Transcript Elizabeth Jury

Can we use cellular markers to
predict immunogenicity?
Elizabeth Jury
Centre for Rheumatology
Division of Medicine
University College London
26-Feb-2014
www.imi.europa.eu
The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement n° [115303], resources of
which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution
1
WP2: Cellular characterization and
mechanisms of the AD immune response
● WP2.1: To understand the cellular mechanisms causing AD
●
●
●
responses
WP2.2: To characterise ADA structurally and functionally
WP2.3: To identify genetic markers predisposing to BP
immunogenicity
Patient cohorts:
● RA patients treated with TNF inhibitors (infliximab,
adalimumab, etanercept), rituximab
● IBD patients treated with TNF inhibitors
● MS patients treated with IFNβ and/or natalizumab
● HA patients treated with FVIII
● SLE patients treated with rituximab
● Considering including new BPs
|
2
UCL WP2 objectives
● WP2.1.1: Evaluation of early activation biomarkers as
potential predictors of immunogenicity
● Prospective: RA, MS, IBD
● Cross sectional: RA, MS, SLE, IBD.
● WP2.1.7 : Evaluation of B cell AD cellular response.
● Cross-sectional: RA, MS, IBD, HA
● WP2.1.8 : Numerical and functional analysis of regulatory B
cells in ADA+/ADA- patients
● Cross-sectional: pilot with RA, SLE then MS and HA
|
3
Global immunophenotyping as a tool
to investigate immunogenicity
● A new methodology
● Validation with healthy donors
● Early Results
● Studying B cell populations
● Patients with MS
● Ongoing plans
|
4
Novel flow cytometry platform:
LEGENDScreen platform
PBMC
CD4 T cells
CD19 B cells
Immature
Mature
Memory
High throughput flow cytometry
332 CD antigens in parallel
Validation: reproducibility
PBMC
HC1
HC2
HC3
HC4
HC5
CD19+
CD4+
Validation: CD19+ gate
Validation: CD4+ gate
Healthy donors compared to
RA patients
PBMC cell surface signature:
Healthy vs RA
Healthy control PBMC
RA patient PBMC
HC1
RA1
HC2
RA2
HC3
RA3
HC4
RA4
HC5
RA5
CD19+ profiling Healthy versus RA
Healthy
RA
Global immunophenotyping as a tool
to investigate immunogenicity
● A new methodology
● Validation with healthy donors
● Early Results
● Studying B cell populations
● Patients with RA, SLE and MS
● Ongoing plans
|
12
B cell subpopulations in PBMCs
Gating strategy
Gated on CD19+
Immature
Memory
CD24
SSC
SSC
B cells
Mature
Live gate
FSC
CD19
13
B cell subsets: Imm- | Immature,
Mat- Mature, Mem- Memory
CD38
|
13
Immature B cells produce IL-10
memory
CD24hiCD38-
mature
CD24intCD38int
***
**
IL-10 producing B cells
are enriched in the
CD24hiCD38hi gate
immature
CD24hiCD38hi
IL-10 (pg/mL)
Blair et al. Immunity; 2010
Immature B cells suppress T cells
IFN-ɣ
TNF-
CD24hiCD38hi B cells suppress T helper cell differentiation
Blair et al. Immunity; 2010
Flores-Borja et al. Science Trans Med; 2013
B cell sub-populations
Relevance to Autoimmunity
Number and frequency of CD24hiCD38hi B cells
are reduced in patients with active RA
Flores-Borja et al. Science Trans Med; 2013
LegendScreen gating strategy
Gating strategy
Gated on CD19+
Immature
Memory
CD24
SSC
SSC
B cells
Mature
Live gate
FSC
CD19
CD38
PLUS:
332 cell surface markers
17
B cell subsets: Imm- | Immature,
Mat- Mature, Mem- Memory
|
17
Top 50 CD markers with highest expression
Results: B cell subsets have distinct
expression profiles in healthy donors
Comparing expression of 332 markers
revealed that B cell subsets can be
distinguished
Immature
Memory
Pierre Dönnes: WP4
Mature
Immature
Specific markers show significantly
altered expression in B cell subsets
Immature vs. Mature
Immature vs.
