Digital Molecular Medicine

Advanced medical therapies like cellular immunotherapies are living drugs that modulate the patient’s immune system to fight disease. They belong to a special group of medical products known as advanced therapy medicinal products (ATMPs) and have achieved clinical success in treating some diseases. As they actively modulate cell biology in vivo, a thorough understanding of their complex mechanisms of action is essential. This necessitates digital transformation in medical bioinformatics and translational molecular medicine.

Machine learning for (single-cell) multiomics and spatial cell biology: We combine omics-wide measurements, including single-cell multiomics and spatial transcriptomics profiling, with biomedical data science, machine learning, and integrative bioinformatics to reveal the mode of action of advanced medical therapies, especially of cell and gene therapies.

Selected publications:

Multiomic profiling of T cell lymphoma after therapy with anti-BCMA CAR T cells and GPRC5D-directed bispecific antibody – PubMed

Single-cell multiomic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma – PubMed

Patient-individual in silico models for risk and pathogenesis prediction: Virtual patient twins promise personalized, risk-adapted clinical decision support. However, virtual patient twins capable of guiding decisions throughout the entire patient path remain underdeveloped. Especially for ATMPs, this challenge is immense, because the patient’s and the medicinal product’s path are two parallel trajectories requiring integrative in silico modelling. We aim to improve personalized virtual twin models for ATMP applications by integrating molecular and cell biological data into virtual patient twin concepts.

Selected publications:

Design specifications for biomedical virtual twins in engineered adoptive cellular immunotherapies – PubMed

Molecular biomarker discovery and confirmation: We develop prospective or predictive biomarkers in oncology and immuno-oncology. We evaluate treatment responses to adoptive cellular immunotherapies using single-cell multi-omics and spatial transcriptomics data. We investigate how machine learning models that interrogate molecular biological data for personalized medicine can be transferred into clinical applications in accordance with the In Vitro Diagnostic Regulation (IVDR).

Selected publications:

A longitudinal single-cell atlas to predict outcome and toxicity after BCMA-directed CAR T cell therapy in multiple myeloma – PubMed

A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer – PubMed

ProstaTrend-A Multivariable Prognostic RNA Expression Score for Aggressive Prostate Cancer – PubMed

Software engineering and interoperability for clinical bioinformatics: Integrating (single-cell) multi-omics data into ATMP product development and treatment decisions requires validated, interoperable bioinformatics pipeline. We conduct research on developing standardized data processing frameworks for (single-cell) multiomics data that ensure reproducibility of research results and interoperability within the ATMP life cycle.  

Selected preprint:

CERTOMICS : Trusted Single-Cell Multiomics Pipeline for High-Resolution Profiling of Adoptive Cellular Immunotherapies | Research Square