Solving the equation for personalized cancer care
The issue can be downloaded here
Solving the equation for personalized cancer care
The issue can be downloaded here
The Precision Neurotherapeutics Program at the Mayo Clinic, a joint venture that includes the Mathematical Neuro-Oncology Lab and the Neurosurgery Innovations Lab, was featured in an issue of The Phoenix Business Journal in October of 2016. The issue can be downloaded here (warning: the file is 13MB).
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.
Click here for video
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.
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.
Glioblastoma multiforme (GBM) is the most common malignant primary brain tumor associated with a poor median survival of 15–18 months, yet there is wide heterogeneity across and within patients. This heterogeneity has been the source of significant clinical challenges facing patients with GBM and has hampered the drive toward more precision or personalized medicine approaches to treating these challenging tumors. Over the last two decades, the field of Mathematical Neuro-oncology has grown out of desire to use (often patient-specific) mathematical modeling to better treat GBMs. Here, we will focus on a series of clinically relevant results using patient-specific mathematical modeling. The core model at the center of these results incorporates two hallmark features of GBM, proliferation \((\rho )\) and invasion (D), as key parameters. Based on routinely obtained magnetic resonance images, each patient’s tumor can be characterized using these two parameters. The Proliferation-Invasion (PI) model uses \(\rho \) and D to create patient-specific growth predictions. The PI model, its predictions, and parameters have been used in a number of ways to derive biological insight. Beyond predicting growth, the PI model has been utilized to identify patients who benefit from different surgery strategies, to prognosticate response to radiation therapy, to develop a treatment response metric, and to connect clinical imaging features and genetic information. Demonstration of the PI model’s clinical relevance supports the growing role for it and other mathematical models in routine clinical practice.
A dear friend and future star was tragically taken from us recently. Anne Baldock was killed by a drunk driver early in the morning of May 16, 2015. She will be sorely missed.
Anne joined our lab in the summer of 2009 as an 18-year-old undergraduate and quickly proved that brilliance and hard work can make things happen. Anne showed herself to be a superstar capable of mastering her education work load while maintaining a strong and productive work ethic in the lab. Even though she was a full-time student, she was offered a staff research scientist job, which is a very unusual and impressive achievement for a then-sophomore in college! After that, she played a pivotal role in the success of the lab. Anne assumed a leadership role among the researchers, leading the image measurement team responsible for collecting most of our data. Anne authored or co-authored over 12 papers and articles by the time she started Medical School in 2013. Anne had applied to, and been accepted by, a dozen different medical or MD/PhD programs. She was just finishing her second year at the University of California San Diego when she was killed. Anne had the skills, talent, and work ethic to make a major difference in the world. Medicine is left without a bright star.
The Anne Baldock memorial has been established in loving memory of Anne Baldock by her family. Funds donated in memory of Anne will be used to further translational research in malignant brain tumors using mathematical modeling, a cause to which she devoted her innate and emerging scientific talents. If you would like to make a donation to Anne’s memorial, please choose one of these two organizations: TGen Foundation or The Mayo Clinic.
You may also send a gift to TGen by mail to:
445 N. 5th Street, Suite 120
Phoenix, AZ 85004
To designate your gift to The Mayo Clinic, please follow these few steps:
The Mathematical Neuro-Oncology Research Lab Presents
David R. Grimes, PH.D.
Post-Doctoral Research Associate
CR UK/MRC Oxford Institute for Radiation Oncology
Gray Labs, Radiation Research Institute
University of Oxford
Modeling the role of oxygen in the tumor micro-environment
MONDAY, MARCH 2ND, 2015
2:00 PM–3:00 PM
676 N. SAINT CLAIR ST. SUITE 1300
MATHEMATICAL NEURO-ONCOLOGY LAB
In tumours, hypoxia is associated with poor prognosis, increased likelihood of metastasis and a marked resistance to radiotherapy. While imaging techniques, such as PET with hypoxia tracers can indicate hypoxic sub-volumes inside a tumour, these modalities are limited by the physics of the system to a resolution in the millimetre regime, whereas tumour oxygen levels can vary over a micron scale. Mathematical models of cellular oxygen distribution are of paramount importance to bridge this scale gap and aid interpretation of clinical image data, and ultimately treatment prescription. This talk discusses some approaches to -and challenges of -the oxygen micro-environment and its significance to treatment.