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.
 

https://link.springer.com/article/10.1007/s11538-015-0067-7

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.

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
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.

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.

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.

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.

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.

https:://www.jci.org/articles/view/76739

Gene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patients

Jennifer E. Adair^1,2, Sandra K. Johnston³, Maciej M. Mrugala^4,5, Brian C. Beard^1,2, Laura A. Guyman^6,7, Anne L. Baldock^6,7, Carly A. Bridge^6,7, Andrea Hawkins-Daarud^6,7, Jennifer L. Gori¹, Donald E. Born⁸, Luis F. Gonzalez-Cuyar⁹, Daniel L. Silbergeld^3,9, Russell C. Rockne^6,7, Barry E. Storer^1,10, Jason K. Rockhill^3,11, Kristin R. Swanson^6,7,12 and Hans-Peter Kiem^1,2,9

¹Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. ²Department of Medicine, ³Department of Radiology, ⁴Department of Neurosurgery, and ⁵Department of Neurology, University of Washington (UW), Seattle, Washington, USA. ⁶Department of Neurological Surgery and ⁷Northwestern Brain Tumor Institute, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. ⁸Department of Pathology, Stanford University, Stanford, California, USA. ⁹Department of Pathology, ¹⁰Department of Biostatistics, and ¹¹Department of Radiation Oncology, UW, Seattle, Washington, USA. ¹²Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

Address correspondence to: Hans-Peter Kiem, Fred Hutchinson Cancer Research Center, Mail Stop D1-100, P.O. Box 19024, Seattle, Washington 98109-1024, USA. Phone: 206.667.4425; E-mail: hkiem@fhcrc.org.

Published August 8, 2014 Received for publication April 29, 2014, and accepted in revised form July 1, 2014.