Background Psoriatic Arthritis (PsA) is a form of seronegative inflammatory arthritis (IA) frequently associated with psoriasis. Similar to other forms of IA, it is a complex heterogeneous disease. While PsA can be characterised by distinct clinical manifestations, at early onset, it often resembles other disease types - especially rheumatoid arthritis (RA). During the treatment and management of the disease important clinical decisions are made and these have a significant impact on patient outcomes. Making an accurate and early diagnosis is particularly important to ensuring that individual patients receive effective and safe medication. Thus, it is widely acknowledged by physicians and patients alike that a new test is needed to facilitate the early and specific diagnosis of PsA .
Objectives To (i) identify candidate biomarkers with the potential to segregate patients with PsA from those with RA (ii) explore the value of combining different omic discovery platforms.
Methods Serum samples were obtained from a cohort of 64 patients (32 PsA and 32 RA) defined as early onset (<12 months) and DMARD naïve. Individual baseline samples were analysed using microRNA (miRNA) (n=63), Luminex xMAP (n=62), an aptamer based platform called SOMAscan (n=36) and label free LC-MS/MS (n=64), platforms. Data was imported into Perseus (version 126.96.36.199) for statistical analysis (p≤0.05). Additionally, data was analysed using a random forest model in R software (version 3.1.0).
Results In this study, by combining 4 different omic technologies it was possible to quantify 376, 48, 1129, 387 analytes by miRNA, Luminex, SOMAscan and LC-MS/MS analysis, respectively. Statistical analysis revealed that a total of 287 analytes were differentially expressed across the two conditions (p≤0.05). Furthermore, random forest analysis revealed the most discriminatory biomarkers from the miRNA, Luminex and SOMAscan analysis. Figure 1 A-C illustrates the receiver operating characteristic (ROC) curves generated from these analyses. The application of the random forest model to the LC-MS/MS data is part of ongoing work.
Conclusions By combining the most discriminatory analytes from each type of analysis into a single algorithm, a biomarker signature with increased predictive potency should be identified. Thus, these molecules represent candidates for inclusion into blood based mulit-analyte test that could be used in the diagnosis of PsA.
Mc Ardle. A. Butt, AQ. Szentpetery, De Jager, Wilco. De Rook, Sytze. FitzGerald, O. Pennington, SR.
Developing Clincially Relevant Biomarerks In Inflammatory Arthritis: A Mutli-Platform Approach for Serum Candidate Protein Discovery. Clincal Proteomics 2015, doi: 10.1oo2/prca.201500046
Acknowledgement Kieran Wynne is thanked for the technical support provided during this project.
Disclosure of Interest None declared
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