Patent Description:
Arthritis is a general term for conditions that affect the joints - the complex structures located where two or more bones meet. Despite this broad classification, there are more than <NUM> forms of arthritis, many of which can be grouped based on similar characteristics. <NPL> is a review of established and experimental biomarkers in rheumatoid and psoriatic arthritis. <NPL> is a review of candidate biomarkers of joint damage in rheumatoid and psoriatic arthritis.

Rheumatoid Arthritis (RA) is the second most prevalent form of arthritis, affecting ~<NUM>% of the population. RA is classified as seropositive since rheumatoid factor (RF) is present in high titre in <NUM>% of patients. Women are at a higher risk of developing RA compared to men, at a ratio of <NUM>:<NUM>. RA is also associated with a strong genetic component - susceptibility has been linked to polymorphisms in the hypervariable region of Human leukocyte antigen (HLA)-DRβ1. RA is most prevalent in the small diarthridal joints of the hands and feet, but large joints (elbow, shoulder, hip, knee, ankles) can also be affected, and the pattern of joint involvement is typically symmetrical. RA is a debilitating disease, whereby joint damage leads to pain and disability; and up to one third of patients become work disabled <NUM> years after onset.

Psoriatic Arthritis (PsA) can be defined as arthritis with psoriasis (Ps), predominantly Ps vulgaris (a form of plaque Ps). Usually negative for RF (seronegative), PsA is characterised radiographically by both bone resorption and periarticular new bone formation. PsA is a form of inflammatory arthritis (IA), affecting approximately <NUM>% of the population. It is a heterogeneous disorder associated with joint damage, disability, disfiguring skin disease and, in severe cases, mortality. Inherently irreversible and frequently progressive, the process of joint damage begins at, or before, the clinical onset of disease. Early recognition and intervention is thus crucial to patient outcome.

PsA is most often diagnosed by history and physical examination, and onset of disease is clinically recognised when a patient presents with musculoskeletal inflammation, presence of psoriasis, and an absence of rheumatoid factor. Currently there are no diagnostic criteria for PsA, and recognition of the disease is dependent on the expertise of the treating clinician. Therefore, the diagnosis of PsA is often missed or delayed and this has been associated with functional consequences for the patient. From a rheumatologists perspective, at disease onset, PsA is particularly difficult to distinguish from other forms of arthritis, especially RA (as both can present with peripheral arthritis and Ps).

In the context of PsA and RA, making an accurate diagnosis is not the only challenge faced by rheumatologists - despite the similarities between PsA and RA, their distinctive pathologies require different treatments. For example, drugs that are effective in RA may not be effective in PsA and can even cause adverse effects. For instance, while there are some medications which are effective for both PsA and RA (e.g. methotrexate; or anti-TNFα inhibitors), there are others which would be best avoided in PsA because of adverse effects (e.g. hydroxychloroquine, or corticosteroid), some which have proven efficacy in RA and not PsA (e.g. rituximab, or tocilizumab) and some with proven efficacy in PsA and not in RA (e.g. ustekinumab, apremilast, or anti-IL17 therapies). Evidence suggests that the early introduction of the appropriate, effective medication would result in better short-term and long-term patient outcomes. When a patient presents with PsA, a number of treatment options become available. Currently, the therapeutic strategy follows a period of trial and error, since many patients do not respond, cannot tolerate, or remit upon cessation of any given therapy. For the patient, several months may be lost as a result of trial and error testing - meanwhile irreversible joint damage may occur.

Clearly more effective clinical tests are urgently needed to improve personalized patient care in PsA. Specifically there is need to develop minimally invasive tests predictive of diagnosis that would allow for early intervention. Such a diagnostic test in PsA would facilitate early detection and therapeutic intervention. This in turn would have a positive impact on patient outcome and relieve both individual suffers and society from a substantial financial burden. Finally, it is likely that such a test would improve the reliability of data from epidemiological studies and intervention trials and therefore enhance research in PsA.

According to the present invention, there is provided a method of differentiating psoriatic arthritis from rheumatoid arthritis in a subject, the method comprising the steps of:.

wherein the or each biomarker comprises Rheumatoid factor C6 light chain.

