Computational Methods to Find and Rank MHC-I Restricted Tumor-Associated Antigens to Improve Therapeutic Efficacy and Tolerability of Antigen-based Cancer Immunotherapy
Creators: name: Lischer, Christopher
Publisher: name: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Year: 2024

description: The development of targeted immunotherapies in the last decade has opened novel treatment modalities for many cancer entities. In particular, antigen-based treatment systems have received significant attention. These methods deliver tumor-derived antigens to the patient’s immune system intending to stimulate a specific and lasting immune response. Prominent methods in use for the delivery of antigens are antigen-encoding mRNA- or DNA-laden vectors like lipid nanoparticles or adenoviruses, as well as externally matured autologous dendritic cells or externally expanded autologous T cells. During the application of these immunotherapies, several challenges became apparent. First, the discovery of suitable target genes has proven difficult since the antigens need to be restricted to the tumor, i.e., not found or, at most, very lowly expressed in the rest of the body. Secondly, antigen loss from the tumor under pressure by the immune system is a repeated occurrence and must be mitigated to ensure long-lasting therapeutic efficacy. Finally, unavoidable off-target effects must be limited in their severity. This thesis aimed to develop novel computational tools and algorithms to address and overcome the above-presented issues. In the first part of this project, we created a pipeline based on next-generation sequencing data to select overly expressed genes in a tumor model which are not or only minimally expressed in survival-critical tissues. As a feasibility study, we predicted antigens against metastatic melanoma and found 35 candidate genes. We predicted all possible peptides with a length of 9 to 12 amino acids and their corresponding binding affinity to different HLA Class I alleles. Using a multivariate score, we ranked all derived peptides and their allele-specific epitopes and provided them to the community in an online database. With our algorithm being free of prior knowledge and based only on primary data, we deemed the selection of the well-known metastatic melanoma marker MAGE-A3 as validation of our approach. In addition, our tolerability evaluation effectively filtered out a known MAGE-A3-derived antigen that had caused severe side effects in a clinical trial. In the second part of this project, we set out to improve several aspects of our pipeline. First, to implement a generalizable prediction procedure; second, to evaluate the biological relevance of the antigen for the tumor and third, to perform experimental validation of the efficacy of our candidates. We adapted our prediction system by integrating a machine learning model that evaluates both binding and immunogenic activity probability in a generalized manner. We also developed a network model that tries to gauge an antigen's relevance for the tumor to reduce the chances of antigen loss. We implemented this approach with data derived from metastasized primary uveal melanoma and found a set of 22 candidate genes. Several experiments with autologous T-cells were performed for validation, showing that our predicted peptides elicited an immune response in an in vitro setting for some of our healthy donors. Further, a cytotoxicity assay showed that the peptide-stimulated T-cells killed the uveal melanoma cell line 92.1 in an antigen-specific fashion. Using in silico and in vitro methods, we strived to discover novel tumor antigens and to provide a decision support system to facilitate applicability.