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 Identification of FGA peptides LDA analysis 6 peptide biomarker panel ROC analysis 500 bootstrap samples 3 Classification ND.SAF vs. KD.FI ND.SAF vs. QOM.RD SJIA flare signature analysis 4 Discovery set (SAF, QOM) NSC feature selection Ten-fold Cross-validation Feature selection LDA analysis 6 protein biomarker panel ROC analysis 5 Classification Training Samples SAF vs. QOM “bootstrap” samples SAF vs. QOM Literature review Antibody array construction Hypothesis generation Cherry pick 43 protein antibodies Antibody array assay Antibody array profiling Plasma biomarker analysis (NSC, LDA, ROC) 6 FIGURE 1

TABLE 1

TABLE 2

FGA(20-35) ADSGEGDFLAEGGGVR FGA( ) AGSEADHEGTHSTKRG FGA( ) DEAGSEADHEGTHSTKR FGA( ) DEAGSEADHEGTHSTKRG FGA( )2560.2DEAGSEADHEGTHSTKRGHAKSRP FGA( )* DEAGSEADHEGTHSTKRGHAKSRPV MH+ProteinSequence Relative abundance ND.SAF AF.QOM.RD.KD.FI TABLE 3

FGA( ) FGA( ) FGA( )* SAF AF QOM RDKDFIHCND FGA( ) FGA( ) FGA(20-38) SAF AF QOM RDKDFIHCND FGA peptide marker distribution Relative abundance FIGURE 2

Classification Clinical diagnosis ND.SAFKD.FI n = LDA Predicted as SJIA F Predicted as non SJIA F Percent Agreement with clinical diagnosis 70%97.8% % Overall P = 6.7X10 -9 AB ND SAFKD FI Predicted probabilities Patient samples Sensitivity 1- Specificity Mean(AUC): 95.6% C FIGURE 3

Classification Clinical diagnosis ND.SAFQOM.RD n = LDA Predicted as SJIA F Predicted As SJIA Q Percent Agreement with clinical diagnosis 65%92.6% % Overall P = 6.1X10 -5 AB ND SAFQOM RD Predicted probabilities Patient samples Sensitivity 1- Specificity Mean(AUC): 91.0% C FIGURE 4

MH+SequenceFGA location DSGEGDFLAEGGGVR ADSGEGDFLAEGGGVR DSGEGDFLAEGGGVRGPR ADSGEGDFLAEGGGVRGPR SQLQKVPPEWK QLQKVPPEWK GGSTSYGTGSETESPRN GGSTSYGTGSETESPRNPSSAGSWN GSTGNRNPGSSGTGGTATWKPGSSGP PGSSGTGGTATWKPG DGFRHRHPDEAAF SSSYSKQFTSSTSYNRGDSTFES SSSYSKQFTSSTSYNRGDSTFESKS SSSYSKQFTSSTSYNRGDSTFESKSY QFTSSTSYNRGDSTFES DEAGSEADHEGTH DEAGSEADHEGTHST DEAGSEADHEGTHSTKRGH DEAGSEADHEGTHSTKRGHA DEAGSEADHEGTHSTKRGHAKSRP DEAGSEADHEGTHSTKRGHAKSRPV GSEADHEGTHSTKRGHAKSRPV ADEAGSEADHEGTHSTKRGHAKSRPV MADEAGSEADHEGTHSTKRGHA MADEAGSEADHEGTHSTKRGHAKSRP MADEAGSEADHEGTHSTKRGHAKSRPV MADEAGSEADHEGTHSTKRGHAKSRPV* SYKMADEAGSEADHEGTHST SYKMADEAGSEADHEGTHSTKRGHA SYKMADEAGSEADHEGTHSTKRGHAKSRPV SYKMADEAGSEADHEGTHSTKRGHAKSRPV* GHAKSRPV I II III IV V VI VII TABLE 4

B D TrainingBootstrapping testing A FIGURE 5 SAFQOM Predicted probabilities Patient samples SAFQOM Training samples n = Clinical diagnosis FQ n = LDA Classified as F Classified as Q Percent Agreement with clinical diagnosis 92%71.4% % Overall P = 7.9X Bootstrapping samples n = Clinical diagnosis FQ n = Testing Classified as F Classified as Q Percent Agreement with clinical diagnosis 87.8%81.8% % Overall P = 2.4 X C SJIA Relative abundance biomarkersSAFQOM TIMP IL RANTES P-Selectin MMP L-Selectin

Sensitivity 1- Specificity Mean(AUC): 92.2% Sensitivity 1- Specificity Mean(AUC): 90.7% TrainingBootstrapping testing FIGURE 6

A C TrainingBootstrapping testing FIGURE 5 SAFQOM Predicted probabilities Patient samples SAFQOM Training samples n = 47 LDA Classified as F Classified as Q Percent Agreement with clinical diagnosis 65%92.6% % Overall P = 6.1X Bootstrapping samples n = Clinical diagnosis FQ n = Testing Classified as F Classified as Q Percent Agreement with clinical diagnosis 87.8%81.8% % Overall P = 2.4 X B SJIA Clinical diagnosis ND.SAFQOM.RD n = SJIA

Sensitivity 1- Specificity Mean(AUC): 90.8% Sensitivity 1- Specificity Mean(AUC): 80.9% TrainingBootstrapping testing FIGURE 6