Optionally, the or each biomarker is further selected from: Leucine-rich alpha-<NUM>-glycoprotein; Alpha-<NUM>-antichymotrypsin; Complement C4-B; Coagulation factor XI; Haptoglobin; Haptoglobin-related protein; and Thrombospondin-<NUM>.

Optionally or additionally, the or each biomarker is selected from: Alpha-<NUM>-acid glycoprotein <NUM>; Alpha-<NUM>-antitrypsin; Insulin-like growth factor-binding protein complex acid labile subunit; Antithrombin; C4b-binding protein alpha chain; Ceruloplasmin; Complement factor B; Clusterin; Platelet basic protein; Extracellular matrix protein <NUM>; Inter-alpha-trypsin inhibitor heavy chain H4; Kininogen-<NUM>; Lipopolysaccharide-binding protein; Pigment epithelium-derived factor; Vitamin K-dependent protein C; and Prothrombin.

Optionally or additionally, the or each biomarker is selected from: Gelsolin; Filamin-C; Complement component C9b; Peroxiredoxin-<NUM>; Plasma serine protease inhibitor; Adenosine deaminase <NUM>; Pregnancy zone protein; Myomegalin; Apolipoprotein D; Glycocalicin; Afamin; Plasma protease C1 inhibitor; Inter-alpha-trypsin inhibitor heavy chain H3; Insulin-like growth factor-binding protein <NUM>; Galectin-<NUM>-binding protein; Alpha-<NUM>-HS-glycoprotein chain B; and Antithrombin-III.

Optionally, the method of differentiating psoriatic arthritis from rheumatoid arthritis in a subject comprises differentiating subjects suffering from psoriatic arthritis from subjects suffering from rheumatoid arthritis based on the quantitative or qualitative level of the or each biomarker in the biological sample.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of two or more biomarkers in the biological sample from the subject.

Further optionally, the determining step (a) comprises determining the quantitative or qualitative level of three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one biomarkers in the biological sample from the subject.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of all of the biomarkers in the biological sample from the subject.

Optionally or additionally, the determining step (a) comprises determining the quantitative or qualitative level of each of the biomarkers in the biological sample from the subject.

The or each biomarker described herein may be defined by a corresponding UniProt Accession Number selected from: A0N5G1; P02750; P01011; P0C0L5; P03951; P00738; P00739; P07996; P02763; P01009; P35858; Q8J001; P04003; P00450; P00751; P10909; P02775; Q16610; Q14624; P01042; P18428; P36955; P04070; P00734; P06396; Q14315; P32119; P05155; P05154; P02748; P20742; Q5VU43; P05090; P07359; P43652; Q06033; Q08380; P02765; and P01008.

The or each biomarker described herein may be defined by a corresponding Genbank Accession/Version Number selected from: AAB25742. <NUM>; NP_443204. <NUM>; K01500. <NUM>; NP_001002029. <NUM>; NP_000119. <NUM>; NP_005134. <NUM>; NP_066275. <NUM>; and NP_003237. <NUM>; NP_000598. <NUM>; NP_001002235. <NUM>; NP_001139478. <NUM>; BAC21173. <NUM>; NP_000706. <NUM>; NP_000087. <NUM>; NP_001701. <NUM>; NP_001822. <NUM>; NP_002695. <NUM>; NP_004416. <NUM>; NP_002209. <NUM>; NP_001095886. <NUM>; NP_004130. <NUM>; NP_002606. <NUM>; NP_000303. <NUM>; NP_000497. <NUM>; NP_000168. <NUM>; NP_001120959. <NUM>; NP_001728. <NUM>; NP_005800. <NUM>; NP_000053. <NUM>; NP_000615. <NUM>; NP_002855. <NUM>; NP_001002810. <NUM>; NP_001638. <NUM>; NP_000164. <NUM>; NP_001124. <NUM>; NP_002208. <NUM>; NP_005558. <NUM>; NP_001613. <NUM>; and NP_000479.

Optionally, the or each biomarker is a protein comprising an amino acid sequence selected from any one of SEQ ID NOs: <NUM>-<NUM>.

