Imaging

Research projects

Perspectives of Targeted Radionuclide Imaging and Therapy of Fibroblast Activation Protein (FAP) in Cancer

Radionuclide-based FAP Targeting for Cancer Imaging and Therapy. – Reference source: Imlimthan S. et al., Pharmaceuticals 2021, 14(10), 1023 (https://doi.org/10.3390/ph14101023)

Gourni group  PD Dr. Eleni Gourni

Tumors develop within a complex microenvironment consisted of diverse cell types surrounded by a matrix rich of proteins, termed tumor stroma. Stroma includes immune cells, fibroblasts and vascular enothelial cells. 

Cancer cells rely on extensive support from the stroma to survive, proliferate and invade, thus making stroma an important potential target for anti-cancer therapy. Targeting elements of stroma, may be a useful therapeutic strategy to prevent tumor growth and progression. One of those elements is the fibroblast activation protein (FAP) which is overexpressed on activated fibroblasts on several tumors types. 

The current project aims at designing and evaluating novel FAP-specific inhibitors for the generation of radiotracers with the potential to be used for the diagnosis and treatment of FAP-positive tumors. The novel radiotracers are thoroughly investigated in vitro and in vivo using cell lines and xenografted tumor models to understand their binding properties and their in vivo performance.

Determing tumor lipid heterogeneity in lung cancer

Cancer cells located within a microvasculature network. Top row: Holoclones remain within the endothelial lining marked by PECAM1 (left), whereas paraclones extravasate in the surrounding matrix (right). PECAM1 = red, A549 subclones = green. Bottom row: 3D surface rendering illustrates the different extravasation dynamics of both A549 subclones. Scale bar = 20 µm. Schmid KF, Zeinali S, Moser SK, Dubey C, Schneider S, Deng H, Haefliger S, Marti TM, Guenat OT. Assessing the metastatic potential of circulating tumor cells using an organ-on-chip model. Front Bioeng Biotechnol. 2024 Oct 8;12:1457884. doi: 10.3389/fbioe.2024.1457884. PMID: 39439549; PMCID: PMC11493642.

Guenat group Prof. Dr. Olivier T. Guenat

The Metastasis-on-Chip project aims to replicate the metastatic process, focusing specifically on extravasation and colony formation. 

Our initial studies evaluate the metastatic potential of cancer cells based on their phenotypes, using the A549 non-small cell lung cancer (NSCLC) cell line, which exhibits distinct phenotypic variations. We discovered that paraclones, characterized by a mesenchymal phenotype, successfully extravasate, while holoclones, with an epithelial phenotype, do not. Additionally, paraclones demonstrated significantly greater migratory behavior compared to holoclones. 

These findings provide valuable insights into the mechanisms of metastasis and lay the groundwork for further exploration of targeted therapies.

Targeting cellular metabolism to augment cancer therapy

Inhibition of lactate metabolism selectively damages the mitochondria of cancer cells, and when combined with radiotherapy, leads to a marked reduction in tumor growth in a mouse model of NSCLC – Reference source: Deng H. et al., 2025 (https://www.nature.com/articles/s41467-025-57906-3)

Marti Group PD Dr. med. Thomas Marti

The aim of this project is to investigate how the nucleotide/lactate metabolism and the DNA damage response machinery are associated with the tumor initiating capacity, the chemotherapy response, and the metastatic capacity of lung and mesothelioma cancer stem cells. In addition, we are exploiting treatment induced cellular adaptations as novel targets for cancer therapy.

 

Artificial Intelligence for Automated QUality Assurance in RadioTherapy for glioblastoma target volume and organs at risk delineation in clinical trials - AQUA RT

Schematic of the proposed AQUA RT for automated delineation quality assurance in glioblastoma patients. Top part shows the current manual delineation, which is compared to the proposed AQUA RT approach (bottom part).

Reyes group Prof. Dr. Mauricio Reyes

In this project we aim to test the hypothesis that AI-based auto-segmentation technologies can be used for an AI-assisted multi-criteria quality assurance assessment in radiation therapy. The proposed multicriteria evaluation model is expected to provide a more objective review than traditional approaches, while at the same time focussing on clinically relevant radiotherapy aspects. The proposed Automated QUality Assurance in RadioTherapy (AQUA-RT) framework has the potential to increase consistency, improve delineation quality and reduce workload for routinely challenging quality assurance procedures.