This is the first issue of The Moffitt Cancer Centers, Integrated Mathematical Oncology (IMO) department and presents our vision for IMO as well as some articles motivated by a mathematical view of cancer.
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1 That is the definition of integrate and the key word that unifies mathematics with oncology. It is appropriate on more than one level, as the power of mathematical modeling is its ability to integrate multiple interacting variables at once and predict in a dynamic manner how these variables change in space and time. Integration is not the antithesis of reductionism but is in fact a means to bridge the perspectives of reductionism and holism, as the component parts are vitally important but how they interact to produce the emergent whole is also critical. Cancer is a complex, multiscale process, in which genetic mutations occurring at a subcellular level manifest themselves as functional changes at the cellular and tissue scale. The multiscale nature of cancer requires mathematical modeling approaches of a similar nature - within the IMO we have been developing a suite of mathematical and computational models that allow us to consider each of these scales in detail as well as bridge them. We are open minded when it comes to which theoretical tools should be used, be they individual, hybrid, or purely continuum based, in order to better capture the complexity of cancer. Integrate: to combine one thing with another so that they become a whole A crucial part of any mathematicians lab - the blackboard! Dusty, messy brainstorming fun! Gene Protein Pathway Cell Tissue Organ Organism
Transcript
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That is the definition of integrate and the key word that unifies mathematics with oncology. It is appropriate on more than one level, as the power of
mathematical modeling is its ability to integrate multiple interacting variables at once and predict in a dynamic manner how these variables change in
space and time. Integration is not the antithesis of reductionism
but is in fact a means to bridge the perspectives of reductionism
and holism, as the component parts are vitally important but how
they interact to produce the emergent whole is also critical.
Cancer is a complex, multiscale process, in which genetic
mutations occurring at a subcellular level manifest themselves as
functional changes at the cellular and tissue scale. The multiscale
nature of cancer requires mathematical modeling approaches of a
similar nature - within the IMO we have been developing a suite of
mathematical and computational models that allow us to consider
each of these scales in detail as well as bridge them. We are open
minded when it comes to which theoretical tools should be used,
be they individual, hybrid, or purely continuum based, in order to
better capture the complexity of cancer.
Integrate:to combine one thing with another so that they become a whole
CONTENTS ISSUE 1IMO 1, 4DOES “CURING” CANCER KILL PATIENTS? 5
MEET THE FACULTY 6MEET THE POSTDOCS 7
ECOLOGY AND EVOLUTION OF CANCER 8-9BIG CITY SLUMS AND TUMOR INVASION 10
WORKSHOPS 11IMO SEMINARS 12
BOOK BY IMO MEMBERS 13COMPUTATIONAL IMAGES FROM IMO 14
CAN YOU BUILD ME A MODEL PLEASE? 15LAST WORD 16
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IMO - Continued
This bridging nature of mathematical models is also important for
understanding how the different biological scales of cancer impact upon
one another. Mutations at the molecular scale affect protein formation which effect signaling pathways which modulate cell behavior that
transforms the tissue. This complex multiscale process can be broken
down into smaller units that are more amenable to both experimental and
theoretical approaches. Whilst its clear that the M in IMO is a fundamental
tool to bridge the different scales of cancer, it also has the power to make predictions and generate hypothesis. However, we need some means to
validate these mathematical models and to test the predictions they make.
This will require sophisticated imaging techniques and potentially new
experimental & clinical protocols. As such IMO, is a truly interdisciplinary
group of scientists incorporating experts in the field of experimental biology, mathematics, computer science, imaging, clinical science and
visualization.
One of our major goals is to begin to focus on specific cancers and
their treatment, and develop in silico models both as a compliment to in
vitro and in vivo models but also as a means to bridge them. There is an unspoken void between in vitro and in
vivo models and between in vivo and the
clinic. In silico models have the power to
link these approaches and in doing
so can give some insight into the processes that translate well
between them and those that
don’t.
Focussing on a specific
cancers means we can also consider specific treatments. To this
end we have began to develop specific
models of cancers such as breast, prostate,
melanoma, and myeloma. These models
incorporate specific cellular and microenvironmental properties of these cancers as well as structural and signaling aspects. Treatment is
a natural place for IMO to play a role in because by its very definition
treatment needs to consider a multitude of interacting variables and what
happens to them once they are perturbed. We ultimately see in silico
models as a pre-treatment protocol to suggest the best therapeutic regime or to indicate which should be avoided. They can also be
considered as an adjuvant in terms of adjusting treatment in a more
dynamic manner, where the current patient state determines the therapy
i.e. an adaptive therapy. Patient specific treatments should be a natural
byproduct of properly parameterized in silico models, since changes in parameters can lead to different outcomes. Understanding where the
current state of a patient is in the “parameter space” will allow us to
predict how the cancer will progress and therefore how best to treat it.
This is very much in line with the Total Cancer Care program at Moffitt.
