imogen S., Beattie, Robert M., Williams, Anthony P. and Ennis, Sarah
(2020)
Immunological profiling of paediatric inflammatory bowel disease using unsupervised machine learning.
Journal of Pediatric Gastroenterology and Nutrition, 70 (6), .
(doi:10.1097/MPG.0000000000002719).
Abstract
Objectives:
The current classification of inflammatory bowel disease (IBD) is based on clinical phenotypes, which is blind to the molecular basis of the disease. The aim of this study was to stratify a treatment-naïve paediatric IBD cohort through specific innate immunity pathway profiling and application of unsupervised machine learning (UML).
Methods:
In order to test the molecular integrity of biological pathways implicated in IBD, innate immune responses were assessed at diagnosis in 22 paediatric patients and 10 age-matched controls. Peripheral blood mononuclear cells (PBMCs) were selectively stimulated for assessing the functionality of upstream activation receptors including NOD2, toll-like receptor (TLR) 1-2 and TLR4, and the downstream cytokine responses (IL-10, IL-1β, IL-6, and TNF-α) using multiplex assays. Cytokine data generated were subjected to hierarchical clustering to assess for patient stratification.
Results:
Combined immune responses in patients across 12 effector responses were significantly reduced compared with controls (P = 0.003) and driven primarily by “hypofunctional” TLR responses (P values 0.045, 0.010, and 0.018 for TLR4-mediated IL-10, IL-1β, and TNF-α, respectively; 0.018 and 0.015 for TLR1-2 -mediated IL-10 and IL-1β). Hierarchical clustering generated 3 distinct clusters of patients and a fourth group of “unclustered” individuals. No relationship was observed between the observed immune clusters and the clinical disease phenotype.
Conclusions:
Although a clinically useful outcome was not observed through hierarchical clustering, our study provides a rationale for using an UML approach to stratify patients. The study also highlights the predominance of hypo-inflammatory innate immune responses as a key mechanism in the pathogenesis of IBD.
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Accepted/In Press date: 17 March 2020
e-pub ahead of print date: 3 April 2020
Published date: 1 June 2020
Identifiers
Local EPrints ID: 441226
URI: http://eprints.soton.ac.uk/id/eprint/441226
ISSN: 0277-2116
PURE UUID: 152e9629-b428-4334-927a-c3006b8d4f9a
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Date deposited: 05 Jun 2020 16:31
Last modified: 17 Mar 2024 05:36
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Contributors
Author:
Tracy Coelho
Author:
Enrico Mossotto
Author:
Yifang Gao
Author:
Rachel Haggarty
Author:
Akshay Batra
Author:
Imogen S. Stafford
Author:
Robert M. Beattie
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