Optionally or additionally, the or each biomarker is a protein having an amino acid sequence selected from any one of SEQ ID NOs: <NUM>-<NUM>.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of one or more subsets of one or more biomarkers in the biological sample from the subject.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of two or more subsets of one or more biomarkers in the biological sample from the subject.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of one or more of a first or second subset of one or more biomarkers in the biological sample from the subject.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of a first and second subset of one or more biomarkers in the biological sample from the subject.

Optionally, the first subset comprises one or more biomarkers selected from: Complement C4-B; Extracellular matrix protein <NUM>; Coagulation factor XI; Pigment epithelium-derived factor; and Prothrombin.

Optionally, the second subset comprises one or more biomarkers selected from: Alpha-<NUM>-acid glycoprotein <NUM>; Alpha-<NUM>-antitrypsin; Leucine-rich alpha-<NUM>-glycoprotein; alpha-<NUM>-antichymotrypsin; Ceruloplasmin; Haptoglobin; Haptoglobin-related protein; Inter-alpha-trypsin inhibitor heavy chain H4; Lipopolysaccharide-binding protein; and Thrombospondin-<NUM>.

Optionally, the first and second subset comprise one or more biomarkers selected from: Complement C4-B; Extracellular matrix protein <NUM>; Coagulation factor XI; Pigment epithelium-derived factor; Prothrombin; Alpha-<NUM>-acid glycoprotein <NUM>; Alpha-<NUM>-antitrypsin; Leucine-rich alpha-<NUM>-glycoprotein; alpha-<NUM>-antichymotrypsin; Ceruloplasmin; Haptoglobin; Haptoglobin-related protein; Inter-alpha-trypsin inhibitor heavy chain H4; Lipopolysaccharide-binding protein; and Thrombospondin-<NUM>.

Optionally, the differentiating step (b) comprises comparing the quantitative or qualitative level of the or each biomarker in the biological sample from the subject with the quantitative or qualitative level of the or each respective biomarker in a normal sample.

Optionally, the normal sample is a biological sample from a subject not suffering from psoriatic arthritis. Optionally or additionally, the normal sample is a biological sample from a subject not suffering from rheumatoid arthritis.

Optionally, a quantitative or qualitative level of the or each biomarker in the biological sample from the subject greater than the quantitative or qualitative level of the or each respective biomarker in a normal sample is indicative of the quantitative or qualitative level of psoriatic arthritis.

Optionally, a quantitative or qualitative level of the or each biomarker in the biological sample from the subject greater than the quantitative or qualitative level of the or each respective biomarker in a normal sample is indicative of the quantitative or qualitative presence of rheumatoid arthritis.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of all of the biomarkers in one or more of the first or second subsets.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of each of the biomarkers in one or more of the first or second subsets.

Optionally, the determining step (a) comprises determining the quantitative or qualitative level of each of the biomarkers in the first and second subsets.

Optionally, a quantitative or qualitative level of the or each biomarker in the or each subset in the biological sample from the subject greater than the quantitative or qualitative level of the or each respective biomarker in a normal sample is indicative of the quantitative or qualitative level of psoriatic arthritis.

Optionally, a quantitative or qualitative level of the or each biomarker in the or each subset in the biological sample from the subject greater than the quantitative or qualitative level of the or each respective biomarker in a normal sample is indicative of the quantitative or qualitative presence of rheumatoid arthritis.

Optionally, the differentiating step (b) comprises comparing the quantitative or qualitative level of the or each biomarker in the or each subset in the biological sample from the subject with the quantitative or qualitative level of the or each respective biomarker in another subset in the biological sample.

Optionally, the differentiating step (b) comprises comparing the quantitative or qualitative level of the or each biomarker in a first subset in the biological sample from the subject with the quantitative or qualitative level of the or each respective biomarker in another subset in the biological sample.

Optionally, the differentiating step (b) comprises comparing the quantitative or qualitative level of the or each biomarker in a first subset in the biological sample from the subject with the quantitative or qualitative level of the or each biomarker in a second subset in the biological sample.

