Workshop 4: Cancer Development, Angiogenesis, Progression, and Invasion

(January 26,2009 - January 30,2009 )

Organizers


Alexander (Sandy) Anderson
Division of Mathematics, University of Dundee
Kristin Swanson
Department of Pathology, University of Washington

Cancer and tumor-induced angiogenesis has a natural place in the Special Year on Developmental Biology as cancer is often thought of as a result of a faulty development process. Experimental and clinical oncology forms a massive literature aimed at understanding and treating cancer. Despite the enormity of the data available, clinical oncologists and tumor biologists proceed without a comprehensive theoretical model to help guide the organization and understanding of such data. To quote a recent Nature article on the topic:

Heeding lessons from the physical sciences, one might expect to find oncology aggressively, almost desperately, pursuing quantitative methods to consolidate its vast body of data and integrate the rapidly accumulating new information. In fact, quite the contrary situation exists. Mathematical models are typically denounced as "too simplistic" for complex tumour-related phenomena (ignoring, of course, the fact that similar simplifying assumptions are required in most experimental designs). Articles in cancer journals rarely feature equations. Clinical oncologists and those who are interested in the mathematical modelling of cancer seldom share the same conference platforms. -- Nature 421, 321 (2003).

Naturally, successful modeling approaches to cancer requires scientists willing to communicate and interact extensively across disciplinary boundaries. This workshop aims to do exactly this by having truly interdisciplinary scientists as well as giving a shared platform for both experienced modellers and state-of-the art experimentalists and clinician-scientists discussing their work covering every level of tumor growth.

Each day of the workshop, will consist of 3 primary speakers (1-hour lectures each) that will include an experimentalist laying out the biological problem, a mathematical modeler describing modeling approaches and a imaging specialist describing the type of data (typically imaging) available for model validation and development. Additionally, other attendees will be invited to present posters at the poster session. An expert panel will comprise of leading modelers and experimentalists to discuss current problems in the efficient translation of mathematical modeling techniques to the laboratory and the clinic.

Significant time will be available during the meeting for discussions of current and future problems in the cancer and tumor-induced angiogenesis area.

Accepted Speakers

Alexander (Sandy) Anderson
Division of Mathematics, University of Dundee
Gustavo Ayala
Departments of Pathology, Urology, and Molecular and Cellular Biology, Baylor University
Vittorio Cristini
School of Health Information Sciences, University of Texas
Carlo Croce
College of Medicine, The Ohio State University
Daniel Gallahan
Division of Cancer Biology, National Cancer Institute
Robert Gatenby
H. Lee Moffitt Cancer Center & Research Institute
Trachette Jackson
Department of Mathematics, University of Michigan
David Morse
H. Lee Moffitt Cancer Center
Lance Munn
Radiation Oncology, Massachusetts General Hospital & Harvard Medical School
Carl Panetta
Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital
Aleksander Popel
Department of Biomedical Engineering, Johns Hopkins University
Vito Quaranta
Vanderbilt Ingram Cancer Biology Center, Vanderbilt University
Jason Rockhill
Department of Radiation Oncology and Neurological Surgery, University of Washington
Kristin Swanson
Department of Pathology, University of Washington
Forest White
Department of Biological Engineering, Massachusetts Institute of Technology
Monday, January 26, 2009
Time Session
11:00 AM
12:00 PM
Kristin Swanson - Current state-of-the-art in mathematical modeling in cancer: Cellular to Organ to Patient

N/A

02:00 PM
03:00 PM
Alexander (Sandy) Anderson - Current state-of-the-art in mathematical modeling in cancer: Subcellular to Cellular to Organ

N/A

03:30 PM
04:30 PM
Robert Gatenby - Does cancer use "spite" as an evolutionary strategy? Warbug revisited

It is generally accepted that carcinogenesis is formally analogous to Darwinian evolution as environmental selection forces act on new phenotypes that are continuously generated through accumulating genetic mutations and epigenetic changes. Those intracellular phenotypes that yield a proliferative advantage are rewarded by clonal expansion and persistence in the population. This process yields progressive fitter populations until a fitness maximum is reached and an invasive cancer emerges.


