RAID Prediction: Pilot Study of Fecal Microbial Signature With Capacity to Predict Response to Anti-TNF Treatment.

ABSTRACT

BACKGROUND AND AIMS: Crohn's disease and ulcerative colitis evolve with alternate outbreaks and remissions of variable duration in both cases. Despite the advances, about 10-30% of patients do not respond to the treatment after the induction period. Besides, between 20% to 50% further patients need an optimization of the dose to respond the treatment. Recent studies have pointed gut microbiota can play a role in the anti-TNF treatment response. This study aimed to define a bacterial signature that could be used to predict the response of patients to anti-TNF treatment. METHODS: There were obtained 38 stool samples from 38 IBD patients before starting anti-TNF treatments Adalimumab, Golimumab or Infliximab. Patients were differentiated in 2 groups responders and non-responders to biological treatment. From each sample, DNA was purified and used in a qPCR for the quantification of the 8 microbial markers. RESULTS: In this proof of concept, the predictive ability to identify anti-TNF treatment responders was analyzed. An algorithm consisting in the combination of 4 bacterial markers showed a high capacity to discriminate between responders and non- responders. The algorithm proved high sensitivity and specificity reporting values of 93.33% and 100% respectively, with a positive predictive value of 100% and a negative predictive value of 75% for predicting response to biologic treatment. CONCLUSIONS: A specific bacterial signature could beneficiate patients with inflammatory bowel disease predicting the therapeutic effectiveness of an anti-TNF treatment, leading to a personalized therapy, improving the patients' quality of life, saving costs and gaining time in patient improvement.

This study aimed to define a microbial signature that could be used to predict the response of patients to anti-TNF treatment in inflammatory bowel disease. An algorithm consisting in the combination of 4 bacterial markers showed a high capacity to discriminate between responders and nonresponders.