Optionally, a quantitative or qualitative level of the or each biomarker in the or each subset in the biological sample from the subject greater than the quantitative or qualitative level of the or each biomarker in another subset in the biological sample is indicative of the quantitative or qualitative level of psoriatic arthritis.

Optionally, a quantitative or qualitative level of the or each biomarker in the first subset in the biological sample from the subject greater than the quantitative or qualitative level of the or each biomarker in the second subset in the biological sample is indicative of the quantitative or qualitative presence of psoriatic arthritis.

Optionally, a quantitative or qualitative level of the or each biomarker in the first subset in the biological sample from the subject less than the quantitative or qualitative level of the or each biomarker in the second subset in the biological sample is indicative of the quantitative or qualitative presence of rheumatoid arthritis.

Optionally, the biological sample is selected from whole blood, serum, plasma, urine, interstitial fluid, peritoneal fluid, tears, saliva, buccal swab, skin, synovial fluid, synovium, and cerebrospinal fluid.

The term "prognosing" is intended to define the usual medical step of prognosis, and is intended to also include predicting a defined outcome, such as a defined medical outcome, including predicting a prospect of: a disease-free outcome, such as recovery; an improvement in disease, such as a reduction in symptom; likelihood of survival, such as life expectancy; and change in disease state, such as progression.

Embodiments of the present invention will now be described with reference to the following nonlimiting examples and accompanying drawings, in which:.

A total number of <NUM> patients were recruited, and a full description of the cohort is described in <NPL>. Recent-onset (symptom duration <<NUM> months), treatment naïve PsA and RA patients with active joint inflammation, aged <NUM> to <NUM> years were enrolled consecutively. PsA patients (n=<NUM>) fulfilled the CASPAR criteria according to <NPL>. and patients with RA (n=<NUM>) met the <NUM> ACR/EULAR classification criteria for RA according to <NPL>. Exclusion criteria were pregnancy, diseases of bone metabolism, previous treatment with disease-modifying anti-rheumatic drugs (DMARDs) or biologic agents, and treatment with anti-resorptive medications, parathyroid hormone or strontium ranelate <NUM> months prior to the study. The use of calcium and vitamin D supplements and a stable dose of steroids of less than <NUM>/day were permitted during the study.

Prior to proteomic analysis, serum samples were depleted of <NUM> high abundant proteins (HAP) using the Agilent Multiple Affinity Removal System comprising a Hu-<NUM> column (HuMARS14) (<NUM> × <NUM>; Agilent Technologies, <NUM>-<NUM>) on a Biocad Vision Workstation and subsequently trypsinized. Samples were run on a Thermo Q Exactive mass spectrometer according to the manufacturer's instructions.

nLC-MS/MS data were visually inspected using Xcalibur software (<NUM> SP1. MaxQuant (<NUM>. <NUM>) was then used for quantitative analysis of the Thermo Scientific. raw files while Perseus software (<NUM>. <NUM>) supported statistical analysis of the data.

Individual patient serum samples were subjected to a multiplexed aptamer-based assay (SOMAscan) developed by Gold et al. to measure the levels of <NUM> proteins as described by <NPL>.

Individual serum samples were subjected to in-house developed and validated multiplexed immunoassays measuring <NUM> analytes using Luminex xMAP proteomics technology (Austin TX, USA). This analysis was undertaken by the Multiplex Core Facility Laboratory of Translational Immunology LTI, in the University Medical Centre Utrecht. The assays were performed as previously described by <NPL>.

Serum RNA was isolated using the miRNeasy serum/plasma kit (Qiagen) according to the manufacturer's instructions. RNA concentration was measured using the NanoDrop Spectrophotometer. For each sample, <NUM>µL of RNA was reverse transcribed using the miScript reverse transcription kit (Qiagen) according to the manufacturer's instructions. Reverse transcription is based on a poly-A tailing of mature miRNAs followed by tailed oligo-dT reverse transcription. As such, all mature miRNAs in the RNA sample are reverse transcribed and amenable for qPCR detection. Individual cDNA samples were pooled, followed by a miRNA-specific pre-amplification and quantification using qPCR. In total, <NUM> individual miRNAs were profiled using version <NUM> of the miRNome platform. Assays are spotted across <NUM> x <NUM>-well plates. The qPCR mix contained a synthetic PCR template (PPC) that is used to assess PCR performance. The PPC assay was measured in duplicate for each sample on each miRNome assay plate. The number of detected miRNAs was determined by applying a Cq detection cut off of <NUM> cycles. miRNA analysis was carried out by Biogazelle, Gent, Belgium.