It is important to understand that because cancer is a complex dynamic process does not mean that we cannot fully understand it. In fact
many complex systems are driven by relatively simple laws, therefore the
in silico models IMO develop will not only be specific but must also be
general to address the fundamental underlying mechanisms of cancer
initiation, progression and control. Mathematicians are ideally suited to this task as they have a long history in discovering the laws of nature
(physics, chemistry etc). There may very well be fundamental laws of
cancer, that if defined and formalized would change completely how we
understand and treat cancer as we do today. We are certainly on the right
road to uncovering such laws and one unifying law that must be
integrated into our fundamental understanding of cancer is evolution.
It is generally well accepted that cancer is a genetic disease driven by mutations in key genes that lead to uncontrolled growth and abnormal
cell behavior. However, the fact that the tumor is an evolving system and
therefore subject to selection pressure and adaptation is largely ignored.
Theoretical models tell us that these evolutionary dynamics are what drive
tumor progression, and treatment resistance. Three other key factors that also need to be understood within any unifying theory of cancer are
homeostasis, heterogeneity and ecology. Ecology and evolution are
intimately linked as one provides the players and the field and the other
the mechanism by which cancer progresses, this is discussed in more
detail by David Basanta and myself (Page 6). Ultimately all cancers originate from a cell within an organ or tissue that was functioning
normally i.e. homeostatic. Normal for many cells in this situation is doing
very little, except when there is a need to, such as to occasionally repair a
wound or react to a viral attack. This disruption of homeostasis is often
one of the initiating events in the development of cancer as the normal control mechanisms of the tissue are damaged or ignored.
Heterogeneity within the tumor cell population is
generally well accepted but is it driven by
genotypic or phenotypic means and
did it emerge or was it always there. He te rogene i t y i s ce r t a i n l y
i m p o r t a n t f o r t r e a t m e n t
resistance but it may also drive
or be driven by evolution.
Understanding these four key factors and their interplay in
cancer progression is one of our
goals. Here is one possible scenario
that encapsulates all four of them - the
ecology of a given organ, that is the cells, chemicals, signaling and structure that define it, all interact to
ensure that homeostasis is maintained. This homeostasis is not static but
dynamic as it reacts to perturbations (such as wounding) to ensure a
return to homeostasis occurs. Natural heterogeneity within the cell
population may allow some cells to escape this homeostatic control easier than others and with additional (probably genetic insults) a true escape
can occur. This then opens the door to evolution and tumor progression.
Its seems appropriate to end this introductory piece by restating why
IMO was created in the first place, to paraphrase myself: “Cancer is a
dynamic complex multiscale system that can only truly be understood via the integration of theory and experiments. The goal of IMO is to use such
an integrated approach to better understand, predict and treat cancer.”
Integration really is the key and if we are serious about impacting
treatment that integration must also encapsulate the clinic. The schematic
at the center of this page highlights the multiple scales that different researchers, involved in cancer research, are gathering data on.
Traditionally the genes to patient jump is made without considering the
scales in between because researchers tend to work within their specific
scale. Using the expertise in the IMO we now have the means to develop
a mechanistic understanding of how these scales connect in cancer and importantly how changes in one affect the others. As we move forward
this understanding will become critical in the prediction and optimization
of treatment response.
BY SANDY ANDERSON
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Patients and politicians increasingly
demand a “cure” for cancer. But controlling the disease may prove to be a better strategy than striving to cure it.
A century ago, the German Nobel laureate
Paul Ehrlich introduced into medicine the concept of “magic bullets” – compounds engineered to target and kill tumor cells or
disease-causing organisms without affecting normal cells. The success of antibiotics 50 years later seemed to validate Ehrlich’s idea. Indeed,
so influential have medicine’s triumphs over bacteria been that the “war on cancer” continues to be driven by the assumption that magic bullets will one day be found for tumor
cells if the search is sufficiently clever and diligent.
Yet lessons learned in dealing with exotic
species, combined with recent mathematical models of the evolutionary dynamics of tumors, indicate that eradicating most cancers may be
impossible. More importantly, trying to do so could worsen the problem.
In 1854, the year Ehrlich was born, the diamondback moth was first observed in Illinois.
Within five decades, the moth had spread throughout North America. It now infests the Americas, Europe, Asia, and Australia. Attempts
to eradicate it using various chemicals worked only fleetingly and, in the late 1980’s, biologists found strains that were resistant to all known
insecticides.So farmers have had to abandon efforts to
eliminate the moth. Instead, most now apply insecticides only when infestation exceeds
some threshold level, with the goal of producing a sustainable and satisfactory crop. Under the banner of “integrated pest management,”
hundreds of invasive species are now successfully controlled by strategies that restrict the pest population growth but do not attempt
to eradicate themThe ability of tumor cells to adapt to a wide
range of environmental conditions, including to toxic chemicals, is similar to the evolutionary
capacities demonstrated by crop pests and other invasive species. As in the case of the Diamondback moth, successful eradication of
disseminated cancer cells is rare. However, despite the paucity of success, the typical goal in cancer therapy remains similar to that of
antimicrobial treatments - killing as many tumor cells as possible under the assumption that this
will, at best, cure the disease and, at worst,
keep the patient alive for as long as possible.To be sure, some types of cancer – for
example, Hodgkin’s lymphoma, testicular cancer, and acute myeloid leukemia – can be
c o n s i s t e n t l y c u re d u s i n g a g g re s s i v e chemotherapy. But these malignant cells seem to have characteristics that make them
particularly responsive to “treatment.” Just as invasive species adapt to pesticides, most cancer cells adapt to therapies. Indeed, the
parallels between cancerous cells and invasive species suggest that the principles for successful cancer therapy might be found not in the magic bullets of microbiology but in the
evolutionary dynamics of applied ecology.Recent research suggests that efforts to
eliminate cancers may actually hasten the
emergence of resistance and tumor recurrence, thus reducing a patient’s chances of survival. The reason arises from a component of tumor
biology not ordinarily investigated: the cost of resistance to treatment. Cancer cells pay a price when they evolve resistance to chemotherapy. For example, to cope with the toxic drugs, a
cancer cell may increase its rate of DNA repair, or actively pump the drug out across the cell membrane. In targeted therapies, in which drugs
interfere with the molecular signaling needed for proliferation and survival, a cell might adapt by activating or following alternative pathways. All
these strategies use up energy that would otherwise be available for invasion into non-cancerous tissues or proliferation, and so reduce a cell’s fitness.
