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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Written on October 21st, 2016. 0 Comments

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


Russell Rockne, 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.

Written on May 25th, 2016. 0 Comments

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


Andrea Hawkins-Daarud, Russell Rockne, David Corwin, Alexander R. A. Anderson and Kristin R. Swanson

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.

Written on May 25th, 2016. 0 Comments

Congratulations to Dr. Pamela Jackson on her new paper in Bulletin of Mathematical Biology

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


Pamela R. Jackson, Joseph Juliano, Russel C. Rockne and Kristin R. Swanson

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.

To designate your gift to The Mayo Clinic, please follow these few steps:

  • 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
Gray Labs, Radiation Research Institute
University of Oxford

Modeling the role of oxygen in the tumor micro-environment

2:00 PM–3:00 PM

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

MNO Lecture Series: November 7, 2014 Natalia Komarova PhD

The Mathematical Neuro-Oncology Research Lab Presents

Natalia Komarova
Professor of Mathematics
University of California – Irvine

Stochastic Modeling of Chronic Lymphocytic Leukemia Treatment
Friday, November 7th, 2014
2:30 pm – 3:30 pm
Arkes Pavilion,
676 n. Saint Clair St. Suite 1300
Mathematical Neuro-Oncology Lab

Chronic lymphocytic leukemia (CLL) is the most common leukemia, mostly arising in patients over the age of 50. The disease has been treated with chemo-immunotherapies with varying outcomes, depending on the genetic make-up of the tumor cells. Recently, a promising new tyrosine kinase inhibitor, ibrutinib, has been developed, which resulted in successful responses in clinical trials, even for the most aggressive chronic lymphocytic leukemia types. The crucial current questions include how long disease control can be maintained in individual patients, when drug resistance is expected to arise, and what can be done to counter it. Computational evolutionary models, based on measured kinetic parameters of patients, allow us to address these questions and to pave the way toward a personalized prognosis.

Written on November 7th, 2014. 0 Comments

MNO Lecture Series: September 19, 2014 Alexander Fletcher, DPhil

The Mathematical Neuro-Oncology Research Lab Presents

Alexander Fletcher, DPhil.
Research Fellow
Wolfson Centre for Mathematical Biology
Oxford University, Oxford, UK

A Computational Modelling Approach for Deriving Biomarkers for Cancer Risk in Premalignant Disease

Friday, September 19th, 2014
1:00 pm – 2:00 pm
Arkes Pavilion,
676 n. Saint Clair St. Suite 1300
Mathematical Neuro-Oncology Lab

Carcinogenesis is an evolutionary process, so biomarkers for cancer prognosis are fundamentally measures that attempt to predict the future course of cancer evolution. How best should we measure the evolutionary process to derive prognostic value? Here we derive evolutionary-motivated biomarkers from an analysis of a computational model of carcinogenesis in premalignant disease. We propose a novel measure of heterogeneity, termed the positive proliferation index, that is the strongest predictor of outcome of all indices studied in our model. These findings suggest biomarkers that may be clinically validated in future studies to ultimately improve risk stratification among patients with premalignant disease.

Alex has held a Research Fellowship in Computational Science associated with the 2020 Science project, a collaborative research programmebased at the University of Oxford, University College London and Microsoft Research, Cambridge since 2011. Dr. Fletcher is a member of the WolfsonCentre for Mathematical Biology (WCMB) at the Mathematical Institute, University of Oxford and a stipendiary lecturer at St Hugh’s College, Oxford. The main focus of his research is to advance the application of, and mathematics underlying, models of epithelial tissues in development, health and disease.

Written on September 10th, 2014. 0 Comments

MNO Lecture Series: August 20th 2014 Phillip Altrock, PhD

The Mathematical Neuro-Oncology Research Lab Presents

Phillip Altrock, PhD
Postdoctoral Fellow
Department of Biostatistics and Computational Biology
Dana-Farber Cancer Institute

Non-cell-autonomous driving of tumor growth support sub-clonal heterogeneity


Wednesday, August 20th, 2014
12:00 pm – 12:30 pm
Arkes Pavilion,
676 n. Saint Clair St. Suite 1300
Mathematical Neuro-Oncology Lab

Philipp studied physics at the University of Leipzig, Germany, where he minored in chemistry and mathematics and focused on theoretical physics. Philipp received his PhD from University of Kiel, Germany in 2011. He gained his first research experience in statistical mechanics working with Prof. Ulrich Behn in Leipzig, and then went on to study evolutionary game theory, evolutionary dynamics, and population genetics with Arne Traulsen and Floyd A. Reed at the Max Planck Institute for Evolutionary Biology.
At the Dana-Farber Cancer Institute, Philipp investigates cancer initiation, progression, diversity, and response to treatment. With a micro-evolutionary framework, Philipp uses computational and mathematical analyses of cancer genomics and expression data to aim at improving cancer mortality and morbidity.

Written on August 15th, 2014. 0 Comments

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