National Cancer Institute (NCI) to further study combining the molecular analysis of glioblastoma with MRI.
The center will be based at Mayo Clinic in Arizona with co-principal investigator Peter Canoll, M.D., Ph.D., Columbia University, and involves collaboration among multiple institutions, including Arizona State University, Columbia University, City of Hope, Inova, Mayo Clinic Rochester, MD Anderson and Washington University.
Glioblastoma is a fast-growing and aggressive brain tumor that begins as a growth of cells in the brain or spinal cord. As it grows, it can invade and destroy healthy tissue. There is no cure, but treatments may slow the cancer’s growth and reduce symptoms.
Glioblastoma is a diverse cancer, which means that biopsies from different regions of each patient’s tumor can represent widely different tumor biology. Treatments that work for one region of the tumor may not work for others.
MRI provides a way to look at the whole tumor but is poor at differentiating regions of unfavorable tumor progression from regions of favorable tumor response which is often mixed with immune response and inflammation. For this reason, it can be difficult for physicians to use imaging to determine if a treatment works for all or part of the patient’s tumor.
This new NCI grant supports a team of researchers to develop the Mathematical Oncology Systems Analysis Imaging Center (MOSAIC). MOSAIC will combine biopsies collected at different regions of each patient’s tumor with features on patient MRIs. MOSAIC researchers rely on these patterns to build mathematical and machine learning models allowing MRI to predict tumor biology in regions that were not biopsied. In this way, MOSAIC researchers can effectively create so-called “virtual biopsies” of patient’s tumors without surgery.
Mathematical oncology is the use of mathematics, modeling and simulation in cancer biology and oncology. This fusion of the quantitative sciences with the study of cancer includes the development of predictive mathematical models that help bridge gaps in knowledge in the understanding of cancer or data-driven machine-learning approaches that detect patterns in cancer data that had not been identified previously.
“This grant is important as it will help us use MRI as a lens to more directly predict changes in the biology of each patient’s tumor during treatment when we normally have very limited access to tissue samples,” says the study’s co-principal investigator, Kristin Swanson, Ph.D., a mathematical oncologist at Mayo Clinic Comprehensive Cancer Center.
MOSAIC’s image-based mathematical models will use MRI to track aspects of tumor heterogeneity previously unattainable during the course of patient care. This is particularly important as the brain is a protected organ which limits tissue sampling during rare surgical interventions.
“This grant will allow us to better understand which of our patients are responding to treatment and to ultimately drive new clinical trials that improve patient outcomes,” adds Dr. Swanson.
The center will be based at Mayo Clinic in Arizona with co-principal investigator Peter Canoll, M.D., Ph.D., Columbia University, and involves collaboration among multiple institutions, including Arizona State University, Columbia University, City of Hope, Inova, Mayo Clinic Rochester, MD Anderson and Washington University.
The grant is supported by NCI of the National Institutes of Health (NIH) under project number 1U54CA274504-01A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
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