1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 Functional Human Variation, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark4 Integrative Systems Biology, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark5 Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark6 CFB - Metagenomic Systems Biology, Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark7 Immunological Bioinformatics, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark8 Department of Bio and Health Informatics, Technical University of Denmark
The human immune system is a highly adaptable system, defending our bodies against pathogens and tumor cells. Cytotoxic T cells (CTL) are cells of the adaptive immune system, capable of inducing a programmed cell death and thus able to eliminate infected or tumor cells. CTLs discriminate between healthy and infected cells based on peptide fragments presented on the cells surface. All nucleated cells present these peptide fragments in complex with Major Histocompatibility Complex (MHC) class I molecules. Peptides that are recognized by CTLs are called epitopes and induce the CTLs to subsequently kill the infected cells. The focus of my PhD project has been on improving a method for CTL epitope pathway prediction, on analyzing the epitope density in the alternative cancer exome, and on a study investigating minor histocompatibility antigens (mHags) associated with leukemia. Part I is an introduction to the fields covered in the thesis. Part II describes a pan-specific, integrative approach for the prediction of CTL epitopes. The presented method, NetCTLpan, an improved and extended version of NetCTL, performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8, 9, 10 and 11-mer epitopes. One of the major benefits of the method is its optimization to achieve high specificity. Its low false positive rate is especially useful in rational reverse immunogenetic epitope discovery approaches. When this method is compared to the NetMHCpan and NetCTL methods, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively. Part III reports the results of an analysis investigating how the alternatively spliced cancer exome differs from the exome of normal tissue in terms of containing predicted MHC class I binding epitopes. We show that peptides unique to cancer splice variants comprise significantly fewer predicted HLA class I epitopes than peptides unique to spliced transcripts in normal tissue. We furthermore find that hydrophilic amino acids are significantly enriched in the unique carcinoma sequences, which contribute to the lower likelihood of carcinoma-specific peptides to be predicted epitopes. Carcinoma is known to have developed mechanisms for evading the host’s immune system. Here, we show for the first time that carcinoma has a bias towards fewer possible epitopes already at the step of mRNA splicing. Part IV of the thesis deals with the analysis of 93 patient-donor pairs that underwent hematopoietic stem cell transplantation (HCT). HCT is a standard treatment for a variety of hematological diseases. Graft-versus-host disease is a possible complication after an HCT, where the recipient´s cells are perceived as foreign and the target of an immune response mediated by the donor´s transplanted immune cells. The immune response is provoked by epitopes unique to the patient, so-called mHags. Here, a gene-specific association between the number of SNPs or predicted mHags and the possible clinical outcome following an HCT is presented.