Urine peptide biomarkers in Systemic Juvenile Idiopathic Arthritis (SJIA) Xuefeng B. Ling 1, Ken Lau 1, Jane Park 2,3, Claudia Macaubas 3, Jane C. Burns 4, John Kanagaye 4, James Schilling 1, Elizabeth Mellins 2,3 1 Pediatric Biotechnology Core, 2 Division of Pediatric Rheumatology, 3 Department of Pediatrics, Stanford University School of Medicine, and 4 Department of Pediatrics, University of California at San Diego

Introduction Systemic Juvenile Idiopathic Arthritis (SJIA) is currently classified as a subtype of JIA and consists of a combination of systemic symptoms (fever, rash, serositis, such as pericarditis, pleuritis) and arthritis. It represents ~10-15% of JIA overall, but accounts for 2/3 of JIA mortality. A similar condition in adults, termed Adult Still’s disease occurs rarely. There is no diagnostic test for SJIA. Outcomes (short and long-term) are quite heterogeneous. For example, disease course can be monocyclic, polycyclic or persistent. Significant joint damage and growth delay occur in a subset of patients (~50%). Complications such as macrophage activation syndrome can occur in a subset of patients (overt MAS in ~10%; “occult MAS” in ~35%). Responses to methotrexate and TNF inhibition are often incomplete; complete responses to IL-1 inhibition and IL-6 inhibition occur in subsets of patients. Molecular signs of disease activity occur in advance of clinical signs, including elevation of ESR (Ling et al, manuscript submitted). Urine peptide diagnostic and prognostic biomarkers would be of clinical use, especially for serial sampling of pediatric patients.

Urine peptidome is a rich source of peptides derived from diverse proteins 2D MS/MSMS analysis of the normal urine peptidome reveals 11,988 different urine peptide sequences from 8519 unique protein precursors; these proteins are involved in numerous biological processes. AB

Sample peptides: - Flare: sample1,2,3… - Quiescence: sample 1,2,3… - Normal: sample 1,2,3… LCMS for each sample LC fraction – m/z – abundance MASS-Conductor © Machine learning feature discovery and classification Biomarker panels MSMS protein ID Independent validation with quantitative mass spec (MRM) LCMS-based, label-free peptidomics platform for urine biomarker discovery LEGEND LC: liquid chromatograpy m/z: mass:charge ratio MRM: multiple reaction monitoring

The order of freezing and centrifugation in urine sample processing does not significantly affect the urine peptidome A B

Longer duration of room temperature (RT) storage progressively degrades the urine peptidome A B

Pooling of samples by clinical class results in artifacts A B C pooling results in averaging pooling results ion suppression Stars (matched color) represent pooled samples. Not all classes give accurate profiles in pooled samples.

Normalization of urine peptide profiles to address analytical and biological variability Urine peptide content in each sample was quantified by the 2,4,6-trinitro-benzenesulfonic acid (TNBS) assay 2. Equal molar amounts of urine peptide samples were profiled in the LCMS analysis. To control for the LC-MALDI analytical variations, each MALDI spot was spiked with a calibrating peptide correct m/z ratios and normalize peptide signals within the spot. Approach to normalization used in SJIA peptide profile analysis 29 endogenous collagen-derived peptides, previously reported to represent “housekeeping” peptides 1, do not further normalize the peptide data. 1 Jantos-Siwy J, Schiffer E, Brand K, et al. Quantitative Urinary Proteome Analysis for Biomarker Evaluation in Chronic Kidney Disease. Journal of Proteome Research 2009; 8: “Before” represents urine peptide data from 130 subjects, using current normalization approaches (left). If effective, normalization with the endogenous panel 1 should decrease the CV of the majority of urine peptide features. However, the CV is substantially increased when using this panel. Random 29-peptide panels serve as controls. 2 Snyder SL and Sobocinski PZ. An improved 2,4,6-trinitro- benzenesulfonic acid method for the determination of amines. Analytical Biochemistry 1975; 64:

ND/SAF vs. AF/QOM/RD/KD/ FI ND/SAF vs. QOM/RD ND/SAF vs. KD/FI Gender2.56E E E-02 Age2.12E E E-02 WBC5.59E E E-02 ESR5.08E E E-02 CRP7.19E E E-02 PLT4.01E E E-01 SJIA patients parameters ND n=2SAF n=18AF n=9QOM n=18RD n=9 obs. media n s.d.Rangeobs. media n s.d.Rangeobs media n s.d.rangeobs media n s.d.rangeobs media n s.d.range Age Gender (male%) 2100%1833%9 1839%922% WBC ESR CRP129.4NA PLT NSAID2100%1688%862%1486%70% PO.PRED20%1650%838%1429%70% MTX20%1747%850%1471%70% TNF20%1612%825%1450%70% IL.1.RA20%1735%812%1436%70% parameters KD n=23FI n=23 obsmedians.d.rangeobsmedians.d.Range Age Gender (male%)2281.8%2360.9% WBC ESR CRP PLT Patient cohort for discovery of an SJIA systemic flare urine peptide signature KD/FI patients Significance analysis (Student T test P value) %: subgroup percentage for categorical variables (P value < 0.01) ND= new onset SJIA disease SAF=active systemic disease +arthritis AF= SJIA with active arthritis only QOM=quiescent SJIA on medication RD=SJIA in remission off medication KD=Kawasaki disease FI=acute febrile illness (various infections)

SJIA flare signature analysis 2 Discovery set (ND,SAF vs. AF,QOM,RD,KD,FI) 1 LCMS raw spectra Peak finding peak alignment feature extraction Urine peptide index NSC feature selection Ten-fold Cross-validation Urine profilingUrine biomarker analysis (NSC, LDA, ROC) Feature selection Biomarker identification LDA analysis 17 peptide biomarker panel ROC analysis 500 bootstrap samples 3 Classification Aim #1 ND.SAF vs. KD.FI Aim #2 ND.SAF vs. QOM.RD Experimental design to discover an SJIA systemic flare urine peptide signature* *Long-term goals: Aim #1: identification of diagnostic urine peptide profile that distinguishes new onset SJIA patients from other systemic inflammatory states, including Kawasaki Disease (KD) and febrile illness (FI). Aim #2: prediction of impending flare during quiescent periods of SJIA.

Proteinm/z U test P value Peptide sequence ND/SAF vs. KD ND/SAF vs. FI ND/SAF vs. AF ND/SAF vs. QOM ND/SAF vs. RD ND/SAF vs. HC* A1AT E E E E E E-03 EAIPMSIPPEVKFNKP A1AT E E E E E E-03 EAIPMSIPPEVKFNKPF COL1A E E E E E E-02GAKGDAGApGApGSQGApG COL1A E E E E E E-02SpGSpGPDGKTGPPGpAG COL1A E E E E E E-01GPpGPpGKNGDDGEAGKPG COL1A E E E E E E-02 GPpGKNGDDGEAGKpGRpG COL1A E E E E E E-02 NGDDGEAGKPGRpGERGPpGP COL1A E E E E E E-01NGApGEAGRDGNpGNDGPpG COL3A E E E E E E-01DGApGKNGERGGpGGpGP COL9A E E E E E E-01PpGPpGYPGKQ FGA E E E E E E-01 DEAGSEADHEGTHSTKR FGA E E E E E E-03 DEAGSEADHEGTHSTKRG FGB E E E E E E-01 EEAPSLRPAPPPISGGG FGB E E E E E E-02 EEAPSLRPAPPPISGGGY UMOD E E E E E E+00 VLNLGPITR UMOD E E E E E E-01SGSVIDQSRVLNLGPI UMOD E E E E E E-01SGSVIDQSRVLNLGPIT Cluster I Cluster II Cluster III Cluster IV Cluster V Urine peptides identified as a 17-peptide signature differentiating ND/SAF from AF/QOM/RD subjects Sequence analysis revealed the signature peptides fall into several tight clusters (P value < 0.005)(0.005 <P value < 0.05) *HC=healthy control; n=10

