3rd Global Forum on TB vaccines,
March 25th, 2013
Systems analysis of TB disease progression & vaccines
 Enhance vaccine development through systems analysis of clinical
studies (reverse translation)
1. Identify biomarkers to predict functional responses
2. Generate hypotheses about mechanisms
 Two case studies
• 1- Correlates of TB disease risk in adolescents
• 2- Comparative analysis of protein+adjuvant vaccines in NHP
2
Case study #1
Correlates of TB disease risk in adolescents

Collaboration with SATVI / University of Cape Town : Willem Hanekom (PI)

Identify prospective RNA signatures that discriminate M.tb infected adolescents
who progress to TB disease from the M.tb infected adolescents who do not
progress

The samples are from the Adolescent Cohort Study (ACS)
•
6,363 adolescents, aged 12-18 years, followed-up for 2 years
~50% latently infected at enrollment TB infected (TST > 10mm or Quantiferon Gold)
~0.5% case rate
Control
Training dataset
• 288 samples (35 cases, 65 controls at 1-4 time points)
• Whole blood RNA-Seq 30 million 50bp paired-end reads
Case
TSS
3
SERPING1
4
Post-treatment
0.5-1yr before TB
0-0.5yr before TB
Incident case
1.5-2yrs before TB
1-1.5yrs before TB
Controls
Over 2 yrs before TB
Prediction accuracy
Pairwise junction-pair ensemble biomarker
Red = higher in cases
Results of 5-fold crossvalidation
78% 7% 44% 45% 67% 71% 71% 65%
Pairwise junction-pair ensemble biomarker
Red = higher in cases
TB
SERPING1 also identified in
Berry et al., 2010 and Maertzdorf et al., 2011
5
Confirmation and validation of the biomarker
 Confirmation by qRT-PCR
• Performed at Seattle BioMed (48X48) and UCT (96x96)
• 2-fold cross-validation
Controls
Controls
>D720
Over 2 yrs before
TB
D540TO720
1.5-2yrs before
TB
1-1.5yrs before
TB
D360TO540
0.5-1yr before
TB
D180TO360
0-0.5yr before
TB
D000TO180
Incident case
ICs
Post-treatment
PostRx
1C accuracy
Prediction
Seattle BioMed
RNA-Seq
qRT-PCR
78%
73%
16%
28%
39%
44%
63%
61%
78%
82%
84%
83%
86%
88%
75%
75%
UCT
RNA-Seq
qRT-PCR
75%
72%
15%
45%
42%
34%
66%
62%
77%
77%
84%
82%
87%
89%
74%
74%
RNA-seq
• Fluidigm Biomark analysis of RNA-Seq cDNA libraries
GBP5
Controls
>D720
D540TO720
D360TO540
D180TO360
D000TO180
ICs
PostRx
2C
Seattle BioMed
RNA-Seq
qRT-PCR
85%
84%
0%
16%
26%
28%
42%
33%
72%
63%
83%
83%
79%
75%
57%
57%
qRT-PCR
 Validation using independent incident case microarray datasets
• Step 1: Parameterize the classifiers using the
UK training set
London Test, 1c
• Step 2: Perform blind predictions on
London Test, 2c
1c
remaining cohorts
1c
London
Test, 2c
SA Validation,
1c
(Berry et al., 2010; Bloom et al., 2012)
6
London
Test, 2c
SA
Validation,
1c
2c
1c
SA
Validation,
2c
Bloom,
1c
Sensitivity
Specificity
(Berry et al., 2010)
Sensitivity
Specificity
0.81
0.71
Sensitivity
Specificity
0.81
0.71
0.76
0.71
0.81
0.76
1.00
0.81
0.81
0.76
1.00
0.81
0.95
0.77
1.00
0.81
0.95
0.77
.90
.92
Confirmation and validation of the biomarker
 Confirmation by qRT-PCR
• Performed at Seattle BioMed (48X48) and UCT (96x96)
• 2-fold cross-validation
Controls
Controls
>D720
Over 2 yrs before
TB
D540TO720
1.5-2yrs before
TB
1-1.5yrs before
TB
D360TO540
0.5-1yr before
TB
D180TO360
0-0.5yr before
TB
D000TO180
Incident case
ICs
Post-treatment
PostRx
1C accuracy
Prediction
Seattle BioMed
RNA-Seq
qRT-PCR
78%
73%
16%
28%
39%
44%
63%
61%
78%
82%
84%
83%
86%
88%
75%
75%
UCT
RNA-Seq
qRT-PCR
75%
72%
15%
45%
42%
34%
66%
62%
77%
77%
84%
82%
87%
89%
74%
74%
RNA-seq
• Fluidigm Biomark analysis of RNA-Seq cDNA libraries
