Post on 24-Feb-2016
description
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Classifier training
Mann Whitney
Predictor discoveryin training set
4
Training setSJIA (12 F, 12 Q)POLY (13 F, 10 Q)
1
DIGEraw gel imagesSJIA (10 F, 10 Q)POLY (5 F, 5 Q)
Spot findingspot alignment
feature extraction
889 discrete spot features
Classifier training
ClassifySJIA F vs QPOLY F vs Q
DIGE analysis Prediction analysis (LDA)
Predictor testin testing set
5
Testing setSJIA (10 F, 10 Q)POLY (10 F, 5 Q)
2
Cluster analysis
2d hierarchicalclustering
heatmap plotting
Normalizationmanual review
Manual review
MSMS ID96 spots
3
8 proteincandidates
Assay development
7 ELISA assays
7
DiscriminateSJIA F
KDFI
SJIA PLASMA BIOMARKER DISCOVERY STUDY DESIGN
ClusteringBox-and-Whisker Analysis
DiscriminateSJIA FSJIA QPOLY FPOLY Q
ELISA assays
ELISA assay
ELISA assayFC (27 )KD (10)
Two class classification
Classification analysis
9
ELISA assayQ->F (11)Q->Q (14)
Test to anticipate SJIA F in Q
LDAFisher exact test
P < 10-5
randomization
Blind testing
ClassifySJIA F vs QPOLY F vs Q
Fisher exact test
Training setSJIA F(12) KD (7), FC (15)
ClassifySJIA F vs Non SJIA F
Testing setSJIA F(10) KD (3), FC (12)
randomization
Blind testing
ClassifySJIA F vs Non SJIA F
Fisher exact test
8
Two dimensional DIGE analysis identified 96 protein spots differentially expressed between SJIA flare and quiescence
A B CF Q F Q
SJIA POLY
F Q
SJIA
F Q
POLY
DIGE analysis reveals a seven protein biomarker panel in plasma clearly differentiating SJIA flare from quiescence
B C DF Q F Q
SJIA POLY
F Q
SJIA
F Q
POLY
A2M
APOA1
SAP
CRP
HP
MRP14
SAAMRP8
A2M
APOA1
SAP
CRP
HP
MRP14
SAAMRP8
F Q F QSJIA POLY
APOA1 SAPCRP HP MRP14 SAAMRP8
Relative expression
2
3
4
1
0
A2M
A
Training setn = 24
12 12
Clinicaldiagnosis F Q
n =
LDA
11 1
1 11
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
91.6% 91.6%+ -
91.6%
Overall
P = 1.0 X 10-3
Testing setn = 20
10 10
Clinicaldiagnosis F Q
n =
Testing
8 3
2 7
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
80% 70%+ -
75%
Overall
P = 7 X 10-2
B CA
Biomarker panelof 7 members
1. A2M
2. APO AI
3. CRP
4. HP
5. MRP8/MRP14
6. SAA
7. SAP
SJIA SJIA
DTraining
SJIA F SJIA Q SJIA F SJIA Q
Testing
Pred
icte
d pr
obab
ilitie
sPatient samples
E
Sen
sitiv
ity
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
Training setn = 23
13 10
Clinicaldiagnosis F Q
n =
LDA
10 5
3 5
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
76.9% 50%+ -
65.2%
Overall
P = 0.41
Testing setn = 15
10 5
Clinicaldiagnosis F Q
n =
Testing
3 0
7 5
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
30% 100%+ -
53.3%
Overall
P = 0.20
A B
POLY POLY
CTraining
PF PQ PF PQ
Testing
Pred
icte
d pr
obab
ilitie
sPatient samples
D
1- Specificity
CRP : AUC=0.64
POLY F vs. Q
panel : AUC=0.64
Sen
sitiv
ity
ELISA analysis invalidates the seven protein biomarker panel in POLY plasma
Training setn = 25
11 14
Clinicaldiagnosis QF QQ
n =
LDA
6 2
5 12
Classified as QF
Classified as QQ
PercentAgreementwith clinical
diagnosis
54.5% 85.7%+ -
72%
Overall
P = 0.08
A
SJIA
B
Pred
icte
d pr
obab
ilitie
s
Patient samples
C
CRP : AUC=0.59
SJIA QF vs. QQ
panel : AUC=0.78
ESR : AUC=0.60
Training
QQ QF
Sen
sitiv
ity
1- Specificity
ELISA analysis shows the ineffectiveness of seven protein plasma biomarker panelin prognosis of impending SJIA flare
B C
MRP14
F KD FC
SJIA
A2M
APOA1
SAP
CRP
HP
SAAMRP8
1210 12
SJIAF KD FCData set
n = 34
10
Clinicaldiagnosis
SJIAF
NOT-SJIAF
n =
Unsupervised clustering
7
3
1
23
Clustered as SJIA F
Clustered as NOT-SJIA F
PercentAgreementwith clinical
diagnosis
70% 95.8%+ -
88.2%
Overall
P = 1.6 X 10-4
24
Prot
ein qu
antity
F KD FCSJIA
APOA1 SAPCRP HP MRP14 SAAMRP8A2M
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
Training setn = 34
12 22
Clinicaldiagnosis
n =
LDA
12 0
0 22
PercentAgreementwith clinical
diagnosis
100% 100%+ -
100%
Overall
P = 7.4X 10-7
Testing setn = 25
10 15
Clinicaldiagnosis
n =
Testing
9 0
1 15
PercentAgreementwith clinical
diagnosis
90% 100%+ -
93.3%
Overall
P = 4.9 X 10-6
A B
SJIAF
C
Training
FC
Testing
Pred
icte
d pr
obab
ilitie
s
Patient samples
712 15
SJIAF KD FC
NOTSJIA F
SJIAF
NOTSJIA F
310 12
SJIAF KD FC
KD SJIA F FC KD SJIA 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
Training setn = 39
16 23
Clinicaldiagnosis F Q
n =
LDA
11 3
5 20
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
68.8% 87%+ -
79.5%
Overall
P = 6.0X 10-4
Bootstrap setn = 73
34 39
Clinicaldiagnosis F Q
n =
Testing
29 7
5 32
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
85.3% 82.1%+ -
83.5%
Overall
P = 5.9 X 10-9
B C
SJIA SJIA
DTraining
SF SQ SF SQ
Bootstrap confirmation
Pred
icte
d pr
obab
ilitie
sPatient samples
E
1- Specificity
Bootstrap : AUC=0.90
SJIA F vs. Q
Training : AUC=0.84
Sen
sitiv
ity
A
Biomarker panelof 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
Training setn = 13
6 7
Clinicaldiagnosis F Q
n =
LDA
5 1
0 6
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
100% 85.7%+ -
84.6%
Overall
P = 0.01
Bootstrap setn = 16
6 10
Clinicaldiagnosis F Q
n =
Testing
5 2
1 8
Classified as F
Classified as Q
PercentAgreementwith clinical
diagnosis
83.3% 80%+ -
81.25%
Overall
P = 0.03
A B
POLY POLY
CTraining
PF PQ PF PQ
Pred
icte
d pr
obab
ilitie
sPatient samples
D
1- Specificity
Bootstrap: AUC=0.91
POLY F vs. Q
Training: AUC=1
Sen
sitiv
ity
A
Biomarker panelof 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