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Congratulations to Dr. Hawkins-Daarud on her new paper in the Journal of Royal Society

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.

https:://www.ncbi.nlm.nih.gov/pubmed/29118112

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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”
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Mayo Clinic and MIT receive grant for Physical Sciences-Oncology Center

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.

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Mayo Clinic and Arizona State University Alliance for Health Care

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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Congratulations to Dr. Jacobs and Dr. Hawkins-Daarud on their new paper in Journal of the Royal Society Interface

A patient-specific computational model of hypoxia-modulated radiation-resistance in glioblastoma using 18F-FMISO PET

 

 
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. 

https://rsif.royalsocietypublishing.org/content/12/103/20141174

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Congratulations to Dr. Andrea Hawkins-Daarud on her new paper in Journal of the Royal Society Interface

In Silico Analysis Suggests Differential Response to Bevacizumab and Radiation Combination Therapy in Newly Diagnosed Glioblastoma

 

 
Recently, two phase III studies of bevacizumab, an anti-angiogenic, for newly diagnosed glioblastoma (GBM) patients were released. While they were unable to statistically significantly demonstrate that bevacizumab in combination with other therapies increases the overall survival of GBM patients, there remains a question of potential benefits for subpopulations of patients. We use a mathematical model of GBM growth to investigate differential benefits of combining surgical resection, radiation and bevacizumab across observed tumour growth kinetics. The differential hypoxic burden after gross total resection (GTR) was assessed along with the change in radiation cell kill from bevacizumab-induced tissue re-normalization when starting therapy for tumours at different diagnostic sizes. Depending on the tumour size at the time of treatment, our model predicted that GTR would remove a variable portion of the hypoxic burden ranging from 11% to 99.99%. Further, our model predicted that the combination of bevacizumab with radiation resulted in an additional cell kill ranging from 2.6×107 to 1.1×1010 cells. By considering the outcomes given individual tumour kinetics, our results indicate that the subpopulation of patients who would receive the greatest benefit from bevacizumab and radiation combination therapy are those with large, aggressive tumours and who are not eligible for GTR.
 

https://rsif.royalsocietypublishing.org/content/12/109/20150388