Applied Bioinformatics

The Tumor Heterogeneity and Evolution team lead by Dr. Sadaf Mughal focuses on the application of computational methods to analyze omics data from patient cohorts to understand cancer heterogeneity, tumor evolution and to decipher the immune crosstalk in cancer progression and metastatic spread. We are particularly interested in learning drug resistance mechanisms tumor employ during evolution. For this we are investigating longitudinal samples to predict the evolutionary trajectories of aggressive metastatic subclones. Furthermore, we are studying the tumor-immune coevolution to address inter- and intra-tumoural heterogeneity, dictating progression of disease.

The Computational Oncoimmunology team lead by Charles Imbusch focuses on questions regarding the immune system in general and more specifically under pathogenic conditions such as cancer. To address these questions NGS, as well as array technologies, are routinely utilized.

With the advent of multiple single cell technologies it is now possible to differentiate subpopulations captured in an assay, allowing to address cell heterogeneity and the discovery of rare cell populations. We focus on the downstream analysis of scRNA-seq and scTCR to robustly identify and describe cell populations, study dynamic cross-talk between tumor and immune cells while keeping up to date with most recent algorithmic developments.

Alternatively to single cell assays we apply and develop methods to deconvolute from bulk data cell types, integrating epigenetic and transcriptomic data to describe the tumor microenvironment.

The AI in Computational and Translational Biology team led by Óscar Gonazález-Velasco focuses on AI model development and application using computational biology approaches to bridge molecular data and clinical applications. We integrate multi-omics data and machine learning models to uncover biological mechanisms underlying disease progression and therapy response. Our goal is to translate computational discoveries into actionable models that support precision and personalized medicine.

The Machine Learning in Translational Cancer Research team led by Benedikt Brors develops predictive and prognostic models to guide therapy for cancer patients. We are currently contributing ML models to two planned clinical trials, ESPAC-6 for adjuvant treatment in resectable pancreatic cancer (phase III, sponsor: University Hospital Heidelberg), and NB2015-LR for low- and medium-risk neuroblastoma (phase III, sponsor: University Hospital Cologne).