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Abstract

Persistent infection with high-risk types of human papillomavirus (HPV) can cause several malignancies, in particular oropharyngeal and anogenital cancers. HPV16 has been identified as the most prevalent high-risk type, being related to 60% of cervical cancers, 75% of oropharyngeal cancers, 71% of anal cancers and the majority of precancerous lesions. As standard of care treatment is invasive, and harbors risks and side effects, there is a need for new approaches. For rationally designing a therapeutic vaccine against HPV-induced malignancies, it is essential to identify suitable target epitopes, which are presented on the surface of an HPV-transformed cell and induce immune responses that eventually mediate target cell death. The HPV16 oncoproteins E6 and E7 represent ideal targets for immunotherapy as they mediate the transforming potential of the virus and are constitutively expressed in all malignant cells. In order to define HPV16 target epitopes, in this thesis several algorithms were used to predict potential HPV16 E6- and E7-derived binders of human leukocyte antigen (HLA) class I in silico. Predicted peptides were synthesized and HLA binding capacity was validated in competition-based cellular binding assays. To ensure broad population coverage, predictions and validations were performed for seven frequent HLA alleles: A*01:01, A*02:01, A*03:01, A*11:01, A*24:02, B*07:02 and B*15:01. Including peptides derived from HPV16 E6/E7 variants containing amino acid changes, 271 peptides were experimentally assessed and 69 binders were identified. Combined with previous results, the total HPV16 E6/E7 dataset comprised 779 peptide-HLA measurements. The HPV16 E6/E7 dataset was used to evaluate the performance of employed predictors. No single algorithm was outperforming other methods, but different predictors were found to be best for different settings, depending on investigated HLA type and peptide length. As applying commonly used decision threshold yielded only low sensitivity, criteria for optimal decision thresholds were defined and optimal thresholds were calculated for individual predictors, HLA-types and peptide lengths. Comparing threshold-dependent performance of predictors showed that using criteria-based thresholds allowed more sensitive prediction of HLA-binding peptides without a strong negative influence on prediction accuracy. To identify T cell epitopes among the HPV16 E6- and E7-derived HLA ligands, their capacity to induce immune responses was investigated. To this end, peripheral blood mononuclear cells of healthy donors were HLA-typed and stimulated with respective peptides to generate epitope-specific T cell lines. By assessing interferon-γ-secretion of these T cells, 31 immunogenic peptides were identified. Further characterizing the functionality of epitopes in cytotoxicity assays, five of ten immunogenic HLA-A*02:01-peptides mediated specific killing of HPV16+ target cells by CD8+ T cells. In conclusion, several immunogenic HPV16 E6-and E7-derived epitopes were identified, which are the basis for rational design of a therapeutic HPV vaccine. Additionally, this thesis provides an evaluation of peptide–HLA class-I binding prediction method and recommendations to increase prediction sensitivity to extend the number of potential epitopes as targets for immunotherapy.