GBP5
Controls
>D720
D540TO720
D360TO540
D180TO360
D000TO180
ICs
PostRx
2C
Seattle BioMed
RNA-Seq
qRT-PCR
85%
84%
0%
16%
26%
28%
42%
33%
72%
63%
83%
83%
79%
75%
57%
57%
qRT-PCR
 Validation using independent incident case microarray datasets
• Step 1: Parameterize the classifiers using the
UK training set
London Test, 1c
• Step 2: Perform blind predictions on
London Test, 2c
1c
remaining cohorts
1c
London
Test, 2c
SA Validation,
1c
(Berry et al., 2010; Bloom et al., 2012)
7
London
Test, 2c
SA
Validation,
1c
2c
1c
SA
Validation,
2c
Bloom,
1c
Sensitivity
Specificity
(Berry et al., 2010)
Sensitivity
Specificity
0.81
0.71
Sensitivity
Specificity
0.81
0.71
0.76
0.71
0.81
0.76
1.00
0.81
0.81
0.76
1.00
0.81
0.95
0.77
1.00
0.81
0.95
0.77
.90
.92
Integrating WB transcriptomes and PBMC counts
(See Adam Penn-Nicholson, Tuesday 11:44, Breakout I – Biomarkers)
Adam Penn-Nicholson
Tom Scriba
Willem Hanekom
UCT/SATVI
8
“NK”
Non-CD14
Non-CD3
Non-CD19
Non-DC
Correlations between transcriptomes and
NK cell counts, in cases and controls
NCAM1/CD56
Relative expression
Controls
Cases
%NK cells in PBMC
NK cells can restrict Mtb growth in
macrophages (Millman et al., 2008) and
cytokine-primed NKs can respond to
extracellular Mtb (Portevin et al., 2012).
9
Correlations between transcriptomes and
NK cell counts, in cases and controls
NCAM1/CD56
KLRF1/NKp80
Relative expression
Relative expression
Controls
Cases
%NK cells in PBMC
NK cells can restrict Mtb growth in
macrophages (Millman et al., 2008) and
cytokine-primed NKs can respond to
extracellular Mtb (Portevin et al., 2012).
10
%NK cells in PBMC
NKp80 binding to monocyte AICL promotes mutual
activation as well as cytotoxicity against myeloid
malignancies (Welte et al., 2006).
Many NK efffector molecules exhibit impaired
expression in TB cases compared to controls
Green
= higher in controls; Red = higher in cases
11
Most monocyte correlated genes exhibit case vs. control
differences, and these include major inflammatory networks
Controls
Cases
Significant case/control difference
Not significant
Red
12 = higher in cases
Correlations between transcriptomes and
monocyte counts, in cases and controls
%Monocytes in PBMC
ALOX15B promotes anti-inflammatory
lipoxin (LXA4) production (Wuest et al., 2012),
which is clinically relevant in TB (Tobin et al., 2012).
13
LILRB4/ILT3
Relative expression
Relative expression
ALOX15B
%Monocytes in PBMC
Soluble and APC-expressed ILT3 induces
anergy and Treg phenotype of naïve and primed
CD4+ T cells (Suciu-Foca & Cortesini, 2007).
Case study #2: Modular analysis of
protein+adjuvant vaccines in NHP
How does innate signaling
influence the adaptive response?
Bob Seder &
Joe Francica, NIH VRC
Modular analysis of adjuvant-induced innate immune responses
(Plasma cells)
(Hematopoietic
Precursors)
M4.11
(B cells)
M3.3
1
Inflammation
M4.2
0.75
M4.10
M5.1
0.5
(Cytotoxicity) M4.15
M6.13
0.25
0
M6.19
M1.2
-0.25
-0.5
T cells
M6.15
-0.75
DOWN UP
M3.4
M4.1
Interferon
response
M5.12
M6.9
Lymphoid
lineage
M3.2
M4.7
M4.13
M5.15
(Neutrophils)
Module definitions updated
from Chaussabel et al., 2008
M4.6
M6.6
Evaluate complex transcriptome responses
Myeloid lineage
in terms of “modules” - functionally
associated gene sets that are coordinately
regulated in other systems
Modular analysis of adjuvant-induced innate immune responses
(Plasma cells)
(Hematopoietic
Precursors)
M4.11
(B cells)
M3.3
1
Inflammation
M4.2
0.75
M4.10
M5.1
0.5
(Cytotoxicity) M4.15
M6.13
0.25
0
M6.19
