DOP08 Serum proteomics predict endoscopic remission in patients with Crohn's Disease. (27th May 2021)

• Record Type: Journal Article
• Authors: Alsoud, D; Sudhakar, P; Sabino, J; Ferrante, M; Verstockt, B; Vermeire, S
• Is Part Of: Journal of Crohn's and colitis. Volume 15(2021)Supplement 1
• Page Start: S046
• Page End: S047
• Publication Date: 2021-05-27
• Subjects: Inflammatory bowel diseases -- Periodicals; 616.344005
• DOI: 10.1093/ecco-jcc/jjab073.047
• Languages: English
• ISSNs: 1873-9946

Abstract

Abstract: Background: Recent progress in deciphering the complex pathogenesis of Crohn's disease (CD) has yielded several effective biologicals. However, ambitious therapeutic goals remain unfulfilled as almost 30% of patients are primary non-responders to a particular biological. This underscores the need for easy-to-implement biomarkers that predict (non-)remission. We aimed to identify serum protein biomarkers that predict endoscopic remission in CD patients. Methods: Serum samples from 169 consecutive CD patients with active endoscopic disease (presence of ulcerations) before starting a biological [infliximab (IFX), adalimumab (ADA), vedolizumab (VDZ) or ustekinumab (UST)] to which they were naïve were collected. Patients were prospectively followed with endoscopic re-assessment after 6–12 months. There were 102 patients (Table 1) with endoscopic remission (SES ≤ 2 or disappearance of all ulcers), whereas 67 showed no improvement. Two independent and complementary proteomic platforms were used: 644 proteins belonging to predesigned assays were quantified using Proximity Extension Assay (PEA) technology (Olink Proteomics AB, Sweden). Second, wide protein discovery mass spectrometry (MS)-based technic (Caprion, Canada) was used and quantified another 985 proteins. A multivariate modelling framework was then applied on a randomly selected training sub-cohort (85%). Predictive performance of identified panels was assessed on the remaining test sub-cohort (15%). We sought to implement the same framework on the drug-specific subgroups; however, train/test splitting was not possible in IFX or ADA subgroups due to very few observations in the non-remission arms which diminishes the possibility for reliable predictive modelling. Results: Applying the modelling framework on training sets from the general cohort, VDZ subgroup and UST subgroup, proteomic panels were selected and consisted of 26, 6 and 8 proteins, respectively, and showed high performance in the test sets (Table 2). VDZ and UST panels shared only 2 proteins each with the general panel, and had no predictive power (accuracy ≤ 0.5) when used to predict other subgroups, making them specific to their respective drugs. Selected proteins are involved among others in pro-inflammatory, extracellular matrix modelling, coagulation and cellular-vascular interaction pathways (Table 3). Conclusion: Applying a multivariate machine learning algorithm on a wide pool of serum proteomics analysed through two discovery technics, we were able to identify 3 proteomic panels that can predict endoscopic (non)remission in patients with CD. Exact implication of these proteins in intestinal inflammation and a validation in an independent cohort is being further investigated.