Workshop 5: Treatment, Clinical Trials, Resistance

(February 16,2015 - February 20,2015 )

Organizers


Guido Marcucci
Comprehensive Cancer Center, The Ohio State University
Kristin Swanson
Neurological Surgery, Northwestern University
Jack Tuszynski
Oncology, University of Alberta

Co-sponsor: Physical Sciences-Oncology Program of the Division of Cancer Biology, National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services.

While the primary forms of tumor treatment remain chemotherapy and radiation, generic cytotoxic therapies, the increasing understanding of the nature of the disease as being both heterogeneous and genetically unstable has induced a trend to design and create therapies tailored to the specific tumor (patient-specific) and to combat the many different subpopulations of cells with combination therapies. Despite these efforts, tumor resistance and recurrence remain an unfortunate challenge of clinical trials. However, the clinical focus has been primarily on the genetic heterogeneity in tumor cell populations, with minimal focus on the impacts of treatment on the subpopulations phenotypic interactions, either competitive or cooperative, the induced microenvironment, or the evolutionary pressures created. One likely reason is the inability of traditional clinical trials to quantify or meaningfully analyze these phenomena. Examining the impact of particular drug therapies and their scheduling on the local microenvironment and individual cellular behavior in both the long and short term is almost impossible in a clinical setting and extremely difficult in laboratory experiments. Limiting factors include inadequate observation tools, e.g. most imaging methodologies are too coarse to properly resolve the dynamics, changing the system by observing it, such as when resecting grown tumors in animals for closer observation, time for disease development and money. Mathematical models offer an approach to investigate many different types of therapies along with their impact on the microenvironment, and to explore optimal dosing combinations and schedules while bypassing the many limitations encountered in the clinic and laboratory. There are many different varieties of models, though they can generally be categorized into discrete, continuum, or statistical, each offering its own advantage for considering various scales or effects. They can be designed utilizing a basic understanding of the primary phenotypes and genotypes present in a tumor to investigate the likely induced microenvironment from various therapies and evolutionary selection pressures leading to resistance. It is even possible to use them to perform virtual clinical trials and compare different treatments on theoretical populations. This workshop will focus on two broad topics: Mathematical modeling of cancer treatment strategies and how to model resistance of cancers to drug treatments. Use of mathematical models to compare clinical trial arms and virtually simulate clinical trials outcomes. The workshop will highlight modeling applications that are as close as possible to direct clinical impact including design of multi-institutional clinical trials for patient-specific radiation dose strategies, quantification of patient-specific response to treatment that can be useful in predicting outcomes and treatment design, as well as include discussions of sequencing of drug treatments, optimal scheduling, and modeling of combination therapies which are useful in rapidly mutating diseases, such as cancer and HIV. The workshop will also discuss ways to implement the use of mathematical models in a clinical setting.