Memory
Comparison of Immature B cells from
Healthy vs. RA patients revealed
differential expression of some markers
60000
Immature B cells
Healthy vs. RA
MFI
40000
Healthy
RA
20000
la
ht
lig
Ig
l ig
H
C
X3
C
R
1
C
D
C 23
D
62
L
m
bd
a
19
6
C
D
LA
C
h t D1
ka 8
H p pa
LA
-A
2
C
D
14
8
-D
R
44
D
C
Top 100 CD markers with highest expression
Ig
H
LA
- C
C A,BD9
D ,
45 C
R
A
0
2-fold higher
No Change
2-fold lower
Several cell surface molecules have been identified as
significantly different in RA compared to
healthy immature B cells
10-4
10-3
p value
CD154
CD163 CD148
CD146 CD104
10-2
CD9
Ig light lambda
p = 0.05
10-1
100
0.0625
0.1250
0.2500
0.5000
1
2
4
8
16
Fold Change
Follow-up in ADA+ and ADA- patients
Summary 1
● Tool to define immature B cell phenotype
● Clinical tool to identify differences
in B
cell phenotype in healthy donors and RA
patients
● Functional relevance of selected markers
Global immunophenotyping as a tool
to investigate immunogenicity
● A new methodology
● Validation with healthy donors
● Early Results
● Studying B cell populations
● Patients with MS (ADA+ vs ADA-)
● Ongoing plans
|
24
Differential Immunophenotype of
ADA+ and ADA- MS patients
● 32 MS patient’s (14 Male & 18 Female)
●
●
●
●
●
●
●
●
● 10 CIS (clinically isolated syndrome)
● 22 RRMS (remitting relapsing)patients
EDSS (expanded disability status) between 1 and 4 (mean 2.09)
Average age: 38.5 ± 9.5 years
22 treated with IFN-β 1a (11 Avonex™;11 Rebif™)
10 treated with IFN-β 1b (6 Extavia™; 4 Betaferon™)
Blood sampled 10-14h after last IFN-β injection
18 Assayed for MxA expression (13 high, 2 middle, 3 low)
Neutralizing Abs determination: 11 positive / 25 assayed (44%
of tested)
Neutralizing Abs titre: unknown
|
25
Rational for sample selection
|
Sominanda et al 2008 JNNP
26
|
27
Cell Populations of Interest
●
Peripheral blood Tfh cells
●
●
●
●
●
Peripheral blood CD19+ B cells
●
●
●
●
●
Phenotype: CD4+CXCR5+:
IgM, IgG and IgA secretion (IL-21 & ICOS dependent);
B cell proliferation (IL-21 & ICOS dependent);
B cell differentiation in CD38+CD19lo plasmablasts;
Subpopulations phenotype:
CD19+CD24hiCD38hi (Transitional B cells- Bregs)
CD19+CD24intCD38int (Mature B cells)
CD19+CD24hiCD38- (Memory B cells)
Myeloid Derived Suppressor Cells (MDSCs)
● Phenotype: CD14+DR-/lo
● Reported to both suppress or enhance the autoimmune response in EAE
|
28
LegendScreen™
|
29
Investigate frequency of different cell
populations as well as immune
‘signature’ of specific subpopulations
CD
16
CD
56
CD
183
CD14+CD4-CD19-CD16- = Classic Monocytes
CD14+CD4-CD19-CD16+ = Non-classic Monocytes
CD14-CD4-CD19-CD56+ = NK cells
CD4+CD14-CXCR5+CD183+ = Tfh1 Cells
CD4+CD14-CXCR5+CD183- = Tfh2+Tfh17 Cells
Preliminary Data
High MxA Expression
PT8
PT23
5
10
4
102
103
104
CXCR5
105
103
CD4
104
CXCR5
105
105
3
10
5.23
3
60.8
103
CD38
104
105
5 81.7
104
103
CD38
104
2
0
10
CD14
10
4
10
5
0 102
103
CD38
104
0
10
CD14
10
10
5
73.7
0 102
103
CD38
104
105
104
105
5 84.9
10
104
14.7
103
0
2
10
0
4
105
4.58
3
105
3.12
2
3
104
0
10
0 0.63
3
103
CXCR5
8.82
10
103
2
102
0
104
66.8
104
18.2
10
0 0
3
105
103
10
4.91
16.4
5 96.9
104
103
105
10
HLA-DR
HLA-DR
0 102
10
18.1
104
0
5 81
10
103
CXCR5
105
HLA-DR
0 102
102
10
0
9.29
88.7
0
0
104
17
10
28.4
10
18.6
105
104
3.63
40.1
0
MDSCs
102
0
CD24
CD24
104
78.9
0
CD24
105
4
10
8.38
0
0
5
10
4
90
CD4
CD4
21
73.6
0
B cells
5
10
CD4
4
10
PT13
CD24
10
PT9
HLA-DR
5
10
Tfh
Low MxA Expression
0 0.19
0
3
10
CD14
10
4
10
5
0
103
CD14
|
31
B cells Activation Markers
High MxA Expression
PT8
PT23
105
0
3
10
3
10
0
103
CD83
104
105
0
103
CD83
104
0
0
103
CD83
104
105
103
CD83
104
105
105
104
CD19
3
10
3
103
104
105
104
105
103
104
105
CD83
104
3
104
105
104
105
0
103
104
105
0
103
104
105
CD83
60
40
% of Max
20
0
103
CD83
104
0
105
CD83
100
80
40.1
3
60
40
20
0
103
103
40
80
5.14
10
0
0
60
100
104
37.8
105
0
105
105
0
0
103
104
10
0
0
CD83
104
52.8
10
0
0
0
103
104
CD83
20
103
0
103
80
8.79
105
36.2
0
100
3
105
105
104
28.9
CD83
104
0
CD19
105
103
103
0
0
105
0
0
0
CD83
104
10
104
103
103
104
48.3
3
105
6.62
CD19
CD19
103
0
105
105
104
19.7
105
0
105
104
CD83
CD19
105
104
10
0
0
103
104
27.3
20
0
105
104
3.85
CD19
CD19
CD83
104
105
104
CD19
103
CD19
105
0
40
% of Max
105
0
60
% of Max
CD83
104
103
CD19
103
38.6
CD19
0
Memory B
103
0
80
104
19.7
% of Max
CD19
103
0
Mature B
104
16.3
100
105
CD19
104
28.9
103
Immature B
105
CD19
104
CD19
CD19+ Cells
105
CD19
105
Low MxA Expression
PT9
PT13
0
103
104
105
0
Conclusions
● Performing LegendScreen analysis on ADA+ and ADAsamples from patients with MS, RA and SLE, IBD cohort to
follow
● Developing strategies to analyse the data – advanced
statistical help
● Develop custom phenotyping panels for screening the
prospective cohorts
● Identified markers analysed further for biological significance
|
33
Acknowledgements
UCL
Claudia Mauri: WP2 leader
ABIRISK Partners:
Marsilio Adraini
Pierre Dönnes: WP4
William Sanderson
Anna Fogdell-Hahn
Jessica Manson
Eva Havrdova
David Isenberg
Petra Nytrova
Paul Blair
Paul Creeke
Jamie Evans
Enrico Maggi
Vijay Mhaiskar
www.abirisk.eu