The more complex and costly the mechanisms used, the less fit the resistant population will be. That cancer cells pay a price
for resistance is supported by several observations. Cells in laboratory cultures that are resistant to chemotherapies typically lose
their resistance when the chemicals are removed. Lung cancer cells that are resistant to the chemotherapy gemcitabine are less proliferative, invasive, and motile than their
drug-sensitive counterparts. Although resistant forms are commonly
found in tumors that haven’t yet been exposed
to treatment, they generally occur in small numbers. This suggests that resistant cells are not so unfit that drug-sensitive cells completely
out-competed them, but that they struggle to proliferate when both types are present.
Our models show that in the absence of
therapy, cancer cells that haven’t evolved resistance will proliferate at the expense of the less-fit resistant cells. When a large number of the sensitive cells are killed, say, by aggressive
therapies, resistant types are able to proliferate unconstrained. This means that high doses of chemotherapy might actually increase the
likelihood of a tumor becoming unresponsive to further therapy.
So, just as the judicious use of pesticides
can be used to control invasive species, a therapeutic strategy designed to maintain a stable, tolerable tumor volume could improve a patient’s prospects for survival by allowing
sensitive cells to suppress the growth of resistant ones.
To test this idea, we treated a human
ovarian cancer, grown in mice, with conventional high-dose chemotherapy. The cancer rapidly regressed but then recurred and killed the mice.
Yet when we treated the mice with a drug dose continuously adjusted to maintain a stable tumor volume, the animals, though not cured, survived for a prolonged period of time.
Designing therapies to sustain a stable tumor mass rather than eradicate all cancer cells will require a long-term strategy that looks
beyond the immediate cytotoxic effects of any one treatment. Researchers will need to establish the mechanisms by which cancer cells
achieve resistance and what it costs them. They will also need to understand the evolutionary dynamics of resistant populations, and design strategies to suppress or exploit the adapted
characteristics.I am not sugges t ing tha t cancer
researchers should abandon their search for
ever-more-effective cancer therapies, or even for cures. However, it may be time to temper our quest for Ehrich’s magic bullets and recognize
the cold reality of Darwin’s evolutionary dynamics. Medicine’s goal of a glorious victory over cancer may need to yield to recognition that an uneasy stalemate may be the best we
can achieve.
DOES “CURING” CANCER KILL PATIENTS? BY ROBERT GATENBY
Controlling cancer might be easier than
curing it
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MEET THE FACULTYIMO currently has 4 faculty associated with it, Alexander “Sandy” Anderson, Bob Gatenby, Bob
Gillies and Kasia Rejniak. The short biographies below show more info on each of them and their roles at Moffitt.
Sandy Anderson, PhD is co-director of the Integrated Mathematical Oncology (IMO) department and Senior member at Moffitt Cancer Center. Dr. Anderson performed his doctoral work on hybrid mathematical models of nematode movement in heterogeneous environments at the Scottish Crop Research Institute in Dundee, UK. His postdoctoral work was on hybrid models of tumor-induced angiogenesis with Prof. Mark Chaplain at Bath University, UK. He moved back to Dundee in 1996 where he worked for the next 12 years on developing mathematical models of many different aspects of tumor progression and treatment, including anti-angiogenesis, radiotherapy, tumor invasion, evolution of aggressive phenotypes and the role of the microenvironment. He is widely recognized as one of only a handful of mathematical oncologists that develop truly integrative models that directly impact upon biological experimentation. His pioneering work using evolutionary hybrid cellular automata models has led to new insights into the role of the tumor microenvironment in driving tumor progression. Due to his belief in the crucial role of mathematical models in cancer research he moved his group to the Moffitt Cancer Center in 2008 to establish the Integrated Mathematical Oncology department.
