Epitope matching in solid organ transplantation: T-cell epitopes

Transcription

1 Epitope matching in solid organ transplantation: T-cell epitopes Eric Spierings Laboratory for Translational Immunology, UMC Utrecht conflict of interest: the UMCU has filed a patent application on the prediction of an alloimmune response against mismatched HLA (PCT/EP2013/073386) the UMCU has a license agreement with PIRCHE AG I am listed as inventor on this patent application 1

2 Learning objectives: to understand the role of T-cell help in relation to antibody responses to understand the concept of linked recognition to understand the concept of indirect recognition to understand the effect of PIRCHE on organ transplant outcome 2

3 Evolving from antigen to epitope matching The current allocation strategy considers each mismatch with the same immunological weight The risk for organ rejection may well be better estimated using epitope-based HLA matching Identification of permissible HLA mismatches HLAMatchmaker and others B-cell epitopes PIRCHE-II algoritme T-cell epitopes aim: redefining HLA matching via a better understanding of the mechanisms behind alloimmunity 3

4 Understanding T cells and organ rejection HLA antibodies Plasma cell B cell Cytotoxic T cell somatic hypermutation, isotype switching & memory These processes are driven by T-helper cells 4

5 Understanding T cells and organ rejection HLA antibodies Plasma cell B cell Cytotoxic T cell T-helper cell T-cell help to B cells linked recognition Recipient T-helper cell (CD4+) T-cell Or more dependent simple: antibody responses require the activation of B cells by helper T cells that respond to the same Generating antigen. a mature The exact antibody epitopes response recognized requires by B the cells and helper presence T cells of both may an be antibody/b-cell different but must epitope be physically and an HLA linked; class II-restricted this phenomenon T-cell epitope is called within linked the recognition. same antigen Recipient B cell cytokines costimulation 5

6 B cell T cell 21/07/2017 indirect recognition of mismatched HLA indirect recognition is fact over 350 HLA-derived peptides have been eluted from HLA 1 in solid organ transplantation, indirectly recognizing T cells are correlated to acute and chronic graft rejection 2,3,4,5 T cells against peptides derived from mismatched HLA have been isolated from SCT patients Suciu-Foca, et al. Transplant Proc Liu, et al. J Clin Invest Ciubotariu et al. J Clin Invest Hornick, et al. Circulation Amir et al. BBMT

7 indirect HLA recognition in a historical perspective We conclude that there are at least two human Ir genes, HLA-DRB1*01 and HLA-DRB1*03, that confer a high risk for both humoral allosensitization and renal allograft failure in situations of HLA-Bw4 incompatibility. HLA-DRB1*03 HLA-DRB1*01 Fuller & Fuller Transplantation 1999 HLA-DRB1*07 HLA-antibodies depends on the HLA-DRB1 allele Dankers et al. Hum Immunol

8 computational model for HLA-derived Th epitopes Recipient HLA class I Donor HLA class I Recipient HLA class II PIRCHE [ˈpercə] noun, acronym Predicted Indirectly ReCognizable HLA Epitope PIRCHE-I: presented by HLA class I PIRCHE-II: presented by HLA class II 8

9 example of PIRCHE = Self peptide A*01:01 A*02:01 Non-self peptide Donor: A*01:01 MAVM APRTLLLLLL GALALTQTWA Patient: A*02: V A*01:01 ASQKMEPRAP WIEQEGPEYW DQETRNMKAH A*02:01 ---R G---KV--- Building the PIRCHE infrastructure See also: Geneugelijk et al. Hum Immunol Nov;77(11): Geneugelijk et al. J Immunol Res. 2017;201 / P97 9

10 the HLA system contains > variants March 2017: 12,021 HLA class I alleles 4,230 HLA class II alleles Robinson et al. IMGT-HLA database 2017 Computational power keeps up with HLA My first computer (1984) My last computer (2017) P2000: Z80 microprocessor operating at 2,5 MHz with 4 kib ROM (extendable to 16kiB), 16 kib RAM and 2 kib videoram. Tape storage 16 kib per tape. AWS Amazon: Single instance operating at 2.5 GHz with 3.75 GiB ROM and 4 GB SDD storage. Scalable and option to parallelize processes. 10

11 the IMGT HLA database is incomplete locus number of incomplete sequences (%) HLA class I 6,523 (90.3%) HLA-A 2,121 (91.3%) HLA-B 2,657 (89.3%) HLA-C 1,745 (90.6%) HLA class II 1,761 (91.4%) HLA-DRB1 1,275 (91.5%) HLA-DQB1 486 (91.2%) Geneugelijk et al. Hum Immunol 2016 completing the HLA protein database completion of HLA protein sequences by automated homologybased nearest-neighbor extrapolation of HLA database sequences input: IMGT HLA protein database repeat until the sequence is complete output: complete HLA protein sequence database parse aligment file by locus find best aligned sequence that is more complete than current sequence add amino acid of best aligned sequence store sequence in FASTA file with HLA-ID Geneugelijk et al. Hum Immunol