Accepted Speakers

Zvia Agur
Institute for Medical Biomathematics
Alexander Anderson
Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute
Mathilde Badoual
Physics, Paris Diderot University
Donald Berry
Biostatistics, University of Texas M.D. Anderson Cancer Center
Kenneth Buetow
Helen Byrne
Centre for Mathematical Medicine and Biology, University of Nottingham
Arijit Chakravarty
Robert Gatenby
H. Lee Moffitt Cancer Center & Research Institute
Susan Geyer
Health Informatics Institute, University of South Florida
Farzin Ghaffarizadeh
Center for Applied Molecular Medicine, University of Southern California
Andrea Hawkins-Daarud
Neurological Surgery, Northwestern University
Thomas Hillen
Mathematical and Statistical Sciences, University of Alberta
Pamela Jackson
Neurological Surgery, Northwestern University
Josh Jacobs
Robert Jeraj
Medical Physics, University of Wisconsin
Eugene Koay
Serge Koscielny
Biostatistics and Epidemiology, Gustave Roussy
Brenda Kurland
Biostatistics, University of Pittsburgh
Kevin Leder
Industrial and Systems Engineering, University of Minnesota
Alicia Martínez González
Mathematics, Universidad de Castilla-La Mancha
Bob Parker
Savannah Partridge
Radiology, University of Washington
Ami Radunskaya
Mathematics, Pomona College
Corbin Rayfield
Edward Rietman
pediatric oncology, tufts medical school, Newman Lakka Institute
Russell Rockne
Neurological Surgery, Northwestern University
Jacob Scott
Ariosto Silva
Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center
Andrew Trister
Cancer Biology, Sage Bionetworks
Jan Unkelbach
Radiation Oncology, Harvard Medical School
Jared Weis
Radiology and Radiological Sciences, Vanderbilt University
Monday, February 16, 2015
Time Session
Tuesday, February 17, 2015
Time Session
Wednesday, February 18, 2015
Time Session
Thursday, February 19, 2015
Time Session
Friday, February 20, 2015
Time Session
Name Email Affiliation
Agur, Zvia agur@imbm.org Institute for Medical Biomathematics
Anderson, Alexander alexander.Anderson@moffitt.org Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute
Avendano, Alex avendano.8@osu.edu Mechanical and Aerospace Engineering, The Ohio State University
Badoual, Mathilde badoual@imnc.in2p3.fr Physics, Paris Diderot University
Bell, Erica erica.bell@osumc.edu Radiation Oncology, The Ohio State University
Berry, Donald dberry@mdanderson.org Biostatistics, University of Texas M.D. Anderson Cancer Center
Buetow, Kenneth Kenneth.Buetow@asu.edu
Butuci, Melina butuci@usc.edu Molecular and Computational Biology, USC
Byrne, Helen byrneh@maths.ox.ac.uk Centre for Mathematical Medicine and Biology, University of Nottingham
Cebulla, Colleen colleen.cebulla@osumc.edu Ophthalmology and Visual Science, The Ohio State University
Chakravarty, Arijit Arijit.Chakravarty@takeda.com
Durrett, Rick rtd@math.duke.edu Department of Mathematics, Duke University
Eljanne, Mariam eljannem@mail.nih.gov Physical Sciences-Oncology, National Institutes of Health (NIH)
Gatenby, Robert robert.gatenby@moffitt.org H. Lee Moffitt Cancer Center & Research Institute
Geyer, Susan susan.geyer@epi.usf.edu Health Informatics Institute, University of South Florida
Ghaffarizadeh, Farzin aghaffar@usc.edu Center for Applied Molecular Medicine, University of Southern California
Han, Yang hanyang1122@gmail.com Medical School, University of Exeter
Hawkins-Daarud, Andrea andrea.hawkins-daarud@northwestern.edu Neurological Surgery, Northwestern University
Hillen, Thomas thillen@ualberta.ca Mathematical and Statistical Sciences, University of Alberta
Jackson, Pamela pamela.jackson@northwestern.edu Neurological Surgery, Northwestern University
Jacobs, Josh joshua.jacobs2@northwestern.edu
Jeraj, Robert rjeraj@wisc.edu Medical Physics, University of Wisconsin
Kanwal, Madiha madiha_k14@hotmail.com Laboratory of Molecular and Experimental Pathology, Kunming Institute of Zoology, CAS, China
Kheibarshekan, Leila leila.kheibarshekan.asl@umontreal.ca pharmacy, Universite de Montreal
Koay, Eugene EKoay@mdanderson.org
Koscielny, Serge serge.koscielny@gustaveroussy.fr Biostatistics and Epidemiology, Gustave Roussy
Kurland, Brenda bfk10@pitt.edu Biostatistics, University of Pittsburgh
Leder, Kevin kevin.leder@isye.umn.edu Industrial and Systems Engineering, University of Minnesota
Marcucci, Guido guido.marcucci@osumc.edu Comprehensive Cancer Center, The Ohio State University
Martínez González, Alicia alicia.martinez@uclm.es Mathematics, Universidad de Castilla-La Mancha
Parker, Bob rparker@pitt.edu
Partridge, Savannah spartrid@seattlecca.org Radiology, University of Washington
Radunskaya, Ami aradunskaya@pomona.edu Mathematics, Pomona College
Rayfield, Corbin crayfi2@uic.edu
Rietman, Edward erietman@gmail.com pediatric oncology, tufts medical school, Newman Lakka Institute
Rockne, Russell russell.rockne@northwestern.edu Neurological Surgery, Northwestern University
Scott, Jacob jacob.g.scott@gmail.com
Shivade, Chaitanya shivade.1@osu.edu Department of Computer Science and Engineering, The Ohio State University
Silva, Ariosto ariosto.silva@moffitt.org Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center
Swanson, Kristin kristin.swanson@northwestern.edu Neurological Surgery, Northwestern University
Trister, Andrew trister@u.washington.edu Cancer Biology, Sage Bionetworks
Tuszynski, Jack jackt@ualberta.ca Oncology, University of Alberta
Unkelbach, Jan junkelbach@partners.org Radiation Oncology, Harvard Medical School
Weis, Jared jared.a.weis@vanderbilt.edu Radiology and Radiological Sciences, Vanderbilt University
Predicting Response to Hormone Therapy in Advanced Prostate Cancer Patients by Integrating a Mathematical Mechanistic Model of Tumor Progression with Data from Clinical Trials

