Scientific background
Personalized cancer vaccines have demonstrated promising and long-lasting results across various tumor entities by priming effective anti-tumor T-cell responses against individual and tumor-specific antigen candidates. However, there is still an urgent need to identify additional types of tumor-specific neoepitopes from different variant classes, such as aberrant protein translation. In addition, we need a better and more systematic understanding of how the neoantigen landscape changes in response to cancer therapies and other mechanisms that affect protein translation. Here, we aim to broaden the neoantigen repertoire by non-canonical antigens induced by aberrant translation and to systematically analyze the plasticity of the actionable neoantigen landscape.
PhD project description
The goal of this project is to make the class of translation variants more accessible for use as targets for personalized cancer immunotherapies and as prognostic and predictive biomarkers. By integrating publicly available and internal data, we will analyze translation variants as a novel variant class and predict their tumor-specificity in across multiple cancer entities. Additionally, the project aims to systematically analyze the plasticity of the actionable neoantigen landscape in tumor cells depending on cancer (immuno-) therapy and translation dynamics. We will profile the dynamic changes of the novel candidate category of translation variants as well as genomic and transcript variants. The target discovery will be complemented by utilizing state-of-the-art proteomics and immunopeptidomics methods to confirm antigen expression and presentation on the tumor cell surface, respectively. A deeper understanding of the plasticity of the neoantigen landscape will help us to more efficiently tailor holistic cancer immunotherapies to individual cancer patients.
Required profile of the candidate
The ideal candidate should have:
A background in computational biology and bioinformatics
Strong programming skills for reproducible data analysis, preferably in R or python
Experience with immunology, Next-Generation Sequencing or Proteomics is a plus
Eagerness to learn, establish and develop cutting-edge methodologies
Curiosity, enthusiasm and ability to work interdisciplinary
Publications relevant to the project
Bartok et al. Anti-tumour immunity induces aberrant peptide presentation in melanoma, Nature 2021
Weller et al. Translation dysregulation in cancer as a source for targetable antigens, Cancer Cell, 2025
Lang et al. Identification of neoantigens for individualized therapeutic cancer vaccines, Nat Rev Drug Discov, 2022
Hausmann et al., Computing tumor specificity of cancer antigen targets by k-mer indexing of healthy tissue transcriptomes, bioRxiv, 2026