Since the pioneering studies of Warburg, it has been consistently demonstrated that invasive cancers maintain a high rate of anaerobic glucose metabolism even in the presence of oxygen. Widespread application clinical of FDG-PET imaging has demonstrated the vast majority (perhaps all) clinical primary and metastatic cancers exhibit significantly increased glucose flux as a result of glycolytic metabolism.


Within the context of somatic evolution, selective use of glycolytic pathways even in the presence of oxygen seems paradoxic. Anaerobic metabolism of glucose is inefficient (yielding 2 ATP /glucose vs. 36-38 ATP/glucose for aerobic metabolism) and produces acid as a byproduct. It would seem that, in general, Darwinian principles would favor more efficient and less potentially toxic metabolism.


We investigate development of aerobic glycolysis using quantitative methods from evolutionary game theory. The models demonstrate a previously unknown era during carcinogenesis in which cellular evolution is driven by limited substrate availability. Specifically we find that adaptation to cyclical hypoxia within premalignant lesions will result in constitutive upregulation of glycolysis. The reduction in extracellular pH caused by upregulation of glycolysis then requires additional cellular evolution to overcome acid-induced toxicity. We find this evolutionary sequence is critical to formation of an invasive cancer because it produces a phenotype that alters its environment (through increased acid production) in a way that is toxic to its competitors but less harmful to itself.


This suggests that cancer cells use an evolutionary strategy previously described as "spite." That is, they reduce their own fitness through aerobic glycolysis but, by doing so, reduce the fitness of their competitors even more.


Experimental support for the acid-mediated tumor invasion hypothesis will be presented along with new treatment strategies that emerge from the model.

Tuesday, January 27, 2009
Time Session
09:00 AM
10:00 AM
Gustavo Ayala - Molecular Markers of Cancer

N/A

11:00 AM
12:00 PM
Forest White - Quantitative Analysis of Receptor Tyrosine Kinase Signaling Networks

To effectively monitor protein phosphorylation events governing signaling cascades, we have developed a mass spectrometry-based methodology enabling the simultaneous quantification of tyrosine phosphorylation of specific residues on dozens of key proteins at multiple time points under a variety of perturbations. We have recently applied this technique to identify key signaling nodes regulating tamoxifen resistance in breast cancer as well as proliferation in glioblastoma. Inhibition of these nodes with small molecule kinase inhibitors results in reversion of resistance or decrease in proliferation in each system. Overall, we have now demonstrated that the combination of mass spectrometry-based analysis of protein phosphorylation with phenotypic measurements and computational modeling yields novel insights into the regulation of cellular signaling on a network scale.

03:00 PM
04:00 PM
Georg Luebeck - A Biomathematical Model for Colorectal Cancer

N/A

Wednesday, January 28, 2009
Time Session
09:00 AM
10:00 AM
Vito Quaranta - Experimental/Mathematical Models of Cancer Invasion

Within the NCI Integrative Cancer Biology Program, our Center focuses on cell scale models of cancer invasion. In the Evolutionary Hybrid Cellular Automata (EHCA) model, each cell is a grid point containing a neural network linking genotype to phenotype. The grid represents tumor microenvironment (mE) with oxygen level controlled by a partial differential equation. At cell doublings, the neural network is copied to daughter cells with an error probability, to capture phenotypic adaptation in cancer progression. The Immersed Boundary Cell (IBCell) model represents cells as 2D deformable objects bounded by linear spring nets (plasma membranes) studded with discrete receptors controlling growth, division, death or polarisation. The mE is represented as physical forces. In IBCell, cells build realistic epithelial structures (acini, ducts) that capture invasion dynamics if perturbed by cancerous cells. The Hybrid Discrete-Continuum (HDC) model represents tumor growth in a one-cell thick 2D slice. The mE contains extracellular matrix, oxygen and matrix degrading proteases controlled by continuous reaction-diffusion equations, while tumor cells are discrete individuals on single lattice points, containing predefined random aggregates of traits (e.g., proliferation, death, motility rates). HDC examines effects on tumor morphology of cell adaptation to mE. We parameterize these models with homogeneous datasets from a platform breast epithelial cell, MCF10A, and its invasive variants. Data include oxygen consumption, proliferation, survival, matrix-degrading enzyme secretion, growth patterns in 3D. High-throughput data collection is being developed for EHCA model parameterization. IBCell, tuned with 2D and 3D growth data, is being tested for ability to predict receptor value ranges that lead to invasive morphology of epithelial structures. Parameterized simulations of HDC confirm its prediction that invasion requires competition between cell phenotypes with distinct adaptive value. For empyrical validation, we developed an Island Invasion Assay that closely mimics the spatial 2D arrangement of HDC tumor slices. Preliminary results support HDC predictions: invasion (fingering) occurs when competing phenotypes adapt to stressful mE conditions. For in vivo validation, we are performing orthotopic versus subcutaneous mouse xenografts of MCF10A tumorigenic variants. In line with ICBP goals, this mathematical oncology strategy closely integrates experimental biologists with physical scientists. It should produce novel insights in cancer by theory-driven experimentation and experiment-driven theory.