Bob Gatenby, MD is the chairman of the department of Radiology and co-director of the Integrated Mathematical Oncology at H. Lee Moffitt Cancer Center. He joined Moffitt in 2008 from the University of Arizona where he was Professor, Department Radiology and Professor, Department of Applied Mathematics since 2000. Bob received a B.S.E. in Bioengineering and Mechanical Sciences from Princeton University and an M.D. from the University of Pennsylvania. He completed his residency in radiology at the University of Pennsylvania where he served as chief resident. Bob remains an active clinical radiologist specializing in body imaging. While working at the Fox Chase Cancer Center after residency, Bob perceived that cancer biology and oncology were awash in data but lacked coherent frameworks of understanding to organize this information and integrate new results. Since 1990, most of Bob’s research has focused on exploring mathematical methods to generate theoretical models for cancer biology and oncology. His current modeling interests include: 1. the tumor microenvironment and its role in tumor biology. 2. evolutionary dynamics in carcinogenesis, tumor progression and therapy. 3. information flow in living systems and its role in maintaining thermodynamic stability.
Bob Gillies, PhD is vice chair of Radiology and director of research imaging at Moffitt. He received his PhD in Zoology from University California, Davis in 1979 and did post-doctoral work on in-vivo Magnetic Resonance Spectroscopy with Robert Shulman, first at the Bell Labs (Summit, NJ) and then at Yale University. He joined the faculty at Colorado State University as an Assistant Professor of Biochemistry in 1982. He moved to the University of Arizona as an associate professor with tenure in 1988 to establish a research program in biomedical MR spectroscopy, which over the years has grown to include biomedical MRI. He relocated to the Moffitt in 2008 as part of a major investment in radiology and imaging research. Bob has received numerous local, national and international awards for his teaching and research, including the Furrow award for innovative teaching (U. Arizona), the Yuhas award for radiation oncology research (U. Penn), a TEFAF professorship (U. Maastricht) and the distinguished Basic Scientist award from the Academy for Molecular Imaging.. The vision for the Moffitt Imaging Institute ate to develop new applications to diagnose, predict and monitor therapy response using noninvasive imaging. This work spans a breadth from molecular and chemical work, to animal studies and to human clinical trials and patient care. Personally, he is principal investigator on four NIH grants dealing with tumor imaging and tumor physiology.
Kasia Rejniak, PhD is assistant member at Moffitt. Dr. Rejniak performed her doctoral work on the mechanics of growth of a trophoblast tissue at Tulane University under the supervision of Lisa Fauci. Her postdoctoral work has centered around the development of a single-cell-based numerical technique for modeling the growth and development of non-homogenous tissues and multicellular organisms at the Mathematical Biosciences Institute, Ohio State University. Subsequent work at Dundee University, UK with Dr. Anderson led to the development of the IBCell (Immersed Boundary model of a Cell) model to understand the mechanics of the formation of epithelial acini. A unique aspect of the computational models she develops is their ability to accurately represent morphological and biomechanical properties of cells and in particular how these properties differ in cancer versus normal. As part of the group brought from Dundee in 2008, she is also very passionate about integration and sees the IMO as a unique opportunity to fully realize her own computational lab driven by experimental and clinical data.
IMO FACULTY
!
There are currently 4 main faculty within or associated with the IMO. There are also 4 post docs and many other researchers, including clinical faculty, directly involved in the IMO activities.
Alexander Anderson
Robert Gatenby
Robert Gillies
Kasia Rejniak
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MEET THE POST DOCSThe diversity of interests of the 4 postdocs that are currently employed by the IMO is precisely
what drives the integrated research that we carry out. Getting to know the group and there interests is one of the reasons for this newsletter and hopefully will stimulate further collaborations both within and outwith Moffitt. We currently have funds for 3 other positions (see the back page) so this list will grow but here are the current team:
David Basanta, PhD is a postdoctoral fellow at the Integrated Mathematical Oncology (IMO) department. He got his undergraduate degree in computer science from the University of Oviedo (Spain) with a thesis on information processing and, after a rather brief stay in industry, performed doctoral work on evolution inspired computing at the department of mechanical engineering at King's College London (University of London, UK). After his PhD, his work shifted to the study of the evolutionary dynamics of cancer, first at the Technical
University of Dresden (Germany) and eventually in Sandy Anderson's group (now at Moffitt). David uses mathematical tools such as Cellular Automata and Evolutionary Game Theory to study how the interactions between tumour cells and other tumour cells and with the tumour microenvironment drive the evolution towards potentially more aggressive cancers. His work has provided novel insights on the role of homeostasis as a set of mechanisms that need to be disrupted during carcinogenesis, and on competition and cooperation's effect on the progression of cancers like glioma and prostate.
Edward Flach, DPhil I arrived recently in Tampa but am feeling integrated already! I came because the ideology of the group is to apply mathematical modeling to practical problems in biology. The other benefit is to be surrounded by actual biologists and even real doctors! This gives the kind of insight that I've never had access to before (and hopefully plenty of hard data to follow). I came from Dresden, Germany most recently where I was working on developmental biology with Andreas Deutsch and Andy Oates. I used the cellular Potts model, which I am now applying to tumour modelling, with a focus on stromal interaction. Before Germany I was in Bloomington, Indiana. There I was looking at models of biochemistry with Santiago Schnell: investigating enzyme action. This style of model is proving useful for understanding the effect of drug application on cell cultures. My doctorate in Philip Maini's group in Oxford was looking at spatial pattern formation with John Norbury. I was interested in travelling wave invasions of pattern. This exploration will give a foundation to a model for predicting clinical progression.
