# PNT Lab featured in January Issue of Cytopathology

Solving the equation for personalized cancer care

Written on January 20th, 2017. 0 Comments

# Mathematical Neuro-oncology Lab and Neurosurgery Innovations Lab Introduce Joint Program

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.

Written on October 27th, 2016. 0 Comments

# 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.”

Written on October 21st, 2016. 0 Comments

# 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.

Written on October 21st, 2016. 0 Comments

# 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.

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

Written on May 25th, 2016. 0 Comments

# 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.

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

Written on May 25th, 2016. 0 Comments

# Patient-Specific Mathematical Neuro-Oncology: Using a Simple Proliferation and Invasion Tumor Model to Inform Clinical Practice

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.

Written on May 25th, 2016. 0 Comments

# In Loving Memory of Anne Baldock

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 send your donation to The Mayo Clinic.

• Click on the “Give Now” button
• In the “Make Donation to:” box, select “Other – please specify below”
• When the “Specify Designation” box opens, enter “Neurologic Surgery Fund”
• Click the “Tribute Information” box, this will open some additional fields
• Click on the “in memory of” button
• Enter “Anne Baldock” in the “Specify Tribute Name” box
• Fill out the rest of the form as you wish

Written on May 20th, 2015. 0 Comments

# MNO Lecture Series: DAVID R. GRIMES, PH.D.

The Mathematical Neuro-Oncology Research Lab Presents

David R. Grimes, PH.D.
Post-Doctoral Research Associate
CR UK/MRC Oxford Institute for Radiation Oncology
University of Oxford

Modeling the role of oxygen in the tumor micro-environment

MONDAY, MARCH 2ND, 2015
2:00 PM–3:00 PM
ARKES PAVILION,
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.

Written on February 26th, 2015. 0 Comments

# MNO Lecture Series: December 11th 2014, Kit Curtius

The Mathematical Neuro-Oncology Research Lab Presents

Kathleen (Kit) Curtius
NSF Research Fellow
Department of Applied Mathematics
University of Washington
Fred Hutchinson Cancer Research Center

How long has that been there?
Multi-scale modeling of Barrett’s Esophagus

Thursday, December 11th, 2014
12:00 pm – 1:00 pm
Arkes Pavilion,
676 n. Saint Clair St. Suite 1300
Mathematical Neuro-Oncology Lab

Although the development of Barrett’s esophagus (BE) is considered an important first step in the progression to esophageal adenocarcinoma (EAC), BE is asymptomatic – so the duration of time a patient has harbored BE is generally not known when she/he is first diagnosed. This is particularly unfortunate because the duration that BE has been present in a patient correlates strongly with the risk of BE transforming into EAC. Recently identified clock-CpGs allow a novel characterization of a tissue in terms of its biological age, and these markers are used to show accelerated tissue aging in a variety of tumors. We seek markers of differential epigenetic drift from genome-wide DNA-methylation array data from BE patients in order to predict BE tissue age. We then estimate individual-level BE onset times and the subsequent risk of progressing to dysplasia and EAC using a mathematical model. This work translates DNA-methylation “footprints” of tissue-aging into “time” information to estimate important time scales in the step-wise progression to dysplasia and cancer in BE patients.

Written on December 10th, 2014. 0 Comments