  1. Anderson ARA and Quaranta V, Nat Rev Cancer. 2008, 8:227-34, doi:10.1038/nrc2329

  2. QuarantaV et al, Sem Cancer Biol. 2008, in press, doi:10.1016/j.semcancer.2008.03.018

09:00 AM
10:00 AM
Lance Munn - Multi-scale tumor physiology and blood vessel dynamics

Recent cancer therapies have targeted tumor blood vessels with inconsistent results. Some treatments show promise while others fail, underscoring a frustrating lack of understanding of the mechanisms that control blood vessel formation, destruction and function. A major difficulty lies in the fact that the mechanisms of vessel formation and remodeling operate at multiple scales, each with its own set of controls, and each critical to the overall function of the blood vessel network. Most importantly, "rare" events occurring at the single cell level can dominate overall vessel network function, and therefore, tumor growth. Analytical approaches--both experimental and computational-- that span the size scale from single cells to the bulk tumor should incorporate the relevant parameters critical for understanding tumor growth. Experimentally, intravital microscopy allows determination of single-vessel hematocrit, blood velocity, permeability as well as vessel and network morphology over time. Mathematical models of blood flow, vessel growth & remodeling, and tumor growth and invasion span the size scale from cells to tissue to elucidate the cellular events that influence tissue-scale physiology. These tools will provide a framework for studying the effects of anti-tumor therapies and improving their efficacy.

11:00 AM
12:00 PM
Muhammad Zaman - Modeling Tumor Cell Invasion

N/A

02:00 PM
03:00 PM
David Morse - Imaging the Hallmarks of Cancer in the Tumor Microenvironment

It was proposed by Hanahan & Weinberg (Cell 2000, 100: 57-70) that most if not all cancers acquire the same set of universal phenotypic traits, or "Hallmarks," through a variety of mechanistic strategies. Namely, the ability to evade programmed cell death, self-sufficiency in growth signals, insensitivity to anti-growth signals, limitless replicative potential, sustained angiogenesis and tissue invasion and metastasis. More recently, Gatenby and Gillies (Nature Reviews Cancer 2008, 8: 56-61) have proposed a microenvironmental model of carcinogenesis that includes the glycolytic phenotype (Warburg effect) and adaptation to growth in the presence of chronic acidosis as an additional "Hallmark." A number of ex vivo and in vivo imaging strategies have been developed which interrogate the morphological, physiological and metabolic phenotype of the evolving tumor microenvironment. Diffusion-weighted magnetic resonance imaging (DW-MRI) and magnetic resonance spectroscopic imaging (MRSI) of choline metabolites can both be used to observe cell proliferation and death. Positron emission tomography (PET) is used to image hypoxia and glucose uptake by uptake of 18F-fluoromisonidazole (FMISO) or 18F-2-fluoro-2-deoxy-D-glucose (FDG) respectively. FMISO accumulates in hypoxic cells but there is no accumulation at pO2 > 10mmHg. FDG is an analog of glucose. Tumor pH is measured by MRSI or fluorescence imaging of pH sensitive agents, e.g. 3-aminopropylphosphonate and SNARF-1 fluorescent dye. Metastasis can be observed and quantified by optical imaging of metastases originating from cells expressing fluorescent protein or luciferase. Hence, these imaging modalities can be used to study tumor phenotypic parameters that are related to the hallmarks of cancer.