Ariosto Silva, PhD is a research scientist at the Imaging and Integrated Mathematical Oncology (IMO) departments. He got his undergraduate degree in computer engineering from the Instituto Tecnologico de Aeronautica (2000, Brazil) with a thesis on development of e-commerce applications in multiple tiers (presentation, control and business logic). He did his undergraduate internship at Motorola (1998, Brazil) developing embedded applications and at Accenture (2000, Brazil) as a technology consultant in banking industry. After a traineeship
in Portugal Telecom (2001, Portugal) developing web based applications for mobile phone account management, he spent 3 years at Gemalto (former Schlumberger Sema, 2001-2004, France) working with security of electronic transactions. His PhD comes from the University of Campinas (2008, Brazil) in the department of Genetics and Molecular Biology with the thesis entitled "A computational approach for simulation of biological processes: tridimensional simulation of tumor metabolism and development". His recent work has focused on the progression and mechanisms of resistance to therapy in Multiple Myeloma, a rare incurable disease where hematologic cancer cells proliferate and take control of the bone marrow. His goal is to use computational models fed by in vitro experiments and clinical specimens to predict optimized forms of therapy by combining drugs in “evolutionarily enlightened” protocols.
Tedman Torres, PhD obtained his undergraduate degree in physics from Sonoma State University, California. There he spent time assisting in experiments relating to Near-Field Optical Microscopy. Following this, he pursued doctoral work at Arizona State University where he worked on experimental and theoretical aspects of Fluorescence Correlation Spectroscopy. His doctoral degree was completed there in the application of Stochastic Process Theory to Fluorescence Correlation Spectroscopy. His current work is in developing a model of drug penetration into cancerous tissue.
A place to relax, read, debate...Its only a compact little love seat but has been the center piece for many discussions and late night grant marathons we’ve come to realize this sofa is a truly integral part of IMO.
IMO SOFA
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It might not seem intuitive, but a small albeit growing number of people found interesting parallels between ecosystems as studied by ecologists (yes, think of the Savannah or the Amazon rain forest or a Coral reef) and tumors. The idea of viewing cancer from an ecological perspective is not just about presenting old facts in a new light but, fundamentally, means that we cannot just consider cancer as a collection of mutated cells but as part of a complex balance o f m a n y i n t e r a c t i n g c e l l u l a r a n d microenvironmental elements. This perspective means that t issues should be seen as sophisticated ecosystems in a homeostatic equilibrium that cancer cells can disrupt. Therefore, as convenient as it would be for cancer biologists to study tumor cells in isolation, that makes as much sense as trying to understand frogs without considering that they tend to live near swamps and feast on insects. Suddenly a frog’s sticky tongue makes much more sense when you consider how convenient it is to have one of those if you want to catch flies. Suddenly it makes sense that a cancer cell that is close to a blood vessel and is capable of producing Vascular Endothelial Growth Factors can benefit from co-opting endothelial cells to grow its very own vasculature and obtain nutrients and oxygen.
An ecosystem is made of individuals (plants, animals, bacteria, independent cells,...) and the environment they inhabit (water, soil, oxygen, nutrients,...). The success of an individual in surviving (and procreating), which is what matters at the evolutionary level, depends on how well it competes (for the existing resources) and cooperates (to produce new ones) with other individuals in the same, or different, species. Even a simplified ecosystem should showcase the interdependence of species and how important the interactions between them are. In a stable ecosystem the number and types of interactions between species does not change significantly over time, leading to a dynamic equilibrium of species and individuals known as ecological homeostasis.
Species that are viable in a homeostatic ecosystem are not necessarily viable in a different one, or in the same one if homeostasis is disrupted.
The local environment is an important factor, not only in traditional ecosystems but also in cancer. This idea dates back to the late 19th century with Paget's well-known seed-soil hypothesis [1] which suggests that in order to understand metastases, the soil (the site of a metastasis) is as important as the seed (the metastatic cells). It is beginning to be accepted that cancer is not just a genetic disease but one in which evolution plays a crucial role [2]. The implications are that tumor cells evolve, adapt to and change the environment in which they live (which includes other cellular species). The ones that fail to do so will eventually become extinct. The ones that do, have a chance to invade and metastasize. The capacity of a tumor cell to adapt to a new environment will thus be determined by the environment and the other cellular species from the original site, to which it has already painstakingly adapted.
Adaptation is thus a critical process in any system subject to Darwinian evolution, and cancer is no exception [3]. Although it's role in Cancer has recently started to be explored, the full implications have yet to impact the cancer research community at large. Tumor cell adaptation to a complex environment like a tissue ecosystem not only means that finding the roots of the disease got a whole lot more challenging (as it is not restricted to the role of a few genes) but this view also opens new routes to stop or even reverse cancer progression [4]. In most cases the ecosystem maintains a dynamic balance or homeostasis from which it can be disrupted by certain events (such a invading species, drought, or a fire).