12 constructing the PIRCHE database obtain protein sequences from HLA database (currently > alleles) complete sequence data for missing exons at the protein level PIRCHE-I proteasome-mediated cleavage binding affinity to HLA class I alleles PIRCHE-II binding affinity to HLA class II alleles (15-mer peptides) trim T-cell epitope to 9-mer core peptides PIRCHE-II calculations using lower level resolution Patient: A1 A3 B8 B18 Cw5 Cw7 DR3 DR3 DQ2 DQ2 Donor: A1 A3 B8 B14 Cw7 Cw8 DR3 DR13 DQ2 DQ6 HLA frequency tables Patient: A*01:01, A*03:01, B*08:01, B*18:01, C*05:01, C*07:01, DRB1*03:01, DRB1*03:01, DQB1*02:01, DQB1*02:01 Donor 1: A*01:01, A*03:01, B*08:01, B*14:02, C*07:01, C*08:02, DRB1*03:01, DRB1*13:01, DQB1*02:01, DQB1*06:03 Donor 2: A*01:01, A*03:01, B*08:01, B*14:02, C*07:01, C*08:02, DRB1*03:01, DRB1*13:02, DQB1*02:01, DQB1*06:04 Donor 3: A*01:01, A*03:01, B*08:01, B*14:02, C*07:01, C*08:02, DRB1*03:01, DRB1*13:02, DQB1*02:01, DQB1*06:09 Donor 1: 10 PIRCHE-II Donor 2: 10 PIRCHE-II Donor 3: 11 PIRCHE-II Calculate PIRCHE-II values for each option Weighted average based upon the relative frequency of all options 12

13 PIRCHE-II calculations PIRCHE and antibody formation See also: Otten et al, Hum Immunol Mar;74(3):290-6 Geneugelijk et al. Am J Transplant Dec;15(12): Chaigne et al. Cell Transplant Nov;25(11): Lachman et al. abstract O18 13

14 T-cell help to B cells linked recognition Recipient T-helper cell (CD4+) Generating a mature antibody response requires the presence of both an antibody/b-cell epitope and an HLA class II-restricted T-cell epitope within the same antigen Recipient B cell cytokines costimulation anti-hla IgG antibodies and T-helper epitopes recipient-specific HLA-DRB1 background scrambled HLA-DRB1 background Otten et al. Hum Immunol,

15 overlap between PIRCHE-II and eplets is only 38% HLA antibody and PIRCHE-II in pregnancy Geneugelijk et al. Am J Transplant

16 PIRCHE and the prediction of de novo DSA Lachmann et al, DGI Meeting 2016 Submission accepted at AJT T-cell help to B cells linked recognition AUC* R 2 ** ln(pirche-ii-lr) ln(pirche-ii) HLAMatchmaker ABCDRDQ-mismatches * Heagerty PJ et al. Biometrics. 2000;56(2): ** Nagelkerke NJ et al. Biometrika. 1991;78(3): Recipient T-helper cell (CD4+) Recipient B cell EFI conference 2017 Lachmann et al, Abstract O18 16

17 lnpirche-ii-linked Recognition score ABCDRDQ mismatches per MM PIRCHE and the prediction of de novo DSA Univariate Hazard ratio (95% CI) p Multivariate Hazard ratio (95% CI) 1.47 ( ) < ( ) < ( ) < ( ) PIRCHE Score and Matchmaker score independently predict de novo DSA formation and allograft survival combined PIRCHE/Eplet score is independent risk stratifier for de novo DSA p EFI conference 2017 Lachmann et al, Abstract O18 PIRCHE and kidney transplant outcome See also: Geneugelijk et al. abstract MO4 Lachman et al., DGI Meeting 2016 / Accepted by Am J Transpl, 17

18 PIRCHE and organ transplant outcome Donor and recipient typed at least for HLA-A, -B and DRB1, split level Exclusion criteria: A never functioning kidney (n=134) 5 or 6 broad mismatches excluded 3061 cases included Geneugelijk et al. Submitted HLA factors in kidney transplant outcome Univariate Multivariable model with forward stepwise selection HR 95%-CI p-value HR 95%-CI p-value ln(pirche-ii) < Eplets Number of A/B/DR mismatches <0.001 Recipient age <0.001 Donor age < <0.001 Transplantation year Recipient immunization status Cox proportional hazard analysis. All parameters as continuous variable Geneugelijk et al. Submitted

19 Risk model for PIRCHE-II mismatching Geneugelijk et al. Submitted Implementation 19

20 prospective PIRCHE-II mismatch probability prospective PIRCHE-II mismatch probability 0% 24% 52% 24% 20

21 prospective PIRCHE-II mismatch probability 10% 50% 40% 0% 0% 1% 20% 79% conclusions PIRCHE-II correlates with de novo antibody formation PIRCHE-II can classify lower and higher risk kidney transplants These data are in line with a parallel study (Lachman et al. Accepted for publication), which additionally shows that PIRCHE-II scores are an independent predictor for de novo DSA formation Risk profiling could be used to anticipate on the PIRCHE match probability 21

22 conclusions PIRCHE-II can reliably be estimated using serological splits PIRCHE-II estimations significantly increase in precision when using high resolution recipient typing Risk profiling can be used to anticipate on the PIRCHE match probability The PIRCHE team Kirsten Geneugelijk (UMC Utrecht) Jeroen Wissing (UMC Utrecht) Dirk Koppenaal (UMC Utrecht) Eric Borst (UMC Utrecht) Matthias Niemann (PIRCHE AG, Berlin) Laboratory for Translational Immunology (UMC Utrecht) Julia Drylewicz The PROCARE team (UMC Utrecht) Elena Kamburova Henny Otten Computational biology Hanneke van Deutekom (University Utrecht) Jorg Calis (University Utrecht) Can Kesmir (University Utrecht) Basel University Gideon Hönger Stefan Schaub Charite University Nils Lachmann Oliver Staeck Constanze Schönemann 22

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