Systematic review: genetic biomarkers associated with anti-TNF treatment response in inflammatory bowel diseases.

Authors:
S Bek, J V Nielsen, A B Bojesen, A Franke, S Bank, U Vogel, V Andersen
Year of publication:
2016
Volume:
-
Issue:
-
Issn:
0269-2813
Journal title abbreviated:
ALIMENT PHARM THER
Journal title long:
Alimentary pharmacology & therapeutics
Impact factor:
6.320
Abstract:
We performed a PubMed literature search and retrieved studies reporting original data on association between polymorphisms and anti-TNF treatment response and conducted a meta-analysis.Personalised medicine, including biomarkers for treatment selection, may provide new algorithms for more effective treatment of patients. Genetic variation may impact drug response and genetic markers could help selecting the best treatment strategy for the individual patient.To identify polymorphisms and candidate genes from the literature that are associated with anti-tumour necrosis factor (TNF) treatment response in patients with inflammatory bowel diseases (IBD), Crohn's disease (CD) and ulcerative colitis.A functional polymorphism in FCGR3A was significantly associated with anti-TNF treatment response among CD patients using biological response criterion (decrease in C-reactive protein, levels). Meta-analyses showed that polymorphisms in TLR2 (rs3804099, OR (95% CI) = 2.17 (1.35-3.47)], rs11938228 [OR = 0.64 (0.43-0.96)], TLR4 (rs5030728) [OR = 3.18 (1.63-6.21)], TLR9 (rs352139) [OR = 0.43 (0.21-0.88)], TNFRSF1A (rs4149570) [OR = 2.06 (1.02-4.17)], IFNG (rs2430561) [OR = 1.66 (1.05-2.63)], IL6 (rs10499563) [OR = 1.65 (1.04-2.63)] and IL1B (rs4848306) [OR = 1.88 (1.05-3.35)] were significantly associated with response among IBD patients using clinical response criteria. A positive predictive value of 0.96 was achieved by combining five genetic markers in an explorative analysis.There are no genetic markers currently available which are adequately predictive of anti-TNF response for use in the clinic. Genetic markers bear the advantage that they do not change over time. Therefore, hypothesis-free approaches, testing a large number of polymorphisms in large, well-characterised cohorts, are required in order to identify genetic profiles with larger effect sizes, which could be employed as biomarkers for treatment selection in clinical settings.