The National Institutes of Health (NIH) awarded Mayo Clinic researchers in Arizona a $3.4 million grant to study how mathematical modeling can be used to help treat patients with glioblastoma - the most common type of malignant brain cancer.
Glioblastomas are made up of many different cell types and tumor cell subtypes. These cells can invade far into the brain and well beyond where the tumor can be seen on clinical imaging, such as MRIs. Surgical removal of these invasive tumor cells is risky. Yet, little is known about these residual tumor invaded tumor cells and how best to treat them using other treatments such as radiation or chemotherapy.
Kristin Swanson, Ph.D., vice chair of research, Mayo Clinic Department of Neurosurgery, is utilizing mathematical modeling to extract new information from MRI scans to unlock clues for how to best treat these residual tumor cells.
Achieving this project requires a team with complementary skills. Dr. Swanson teamed with neuroradiologist, Leland Hu, M.D., molecular biologist, Nhan Tran, Ph.D. and imaging informaticist, Joseph Ross Mitchell, Ph.D. Using MRI data, their team produce maps of the different tumor subtypes found within a patient’s brain.
“MRI-based mathematical models can be used to to predict genomic content of these invasive tumor region. These models provide a non-invasive way to identify the different tumor subpopulations in this invasive region for each patient. If we know the genetic content of the different parts of each patient’s tumor, we can match treatments that target each of the different genetic abnormalities,” says Dr. Swanson, whose team also leverages genomics, computer vision and artificial intelligence as part of their approach.
Dr. Swanson says these new mathematical models can be applied to each patient’s MRIs over time, providing crucial data on how tumor cells grow or respond to treatment in each patient.
“This knowledge allows us to better advance individualized medicine,” she says. “We can better match each patient with the combination of treatments that will best target the different populations of tumor cells within each tumor. This information may also reveal new treatment targets, open up additional treatment pathways and improve a physician’s ability to monitor the effects of treatment.”