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

Bioinformatics in transplantation immunology

PhD defense

Malene Erup Larsen

October 27th 2010

27/10/2010Bioinformatics in transplantation immunology2 CBS, Department of Systems Biology

Hematopoietic cell transplantations

Matching donor

Leukemia patient

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

Main topic of PhD:

Prediction of minor histocompatibility antigens (mHags)


Outline of talk

• Introduction

• Prediction of H-Y antigens

• Prediction of nsSNP-derived mHags

• Survival study

• HLArestrictor

• Summary


Antigen presentation

MHC = Major Histocompatibility ComplexHLA = Human Leukocyte AntigenTAP = Transporter associated with Antigen ProcessingER = Endoplasmic reticulum

Figure by Mette Voldby Larsen


Peptide / MHC binding

HLA-A*0201LLFGYPVYV

VLHDDLLEA

YIGEVLVSV

RTLDKVLEV

FIDSYICQV

SLYNTVATL

.........

.........

Acidic Basic Hydrophobic Neutral

PDB structure 1DUZ

From: MHC motif viewer


NetMHCpan

Artificial neural network trained on available binding data and residues in contact with the peptide

M. Nielsen, et al., PLoS ONE 2, e796 (2007)

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


T cell education

Hematopoietic stem cells develop into T cell precursors

T cells are educated in the thymus

Naive T cells await to be activated


Thymal training

Illustration from Elsevier images


Allogeneic hematopoietic cell transplantation - minor histocompatibility antigens

MHCIMHCI

T-cells T-cells

Patient cells

T-cell

Stem cells

T cell education in the thymus

After allo-HCT

Patient Donor

mHagFigure by Mette Voldby Larsen


Known mHags

• dbMinor lists ~30 mHags

• ~50 mHags are known

• 3 million genetic differences

between any two individuals

• Tip of the iceberg....


Why identify more mHags?

• To better predict occurrence of GVHD

• Therapeutic mHags - adoptive immunotherapy


Outline of talk

• Introduction



• Survival study

• HLArestrictor

• Summary


Sex-mismatched transplantations

Female donorMale patient

Proteins encoded by the Y chromosome are unknown to the female immune system


Correlation with outcome

From Stern et al. 2008

Based on data from 54,000 patients


Aim of the study- Apply reverse immunology to

identify novel H-Y antigens

Standard immunology Reverse immunology

Predict epitopes

High throughput peptide testing

Isolated T cell clone recognizing

unknown epitope

Experiments

SPSVDKARAEL


Patients

32 male patients26 sisters1 mother5 unrelated donors

15 most common HLA alleles (from a total of 31 different alleles)

Allele No. patients Allele No. patients HLA-A*02:01 16 HLA-B*44:02 8 HLA-A*03:01 7 HLA-B*08:01 8 HLA-A*01:01 6 HLA-B*40:01 7 HLA-A*11:01 6 HLA-B*07:02 6 HLA-A*24:02 6 HLA-B*51:01 5 HLA-A*68:01 6 HLA-B*35:01 4 HLA-A*32:01 2 HLA-B*15:01 4

HLA-B*13:02 3


Predictions

>SMCY (length 1570 aa)

MEPGCDEFLPPPECPVFEPSWAEFQDPLGYIAKIRPIAEKSGICKIRPPADWQPPFAVEVDNFRFTPRVQRLNELEAQTRVKLNYLDQIAKFWEIQGSSLKIPNVERKILDLYSLSKIVI....

NetMHCpan was used to predict 8, 9, 10, and 11mers

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


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


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

• Next step: Tetramer validations

• Identify the exact sequence of the H-Y antigens

• Identify the restricting HLA alleles

Tetramer validations


Outline of talk

• Introduction



• Survival study

• HLArestrictor

• Summary


Aim of the study

- Apply reverse immunology to identify novel nsSNP derived mHags

• 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

Challenges


Selected proteins

Proteins with known mHags

Expression Additional proteins

Expression

AKAP13 SP110 BCL2A1 KIA0020 MYO1G HMHB1 USP9Y DDX3Y RPS4Y1 SMCY UTY HMHA1 CTSH ECGF1 LHR1 TOR3A

Broad Hematopoietic Hematopoietic Broad Hematopoietic B cell specific Broad Broad Broad Broad Broad Hematopoietic Broad Broad Tumor specific Broad

BCL6 CD99 TYR MAGEA1 CD3D CD79B CMRF35 IL2 TP53 WT1 TAL1 MPL NOV PLAT

B cell leukemia T cell specific Melanoma Melanoma T cell specific B cell specific Hematopoietic Hematopoietic Tumor specific Tumor specific T cell leukemia Leukemia Broad / Cancer Endothelial cells

Proteins with known mHags

Expression Additional proteins

Expression

AKAP13 SP110 BCL2A1 KIA0020 MYO1G HMHB1 USP9Y DDX3Y RPS4Y1 SMCY UTY HMHA1 CTSH ECGF1 LHR1 TOR3A

Broad Hematopoietic Hematopoietic Broad Hematopoietic B cell specific Broad Broad Broad Broad Broad Hematopoietic Broad Broad Tumor specific Broad

BCL6 CD99 TYR MAGEA1 CD3D CD79B CMRF35 IL2 TP53 WT1 TAL1 MPL NOV PLAT

B cell leukemia T cell specific Melanoma Melanoma T cell specific B cell specific Hematopoietic Hematopoietic Tumor specific Tumor specific T cell leukemia Leukemia Broad / Cancer Endothelial cells


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


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

Protein

nsSNP Patient Donor Reference peptide

Prediction, A*03:01

Missense peptide

Prediction, A*03:01

AKAP13 rs7177107 KE EE KLCDNIVSE 10124 nM KLCDNIVSK 38 nM

(Heterozygote frequency = 11 %)


Outline of talk

• Introduction



• Survival study

• HLArestrictor

• Summary


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


Correlation with number of nsSNP disparities?



Correlation with number of mHag disparities



Multivariate analysis

• Patient-donor relation

• Sex-mismatch

• Disease level (Kahl score)

• CMV status

• Patient and donor age

• Acute and Chronic GVHD

Correlation between number of predicted mHags and survival still significant (P=0.014) when including the following covariates:



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



Outline of talk

• Introduction



• Survival study

• HLArestrictor

• Summary


Scientific questions

What is the binding affinity between the 9mer

SLYNTVATL and HLA-A*02:01?

NetMHCpan:

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


Interface


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 0.8538 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 0.8537 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 0.4867 11 RSLYNTVATLY HLA-C07:01 0.418 544 0.25 Strong binder 0.4867 8 RSLYNTVA HLA-C07:01 0.304 1867 1.5 Weak binder 0.4867 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 0.5647 10 RSLYNTVATL HLA-C16:02 0.106 NA 0.8 Weak binder 0.564



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

Binding threshold (%)

Perf

orm

an

ce

2% rank threshold:

~90% of positives correctly predicted

~60% of negatives correctly predicted

MCC ~0.4



Benchmark 18 tetramer validations

Investigating 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)



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


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

Documents

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