Computational Methods to Find and Rank MHC-I Restricted Tumor-Associated Antigens to Improve Therapeutic Efficacy and Tolerability of Antigen-based Cancer Immunotherapy

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

Network- and systems-based re-engineering of dendritic cells with non-coding RNAs for cancer immunotherapy

Dendritic cells (DCs) are professional antigen-presenting cells that induce and regulate adaptive immunity by presenting antigens to T cells. Due to their coordinative role in adaptive immune responses, DCs have been used as cell-based therapeutic vaccination against cancer. The capacity of DCs to induce a therapeutic immune response can be enhanced by re-wiring of cellular signalling pathways with microRNAs (miRNAs). Methods: Since the activation and maturation of DCs is controlled by an interconnected signalling network, we deploy an approach that combines RNA sequencing data and systems biology methods to delineate miRNA-based strategies that enhance DC-elicited immune responses. Results: Through RNA sequencing of IKKβ-matured DCs that are currently being tested in a clinical trial on therapeutic anti-cancer vaccination, we identified 44 differentially expressed miRNAs. According to a network analysis, most of these miRNAs regulate targets that are linked to immune pathways, such as cytokine and interleukin signalling. We employed a network topology-oriented scoring model to rank the miRNAs, analysed their impact on immunogenic potency of DCs, and identified dozens of promising miRNA candidates, with miR-15a and miR-16 as the top ones. The results of our analysis are presented in a database that constitutes a tool to identify DC-relevant miRNA-gene interactions with therapeutic potential (https://www.synmirapy.net/dc-optimization). Conclusions: Our approach enables the systematic analysis and identification of functional miRNA-gene interactions that can be experimentally tested for improving DC immunogenic potency

Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic

In most disciplines of natural sciences and engineering, mathematical and computational modelling are mainstay methods which are usefulness beyond doubt. These disciplines would not have reached today’s level of sophistication without an intensive use of mathematical and computational models together with quantitative data. This approach has not been followed in much of molecular biology and biomedicine, however, where qualitative descriptions are accepted as a satisfactory replacement for mathematical rigor and the use of computational models is seen by many as a fringe practice rather than as a powerful scientific method. This position disregards mathematical thinking as having contributed key discoveries in biology for more than a century, e.g., in the connection between genes, inheritance, and evolution or in the mechanisms of enzymatic catalysis. Here, we discuss the role of computational modelling in the arsenal of modern scientific methods in biomedicine. We list frequent misconceptions about mathematical modelling found among biomedical experimentalists and suggest some good practices that can help bridge the cognitive gap between modelers and experimental researchers in biomedicine. This manuscript was written with two readers in mind. Firstly, it is intended for mathematical modelers with a background in physics, mathematics, or engineering who want to jump into biomedicine. We provide them with ideas to motivate the use of mathematical modelling when discussing with experimental partners. Secondly, this is a text for biomedical researchers intrigued with utilizing mathematical modelling to investigate the pathophysiology of human diseases to improve their diagnostics and treatment

The IKZF1–IRF4/IRF5 Axis Controls Polarization of Myeloma-Associated Macrophages

The bone marrow niche has a pivotal role in progression, survival, and drug resistance of multiple myeloma cells. Therefore, it is important to develop means for targeting the multiple myeloma bone marrow microenvironment. Myeloma-associated macrophages (MAM) in the bone marrow niche are M2 like. They provide nurturing signals to multiple myeloma cells and promote immune escape. Reprogramming M2-like macrophages toward a tumoricidal M1 phenotype represents an intriguing therapeutic strategy. This is especially interesting in view of the successful use of mAbs against multiple myeloma cells, as these therapies hold the potential to trigger macrophage-mediated phagocytosis and cytotoxicity. In this study, we observed that MAMs derived from patients treated with the immunomodulatory drug (IMiD) lenalidomide skewed phenotypically and functionally toward an M1 phenotype. Lenalidomide is known to exert its beneficial effects by modulating the CRBN-CRL4 E3 ligase to ubiquitinate and degrade the transcription factor IKAROS family zinc finger 1 ( IKZF1). In M2-like MAMs, we observed enhanced IKZF1 levels that vanished through treatment with lenalidomide, yielding MAMs with a bioenergetic profile, T-cell stimulatory properties, and loss of tumor-promoting capabilities that resemble M1 cells. We also provide evidence that IMiDs interfere epigenetically, via degradation of IKZF1, with IFN regulatory factors 4 and 5, which in turn alters the balance of M1/M2 polarization. We validated our observations in vivo using the CrbnI391V mouse model that recapitulates the IMiD-triggered IKZF1 degradation. These data show a role for IKZF1 in macrophage polarization and can provide explanations for the clinical benefits observed when combining IMiDs with therapeutic antibodies