Predictor discovery in training set 6 Training set SJIA (24 F, 14 Q) POLY (15 F, 10 Q) 1 DIGE raw gel images SJIA (13 F, 13 Q) POLY (5 F, 5 Q) Spot finding.

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Predictor discovery in training set 6 Training set SJIA (24 F, 14 Q) POLY (15 F, 10 Q) 1 DIGE raw gel images SJIA (13 F, 13 Q) POLY (5 F, 5 Q) Spot finding spot alignment feature extraction 889 discrete spot features Classifier training Ten-fold cross-validation Classify SJIA F vs Q POLY F vs Q DIGE analysis Prediction analysis of microarray (PAM) Class prediction in testing set 7 Testing set SJIA (24 F, 14 Q) POLY (15 F, 11 Q) Predictors of 4 ~ 12 features PAM class prediction algorithm Construct estimates of predicted class probabilities Analysis of goodness of class separation 2 Cluster analysis 7 feature biomarker panel 2d hierarchical clustering heatmap plotting Normalization manual review Manual review MSMS ID 97 spots P < Mann Whitney 10 protein candidates 4 Literature review + literature candidates - check antibody availability 5 12 ELISA assays Figure 1 8 Prospective Study N.D. Discriminate SJIA F KD FI

FQFQ SJIAPOLY Figure 2

FQ SJIA Figure 3

A2M CFHR1 ATIII GSN A2M GSN TTR APOA1 SAP APOIV HP CRP HP MRP8 MRP14 SAA SAP APOAIV HP CRP HP A2M CFHR1 GSN A2M ATIII TTR APOA1 APOIV APOA1 MRP14 HP MRP8 SAA FQFQSJIAPOLY Figure 4

POLY Q POLY F POLY Q POLY F TRAININGTESTING Figure 5A

SJIA Q SJIA F SJIA Q SJIA F TRAININGTESTING Figure 5B

Figure 6A Goodness of class separation –  probability POLY TrainingPOLY Testing Feature#

Figure 6B Goodness of class separation –  probability SJIA TrainingSJIA Testing Feature#

SJIA Q SJIA F SJIA Q SJIA F TRAININGTESTING Figure 7

Training set n = Clinical diagnosis FQ n = PAM Classified as F Classified as Q Percent Agreement with clinical diagnosis 75%64% +- 71% Overall P < 0.05 Testing set n = Clinical diagnosis FQ n = PAM Classified as F Classified as Q Percent Agreement with clinical diagnosis 88%71% +- 82% Overall P < A B Figure 8 C Biomarker panel of 7 members 1.HP 2.APO AI 3.A2M 4.SAP 5.CRP 6.MRP8/MRP14 7.SAA SJIA

SJIA F KDFI SAP SAA MRP8 MRP14 HP CRP APOA1 A2M Data set n = Clinical diagnosis SJIA F NOT-SJIA F n = Unsupervised clustering Clustered as SJIA F Clustered as NOT-SJIA F Percent Agreement with clinical diagnosis 77%96% % Overall P < Figure 9

Spot Cluster IDProtein IDT test P valueMann Whitney Test P value 6A2M1.05 X X CFHR12.31 X X ATIII1.73 X X ATIII1.17 X X GSN7.68 X X A2M1.79 X X GSN4.23 X X TTR2.16 X X APOA11.84 X X APOA13.27 X X APOA14.28 X X APOA11.32 X X APOA11.10 X X APOA13.50 X X SAP4.16 X X APOAIV1.30 X X HP3.93 X X CRP8.99 X X HP4.03 X X HP8.23 X X HP1.22 X X HP5.38 X X HP9.54 X X HP8.86 X X MRP82.32 X X MRP81.97 X X MRP X X SAA1.26 X X SAA3.03 X X SAA1.22 X X Supplement Table 1