04:00 PM
05:00 PM
Frank Tobin, Dean Bottino - Industry perspectives on Mathematical Modeling of Cancer Therapeutics

N/A

Thursday, January 29, 2009
Time Session
11:00 AM
12:00 PM
Aleksander Popel - Mathematical Modeling of Tumor-Induced Angiogenesis

N/A

Friday, January 30, 2009
Time Session
09:00 AM
10:00 AM
Jason Rockhill - Current Challenges in Radiation Oncology

N/A

10:45 AM
11:45 AM
Carl Panetta - An Introduction to Pharmacokinetic and Pharmacodynamic Modeling

Pharmacokinetics (PK) is the study of the disposition of drugs (absorption, distribution, metabolism, and elimination) in the body and pharmacodynamics (PD) is the study of the effects of the drugs on the body. Over the last several decades PK/PD modeling has evolved into a complete mathematical/statistical subfield in pharmaceutical research and is now involved in all aspects of drug development from in vitro to clinical studies. There are several reasons why PK/PD models are developed. First, they are used to describe data such as plasma concentrations of a drug and/or its metabolite (PK) or the effect of the drug on a target such as a cell or receptor (PD). This descriptive information can be used to determine if effective concentrations are being obtained to cause the desired effect without causing excessive toxicity. In addition, PK/PD models are used to predict drug concentrations and/or effects. For example the drug disposition for a multiple dosing regimen can be predicted given the data from just one dose. The PK/PD modeling process first involves model building which is as much of an art as a science. This is followed by model parameter estimation using methods such as weighted least squares, maximum likelihood estimation, or maximum a posteriori probability estimation (Bayesian estimation). This session will provide an introduction to the process of PK/PD modeling using examples from pediatric oncology.

