NIH Awards Mayo Clinic Researchers $3.4 Million Glioblastoma Grant
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 cells and how best to treat them using other treatments such as radiation or chemotherapy.
Kristin Swanson, Ph.D., vice chair of research at Mayo Clinic’s 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. Swanson teamed with neuroradiologist, Leland Hu, M.D., molecular biologist, Nhan Tran, Ph.D. and imaging informaticist, Ross Mitchell, Ph.D. Using MRI data; their team produces maps of the different tumor subtypes found within a patient’s brain. “MRI-based mathematical models can be used to predict genomic content of these invasive tumor regions. 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”
“This knowledge allows us to better advance individualized medicine,” says Dr. Swanson. “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.”
The biology and mathematical modelling of glioma invasion: a review
Adult gliomas are aggressive brain tumours associated with low patient survival rates and limited life expectancy. The most important hallmark of this type of tumour is its invasive behaviour, characterized by a markedly phenotypic plasticity, infiltrative tumour morphologies and the ability of malignant progression from low- to high-grade tumour types. Indeed, the widespread infiltration of healthy brain tissue by glioma cells is largely responsible for poor prognosis and the difficulty of finding curative therapies. Meanwhile, mathematical models have been established to analyse potential mechanisms of glioma invasion. In this review, we start with a brief introduction to current biological knowledge about glioma invasion, and then critically review and highlight future challenges for mathematical models of glioma invasion.
Five outstanding ASU students chosen to attend Mayo medical conference
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The Mathematical Neuro-oncology Lab had a strong showing at the 2017 Society for Neuro-Oncology annual meeting. Eight members presented a variety of research ranging from the evaluation of response metrics in GBM treatment to novel methods for applying machine learning methods to radiomics.
- Gustavo De Leon, BS – “Identifying Early Indicators of Immunotherapeutic Response: CAR T-Cell Therapy”
- Susan Christine Massey, PhD – “Extent of glioblastoma invasion predicts overall survival following upfront radiotherapy concurrent with temozolomide”
- Kyle W. Singleton, PhD – “Discrimination of clinically impactful treatment response in recurrent glioblastoma patients receiving bevacizumab treatment”
- Kyle W. Singleton, PhD – “Role of pretreatment tumor dynamics and imaging response in discriminating glioblastoma survival following gamma knife”
- Michael Vogelbaum, MD, PhD – “Impact of post-surgical enhancing tumor volume and T2/FLAIR volume on the survival impact of bevacizumab in NRG Oncology/ RTOG 0825”
- Pamela R Jackson, PhD – “P53 amplification modifies the glioblastoma microenvironment: Differentiating the contribution of cells vs edema in the T2 weighted MRI signal”
- Leland Hu, MD – “Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning”
- Kristin R Swanson, PhD – “Radiomics of tumor invasion 2.0: combining mechanistic tumor invasion models with machine learning models to accurately predict tumor invasion in human glioblastoma patients”
Susan Massey, PhD, presented part of her dissertation research today at the Society for Mathematical Biology Conference in Salt Lake City, Utah – “Biomathematical proneural glioma model suggest giving PDGF inhibitors earlier”
Solving the equation for personalized cancer care
The issue can be downloaded here
The Mathematical Neuro-Oncology Lab and the Neurosurgery Innovations Lab at the Mayo Clinic in Phoenix, Arizona, have joined to create the Precision Neurotherapeutics Program which is introduced in this new video featuring Dr. Bernard Bendok, MD, and Dr. Kristin Swanson, PhD.
Mayo Clinic and the Massachusetts Institute of Technology (MIT) have been awarded a five-year, $9.7 million grant from the National Cancer Institute (NCI) to support a Physical Sciences-Oncology Center (PS-OC). Researchers hope to learn more about the physical parameters that limit drug delivery into brain tumors and use this information to build models that will help physicians better predict how the body will distribute a particular drug to brain tumors and help them select the best drug to treat each patient based on their unique tumor.
Mayo Clinic and MIT are among 10 institutions selected to participate in the NCI Physical Sciences-Oncology Network. The network supports innovative ideas that blend perspectives and approaches from the physical sciences, engineering, and cancer research, with the goal of improving the understanding of cancer biology and oncology.
“The most common types of malignant brain tumors — brain metastases originating from cancers outside of the brain, and glioblastoma — have regions that are protected from most drugs,” says co-principal investigator Jann Sarkaria, M.D., of Mayo Clinic. “Low-level drug exposure in these regions can promote drug resistance and that may be why there have been no new effective drug treatments for brain tumors in more than a decade.”
This article originally appeared on the Mayo Clinic News Network.
The Mayo Clinic and Arizona State University (ASU) Alliance for Health Care, a comprehensive new model for health care education and research, was announced today, Friday, October 21. Our relationship with ASU is a long-term priority for Mayo Clinic. The Alliance draws from the strengths of each organization and will accelerate cutting-edge research discoveries, improve patient care through health care innovation and transform medical education.
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A patient-specific computational model of hypoxia-modulated radiation-resistance in glioblastoma using 18F-FMISO PET
, Andrew D. Trister
, Joshua Jacobs
, Andrea J. Hawkins-Daarud
, Maxwell L. Neal
, Kristi Hendrickson
, Maciej M. Mrugala
, Jason K. Rockhill
, Paul Kinahan
, Kenneth A. Krohn
, and Kristin R. Swanson
Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient’s disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.