Bioinformatics in transplantation immunology

1. Bioinformatics in transplantation immunology PhD defense Malene Erup Larsen October 27th 2010

2. Hematopoietic cell transplantations Hematopoietic stem cells + leukocytes • Ideal outcome: • Hematopoietic system is replaced by a healthy one • Leukemia relapse is prevented due to the graft-versus-leukemia effect • Complications: • Graft-versus-host disease • Relapse • Graft rejection Leukemia patient Matching donor Main topic of PhD: Prediction of minor histocompatibility antigens (mHags)

3. Outline of talk • Introduction • Prediction of H-Y antigens • Prediction of nsSNP-derived mHags • Survival study • HLArestrictor • Summary

4. Antigen presentation MHC = Major Histocompatibility ComplexHLA = Human Leukocyte AntigenTAP = Transporter associated with Antigen ProcessingER = Endoplasmic reticulum Figure by Mette Voldby Larsen

5. Peptide / MHC binding HLA-A*0201 LLFGYPVYV VLHDDLLEA YIGEVLVSV RTLDKVLEV FIDSYICQV SLYNTVATL ......... ......... PDB structure 1DUZ From: MHC motif viewer Acidic Basic Hydrophobic Neutral

6. NetMHCpan Artificial neural network trained on available binding data and residues in contact with the peptide Standard binding threshold:Affinity = IC50 = 500 nMConcentration of peptides at which half of the HLA molecules are occupied % rank value:at 2% rank, only 2% of random peptides are predicted to have a stronger binding than the query peptide M. Nielsen, et al., PLoS ONE 2, e796 (2007)

7. T cell education T cells are educated in the thymus Naive T cells await to be activated Hematopoietic stem cells develop into T cell precursors

8. Thymal training Illustration from Elsevier images

9. Allogeneic hematopoietic cell transplantation - minor histocompatibility antigens Patient Donor T-cells T-cells T cell education in the thymus MHCI MHCI Patient cells Stem cells After allo-HCT T-cell mHag Figure by Mette Voldby Larsen

10. Known mHags • dbMinor lists ~30 mHags • ~50 mHags are known • 3 million genetic differences • between any two individuals • Tip of the iceberg....

11. Why identify more mHags? • To better predict occurrence of GVHD • Therapeutic mHags - adoptive immunotherapy

12. Outline of talk • Introduction • Prediction of H-Y antigens • Prediction of nsSNP-derived mHags • Survival study • HLArestrictor • Summary

13. Sex-mismatched transplantations Proteins encoded by the Y chromosome are unknown to the female immune system Male patient Female donor

14. Correlation with outcome From Stern et al. 2008 Based on data from 54,000 patients

15. Aim of the study Predict epitopes Isolated T cell clone recognizing unknown epitope Experiments High throughput peptide testing SPSVDKARAEL - Apply reverse immunology to identify novel H-Y antigens Standard immunology Reverse immunology

16. Patients 26 sisters 1 mother 5 unrelated donors 32 male patients 15 most common HLA alleles (from a total of 31 different alleles)

17. Predictions NetMHCpan was used to predict 8, 9, 10, and 11mers >SMCY (length 1570 aa) MEPGCDEFLPPPECPVFEPSWAEFQDPLGYIAKIRPIAEKSGICKIRPPADWQPPFAVEVDNFRFTPRVQRLNELEAQTRVKLNYLDQIAKFWEIQGSSLKIPNVERKILDLYSLSKIVI.... Gives MEPGCDEFLPP MEPGCDEFLP MEPGCDEFL MEPGCDEF EPGCDEFLPPP EPGCDEFLPP EPGCDEFLP EPGCDEFL Predictions were run for all 31 HLA alleles using a standard binding threshold of 500 nM resulting in 7390 predicted binders

18. Filtering steps • Exclude peptides also found in homologous proteins encoded by the X chromosome • Exclude shorter peptide version of a predicted binder • Only include peptides predicted to bind to the 15 most common HLA alleles • Include only the 30 strongest binders for each HLA allele • Result: 324 predicted H-Y antigens to test experimentally

19. Experimental validations- at Laboratory of Experimental Immunology, Panum, University of Copenhagen Intracellular cytokine staining • 8 patients have been tested • 35 CD8+ T cell responses to 30 different peptides • (1 known H-Y antigen) • Binding of peptides or submers confirmed in 26 / 35 cases Tetramer validations • Next step: Tetramer validations • Identify the exact sequence of the H-Y antigens • Identify the restricting HLA alleles

20. Outline of talk • Introduction • Prediction of H-Y antigens • Prediction of nsSNP-derived mHags • Survival study • HLArestrictor • Summary

21. Aim of the study - Apply reverse immunology to identify novel nsSNP derived mHags Challenges • Size of the human genome compared to the • Y chromosome or viral or bacterial genomes • nsSNP variants are individual - genotyping is necessary • mHags need to be expressed in relevant tissues

22. Selected proteins

23. Patients • 164 patients treated with an allo-HCT between years 2000-2008 • HLA identical related or fully matched unrelated donors were used • 46 different HLA alleles