Homeostasis is also a crucial feature of normal body tissues (those in which cancer has not been initiated). Evolution selects for homeostatic organisms that are capable of recovering from environmental and genetic insults [5]. The normal form and function of most tissues (defined by the integration of multiple cellular, extracellular, chemical and physical signals/constraints) is to maintain a homeostatic balance and carry out the role they are required to perform. Homeostasis loss is traditionally seen as a key initial step on the route to cancer development [6]. At its simplest tissue homeostasis is the balance between cell proliferation and apoptosis such that the tissue architecture and function remains constant. It is no accident that disruptions in these processes are considered as key features of oncogenic transformation. Fortunately, there are multiple mechanisms that regulate these processes and actively ensure homeostasis maintenance,
main ly through the regulat ion of both proliferation and apoptosis. These mechanisms fall into the two broad camps of cellular (e. g. cell-cell adhesion, cell-ECM adhesion), and environmental (e.g. metabolic factors, growth factors, stroma) although there is a great deal of feedback between these camps with changes in one driving the other. Therefore to escape homeostatic control mutant cells need to significantly modify their baseline phenotypes and effectively ignore environmental signals. This will be profoundly influenced by both cellular (in terms of phenotypic traits such as cell adhesion) and environmental heterogeneity (in terms of metabolite levels and stromal communication) and the feedback between them. The cellular heterogeneity represents an intrinsic variability that may be driven by genetic or non-genetic means but provides the means for homeostatic d is rupt ion . Th is he te rogene i ty fu r the r emphasizes the need to understand interactions that occur within the cancer ecosystem i.e. between cells and between cells and their environment.
Given the complexity of the homeostatic process that emerges from the interactions between individuals and their environment in an ecosystem, how can we hope to understand, never mind cure, cancer? Fortunately for us mathematical oncologists, theoretical ecologists have already developed a number of tools that can be used to study ecosystems, and these tools are suitable for both big and small ecosystems.
One tool that is ideal for this is game theory (GT). Interestingly GT was initially introduced to understand human and sociological behavior. The idea is that one can study games in which the outcome affecting a player depends, not only on the strategy used, but on the strategies employed by the other players. A key aspect is that a strategy to play a game is not good or bad considered in isolation but only when compared with the strategies employed by other players: it is the interactions between the players that matter. John Maynard Smith pioneered the use of this tool to study evolutionary dynamics in ecosystems. This is known as evolutionary game theory (EGT). The GT assumption that players have to be rational is, paradoxically, better suited to the individuals in an ecosystem than to humans playing either games in economics or war. The force of natural selection keeps ecosystem denizens focused on optimizing the bottom line: reproduction. In the games studied by evolutionary game theoreticians, individuals compete for available resources using a variety of strategies, that is, by presenting different features and behaviors that can affect their chances of survival and reproduction. These features and behaviors, known as the phenotypic
THE ECOLOGY & EVOLUTION OF CANCER
A lake ecosystem: it can remain in equilibrium for long periods of time before a disruption sets the ecosystem evolving in a different direction.
BY DAVID BASANTA & SANDY ANDERSON
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strategy, determine the winners and losers of the evolutionary race. Simple mathematical analysis using EGT can be used to investigate the evolutionary dynamics. If some equilibrium is achieved then this could form the foundation for homeostasis.
One crucial lesson from EGT for potential anti-cancer therapies, is that focusing on indiscriminately destroying as many cancer cells as possible is not necessarily the smartest thing to do. In EGT, the long term (equilibrium) outcome of a game depends on the interactions between the players, not on the size of the population. A treatment based exclusively on removing cancer cells is likely to have only a temporal effect as in most cases the original number of tumor cells will eventually be restored and exceeded. A more effective alternative would be based on changing the way cells interact with each other and their environment which would affect their fitness and thus, potentially, drive tumor evolution towards less aggressive cell types or at least to a stable coexistence that would be less harmful to the patient [7].
Another potential application of EGT emerging from the cancer-ecosystem viewpoint is the study evolutionary dynamics leading to the emergence of cooperation [8]. A common misunderstanding about evolution is that the survival of the fittest means that only the strongest and meanest survive. But nature is abundant with examples of inter and intra species cooperation. The trick is that cooperation can only emerge within the constraints of selection, so it can only be sustainable when everybody (or their genes) benefits. Which is not to say that all parties should benefit equally.
EGT is particularly useful at studying the interactions between the players, how those affect tumor evolution (as in Darwinian evolution), and how evolution might lead to or away from homeostatic equilibrium. However, they do not incorporate space, individual based models (IBM) do consider both space and time explicitly and treat each cell as a distinct entities, offer and ideal methodology to integrate some features of EGT within a spatial framework. Specifically they
can incorporate detailed descriptions of the individual (tumor cell, fish, fox etc) defining its behavior (migrate, reproduce, die etc) in a given context (Savannah, lake, muscle tissue etc). IBMs therefore capture the spatial and temporal variation that characterizes real ecosystems allowing us to explore the robustness of key homeostatic mechanisms. Moreover, they have been extensively used by the modeling community to look at many different biological systems [9] focussing on how individuals and their interactions collectively drive different evolutionary outcomes.
