1. Classifier training Mann Whitney Predictor discovery in training set 4 Training set SJIA (12 F, 12 Q) POLY (13 F, 10 Q) 1 DIGE raw gel images SJIA (10 F, 10 Q) POLY (5 F, 5 Q) Spot finding spot alignment feature extraction 889 discrete spot features Classifier training Classify SJIA F vs Q POLY F vs Q DIGE analysis Prediction analysis (LDA) Predictor test in testing set 5 Testing set SJIA (10 F, 10 Q) POLY (10 F, 5 Q) 2 Cluster analysis 2d hierarchical clustering heatmap plotting Normalization manual review Manual review MSMS ID 96 spots 3 8 protein candidates Assay development 7 ELISA assays 7 Discriminate SJIA F KD FI SJIA PLASMA BIOMARKER DISCOVERY STUDY DESIGN Clustering Box-and-Whisker Analysis Discriminate SJIA F SJIA Q POLY F POLY Q ELISA assays ELISA assay FC (27 ) KD (10) Two class classification Classification analysis 9 ELISA assay Q->F (11) Q->Q (14) Test to anticipate SJIA F in Q LDA Fisher exact test P < 10 -5 randomization Blind testing Classify SJIA F vs Q POLY F vs Q Fisher exact test Training set SJIA F(12) KD (7), FC (15) Classify SJIA F vs Non SJIA F Testing set SJIA F(10) KD (3), FC (12) randomization Blind testing Classify SJIA F vs Non SJIA F Fisher exact test 8

2. Two dimensional DIGE analysis identified 96 protein spots differentially expressed between SJIA flare and quiescence A BC FQFQ SJIAPOLY FQ SJIA FQ POLY

3. DIGE analysis reveals a seven protein biomarker panel in plasma clearly differentiating SJIA flare from quiescence BCD FQFQ SJIAPOLY FQ SJIA FQ POLY A2M APOA1 SAP CRP HP MRP14 SAA MRP8 A2M APOA1 SAP CRP HP MRP14 SAA MRP8 FQFQ SJIAPOLY APOA1SAPCRPHPMRP14SAAMRP8 Relative expression 2 3 4 1 0 A2M A

4. Training set n = 24 12 Clinical diagnosis FQ n = LDA 111 1 Classified as F Classified as Q Percent Agreement with clinical diagnosis 91.6% +- Overall P = 1.0 X 10 -3 Testing set n = 20 10 Clinical diagnosis FQ n = Testing 83 27 Classified as F Classified as Q Percent Agreement with clinical diagnosis 80%70% +- 75% Overall P = 7 X 10 -2 B CA Biomarker panel of 7 members 1.A2M 2.APO AI 3.CRP 4.HP 5.MRP8/MRP14 6.SAA 7.SAP SJIA D Training SJIA FSJIA QSJIA FSJIA Q Testing Predicted probabilities Patient samples E Sensitivity 1- Specificity CRP : AUC=0.58 SJIA F vs. Q panel : AUC=0.82 ESR : AUC=0.86 ELISA analysis validates the seven protein biomarker panel in plasma

5. Training set n = 23 1310 Clinical diagnosis FQ n = LDA 105 35 Classified as F Classified as Q Percent Agreement with clinical diagnosis 76.9%50% +- 65.2% Overall P = 0.41 Testing set n = 15 105 Clinical diagnosis FQ n = Testing 30 75 Classified as F Classified as Q Percent Agreement with clinical diagnosis 30%100% +- 53.3% Overall P = 0.20 A B POLY C Training PFPQPFPQ Testing Predicted probabilities Patient samples D 1- Specificity CRP : AUC=0.64 POLY F vs. Q panel : AUC=0.64 Sensitivity ELISA analysis invalidates the seven protein biomarker panel in POLY plasma

6. Training set n = 25 1114 Clinical diagnosis QFQQ n = LDA 62 512 Classified as QF Classified as QQ Percent Agreement with clinical diagnosis 54.5%85.7% +- 72% Overall P = 0.08 A SJIA B Predicted probabilities Patient samples C CRP : AUC=0.59 SJIA QF vs. QQ panel : AUC=0.78 ESR : AUC=0.60 Training QQQF Sensitivity 1- Specificity ELISA analysis shows the ineffectiveness of seven protein plasma biomarker panel in prognosis of impending SJIA flare

7. B C MRP14 FKDFC SJIA A2M APOA1 SAP CRP HP SAA MRP8 121012 SJIA F KDFCData set n = 34 10 Clinical diagnosis SJIA F NOT-SJIA F n = Unsupervised clustering 7 3 1 23 Clustered as SJIA F Clustered as NOT-SJIA F Percent Agreement with clinical diagnosis 70%95.8% + - 88.2% Overall P = 1.6 X 10 -4 24 Protein quantity FKDFC SJIA APOA1SAPCRPHPMRP14SAAMRP8A2M 2 3 4 1 0 A DIGE analysis shows that seven protein SJIA flare panel in plasma clearly differentiating SJIA flare from confounding Kawasaki and febrile illness

8. Training set n = 34 1222 Clinical diagnosis n = LDA 120 022 Percent Agreement with clinical diagnosis 100% +- Overall P = 7.4X 10 -7 Testing set n = 25 1015 Clinical diagnosis n = Testing 90 115 Percent Agreement with clinical diagnosis 90% 100% +- 93.3% Overall P = 4.9 X 10 -6 AB SJIA F C Training FC Testing Predicted probabilities Patient samples 71215 SJIA F KDFC NOT SJIA F SJIA F NOT SJIA F 31012 SJIA F KDFC KDSJIA FFCKDSJIA F Clustered as SJIA F Clustered as NOT SJIA F ELISA analysis validates the utility of the seven protein SJIA flare panel in plasma to discriminate SJIA flare from confounding Kawasaki and febrile illness

9. Training set n = 39 1623 Clinical diagnosis FQ n = LDA 113 520 Classified as F Classified as Q Percent Agreement with clinical diagnosis 68.8%87% +- 79.5% Overall P = 6.0X 10 -4 Bootstrap set n = 73 3439 Clinical diagnosis FQ n = Testing 297 532 Classified as F Classified as Q Percent Agreement with clinical diagnosis 85.3%82.1% +- 83.5% Overall P = 5.9 X 10 -9 B C SJIA D Training SFSQSFSQ Bootstrap confirmation Predicted probabilities Patient samples E 1- Specificity Bootstrap : AUC=0.90 SJIA F vs. Q Training : AUC=0.84 Sensitivity A Biomarker panel of 7 members 1.TIMP1 2.MMP9 3.IL18 4.RANTES Agilent protein array analysis reveals a four protein SJIA flare panel in plasma clearly differentiating SJIA flare from quiescence

10. Training set n = 13 67 Clinical diagnosis FQ n = LDA 51 06 Classified as F Classified as Q Percent Agreement with clinical diagnosis 100%85.7% +- 84.6% Overall P = 0.01 Bootstrap set n = 16 610 Clinical diagnosis FQ n = Testing 52 18 Classified as F Classified as Q Percent Agreement with clinical diagnosis 83.3%80% +- 81.25% Overall P = 0.03 A B POLY C Training PFPQPFPQ Predicted probabilities Patient samples D 1- Specificity Bootstrap: AUC=0.91 POLY F vs. Q Training: AUC=1 Sensitivity A Biomarker panel of 4 members 1.TIMP2 2.IGFBP-3 3.IGFBP-6 4.VCAM-1 Agilent protein array analysis reveals a four protein POLY flare panel in plasma clearly differentiating POLY flare from quiescence Bootstrap confirmation