Abstract not submitted.

Modeling the response of low-grade gliomas to radiotherapy

After years of slow growth, diffuse low-grade gliomas transform inexorably into more aggressive forms, jeopardizing the patient’s life. Mathematical modeling could help clinicians to have a better understanding of the natural history of these tumors, to optimize treatments, but also to predict their evolution. We present here a model for the effect of radiotherapy on these tumors. To build this model, we first analyzed histological samples from patients’ biopsies. The samples were prepared in Hospital Sainte-Anne (Paris, France). We were able to correlate the amount of edema in the samples with the MRI signal abnormalities. A mathematical model was then designed from these observations, involving the production and the draining of edema by tumor cells. The model is applied to clinical data consisting of the tumor radius along time, for a population of 28 patients. We show that the draining of edema accounts for the observed delay of tumor regrowth following radiotherapy, and we are able to fit the clinical data in a robust way. We argue that, within reasonable assumptions, it is possible to predict (with a precision around 20%) the regrowth delay after radiotherapy and the gain of lifetime due to radiotherapy.

Platform Clinical Trials in Oncology and Neurology

Abstract not submitted.

Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations

Genetic and epigenetic changes in cancer cells are typically divided into "drivers" and "passengers" Drug development strategies target driver mutations, but inter- and intra-tumoral heterogeneity usually results in emergence of resistance. Here we model intratumoral evolution in the context of a fecundity/survivorship trade-off. Simulations demonstrate the fitness value, of any genetic change is not fixed but dependent on evolutionary triage governed by initial cell properties, current selection forces, and prior genotypic/phenotypic trajectories. We demonstrate spatial variations in molecular properties of tumor cells are the result of changes in environmental selection forces such as blood flow. Simulated therapies targeting fitness-increasing (driver) mutations usually decrease the tumor burden but almost inevitably fail due to population heterogeneity. An alternative strategy targets gene mutations that are never observed. Because up or down regulation of these genes unconditionally reduces cellular fitness, they are eliminated by evolutionary triage but can be exploited for targeted therapy.

Leveraging Mathematical Modeling of Gliomas for Predicting Therapeutic Efficacy

Despite growing interest in anti-angiogenic therapies for glioblastoma (GBM), no statistically significant evidence has shown that bevacizumab, an anti-angiogenic, in combination with other therapies increases the overall survival of GBM patients. Clinical trials, however, have been considering populations without regard for the size or growth kinetics of the individual tumors. We utilized a mathematical model of GBM growth to investigate if there are predictable subpopulations, defined through these metrics, which may be receiving benefit from combination therapies The model chosen incorporates vascular density, hypoxia and necrosis and assumes that bevacizumab normalizes vasculature and decreases hypoxia. Growth rates of the simulated tumors were varied across dynamics observed in human GBMs. The differential hypoxic burden after a gross total resection (GTR) was assessed along with the change in radiation cell kill from bevacizumab induced tissue re-normalization. We found patients undergoing a GTR would not likely benefit from combination therapy as the majority of the hypoxic burden would have been surgically removed. For patients not undergoing a GTR, we found a sub-population of patients, those with large tumors and high proliferation rates, who would have a dramatic change in the total radiation cell kill when combined with bevacizumab. Our results indicate that there is a likely subpopulation of patients who would benefit from bevacizumab and radiation combination therapy, namely, those with large, aggressive tumors, and who are not eligible for a GTR.