The development and optimisation of MRM assays was performed using Skyline software (version <NUM>. <NUM>) (MacCoss laboratory, Washington DC). Assays were developed to prototypic peptides for all proteins of interest according to the following criteria: no missed cleavages or 'ragged ends', sequence length between <NUM>-<NUM> amino acids. Where possible, peptides sequences with reactive (C) or methionine (M) residues were avoided but not excluded. A working MRM was determined based on the dot product ≥<NUM>, signal to noise ≥<NUM>, data points under the curve ≥<NUM> and percentage coefficient of variance (retention time ≤ <NUM>% , area ≤ <NUM>%).

Crude serum (2µL) was added to the wells of a <NUM> deep-well plate (Thermo) and diluted <NUM> in <NUM> with NH<NUM>CO<NUM>. Rapigest™ SF surfactant/denaturant (Waters) was re-suspended in <NUM> NH<NUM>CO<NUM> to give a stock solution of <NUM>% w/v. The stock solution was added to each sample so that the final concentration of Rapigest™ was <NUM>%. Plates were covered with adhesive foil and samples were incubated in the dark at <NUM> for <NUM>. After incubation, plates were centrifuged at 2000rcf, <NUM> for <NUM> to condense droplets. Following this, DTT was added to each sample at a final concentration of <NUM>. Samples were then incubated at <NUM> for 1hr followed by centrifugation at 2000rcf, <NUM> for <NUM>. Next, IAA was added to each sample to give a final concentration of <NUM> and plates were incubated at <NUM> in the dark for <NUM>. Again, plates were centrifuged at 2000rcf at <NUM> for <NUM> and samples were next diluted with LC-MS/MS grade H<NUM>O to give a final concertation of <NUM> NH<NUM>CO<NUM>. Trypsin was then added to each sample so that the protein enzyme ratio was <NUM>:<NUM>. The reaction was stopped with the addition of 2uL of neat TFA to each sample and incubation for a further <NUM> at <NUM>. In order to pellet Rapigest™, digests were transferred from <NUM>-well plates to <NUM> to bind eppendorfs (Eppendorf) and centrifuged for <NUM> at 12000rcf. Supernatants were removed and transferred into clean eppendorfs and lyophilised by speed vacuum at <NUM> for 2hr. Lyophilised samples were stored at -<NUM> until further use.

MRM analysis was performed using an Agilent <NUM> QqQ mass spectrometer with a JetStream electrospray source (Agilent) coupled to a <NUM> Quaternary Pump HPLC system. Peptides were separated on an analytical on a Zorbax Eclipse plus C18, rapid resolution HT: <NUM> x <NUM>, <NUM>, 600Bar column (Agilent) before introduction to the QqQ. A linear gradient of <NUM> - <NUM>% over 17mins was applied at a flow rate of <NUM>. 400µL/min with a column oven temperature of <NUM>. Source parameters were as follows; gas temp: <NUM>, gas flow <NUM>/min, nebuliser psi30, sheath gas temp <NUM> sheath gas flow <NUM>/min. Peptide retention times and optimised collision energies were supplied to MassHunter (B0. <NUM> Agilent Technologies) to establish a dynamic MRM scheduling method based on input parameters of <NUM> cycle times and <NUM> retention time windows. Percentage coefficient of variance (% Cv) of biological and technical replicates was used as a measure of variance and was calculated using the standard calculation of % Cv = (standard deviation/mean)*<NUM>.

CRP levels were evaluated using the "gold" standard clinical grade assay in St Vincent's University Hospital, Dublin. 125µL of serum from each patient was analysed for levels of CRP using an automated CRPL3 Tina-quant C-Reactive Protein assay (Roche Diagnostics, GmbH).

