Computational Neuropathology – Multi-Omics Strategies for Characterizing Molecular Tumor Heterogeneity

Brain tumors pose a major clinical challenge due to their pronounced molecular heterogeneity and limited treatment options. Despite significant advances in molecular diagnostics, there are still gaps in our understanding of how (epi)genomic aberrations influence the entire gene expression axis – from the activation of alternative transcript isoforms to the expression of specific proteoforms.

Our research group develops and applies computational multi-omics approaches to systematically identify and characterize brain tumor-specific aberrations at the (epi)genomic, transcriptomic, and proteomic levels – with a particular focus on their diagnostic and therapeutic potential.

Our work focuses on two key areas:

Analysis of molecular heterogeneity across different tumors using large patient cohorts. We develop and apply methods for multi-omics data integration to precisely capture the relationships between genomic, transcriptomic, and proteomic alterations. Our extensive tumor database enables in-depth and robust analyses.

Investigation of intratumoral heterogeneity using innovative technologies such as single-cell and spatial transcriptomics, combined with high-resolution digital microscopy and spatial proteomics. This provides the foundation for the targeted identification and characterization of distinct tumor subclones.

For analysis, we employ and further develop classical statistical and bioinformatics methods as well as novel AI-driven algorithms to gain deeper insights into tumor heterogeneity and identify potential diagnostic and therapeutic targets.