Classification Clinical diagnosis ND.SAFKD.FI n = LDA Predicted as SJIA F Predicted as non SJIA F Percent Agreement with clinical diagnosis 85%100% % Overall P = 4.53X Urine peptide panel provides accurate class predictions discriminating ND/SAF from KD/FI subjects ND SAFKD FI Predicted probabilities Patient samples Sensitivity 1- Specificity Mean(AUC): 99.9% 17-urine-peptide panel effectively differentiates samples from subjects with active SJIA from samples from subjects with systemic inflammation due to KD/FI ROC analysis demonstrates the effectiveness of the panel classifier

Classification Clinical diagnosis ND.SAFQOM.RD n = LDA Predicted as SJIA F Predicted As SJIA Q Percent Agreement with clinical diagnosis 90%100% % Overall P = 4.16X ND SAFQOM RD Predicted probabilities Patient samples Sensitivity 1- Specificity Mean(AUC): 99.8% Urine peptide panel provides accurate class predictions discriminating ND/SAF from QOM/RD subjects 17-urine-peptide panel effectively discriminates samples from SJIA subjects with active disease (ND/SAF) from samples from SJIA subjects with inactive disease (QOM/RD) ROC analysis demonstrates the effectiveness of the panel classifier

Future experiments to validate SJIA urine peptide biomarkers will use MRM MRM standard curve analysis for THP 1912 peptide in urine. As a feasibility experiment for MRM in urine, we tested a urinary peptide of mass This peptide was infused at a concentration of ~200 fmol/uL to determine the best parent and daughter ions to analyze in subsequent MRM transitions. For peptide 1912, the prominent parent ion is the triply charged ion and the most prominent daughter ion is the doubly charged y ion. The linearity of the standard curve spans >4 orders of magnitude. In 20% acetonitrile, the LOD for this peptide is 0.2 pg (~104 amol). In urine, the LOD is 0.2 pg (~104 amol, S/N ~4), while the LOQ is ~ 0.8 pg (~480 amol).

Proteolytic and anti-proteolytic pathway changes IL10, RANTES … Systemic disease Circulating inflammatory mediators TIMP1, MMP9 … Disease specific degradation of plasma proteins plus Disease specific shedding from other organs A1AT, COL1A1,1A2,3A1,9A2, FGA, FGB peptides … Filtered proteases and inhibitors TIMP1, MMP9 … Disease specific degradation of renal proteins UMOD peptides… Blood filtrate in the kidney Disease specific peptides derived from circulation A1AT, COL1A1,1A2,3A1,9A2, FGA, FGB peptides … Disease diagnostic/prognostic signature Biomarker panel of urine peptides Plasma origin peptides Renal origin peptides Amino acids Proteolytic and anti-proteolytic pathway changes Inflammatory mediators Current Model: SJIA urine peptide biomarkers reflect changes in expression of inflammatory mediators and proteolytic and anti- proteolytic activities during active SJIA

Conclusions: A protocol for urine peptide profiling has been developed that will allow collection of samples at distant sites using freezing of urine samples after a controlled (not more that 4 hours) period at room temperature. In addition, we find that MRM analysis, an initial approach for quantitative validation of urine peptide biomarkers is robust in urine. A candidate biomarker panel of 17 peptides has been identified that is effective in discriminating active systemic JIA from new onset Kawasaki disease or acute febrile illness (from various infectious causes). This panel requires validation in a larger cohort of new onset SJIA subjects in comparison to other acute systemic inflammatory disease, but it has the potential to be a diagnostic test for SJIA. The same biomarker panel discriminates active from inactive systemic disease in SJIA. This panel may detect incipient disease activity prior to clinical evidence of disease. Serial evaluation (q 2wks) of urine samples from SJIA subjects using MRM analysis will test this hypothesis. We propose a model in which urine peptides may be disease-specific due to the effects of inflammatory mediators on proteases and anti-proteases at sites of inflammation, in circulation and in the urinary tract.