Multi modal prediction of therapy response for rectal cancer patients (P02)

• Subject Area: Medical Informatics and Medical Bioinformatics ; Pathology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 540805631

The project will focus on the impact of the TME on survival of rectal cancer patients and develop machine learning methods that integrate molecular omics data to predict treatment outcome. The project is based on the notion that networks based on multi-modal data (histopathology and molecular omics) will outperform networks trained with only one of the two data modalities. Thus, it will couple multi-view data via graph neuronal networks where the individual nodes represent cells, which carry RNA expression and morphological features, and edges connect neighboring cells based on their Euclidean distances (cell-graphs).

• DFG Programme: CRC/Transregios
• Subproject of: TRR 417: Cellular Communication in the Stroma of Colorectal Cancer: From Pathophysiology to Clinical Translation
• Applicant Institution: Goethe-Universität Frankfurt am Main
• Project Heads: Professor Florian Buettner, Ph.D.; Dr. Nadine Flinner