In March of 2018, seven MNO lab members presented posters at the Mayo Clinic Young Investigators Research Symposium in Rochester, MN. The group included Aditya Khurana (Mayo Medical School student), Cassandra Rickertsen (Image Analysis Team Lead), Gustavo De Leon (post-baccalaureate), Susan Massey (post-doctoral researcher), Sara Yee (ASU undergraduate), Julia Lorence (ASU undergraduate), and Paula Whitmire (post-baccalaureate). Gustavo De Leon was selected to give a poster teaser presentation of his research on identifying early indicators of immunotherapeutic response in GBM patients. The event served as an excellent opportunity for our young investigators to present their research and network with investigators from the other Mayo Clinic campuses.
April Fleming and Kyle Singleton present at 2017 Biomedical Engineering Society Annual Meeting in October 2017
April Fleming, an ASU summer undergraduate intern with the PNT program, presented a poster on her work “Mathematical model of brain tumor growth facilitates tumor cell quantification from bioluminescence imaging”. Kyle W. Singleton, PhD, presented a poster “Comparison Of Brain Tumor Segmentation Methods For Computing Volumetric and Radial Measurements”.
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
MNO lab presents 8 abstracts at the 22nd Annual Scientific Meeting of the Society for Neuro-Oncology
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”