AB0250 Endotyping of arthritic patients using novel serological biomarkers
Authors: J.P.M. Blair, C. Bager, M. Weinblatt, M. Karsdal, A. Platt, A.-C. Bay-Jensen
Journal: Annals of the Rheumatic Diseases (2018)

<h3>Background</h3> Accurate patient stratification is critical if medical professionals are to adopt a precision medicine approach when planning clinical trials or prescribing medication. This approach results in a superior level of drug response in the target group, a reduction in adverse effects and reduced costs for payers. Best practice treatment recommendations and disease activity markers such as DAS28, as opposed to a treat to target approach, guide current treatment of arthritic disease. There is currently a lack of tools to enable patient stratification, in part due to traditional biomarkers reflecting systemic inflammation rather than the target tissue. <h3>Objectives</h3> In this paper, we explore the use of a combination of novel tissue specific biomarkers for patient clustering with the objective of identifying different disease profiles. <h3>Methods</h3> Four biomarker substudy cohorts were pooled for this study, including two RA studies; LITHE (n=574) and OSKIRA-1 (n=131) and two OA studies; SMC1 (n=447) and SMC2 (n=81) all of which have been described in detail before. Whilst the principle focus was to examine RA patient profiles, OA studies were included to enrich the cohort with a non-RA population. OSKIRA and LITHE both had measurements at 24 weeks with additional measurements at 52 week s in LITHE. Several serological biomarkers were measured in each cohort, selected due to the specific tissue metabolite they represent. These included: C2M (cartilage degradation); CTX-I and PINP (bone resorption and formation); C1M and C3M (interstitial matrix degradation); CRPM (CRP metabolite) and VICM (macrophage activity). Each biomarker was log transformed and min-max normalised in order to allow for direct comparison of each of the variables. Patient clustering was performed using Ward hierarchical clustering and the number of clusters determined using the GAP statistic. ANOVA test was used to identify differences in delta change in radiographic scores at 24 and 52 weeks in the RA placebo groups (n=271) only. <h3>Results</h3> Clustering analysis resulted in five different clusters (A-E). Cluster A and B were both comprised of &gt;98% RA patients. Cluster D was comprised mainly of OA patients whilst clusters C and E were a mix of OA and RA patients. Clusters A and B were characterised by high levels of all biomarkers compared to other clusters except for VICM, which is significantly lower in cluster A than in cluster B (Tukey test p&lt;0.001). Biomarker levels in Cluster C were all close to the median. Cluster D was characterised by low levels of all biomarkers compared to other clusters with significantly lower C2M levels, whilst cluster E also had low levels of markers, yet with significantly higher levels of CTX-1 compared to cluster D. When looking at the RA placebo groups there were no difference in change in SHP score at 24 weeks between the groups, (n=271, LITHE, OSKIRA), but a significant difference in SHP change 52 weeks (n=83, p&lt;0.05, LITHE). <h3>Conclusions</h3> We have identified putative RA profiles based on novel serological biomarker status. Whether patients in particular clusters may benefit from specific targeted treatments, according to their tissue turnover profile, will be investigated further. <h3>Disclosure of Interest</h3> None declared