Research > Focus C > Work Packages C04
Summary
The spatial distribution of amino acid (and other) metabolite profiles in glioblastoma, which are potent promoters of tumor progression and mediators of resistance induced by glioblastoma therapies, will be investigated by statistical mass spectrometry imaging in orthotopic glioma models and clinical samples. This project aims at adopting mass spectrometry imaging (MSI) technology for identification and spatial visualization of metabolite profiles associated with response and resistance in glioblastoma. The visual abstract below shows the aims and planned tasks of the work package.
Task 1
Targeted MS Imaging of amino acid metabolites:
Changes by standard glioblastoma therapies or immunotherapies?
Steps / Workflow
- Treatment of glioma mice and human glioblastoma in bioreactor with radi-ation, small molecule drugs or immunotherapies.
- Targeted MALDI-MS Imaging for distri-bution of drugs and known amino acid metabolites; LC-MS/MS for their quantification.
- Multivariate statistical analysis of inte-grated MSI- and LC-MS/MS- datasets.
- Translate to primary human gliobla- stoma resectates.
Task 2
Unbiased spatially-resolved search for metabolite pharmaco-dynamic biomarkers for small molecule inhibitors targeting amino acid degrading enzymes: Does lack of target engagement contribute to drug resistance?
Steps / Workflow
- Treatment of glioma mice and human glioblastoma in bioreactor with small molecule drugs targeting amino acid metabolism.
- Untargeted “as many metabolites as possible” MALDI-MSI (and IHC to visualize drug targets)
- Employ machine learning to extract putative metabolite patterns that indi-cate drug response or resistance
Task 3
Quantitative spatially resolved PK-PD relation-ships by simultaneous MS Imaging of small molecule inhibitors and suitable metabolites as PD markers
Steps / Workflow
- Treatment of glioma mice and human glioblastoma in bioreactor with inno-vative small molecule drugs targeting amino acid metabolism.
- Untargeted as well as targeted quanti-tative MALDI-MSI (and LC-MS/MS for reference quantification)
- Visualize drug and response distribu-tion by quantitative MSI
Task 4
Translational research on metabolite patterns relating to resistance in patients:
Identification of novel metabolic signatures associated with resistance or response using untargeted MSI in tumor specimens from UNITE-accessible clinical cohorts
Steps / Workflow
- Untargeted MALDI-MSI (supported by LC-MS/MS) and IHC of glioma patient samples (e.g. N2M2, INFORM, AMPLIFY-NEOVAC cohorts)
- Machine learning to extract metabo-lite patterns that may indicate drug response or resistance in patient tissue
PRINCIPLE INVESTIGATORS
HOPF, CARSTEN, PROF. DR. RER. NAT., DIPL.-BIOCHEM.
Head of CeMOS
Hochschule Mannheim, Center for Mass Spectrometry and Optical Spectroscopy (CeMOS), Paul-Wittsack-Str. 10, 68163 Mannheim, Germany
OPITZ, CHRISTIANE, DR. MED.
Junior Group Leader
German Cancer Research CenterBrain Cancer Metabolism Group Im Neuenheimer Feld 280, 69120 Heidelberg, German