Name Email Affiliation
Andasari, Vivi vivi@maths.dundee.ac.uk Division of Mathematics, University of Dundee
Anderson, Alexander (Sandy) Yvette.Mieles@moffitt.org Division of Mathematics, University of Dundee
Ayala, Gustavo kj1@bcm.tmc.edu Departments of Pathology, Urology, and Molecular and Cellular Biology, Baylor University
Bakshi, Shubham bakshi.9@osu.edu College of Medicine, The Ohio State University
Basanta, David dbasanta@gmail.com Cancer Evolutionary dynamics, H. Lee Moffitt Cancer Center and Research Institute
Bhave, Neela neela.bhave@osumc.edu Comprehensive Cancer Center, The Ohio State University
Bottino, Dean dean.bottino@novartis.com Clinical Pharmacology, Takeda Pharmaceuticals
Bronisz, Agnieszka agnieszka.bronisz@osumc.edu Dept. of Molecular and Cellular Biochemistry, The Ohio State University
Camerlengo, Terry camer@bmi.osu.edu Department of Bioinformatics, The Ohio State University
Chakraborty, Gargi gargi@u.washington.edu Department of Pathology, University of Washington
Chen, Chun river6@gmail.com Biology Department, Virginia Polytechnic Institute and State University
Chen, De chend2@mail.nih.gov Advanced Biomedical Computing Center, National Cancer Institute
Chiocca , Ennio Antonio (Nino) EA.Chiocca@osumc.edu Department of Neurological Surgery, The Ohio State University
Cinar, Ali cinar@iit.edu Chemical and Biological Engineering, Illinois Institute of Technology
Cooper, Lee cooperle@gmail.com Electrical Engineering, The Ohio State University
Cristini, Vittorio vittorio.cristini@uth.tmc.edu School of Health Information Sciences, University of Texas
Croce , Carlo carlo.croce@osumc.edu College of Medicine, The Ohio State University
Durrett, Rick rtd1@cornell.edu Mathematics, Cornell University
Eladdadi, Amina eladdadi@yahoo.com Mathematical Sciences, Rensselaer Polytechnic Institute
Enderling, Heiko Heiko.Enderling@tufts.edu Center of Cancer Systems Biology, Caritas St. Elizabeth's Medical Center
Fister , Renee Renee.Fister@murraystate.edu Department of Mathematics and Statistics, Murray State University
Gallahan, Daniel baneye@mail.nih.gov Division of Cancer Biology, National Cancer Institute
Gatenby, Robert julie.silvey@moffitt.org H. Lee Moffitt Cancer Center & Research Institute
Ghosh, Sankha ghosh.44@osu.edu Comprehensive Cancer Center, The Ohio State University
Guan, Bo guan@math.ohio-state.edu Department of Mathematics, The Ohio State University
Guang, Jia jia.11@osu.edu Radiology, The Ohio State University
Guo, Peng guo.119@osu.edu Pharmaceutics, The Ohio State University
Hattori, Harum hhattori@wvu.edu Department of Mathematics, West Virginia University
Hayward, Simon simon.hayward@vanderbilt.edu Medical Center, Vanderbilt University
Hernandez, Gerardo geherco@wpi.edu Mathematical Sciences Department, Worcester Polytechnic Institute
Howk, Cory howkc@iastate.edu Mathematics, Iowa State University
Hsu, Jason hsu.1@osu.edu Department of Statistics, The Ohio State University
Huang, Kun khuang@bmi.osu.edu Biomedical Informatics, The Ohio State University
Isaacson, David isaacd@rpi.edu Mathematical Sciences, Rensselaer Polytechnic Institute
Jackson, Trachette tjacks@umich.edu Department of Mathematics, University of Michigan
Jean, Larry leijean@amath.washington.edu Computational Biology, Fred Hutchinson Cancer Research Center
Jeon, Jihyoun jhjeon@fhcrc.org Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center
Kutzman, Misha misha.kutzman@gmail.com Department of Pathology, University of Washington
Laomettachit, Teeraphan laomett@vt.edu Biology, Virginia Polytechnic Institute and State University
Lawler, Sean sean.lawler@osumc.edu Neurosurgery, The Ohio State University
Ledzewicz , Urszula uledzew@siue.edu Department of Mathematics and Statistics, Southern Illinois University
Li, Fu fu.li@osumc.edu Human Cancer Genetics, The Ohio State University
Li, Yan li.560@osu.edu Department of Statistics, The Ohio State University
Liu, Yi liu.693@osu.edu Department of Statistics, The Ohio State University
Luebeck, Georg gluebeck@fhcrc.org Program in Computational Biology, Fred Hutchinson Cancer Research Center
Machiraju, Raghu machiraju@math.ohio-state.edu Computer Science and Engineering, The Ohio State University
Magi, Ross magi@math.utah.edu Mathematics, University of Utah
Marciniak-Czochra, Anna Anna.Marciniak@iwr.uni-heidelberg.de Institute of Applied Mathematics, University of Heidelberg
Massey, Susan suzyn03@u.washington.edu Department of Pathology, University of Washington
Mathsyaraja, Haritha haritha.mathsyaraja@osumc.edu Molecular Cellular and Developmental Biology, The Ohio State University
Merchant, Anand anand.merchant@osumc.edu Comprehensive Cancer Center, The Ohio State University
Morse, David David.Morse@moffitt.org H. Lee Moffitt Cancer Center
Munn, Lance lance@steele.mgh.harvard.edu Radiation Oncology, Massachusetts General Hospital & Harvard Medical School
Ozer, H. Gulcin ozer@bmi.osu.edu Comprehensive Cancer Center, The Ohio State University
Panetta, Carl Carl.Panetta@stjude.org Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital
Popel, Aleksander apopel@jhu.edu Department of Biomedical Engineering, Johns Hopkins University
Poplawski, Nikodem nipoplaw@indiana.edu Dept. of Physics and Biocomplexity Institute, Indiana University
Powathil, Gibin George ggpowath@math.uwaterloo.ca Applied Mathematics, University of Waterloo
Quaranta, Vito jill.shell@Vanderbilt.Edu Vanderbilt Ingram Cancer Biology Center, Vanderbilt University
Raman, Sundaresan sundaresan.r@gmail.com CSE, The Ohio State University
Rao, Youlan rao.67@osu.edu Department of Statistics, The Ohio State University
Rejniak, Katarzyna Kasia.Rejniak@moffitt.org Integrated Mathematical Oncology, Moffitt Cancer Center & Research Institute
Rockhill, Jason jkrock@u.washington.edu Department of Radiation Oncology and Neurological Surgery, University of Washington
Rockne, Russ rockne@u.washington.edu Department of Pathology, University of Washington
Schaettler, Heinz hms@wustl.edu Engineering, Washington University
Seo, Yeon-jung syeonj@iastate.edu Mathematics, Iowa State University
Sharma, Sudarshana sudarshana.sharma@osumc.edu Molecular and Cellular Biochemistry, The Ohio State University
Shirinifard, Abbas ashirini@indiana.edu Biocomplexity Ins., Physics Department, Indiana University
Singh, Shantanu singh.220@osu.edu CSE, The Ohio State University
Singhania, Rajat rajats@vt.edu Biology, Virginia Polytechnic Institute and State University
Stone, Carmen carmen.cantemir@osumc.edu Cancer Genetics, The Ohio State University
Swanson, Kristin swanson@amath.washington.edu Department of Pathology, University of Washington
Swat, Maciej glazier@indiana.edu Department of Physics, Indiana University
Szeto, Mindy bluesilk@u.washington.edu Biochemistry, University of Washington
Taffany, David david.taffany@osumc.edu Comprehensive Cancer Center, The Ohio State University
Taslim, Cenny taslim.2@osu.edu Biomedical Informatics, The Ohio State University
Tobin, Frank frank@tobins.org Tobin Consulting LLC, Tobin Consulting LLC
Uthandaraman, Mahalinga Raja mahalingaraja@cancerinstitutewia.in Department of Molecular Oncology, Cancer Institute WIA
Viapiano, Mariano viapiano.1@osu.edu Neurological Surgery, The Ohio State University
Wallace, Julie julie.wallace@osumc.edu Comprehensive Cancer Center, The Ohio State University
Wang, Bo bo.wang@osumc.edu Comprehensive Cancer Center, The Ohio State University
Wang, Christina cdub141@u.washington.edu Bioengineering, University of Washington
Weekes, Suzanne L. sweekes@wpi.edu Mathematical Sciences Department, Worcester Polytechnic Institute
White, Forest fwhite@mit.edu Department of Biological Engineering, Massachusetts Institute of Technology
Zaman, Muhammad mhzaman@mail.utexas.edu Department of Biomedical Engineering and Institute of Theoretical Chemistry, University of Texas
Zhang, Jie jie.zhang1@gmail.com Biomedical Informatics, The Ohio State University
Zhang, Tongli tongli@vt.edu Biology Department, Virginia Polytechnic Institute and State University
Current state-of-the-art in mathematical modeling in cancer: Subcellular to Cellular to Organ