24. Predictions • Patients were genotyped for 173 nsSNPs and variation in the GVH-direction was found in 36 nsSNPs • Validation of 128 predicted mHags is ongoing Example, patient 273 (Heterozygote frequency = 11 %)

25. Outline of talk • Introduction • Prediction of H-Y antigens • Prediction of nsSNP-derived mHags • Survival study • HLArestrictor • Summary

26. Aim of the study - Investigate possible correlations between predicted mHags and transplantation outcome M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

27. Correlation with number of nsSNP disparities? M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

28. Correlation with number of mHag disparities M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

29. Multivariate analysis Correlation between number of predicted mHags and survival still significant (P=0.014) when including the following covariates: • Patient-donor relation • Sex-mismatch • Disease level (Kahl score) • CMV status • Patient and donor age • Acute and Chronic GVHD M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

30. Conclusions • First study to demonstrate correlation between the number of predicted mHags and transplantation outcome • The effect is more significant when adding HLA binding predictions instead of only nsSNP-disparities M. E. Larsen et al., Biol Blood Marrow Transplant Oct. 2010, 16(10):1370-81

31. Outline of talk • Introduction • Prediction of H-Y antigens • Prediction of nsSNP-derived mHags • Survival study • HLArestrictor • Summary

32. Scientific questions NetMHCpan: What is the binding affinity between the 9mer SLYNTVATL and HLA-A*02:01? HLArestrictor: What is the optimal epitope and restricting HLA allele of the 17mer TGSEELRSLYNTVATLY known to elicit a T cell response in patient N067 with HLA alleles HLA-A*02:01, HLA-A*02:05, HLA-B*51:01, HLA-B*58:01, HLA-C*07:01, HLA-C*16:02? M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

33. Interface

34. Output - HLA oriented # HLArestrictor with NetMHCpan version 2.3 # HLA types used: HLA-A02:01, HLA-A02:05, HLA-B51:01, HLA-B58:01, HLA-C07:01, HLA-C16:02 # Peptide lengths: 8, 9, 10, 11 # Sort-method: OR. Sort-mode: HLA-oriented # %rank threshold for strong binding peptides: 0.5%rank # %rank threshold for weak binding peptides: 2.0%rank # Affinity threshold for strong binding peptides: 50.0nM # Affinity threshold for weak binding peptides: 500.0nM # Number of predictions per peptide: Not specified # Non-binders shown up to a prediction score of 2.0*(weak binding threshold) Results for Peptide N067_TGSEELRSLYNTVATLY: TGSEELRSLYNTVATLY -------------------------------------------------------------------------- Pos Length Peptide HLA 1-log50k(aff) Affinity(nM) %Rank Label Estimated accuracy 8 9 SLYNTVATL HLA-A02:01 0.43 475 4.0 Combined binder 0.853 8 8 SLYNTVAT HLA-A02:01 0.363 985 5.0 Non-binder 0.853 7 11 RSLYNTVATLY HLA-B58:01 0.601 75 0.4 Strong binder 0.853 7 10 RSLYNTVATL HLA-B58:01 0.512 196 0.8 Weak binder 0.853 7 10 RSLYNTVATL HLA-C07:01 0.476 289 0.1 Strong binder 0.486 7 11 RSLYNTVATLY HLA-C07:01 0.418 544 0.25 Strong binder 0.486 7 8 RSLYNTVA HLA-C07:01 0.304 1867 1.5 Weak binder 0.486 7 9 RSLYNTVAT HLA-C07:01 0.268 2747 3.0 Non-binder 0.486 7 11 RSLYNTVATLY HLA-C16:02 0.112 NA 0.8 Weak binder 0.564 7 10 RSLYNTVATL HLA-C16:02 0.106 NA 0.8 Weak binder 0.564 M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

35. Benchmark 1067 HIV ELIspot responses to 85 distinct peptides to which HLA restriction was assigned through association studies Measuring the ability of HLArestrictor to identify the correct HLA restriction element 2% rank threshold: ~90% of positives correctly predicted Performance ~60% of negatives correctly predicted MCC ~0.4 Binding threshold (%) M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

36. Benchmark 18 tetramer validationsInvestigating the ability of HLArestrictor to identify the correct minimal epitope ............16 / 18 minimal epitopes predicted at 2 %rank threshold 18 / 18 predicted at 2 %rank OR 500 nM threshold (standard setting) M. E. Larsen et al., submitted to Immunogenetics Aug. 2010

37. Summary of talk • Overall theme: Identification of novel mHags • H-Y antigens predicted and 35 CD8+ T cell responses to 30 different peptides observed in 8 patients • nsSNP-derived mHags predicted in 30 selected proteins • Correlation between number of predicted mHags and transplantation outcome demonstrated • New flexible prediction tool - HLArestrictor - developed and benchmarked

38. Acknowledgements • Søren Brunak • Mette Voldby Larsen • Morten Nielsen • Ole Lund and the Immunological Bioinformatics group • The Integrative Systems Biology group • All our collaborators at Rigshospitalet and Panum • Everyone at CBS • Friends and family