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Systematic review: predictive biomarkers of therapeutic response in inflammatory bowel disease-personalised medicine in its infancy.

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Published:1st Dec 2018
Author: Stevens TW, Matheeuwsen M, Lönnkvist MH, Parker CE, Wildenberg ME, Gecse KB et al.
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Ref.:Aliment Pharmacol Ther. 2018;48(11-12):1213-1231.
DOI:10.1111/apt.15033

Background: Inflammatory bowel disease (IBD) is characterised by substantial heterogeneity in treatment response. With an expanding number of therapeutic agents, identifying optimal treatment at the patient level remains a major challenge.

Aim: To systematically review the available literature on predictive biomarkers of therapeutic response in IBD.

Methods: An electronic literature search was performed on 30 January 2018 using MEDLINE, EMBASE and the Cochrane Library. Retrospective, prospective, uncontrolled and controlled studies reporting on biomarkers predicting therapeutic response in paediatric and adult IBD populations were eligible for inclusion. The methodological quality of the included studies was assessed using the QUIPS tool. Due to anticipated heterogeneity and limited data, a qualitative, rather than quantitative, assessment was planned.

Results: Of the 10 638 citations identified, 92 articles met the inclusion criteria. Several potential DNA, mRNA and protein markers were evaluated as predictive biomarkers. Most studies focused on predicting response to anti?TNF agents. Substantial between?study heterogeneity was identified with respect to both the biomarkers studied and the definition of response. None of the included studies received a low risk of bias rating for all six domains. Currently, none of the biomarkers is sufficiently predictive for clinical use.

Conclusions: The search for predictive biomarkers is still in its infancy and current evidence is limited. Future research efforts should take into account the high patient heterogeneity within prospective trials with objective response assessment. Predictive models will most likely comprise a combination of several molecular markers from integrated omics?levels and clinical characteristics.

 

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