N/A

Molecular Markers of Cancer

N/A

Industry perspectives on Mathematical Modeling of Cancer Therapeutics

N/A

Does cancer use "spite" as an evolutionary strategy? Warbug revisited

It is generally accepted that carcinogenesis is formally analogous to Darwinian evolution as environmental selection forces act on new phenotypes that are continuously generated through accumulating genetic mutations and epigenetic changes. Those intracellular phenotypes that yield a proliferative advantage are rewarded by clonal expansion and persistence in the population. This process yields progressive fitter populations until a fitness maximum is reached and an invasive cancer emerges.


Since the pioneering studies of Warburg, it has been consistently demonstrated that invasive cancers maintain a high rate of anaerobic glucose metabolism even in the presence of oxygen. Widespread application clinical of FDG-PET imaging has demonstrated the vast majority (perhaps all) clinical primary and metastatic cancers exhibit significantly increased glucose flux as a result of glycolytic metabolism.


Within the context of somatic evolution, selective use of glycolytic pathways even in the presence of oxygen seems paradoxic. Anaerobic metabolism of glucose is inefficient (yielding 2 ATP /glucose vs. 36-38 ATP/glucose for aerobic metabolism) and produces acid as a byproduct. It would seem that, in general, Darwinian principles would favor more efficient and less potentially toxic metabolism.


We investigate development of aerobic glycolysis using quantitative methods from evolutionary game theory. The models demonstrate a previously unknown era during carcinogenesis in which cellular evolution is driven by limited substrate availability. Specifically we find that adaptation to cyclical hypoxia within premalignant lesions will result in constitutive upregulation of glycolysis. The reduction in extracellular pH caused by upregulation of glycolysis then requires additional cellular evolution to overcome acid-induced toxicity. We find this evolutionary sequence is critical to formation of an invasive cancer because it produces a phenotype that alters its environment (through increased acid production) in a way that is toxic to its competitors but less harmful to itself.


