DOP79 Biomarkers for IBD using OLINK Proteomics inflammation panel: Preliminary results from the COLLIBRI consortium

Abstract Background Circulating serum proteins have provided insights into disease pathogenesis and are being used to identify prognostic, diagnostic and therapeutic biomarkers for chronic inflammatory diseases. With this pilot project, the Collaborative IBD Biomarker Research Initiative (COLLIBRI) consortium aimed to unravel disease heterogeneity in inflammatory bowel disease (IBD). Methods Serum samples were cross-sectionally obtained from 3,390 individuals (Crohn’s disease (CD), n=1815; ulcerative colitis (UC), n=1170; healthy, n=405) recruited at nine centres from Sweden and Belgium. Relative levels of 92 proteins were analysed using the Proseek Multiplex Inflammation I Probe kit 96x96 (Olink Proteomics, Uppsala, Sweden) and reported as arbitrary units, i.e., normalised protein expression on a log2 scale. Using a multivariate integrative approach, we identified protein signatures distinguishing CD and UC samples and attempted to identify clusters or subgroups within patients. Recruiting centre, cohort and batch information were considered for the integrative analysis. Optimisation was performed for identifying the number of components and features per component using 5-fold cross-validation and Leave-One-Group-Out-Cross-Validation, respectively. Information on transcriptional regulators was retrieved from the ReMap project using the orthogonal regulatory resource ChEA3. Results A panel of 8 proteins was identified which could segregate CD and UC patients (Figure 1). FGF19 exhibited a consistent trend of expression (downregulated in CD) across all batches of datasets. An integrated AUC of 72.5% was achieved across the different batches of samples used in the study with the highest AUC (79.2%, P-value 8.5e-07) being recorded for a single batch of samples (CD = 42, UC = 56). On a centre-specific dataset, the cross-centre integrated signature achieved an AUC of 75.1%. We identified three transcription factors (MEF2A, BATF, NFKB2), of which the two latter ones are known to modulate intestinal inflammation and which could potentially regulate the expression of at least half of the genes encoding the proteins in the predictive 8-protein panel. Conclusion We identified an integrated proteomic biomarker panel capable of separating CD and UC patients. Through further integration of confounder variables along with using other supervised and unsupervised approaches, subsequent analyses may further refine the molecular heterogeneity among CD and UC patients. Our results demonstrate the need for large datasets to identify relevant clusters of patients with IBD, since the diagnosis exhibits a high degree of clinical heterogeneity. *Equally contributed