A stochastic model for the normal tissue complication probability (NTCP) in radiation treatment

When radiation therapy is applied to a tumor, then inevitably, healthy tissue is exposed too. It is quite common that side effects arise or that organs fail. The normal tissue complication probability (NTCP) is an attempt to quantify the risks of side effects. So far, NTCP models have been based on statistical outcomes. In my talk I will develop a mechanistic model for tissue complication which is based on organ-specific and patient-specific model parameters. We get a surprisingly simple formulation of the NTCP, which only requires a few, obtainable, physiological characteristics.

Interplay between molecular imaging and tumor modeling of anti-angiogenic therapies

Angiogenesis is one of the key tumor hallmarks. Use of anti-angiogenic therapies, particularly those targeting the VEGF pathway, is a common treatment strategy. However, clinical outcomes have been rather disappointing. One of the key phenomena in response to anti-angiogenic therapies is the so-called "angiogenic flare", rapid rebound of tumors after therapy cessation or during treatment breaks. In addition, prolonged exposure to anti-angiogenic agents leads to build-up of treatment resistance. A series of clinical trials using advanced molecular imaging techniques will be presented that has helped explaining the observed angiogenic flare, giving insights into potential treatment strategies. Furthermore, accompanying tumor modeling based on patient-specific molecular imaging input has been used to investigate potential mechanisms leading to treatment failures. Interplay between molecular imaging-driven clinical trials and tumor modeling will be presented as a role-model for better understanding of clinically observed phenomena and as a guiding tools to guide more successful future treatment strategies.

Biophysical subtypes of pancreatic cancer

The dismal survival rate for patients with pancreatic cancer has been stagnant for decades. Personalized approaches to therapy may help accelerate progress, but requires establishment of robust biomarkers. Partly due to the complex biology of the disease and its significant heterogeneity between patients, the identification of molecular biomarkers has not been fruitful so far. To address this challenge, we have developed methods to characterize the physical properties of pancreatic cancer. Our work supports the notion that the multi-scale mass transport properties of this cancer influence the natural history of the disease and its sensitivity to cytotoxic therapies. Our ongoing efforts to further develop this approach and implement it in the clinic aim to achieve the goal of individualized treatments for this deadly disease.

Targeting Hypoxia in Gliomas: From Mathematics to Bedside

This research explores the use of mathematical models as promising and powerful tools to understand the complexity of tumors and their environment. We focus on gliomas, which are primary brain tumors derived from glial cells, mainly astrocytes or oligodendrocytes. These tumors range from lower-grade astrocytomas, such as the diffuse astrocytoma with slow growth, to the highest grade, epitomized in the most malignant and prevalent one: the glioblastoma multiforme. A variety of mathematical models, based on ordinary differential equations and partial differential equations, have been developed both at the micro and macroscopic levels. Our aim is to describe key mechanisms relevant in tumors in a quantitative way and to design optimal therapeutical strategies. We consider both standard therapies such as radio and chemotherapy together with other novel therapies targeting oxidative stress, thromboembolic phenomena or the cell metabolism.

This study has been the basis of a multidisciplinary collaboration involving, among others, neuro-oncologists, radiation oncologists, pathologists, cancer biologists, surgeons and mathematicians with a common goal: to achieve a deeper understanding of the tumor evolution and to improve its therapeutical management.

Breast Imaging Methods for Clinical Trials: Standard and New Approaches

Neoadjuvant chemotherapy treatment of breast cancer offers several potential advantages. These include enabling more breast conserving surgeries to be performed by shrinking larger tumors prior to surgery and allowing response to be monitored directly to better tailor therapies. There are a number of advanced imaging modalities presently in use in multisite clinical trials for evaluating breast cancer response to therapy, which provide different biological information. In this presentation, we will review the more commonly used imaging techniques of MRI and PET, and discuss the potential value of a variety of newer approaches being implemented in clinical breast cancer trials.