This suggests that cancer cells use an evolutionary strategy previously described as "spite." That is, they reduce their own fitness through aerobic glycolysis but, by doing so, reduce the fitness of their competitors even more.


Experimental support for the acid-mediated tumor invasion hypothesis will be presented along with new treatment strategies that emerge from the model.

A Biomathematical Model for Colorectal Cancer

N/A

Imaging the Hallmarks of Cancer in the Tumor Microenvironment

It was proposed by Hanahan & Weinberg (Cell 2000, 100: 57-70) that most if not all cancers acquire the same set of universal phenotypic traits, or "Hallmarks," through a variety of mechanistic strategies. Namely, the ability to evade programmed cell death, self-sufficiency in growth signals, insensitivity to anti-growth signals, limitless replicative potential, sustained angiogenesis and tissue invasion and metastasis. More recently, Gatenby and Gillies (Nature Reviews Cancer 2008, 8: 56-61) have proposed a microenvironmental model of carcinogenesis that includes the glycolytic phenotype (Warburg effect) and adaptation to growth in the presence of chronic acidosis as an additional "Hallmark." A number of ex vivo and in vivo imaging strategies have been developed which interrogate the morphological, physiological and metabolic phenotype of the evolving tumor microenvironment. Diffusion-weighted magnetic resonance imaging (DW-MRI) and magnetic resonance spectroscopic imaging (MRSI) of choline metabolites can both be used to observe cell proliferation and death. Positron emission tomography (PET) is used to image hypoxia and glucose uptake by uptake of 18F-fluoromisonidazole (FMISO) or 18F-2-fluoro-2-deoxy-D-glucose (FDG) respectively. FMISO accumulates in hypoxic cells but there is no accumulation at pO2 > 10mmHg. FDG is an analog of glucose. Tumor pH is measured by MRSI or fluorescence imaging of pH sensitive agents, e.g. 3-aminopropylphosphonate and SNARF-1 fluorescent dye. Metastasis can be observed and quantified by optical imaging of metastases originating from cells expressing fluorescent protein or luciferase. Hence, these imaging modalities can be used to study tumor phenotypic parameters that are related to the hallmarks of cancer.

Multi-scale tumor physiology and blood vessel dynamics

Recent cancer therapies have targeted tumor blood vessels with inconsistent results. Some treatments show promise while others fail, underscoring a frustrating lack of understanding of the mechanisms that control blood vessel formation, destruction and function. A major difficulty lies in the fact that the mechanisms of vessel formation and remodeling operate at multiple scales, each with its own set of controls, and each critical to the overall function of the blood vessel network. Most importantly, "rare" events occurring at the single cell level can dominate overall vessel network function, and therefore, tumor growth. Analytical approaches--both experimental and computational-- that span the size scale from single cells to the bulk tumor should incorporate the relevant parameters critical for understanding tumor growth. Experimentally, intravital microscopy allows determination of single-vessel hematocrit, blood velocity, permeability as well as vessel and network morphology over time. Mathematical models of blood flow, vessel growth & remodeling, and tumor growth and invasion span the size scale from cells to tissue to elucidate the cellular events that influence tissue-scale physiology. These tools will provide a framework for studying the effects of anti-tumor therapies and improving their efficacy.

An Introduction to Pharmacokinetic and Pharmacodynamic Modeling

Pharmacokinetics (PK) is the study of the disposition of drugs (absorption, distribution, metabolism, and elimination) in the body and pharmacodynamics (PD) is the study of the effects of the drugs on the body. Over the last several decades PK/PD modeling has evolved into a complete mathematical/statistical subfield in pharmaceutical research and is now involved in all aspects of drug development from in vitro to clinical studies. There are several reasons why PK/PD models are developed. First, they are used to describe data such as plasma concentrations of a drug and/or its metabolite (PK) or the effect of the drug on a target such as a cell or receptor (PD). This descriptive information can be used to determine if effective concentrations are being obtained to cause the desired effect without causing excessive toxicity. In addition, PK/PD models are used to predict drug concentrations and/or effects. For example the drug disposition for a multiple dosing regimen can be predicted given the data from just one dose. The PK/PD modeling process first involves model building which is as much of an art as a science. This is followed by model parameter estimation using methods such as weighted least squares, maximum likelihood estimation, or maximum a posteriori probability estimation (Bayesian estimation). This session will provide an introduction to the process of PK/PD modeling using examples from pediatric oncology.