Traditionally the ecological perspective is firmly grounded at the scale of the phenotype (a fox, fish, rabbit etc) and essentially ignores anything below this scale. However, it tends to be more encompassing at the phenotype scale and embraces all the different players of the ecosystem. In contrast with this perspective, the cancer biology view is very much centered on the genetic and molecular scales for which there is a wealth of data. Whilst this provides a solid foundation to work from, this data is unbalanced due to the poorly quantified phenotypic-scale. This imbalance is the result of the dominance and success of reductionism in cancer research. Reductionism is undoubtedly responsible for the exquisite level of understanding of the several genes and pathways that are involved in tumor initiation and progression in a variety of tissues. Unfortunately both of these approaches have limitations but also have their own strengths that in fact compliment one another. Ideally we want to unify this biological-gene-centric view with the ecological-phenotype-centric view, however, experimentally this is difficult if not impossible, without the aid of theoretical approaches discussed above. In fact, IBMs can explicitly bridge the genotypic-phenotypic scales [10-12].
The ecosystem view is, ultimately, a holistic one that sees cancer progression as a process that emerges from the interactions between multiple cellular species and interactions with the tumor microenvironment. An ecosystem may be either under homeostat ic control or in evolutionary driven escape. These states have intr iguing implications for invasion and metastasis. Are metastatic cells the ones that represent the best and most adapted cells at the primary site? Or, on the contrary, does metastasis and invasion represent the only alternative for the less successful phenotypes, capable of escaping the primary site but unable to compete with better adapted ones locally? May it only be a by-product of tumor cells acquiring the abilities to move and detach from the main body of the tumor? Is it the result of cooperation or competition? Regardless of the answer to this question, an ecological interpretation of cancer would predict that metastasis will occur to sites in which the tumor cells will have a better chance of survival and colonization. This will depend not only on the
distance from the primary site or on the availability of lymphatic or blood vessels but also on the suitability of the new site for colonization. Since these cells are likely to be reasonably adapted to specific environmental conditions. A secondary site that somewhat resembles key features of the primary one while providing the metastatic cells with nutrients and room for growth will always be a more likely target for a secondary tumor.
The timing for an ecosystemic view of cancer could not be better: with the development of high throughput automated microscopy the ability to gather substantial amounts of cellular information is becoming a reality. With this new information the cancer ecosystem is becoming more complete and therefore theoretical oncologists will have a better understanding of the key phenotypic strategies and mechanisms of interaction that tumor cells, and other relevant cells employ. Clearly this means we are more likely to be successful at producing models that are both holistic (taking into account the multiple scales at which cancer takes place) and quantitative (in which model parameters and predictions can be compared with experiments) i.e. qolistic approaches [13].
The heart of the matter is that an ecological view of tumors does not invalidate but complements and builds upon decades of cancer research and undoubtedly this will lead to a better understanding of the biology of cancer and to new and improved therapies. If we may use the old analogy but framed slightly differently: we need to properly understand the trees (e.g. every leaf, twig and branch) before we can understand the forest but we cannot afford to ignore the forest because the trees are so interesting on their own.
1. Paget, S. (1889). Lancet 1889; 1:571-3.2. Crespi, B. and K. Summers (2005). Trends Ecol Evol 20: 545-52.3. Anderson, A. R. A., A. M. Weaver, et al. (2006). Cell 127: 905-15.4. Gatenby, R. A., A. S. Silva, et al. (2009). Cancer Res 69: 4894-903.5. Basanta, D., M. Miodownik, et al. (2008). PLoS Comp Biol 4: e1000030.6. Hanahan, D. and R. A. Weinberg (2000). Cell 100(1): 57-70. 7. Gatenby, R. A. (2009). Nature 459(7246): 508-9.8. Axelrod, R., D. E. Axelrod, et al. (2006). Proc Natl Acad Sci 103:13474-9.9. Anderson, A. R. A., Rejniak, K.A. & Chaplain M.A.J. (2008) MBI Book.10.Mansury, Y., M. Diggory, et al. (2006). J. Theor. Biol.238: 146-156.11.Basanta, D., H. Hatzikirou, et al. (2008). Eur. Phys. J. B 63: 393–397.12.Gerlee, P. and A. R. A. Anderson (2008). J. Theor. Biol. 250: 705-22.13.Anderson, A. R. and V. Quaranta (2008). Nat Rev Cancer 8: 227-34.
Parasitism: Parasitic wasp cocoons attached to a caterpillar.
Symbiosis. Both the bee & flower derive benefit from their interaction.
10
I n t h i s a r t i c l e w e c o m p a re t w o
p h e n o m e n a : t u m o r i g e n e s i s a n d t h e development of slums in big cities, and
propose that not only are the rules that control
their existence similar but also that the strategies used to eradicate them are
equivalent and as such can we learn lessons from one problem to resolve the other.
Slums are a grave problem in big cities in
underdeveloped and developing countries. In 2007 in São Paulo, the biggest city in Brazil,
there were approximately 2,000 slums with a total population of more than 400,000 families
living in sub-human conditions. Besides the
social problems of this population, deprived of minimum sanitary conditions, slums are also a
safe haven for organized crime and drug dealers. Slums gradually grow by engulfing
neighborhoods of the city whose real-state is
downgraded by the proximity with them.Tumors are believed to be created by the
relentless replication of genetically unstable cells that, through mutations and selection
from the microenvironment, acquire a set of
phenotypes that allow them to invade healthy tissue, promote angiogenesis and colonize new
regions of the host and create new tumors [1, 2], eventually reaching a state of tumor burden
that is fatal to the host.