A stochastic model for the normal tissue complication probability (NTCP) in radiation treatment

When radiation therapy is applied to a tumor, then inevitably, healthy tissue is exposed too. It is quite common that side effects arise or that organs fail. The normal tissue complication probability (NTCP) is an attempt to quantify the risks of side effects. So far, NTCP models have been based on statistical outcomes. In my talk I will develop a mechanistic model for tissue complication which is based on organ-specific and patient-specific model parameters. We get a surprisingly simple formulation of the NTCP, which only requires a few, obtainable, physiological characteristics.

Clinically targeted mathematical modeling to study response, resistance and recurrence of glioblastoma brain cancer.

Glioblastoma is a lethal primary brain tumor that often recurs after treatment. MRI is the principle means of monitoring the disease progress and response to treatment. Using an MRI-based, patient-specific mathematical model, we study the spatial-temporal evolution of glioblastoma through treatment to better understand how the phenotype and kinetics of the disease are changed by therapy. Specifically, we use patient-specific parameters to quantitatively study response, resistance and the dynamics of tumor recurrence on an individual basis using a macroscopic tumor cell density model. We find that the standard chemo-radiation therapy significantly changes the phenotype and growth kinetics of the disease in a way that may lead to the design of better, more individualized therapies.

Personalized therapeutic regimens in multiple myeloma

We here describe how particular features of cancer, combined with flawed paradigms and bad habits widespread in cancer research, prevent us from curing cancer.

Next, we propose how one may overcome these challenges by combining biological models from biomedical sciences, clinical data, and evolutionary dynamics into a mechanistic framework capable of predicting clinical response.

Through this presentation we use our work in multiple myeloma, a treatable but incurable cancer of the bone marrow, as a proof of principle for adaptive personalized medicine. In other words:

The right drug combination at the right dose and schedule, for the right patient, at the right time, and for the right duration of time, adjusting any of these elements as needed, aiming to maximize survival and quality of life.

Radiotherapy planning based on the biologically equivalent dose model

Fractionation decisions in radiotherapy face a tradeoff between increasing the number of fractions to spare normal tissues and increasing the total dose to keep the same level of tumor control. The biologically equivalent dose (BED) model is the most common model to compare fractionation regimens in the clinic. The basic BED model assumes that the biological dose is given by a quadratic function of the physical dose. Despite its phenomenological nature and mathematical simplicity, the BED model yields surprising implications for fractionation decisions. In this presentation, two aspects of the BED model are discussed: 1) The interdependence of fractionation decisions and the spatial dose distribution, and 2) extensions of the BED model towards concurrent chemoradiation. It is demonstrated that, even in the absence of time dependencies in radiation response, the BED model suggests non-stationary treatment regimens, i.e. the delivery of different dose distributions in different fractions.

Predicting the Response of Breast Cancer to Neoadjuvant Chemotherapy Using a Mechanically Coupled Reaction-Diffusion Model

While there is considerable data on the use of mathematical modeling to describe tumor growth and response to therapy, they are often not of the form that can be easily applied to clinical data to generate testable predictions in individual patients. There is a clear need to develop and apply clinically-relevant oncological models that are amenable to available patient data and yet retain the most salient features of response prediction. We show how a biomechanical model of tumor growth can be initialized and constrained by patient-specific magnetic resonance imaging data, obtained early in the course of therapy, to predict the response for individual patients with breast cancer undergoing neoadjuvant chemotherapy. Using this mechanics-coupled modeling approach, we are able to predict, after the first cycle of therapy, breast cancer patients that would eventually achieve a complete pathological response and those who would not, with receiver operating characteristic area under the curve of 0.87. Our approach significantly outperformed standard (i.e., not mechanically coupled) reaction-diffusion predictive modeling, simple analysis of the tumor cellularity estimated from imaging data, and the Response Evaluation Criteria in Solid Tumors (RECIST). Thus, we show the potential for mathematical model prediction for use as a predictive indicator of response to therapy. The work indicates the considerable promise of image-driven biophysical modeling for predictive frameworks within therapeutic applications.