Graph pad Prism software package (<NUM>) were used to investigate the statistical significance of Luminex and miRNA data whereas SOMAsuite (<NUM>) was used to analyse SOMAscan data. The ability of quantified proteins/peptides and miRNAs to predict the diagnosis (PsA or RA) of individual patients was assessed using the random Forest package in R (version <NUM>. The most important variables in providing the area under the receiver operating curve were selected by use of the variable importance index and the Gini decrease in impurity was used to assess the importance of each variable. All AUC values were determined using the ROCR package in R (version <NUM>.

Serum samples were collected from <NUM> PsA and <NUM> RA patients. The demographic and clinical features of patients are summarised in Table <NUM>.

To investigate differences in serum protein expression between patients, individual depleted samples were analysed by nLC-MS/MS on a QExactive mass spectrometer. A total of <NUM> proteins were identified across all samples analysed.

To identify proteins that were differentially expressed between patients with PsA from those with RA (a) univariate analysis was applied to <NUM> commonly identified proteins in these patient samples and (b) multivariate analysis was applied to the complete data set. Univariate analysis (student T test using a Benjamini Hochberg FDR <NUM>) revealed that <NUM> proteins were significantly differentially expressed between PsA and RA patients.

Hierarchical cluster and principle component analysis was carried out on these <NUM> proteins and this demonstrated in an unbiased manner, the overall differences/similarities between expression levels in the individual PsA and RA patients. Clear within group clustering and between group separations could be observed (see <FIG>).

Random forest analysis on the <NUM> proteins revealed patients could be segregated with an AUC of <NUM> (see Table <NUM>; ROC plot of <FIG>). Thus, this data clearly reflected a difference in the serum protein profile between PsA and RA patients.

To extend the breadth of proteome coverage afforded by nLC-MS/MS, samples were also analysed on <NUM> alternative and complementary protein biomarker discovery platforms. SOMAscan analysis supported the quantification of <NUM> proteins in a subset of patient samples PsA (n=<NUM>) and RA (n=<NUM>). Univariate analysis of these data revealed that <NUM> proteins were significantly differentially expressed between PsA and RA patients (see Table <NUM>).

Multivariate analysis revealed that it was possible to discriminate PsA from RA patients with an AUC of <NUM> (Table <NUM>; ROC plot of <FIG>).

Based on their known importance in PsA and RA, <NUM> proteins were selected for analysis using the Luminex assay. Of the <NUM> proteins targeted, <NUM> were identified in every sample. T-tests revealed that <NUM> proteins; IL-<NUM> (p ≤ <NUM>) II-<NUM> BPa, HGF, and FAS (p ≤ <NUM>) were significantly differentially expressed between PsA and RA samples (see <FIG>).

Random forest analysis of the Luminex data demonstrated patients could be segregated with an AUC of <NUM> (see Table <NUM>; <FIG>). In comparison to the LC-MS/MS analysis, the targeted approach to protein discovery yielded data sets with reduced predictive power.

The miRNAome of baseline PsA (n=<NUM>) and RA (n=<NUM>) samples were analysed using a miRNA array. A total of <NUM> miRNAs were identified of which <NUM> were commonly expressed in each sample. Using a Mann Whitney U-test it was found that of the <NUM> common miRNAs analysed <NUM> were significantly differentially expressed between PsA and RA (see Table <NUM>; <FIG>).

Random forest analysis of the <NUM> miRNA data set revealed it was possible to correctly classify only <NUM>/<NUM> patients resulting an AUC of <NUM> (see ROC plot of <FIG>). This data indicated that the serum miRNA profile between PsA and RA patients was not different.

In an attempt to directly compare platforms, the combined matched data set (i.e. from the same <NUM> samples analysed on each platform) were analysed. Results showed that it was possible to distinguish PsA from RA patients with an AUC of <NUM> (see Table <NUM>; ROC plot of <FIG>). The weighted variable importance, which was assessed by a mean decrease in Gini, demonstrated that, out of the top <NUM> analytes contributing to this AUC, <NUM> were proteins (<NUM> of which were identified by nLC-MS/MS and <NUM> by SOMAscan) and <NUM> were miRNAs. These same proteins and miRNAs were observed as statistically significant during uni- and/or multivariate analysis of the individual protein and miRNA datasets. Taken together, the LC-MS/MS data emerged as the most promising and the proteins identified by nLC-MS/MS were prioritised for further evaluation as the most time- and cost-effective strategy.

