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SAT0444 Detection of psoriatic arthritis at early onset: a multi-proteomic approach to developing a new blood test
  1. AF Mc Ardle1,
  2. A Szentpetery2,
  3. S De Rook3,
  4. B Hernandez1,
  5. O FitzGerald4,
  6. SR Pennington1
  1. 1School of Medicine and Medical Sciences, University College Dublin
  2. 2Rheumatology, St. Vincents University Hospital, Dublin 4, Ireland
  3. 3Wilhelmina Kinderziekenhuis, Hospital, Utrecht, Netherlands
  4. 4Rheumatology, St. Vincents University Hospitla, Dublin 4, Ireland

Abstract

Background Psoriatic Arthritis (PsA) is an inflammatory arthritis (IA) frequently associated with psoriasis. Clinically, it is a complex heterogeneous disease and there are no diagnostic tests or criteria [1]. At first presentation, PsA may resemble other disease types - especially rheumatoid arthritis (RA). Making an accurate and early diagnosis is particularly important to ensure that individual patients receive effective and safe medication and so optimise long-term patient outcomes. Thus, it is widely acknowledged by physicians and patients alike that a new diagnostic test is needed to facilitate the early and specific diagnosis of PsA [2].

Objectives To (i) identify and verify candidate biomarkers with the potential to segregate patients with PsA from those with RA; and (ii) explore the value of combining different proteomic 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 with label free LC-MS/MS (n=64), the Luminex xMAP (n=62) and an aptamer based platform called SOMAscan (n=36). The random forest test was applied to each individual data set as well as to a combined-matched data set (n=36). To verify MS data, a multiple reaction monitoring (MRM) assay was developed for 54 of the most discriminatory proteins to be applied to both pooled (PsA n=9, RA n=9) and individual patient samples (n=64).

Results In this study, it was possible to quantify 387, 48 and 1129 proteins from LC-MS/MS, Luminex and SOMAscan analysis, respectively. Proteins with the ability to segregate PsA patients from those with RA were identified by random forest analysis; LC-MS/MS (AUC 0.94), Luminex (AUC 0.69) and SOMAscan (AUC 0.73). The application of the random forest model to the (i) combined data and (ii) MRM data set is part of ongoing work.

Conclusions To date, statistical analysis revealed LC-MS/MS identified proteins were the most discriminatory. An MRM assay has been developed to the top 54 LC-MS/MS proteins and this assay has been applied to the discovery cohort (data analysis ongoing). The assay will next be applied to additional evaluation cohorts that include patients with spondyloarthritis and psoriasis. Discriminatory proteins verified here represent candidates for inclusion in a blood based multi-analyte test that could ultimately be used in the diagnosis of PsA.

References

  1. Butt AQ, Mc Ardle A, Gibson DS, FitzGerald OF, Pennington SR. Psoriatic arthritis under a proteomic spotlight: application of novel technologies to advance diagnosis and management. Curr Rheumatology Reports 2015;17(5):35. doi: 1007/ss11926-015-0509–0.

  2. Mc Ardle, A. Butt AQ, Szentpetery, A. De Jager, Wilco. De Roock, Sytze. FitzGerald, O. Pennington, SR. Developing Clinically Relevant Biomarkers in Inflammatory Arthritis: A Multiplatform Approach for Serum Candidate Protein Discovery. Proteomics Clinical Applications, 2015. doi: 10.1002/prca.201500046.

References

Disclosure of Interest None declared

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