Mathematical Modeling of Tumor-Induced Angiogenesis

N/A

Experimental/Mathematical Models of Cancer Invasion

Within the NCI Integrative Cancer Biology Program, our Center focuses on cell scale models of cancer invasion. In the Evolutionary Hybrid Cellular Automata (EHCA) model, each cell is a grid point containing a neural network linking genotype to phenotype. The grid represents tumor microenvironment (mE) with oxygen level controlled by a partial differential equation. At cell doublings, the neural network is copied to daughter cells with an error probability, to capture phenotypic adaptation in cancer progression. The Immersed Boundary Cell (IBCell) model represents cells as 2D deformable objects bounded by linear spring nets (plasma membranes) studded with discrete receptors controlling growth, division, death or polarisation. The mE is represented as physical forces. In IBCell, cells build realistic epithelial structures (acini, ducts) that capture invasion dynamics if perturbed by cancerous cells. The Hybrid Discrete-Continuum (HDC) model represents tumor growth in a one-cell thick 2D slice. The mE contains extracellular matrix, oxygen and matrix degrading proteases controlled by continuous reaction-diffusion equations, while tumor cells are discrete individuals on single lattice points, containing predefined random aggregates of traits (e.g., proliferation, death, motility rates). HDC examines effects on tumor morphology of cell adaptation to mE. We parameterize these models with homogeneous datasets from a platform breast epithelial cell, MCF10A, and its invasive variants. Data include oxygen consumption, proliferation, survival, matrix-degrading enzyme secretion, growth patterns in 3D. High-throughput data collection is being developed for EHCA model parameterization. IBCell, tuned with 2D and 3D growth data, is being tested for ability to predict receptor value ranges that lead to invasive morphology of epithelial structures. Parameterized simulations of HDC confirm its prediction that invasion requires competition between cell phenotypes with distinct adaptive value. For empyrical validation, we developed an Island Invasion Assay that closely mimics the spatial 2D arrangement of HDC tumor slices. Preliminary results support HDC predictions: invasion (fingering) occurs when competing phenotypes adapt to stressful mE conditions. For in vivo validation, we are performing orthotopic versus subcutaneous mouse xenografts of MCF10A tumorigenic variants. In line with ICBP goals, this mathematical oncology strategy closely integrates experimental biologists with physical scientists. It should produce novel insights in cancer by theory-driven experimentation and experiment-driven theory.



  1. Anderson ARA and Quaranta V, Nat Rev Cancer. 2008, 8:227-34, doi:10.1038/nrc2329

  2. QuarantaV et al, Sem Cancer Biol. 2008, in press, doi:10.1016/j.semcancer.2008.03.018

Current Challenges in Radiation Oncology

N/A

Current state-of-the-art in mathematical modeling in cancer: Cellular to Organ to Patient

N/A

Industry perspectives on Mathematical Modeling of Cancer Therapeutics

N/A

Quantitative Analysis of Receptor Tyrosine Kinase Signaling Networks

To effectively monitor protein phosphorylation events governing signaling cascades, we have developed a mass spectrometry-based methodology enabling the simultaneous quantification of tyrosine phosphorylation of specific residues on dozens of key proteins at multiple time points under a variety of perturbations. We have recently applied this technique to identify key signaling nodes regulating tamoxifen resistance in breast cancer as well as proliferation in glioblastoma. Inhibition of these nodes with small molecule kinase inhibitors results in reversion of resistance or decrease in proliferation in each system. Overall, we have now demonstrated that the combination of mass spectrometry-based analysis of protein phosphorylation with phenotypic measurements and computational modeling yields novel insights into the regulation of cellular signaling on a network scale.

Modeling Tumor Cell Invasion

N/A