A total of <NUM> proteins represented by <NUM> peptides and <NUM> transitions (<NUM> per peptide) were brought forward for MRM assay development. These candidates included proteins identified by uni-/multi- variate analysis of the discovery data described here in addition to proteins identified during previous studies in pooled patient samples. Of the proteins brought forward, it was possible to develop an assay for <NUM> of them represented by <NUM> peptides. These peptides were measured in the <NUM> clinical samples in a randomised run order. Random forest analysis of the data revealed it was possible to discriminate between PsA and RA patients with an AUC of <NUM> (see Table <NUM>; <FIG>).

The top <NUM> most important peptides in providing this AUC were selected by use of the variable importance index, here the Gini decrease in impurity was used to assess the importance of each variable.

Peptide expression changes observed during LC-MRM analysis were next compared to those observed during nLC-MS/MS analysis. Comparisons could be made for <NUM>/<NUM> peptides since for <NUM> peptides nLC-MS/MS data was not available. Thus, it was found that for <NUM>/<NUM> peptides, expression changes in PsA and RA patients were in agreement when analysed by both MRM and nLC-MS/MS (<NUM> upregulated in PsA and <NUM> upregulated in RA) supporting their genuine value as putative biomarkers. For the remaining <NUM>/<NUM> peptides, a potential reason for discordance in observations may be due to false discoveries introduced during the initial LC-MS/MS analysis whereby workflows employed were less robust compared to those used during MRM analysis (Table <NUM>). Finally, a MRM assay was developed to CRP (see <FIG>) with analysis of this protein by standard lab assay serving as a comparator. It was not surprising to find that serum levels of CRP were significantly upregulated in patients with RA (n=<NUM>) as compared to those PsA (n=<NUM>) when measured by both ELISA (p ≤ <NUM>) and MRM (p ≤ <NUM>) (see <FIG>) and these measurements could be correlated (R<NUM> = <NUM>) (see <FIG>).

The present invention identifies biomarkers for the differentiation of patients with PsA from those with RA. Importantly, the invention is based on multiplexed analysis of serological markers in patients with early onset PsA. Here it was established that patients with PsA could be differentiated from those with RA based on molecular signatures identified in serum. Multi-omic analysis revealed it was possible to discriminate PsA from RA patients with an AUC of <NUM> (nLC-MS/MS), AUC <NUM> (Luminex), AUC <NUM> (SOMAscan) and AUC <NUM> (miRNA), while combining data from a group of matched patients resulted in an AUC of <NUM>.

To further identify serological protein biomarkers for the stratification of patients with psoriatic arthritis (PsA) from those with rheumatoid arthritis (RA) at early stages of the disease; the serum proteome of patients with PsA and RA was interrogated using liquid chromatography mass spectrometry (LC-MS/MS). Multiple reaction monitoring (MRM) assays were developed to <NUM> proteins and subsequently analysed using a triple quadrupole mass spectrometer.

Recent-onset (symptom duration <<NUM> months), treatment-naive PsA and RA patients with active joint inflammation, aged <NUM> to <NUM> years, were enrolled consecutively. PsA patients (n=<NUM>) fulfilled the CASPAR criteria and patients with RA (n=<NUM>) met the <NUM> ACR/EULAR classification criteria for RA. Exclusion criteria were pregnancy, diseases of bone metabolism, previous treatment with disease-modifying anti-rheumatic drugs (DMARDs) or biologic agents, and treatment with anti-resorptive medications, parathyroid hormone or strontium ranelate <NUM> months prior to the study. The use of calcium and vitamin D supplements and a stable dose of steroids of less than <NUM>/day were permitted during the study.