M1.2
-0.25
-0.5
T cells
M6.15
M3.4
-0.75
M4.1
Interferon
response
M5.12
M6.9
Lymphoid
lineage
M4.1
0
M4.7
20
18
16
M4.6
M6.6
M6.1
9
24hrs post-vaccination
M6.1
5
M3.3
M3.2
Env+Alum
M4.2
Env+Alum+TLR4
M5.1
14 M4.13
M4.1
M5.15 5
(Neutrophils)
M4.1
1
12
Env+Alum+TLR7
M6.1
3
Env+MF59
10
Myeloid lineage
Env+ANE/TLR4
8
6
M1.2
Env
4
2
0
Env+ANE/TLR7
M3.4
Modular analysis of adjuvant-induced innate immune responses
(Plasma cells)
(Hematopoietic
Precursors)
M4.11
(B cells)
M3.3
2
Inflammation
M4.2
M4.10
M5.1
(Cytotoxicity) M4.15
1
M6.13
0
M6.19
T cells
M6.15
M1.2
M3.4
-1
M4.1
Interferon
response
M5.12
M6.9
Lymphoid
lineage
M4.1
0
M4.7
20
18
16
M4.6
M6.6
M6.1
9
24hrs post-vaccination
M6.1
5
M3.3
M3.2
Env+Alum
M4.2
Env+Alum+TLR4
M5.1
14 M4.13
M4.1
M5.15 5
(Neutrophils)
M4.1
1
12
Env+Alum+TLR7
M6.1
3
Env+MF59
10
Myeloid lineage
Env+ANE/TLR4
8
6
M1.2
Env
4
2
0
Env+ANE/TLR7
M3.4
(Plasma cells)
(Hematopoietic
Precursors)
M4.11
(B cells)
M3.3
2
Inflammation
M4.2
M4.10
M5.1
(Cytotoxicity) M4.15
1
M6.13
0
M6.19
T cells
M6.15
M1.2
M3.4
-1
M4.1
Interferon
response
Peak midpoint titers (log10) after 4th shot
Total IgG
Modular analysis of adjuvant-induced innate immune responses
M5.12
M6.9
Lymphoid
lineage
M4.1
0
M4.7
20
18
16
M4.6
M6.6
M6.1
9
24hrs post-vaccination
M6.1
5
M3.3
M3.2
Env+Alum
M4.2
Env+Alum+TLR4
M5.1
Env+Alum+TLR7
14 M4.13
M4.1
M5.15 5
(Neutrophils)
M4.1
1
M6.1
3
12
10
Myeloid lineage
Env+ANE/TLR4
8
6
4
Env+MF59
M1.2
Env+ANE/TLR7
Env
2
0
M3.4
(Ab responses: Joe Francica
and Bob Seder)
(Plasma cells)
(Hematopoietic
Precursors)
M4.11
(B cells)
M3.3
1
Inflammation
M4.2
0.75
M4.10
M5.1
0.5
(Cytotoxicity) M4.15
M6.13
0.25
0
M6.19
M1.2
-0.25
-0.5
T cells
M6.15
M3.4
-0.75
M4.1
Interferon
response
Peak midpoint titers (log10) after 4th shot
Total IgG
Modular analysis of adjuvant-induced innate immune responses
M5.12
M6.9
Lymphoid
lineage
M4.1
0
M4.7
M6.1
9
M3.3
20
18
16
M4.6
Env+Alum
M4.2
M6.6
Env+Alum+TLR4
M5.1
Env+Alum+TLR7
M6.1
3
12
10
Myeloid lineage
6
Env+MF59
Env+ANE/TLR4
8
4
M6.1
5
M3.2
14 M4.13
M4.1
M5.15 5
(Neutrophils)
M4.1
1
M1.2
Env+ANE/TLR7
Env
2
0
M3.4
(Ab responses: Joe Francica
and Bob Seder)
The HSF1 module correlates with
Ab response magnitude
Peak midpoint titers (log10) after 4th shot
Total IgG
Heat Shock Factor 1 (HSF1)
Log2(FC) HSF1 module expression
20
Regulation-centric modules:
Analyze coordinate expression of genes
that are targets of the same
transcription factors (InnateDB/CisRED)
The HSF1 module correlates with
Ab response magnitude
Peak midpoint titers (log10) after 4th shot
Total IgG
Heat Shock Factor 1 (HSF1)
MF59
Alum
Log2(FC) HSF1 module expression
21
The HSF1 module correlates with
Ab response magnitude
Peak midpoint titers (log10) after 4th shot
Total IgG
Heat Shock Factor 1 (HSF1)
Inouye et al., 2004
MF59
Alum
HSF1-/-
Log2(FC) HSF1 module expression
22
HSF1+/+
IgG2a and IgG1 production are
impaired in HSF1-null mice
(Sheep RBC model)
Next steps
 Validating TB disease risk biomarkers in independent samples
(ACS) and cohorts (GC6)
 Evaluating sorted cell transcriptomes from ACS
 Functionally evaluating role of HSF1 and other predicted
regulators in murine vaccine models
 Future analyses of candidate TB vaccines (AERAS)
• AERAS-422 (Dan Hoft)
• M72 (GSK)
23
Thank you!
Alan Aderem
Systems Vaccinology Team
Ethan Thompson
Lynn Amon
Joe Valvo
Emilio Siena (Novartis)
Smitha Shankar
Rebecca Podyminogin
24
Willem Hanekom
Thomas Scriba
Adam Penn-Nicholson
Wendy Whatney
Mzwandile Erasmus
Bob Seder
Joe Francica