DOP22 Predictive biomarkers of therapeutic response in Inflammatory Bowel Disease: a step towards personalized medicine
Authors: Baldan-Martin M , Azkargorta M , Aransay A M , Gil-Redondo R , Moreno-Indias I , Iloro I , Soleto I , Orejudo M , Ramírez C , Lucendo A J , Rodríguez Gutiérrez C , Gutiérrez A , Martín Arranz E , Bujanda L , Rodríguez-Lago I , Riestra S , Iglesias-Flores E , Bermejo F , Van Domselaar M , García M J , Fernández-Salazar L , García-Alonso J F , Calvet X , Torrealba L , Bastón Rey I , Pallarés H , Ber Y , Carnerero E L , Casanova M J , Millet Ó , Elortza F , Mato J M , Tinahones F J , Chaparro M , Gisbert J P 
Published: January 2024

Abstract

Background
Inflammatory bowel diseases are complex conditions characterized by heterogeneity at clinical, immunological, molecular, genetic, and microbial levels. The most effective approaches to induce clinical remission and control inflammation in these diseases are biologic and JAK-inhibitor therapies. However, a significant proportion of patients do not respond to treatment. Consequently, the identification of predictive biomarkers of clinical response to the different available therapies could improve the selection of patients who may benefit from the most appropriate treatment.

Methods
A multiomic study (transcriptomic, proteomic, metabolomic and metagenomic) was performed in 53 patients with active Crohn´s disease (CD) and 50 patients with active ulcerative colitis (UC) before and after 14 weeks of treatment (anti-TNFα, ustekinumab, vedolizumab or tofacitinib). Responders and non-responders patients were categorized based on endoscopic findings. Samples (serum, urine, stool and intestinal biopsies) were used for RNA-seq analysis, liquid chromatography-mass spectrometry, nuclear magnetic resonance, and 16S rRNA gene sequencing.

Results
Our findings revealed multiple mRNAs, proteins and metabolites associated with the response to different treatments (Table 1). Differential gene expression analyses in intestinal tissue showed that the main differences were detected between responders versus non-responders UC patients to anti-TNF, vedolizumab and tofacitinib. Proteomic analysis found numerous differentially expressed proteins between the study groups. Additionally, ROC curves revealed two proteins with good predictive value to discriminate between CD and UC patients responders versus non-responders to anti-TNF treatment (AUC=0.81 and AUC=0.96, respectively). Metabolomics showed up-regulation of 21 lipoproteins in serum from CD patients responders to ustekinumab compared to non-responders. Metabolic analysis pathways identified enrichment in ketone and butyrate metabolism, mitochondrial electron transport chain, carnitine synthesis, and oxidation of chain fatty acids. Regarding metagenomic results, only differences were found in microbial composition in responders versus non-responders UC patients treated with anti-TNF (Figure 1).

Conclusion
This study provides early insights into shifts in gene and protein expression in intestinal tissue, alterations in serum and urine metabolites, and changes in gut microbiota as potential predictors of response to biologics and JAK-inhibitor treatment. Further research is needed to validate these results and asses their clinical significance in identifying patients most likely to benefit from each therapeutic approach.