The development and optimisation of MRM assays was performed using Skyline software (MacCoss laboratory, Washington DC). Assays were developed to proteotypic peptides for all proteins of interest according to the following criteria: no missed cleavages or 'ragged ends', sequence length between <NUM>-<NUM> amino acids. Where possible, peptides sequences with reactive (C) or methionine (M) residues were avoided but not excluded. A working MRM was determined based on the dot product ≥<NUM>, signal to noise ≥<NUM>, data points under the curve ≥<NUM> and percentage coefficient of variance (retention time ≤ <NUM> % , area ≤ <NUM> %).

Crude serum (<NUM>µl) was added to the wells of <NUM>-deep-well plates and digested with trypsin. Tryptic digestion was performed in a flat-bottom polystyrene <NUM>-well plate following an in-house developed standard operating procedure (SOP18A). For protein denaturation, <NUM>µL denaturant solution (<NUM> % trifluoroethanol (TFE) in <NUM> NH<NUM>HCO<NUM> with <NUM> dithiothreitol (DTT)) were added to <NUM>µL serum in each well. The <NUM>-well plate was covered with a sterile adhesive foil and incubated for <NUM> at <NUM>. To remove any condensation from the foil, samples were allowed to cool down to room temperature and centrifuged for <NUM> at <NUM>. <NUM>µL of <NUM> iodoacetamide (IAA) solution was added to each sample, the plate was sealed, vortexed and incubated for <NUM> protected from light. To quench the excess of IAA, <NUM>µL of <NUM> DTT was added to each well. The plate was then resealed, vortexed and incubated for <NUM> protected from light before diluting samples by adding <NUM>µL of <NUM> NH<NUM>HCO<NUM> solution. For each well <NUM>µL trypsin solution (<NUM>/mL sequencing grade modified trypsin (Promega) re-suspended <NUM>:<NUM> in trypsin resuspension buffer (Promega) and <NUM> NH<NUM>HCO<NUM>) was used. After <NUM> incubation at <NUM>, <NUM>µL of <NUM> % formic acid (FA) was added to each well in the <NUM>-well plate. The digestion plates were stored at -<NUM> once the digestion process was complete.

A total of <NUM> proteins represented by <NUM> peptides were used for the MRM assay, which was applied to <NUM> patient samples. MRM analysis was performed using an Agilent <NUM> triple quadrupole (QqQ) mass spectrometer with a JetStream electrospray source (Agilent) coupled to a <NUM> Quaternary Pump HPLC system. Peptides were separated on an analytical Zorbax Eclipse plus C18, rapid resolution HT: <NUM> x <NUM>, <NUM>, <NUM> Bar column (Agilent) before introduction to the QqQ. A linear gradient of <NUM> - <NUM> % over <NUM> mins was applied at a flow rate of <NUM>µl/min with a column oven temperature of <NUM>. Source parameters were as follows; gas temp: <NUM>, gas flow <NUM> I/min, nebuliser psi <NUM>, sheath gas temp <NUM> and sheath gas flow <NUM> I/min. Peptide retention times and optimised collision energies were supplied to MassHunter (B0. <NUM> Agilent Technologies) to establish a dynamic MRM scheduled method based on input parameters of <NUM> millisecond (ms) cycle times and <NUM> retention time windows. The percentage coefficient of variance (% Cv) of biological and technical replicates was used as a measure of variance and was calculated using the standard calculation of % Cv = (standard deviation/mean) <NUM>.

The ability of quantified proteins/peptides to predict the diagnosis (PsA or RA) of individual patients was assessed using the random Forest package in R (version <NUM>. The most important variables in providing the area under the receiver operating curve were selected by use of the variable importance index and the Gini decrease in impurity was used to assess the importance of each variable. All area under the curve (AUC) values were determined using the ROCR package in R (version <NUM>.

Claim 1:
A method of differentiating psoriatic arthritis from rheumatoid arthritis in a subject, the method comprising the steps of:
(a) determining the quantitative or qualitative level of one or more biomarkers in a biological sample from the subject; and
(b) differentiating psoriatic arthritis from rheumatoid arthritis in the subject based on the quantitative or qualitative level of the or each biomarker in the biological sample;
wherein the or each biomarker comprises Rheumatoid factor C6 light chain.