Generously Funded by the Liedtke MetaLab Research Fund
Amit Singnurkar – Sunnybrook Health Sciences Center
Project Title: “Mapping of Essential Amino Acid Metabolism for the Detection of Glioblastoma Multiforme Spread in Post-Surgical Patients”
Description of Project:
Glioblastoma (GB) is the most common primary brain malignancy in adults and carries poor prognosis. Despite recent advances in medical imaging and therapy, median survival remains approximately 14 months.
A major barrier to the effectiveness of current therapies is the inability to detect the full extent of disease. While the bulk of the tumour can be identified on magnetic resonance imaging (MRI), GB has a diffuse growth pattern and is prone to microscopic spread to distant parts of the brain.
In this study, we will use positron emission tomography (PET) to image amino acid receptors that are known to be expressed by GB tumour tissue. Using artificial intelligence techniques, will develop a methodology to detect occult diseases on current imaging techniques using high performance computing methods to map the total extent of tumour tissue.
With this information, we hope to provide better information to treating physicians when planning radiation treatment so that there is comprehensive treatment coverage of the tumour, with better sparing of unaffected brain tissue, which we predict will lead to better survival and quality of life for patients.
This work will also support future development and evaluation of targeted therapies for glioblastoma using high energy medical isotopes.
What receiving this award means:
This award will support our work in building a comprehensive research and therapeutics capability for glioblastoma patients by adding nuclear oncology and molecular imaging with positron emission tomography to the already impressive portfolio of research and innovation at Sunnybrook.
Update May 2022
We have successfully developed PET/MRI sequences that can image GBM to provide multi-modality imaging signatures for these tumors. We continue to work on improving imaging quality by addressing patient motion so that we can identify tumor borders with increased confidence and optimize tumor maps for radiation therapy planning. We have developed an alpha version of a software tool that can identify tumor borders and early work has shown that FET-PET can identify viable tumor that lies outside of the area typically appreciated by clinical MRI. Due to restrictions and supply chain constraints related to COVID-19, the above work has been accomplished through use of data from a sister study at Sunnybrook and we are now actively recruiting for MAP-GBM so that we can further sharpen our analysis tools and validate the tumor imaging signatures through longitudinal follow-up. We will also enhance our software tool using machine learning and artificial intelligence to interrogate tracer kinetics for identification of occult tumor.