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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