Both phenomena, slums and tumors,
often develop in the periphery of the host (carcinomas develop from epithelial tissue
separated from the host by basement
membrane while slums have their origin in the outskirts of towns where real state is less
expensive) where resources are limited and uncontrolled growth leads to gradients of
resources and harsh conditions.
Both systems invade by “trashing” their surroundings: tumors invade healthy tissue by
both degradation of the extracellular matrix and by causing death of healthy cells; it is known
that tumors constitutively metabolize glucose
anaerobically producing lactic acid [3, 4] even
in the presence of oxygen. It has been proposed that this glycolytic phenotype could
be a mechanism through which tumors
intoxicate their surroundings in order to kill healthy tissue and make room for new tumor
cells [4]. A similar mechanism is found in the periphery of growing slums: a wave of
devaluation of real state moves outwards of the
slum propagated by criminality which imposes a “bad reputation” on the neighborhood,
scaring the residents away and leaving room for new tenants from the slum periphery or
from outside of the system.
Solid tumors are often avascular during the early steps of tumorigenesis and are only
able to promote angiogenesis as they achieve a critical mass. The fragile infrastructure of
slums is no different from solid tumors: as one
progresses into the settlement, the roads become narrower until cars cannot travel,
which considerably reduces efficiency of law enforcement. This lack of law enforcement and
a minimum infrastructure for the survival of the
slums is similar to what happens in solid tumors. On one hand poor perfusion prevents a
faster growth of the tumor but on the other hand it protects the tumor by preventing the
action of the immune system, chemotherapy
and radiotherapy by limiting diffusion of drugs, inducing quiescence in hypoxic tumor cells and
by generating a heterogeneous tumor microenvironment that confers a greater
robustness to therapeutic attack [5].
We have discussed some aspects on how carcinomas and slums develop in a similar
manner, notably by uncontrolled population growth in an area at the edge of the host/city
with poor infrastructure but also with small or
no interference from immune system/law enforcement, as is the case with carcinomas
which are separate from immune system by a basement membrane.
Both systems appear to be robust to
brute force attacks (toxins and drugs in cancer, and law enforcement and eviction in slums) not
only because these approaches cause higher side effects in the “host” than in the target but
also because the forces that promoted the
initial development of these systems remain u n c h a n g e d ( g e n e t i c i n s t a b i l i t y a n d
microenvironment-imposed selection for cancer, and social inequality in slums) and thus
will promote regrowth of the original system or
similar systems in other areas.
We propose that the most promising
strategies for eradicating and preventing carcinomas and slums are those that target the
forces that promote their emergence. For
carcinomas these strategies would focus on intratumoral pH normalization, use of glucose
inhibitors, use the minimum amount of therapy necessary to arrest tumor growth and delay
patient relapse, and finally assess tumor
response to therapy in a closed-loop approach. For slums, whose emergence is due to a
considerable mass of poor people, the most promising approach would be to invest
resources into bringing this share of the society
into more equal conditions, which can be achieved by full-time public education with
meals and recreational activities in order to keep the children away from one environment
permeated by violence, drugs and poverty.
Work laws that ensure minimum wages and social programs to provide credit to families to
finance homes are also more immediate actions. Finally, the problem of slums in big
cities will never be solved if the flow of
migrants from poorer underdeveloped regions of the same country remains. It is important
thus that such an action for reduction of social disparities happens country-wide.
As a final note, we would like to stress
that even though slums carry within criminality and major social and public health problems,
they only exist and grow because of the initial advantage of cheap labor they offer to the
richer population of the cities. An interesting
point is that in carcinomas the cells that develop as tumors are exactly those that are
isolated in the periphery of the host and considered as “expendable”.
1. Goldie JH: Drug resistance in cancer: a perspective. Cancer Metastasis Rev 2001, 20:63-68.2. Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100:57-70.3. Gatenby RA, Gillies RJ: A microenvironmental model of carcinogenesis. Nat Rev Cancer 2008, 8:56-61.4. Gatenby RA, Gawlinski ET, Gmitro AF, Kaylor B, Gillies RJ: Acid-mediated
tumor invasion: a multidisciplinary study. Cancer Res 2006, 66:5216-5223.
5. Kitano H: Cancer robustness: tumor tactics. Nature 2003, 426:125
BIG CITY SLUMS AND TUMOR INVASION
BY ARIOSTO SILVA
11
WORKSHOPSIts been a busy start to the 2009 for the
IMO with two major meetings being co-
organized by our members. An MBI workshop
and a three part SIAM symposium both dealing
with in silico models of cancer but with their
own distinct focus.
MBI Workshop: Cancer Development, Angiogenesis, Progression, & Invasion
The Mathematical Biosciences Institute
(MBI), is an institute dedicated to the
application of mathematics to biology with the
goal of enhancing both research and education
to foster the growth of an international
community of researchers in this new field.
Co-organized by Kristin Swanson and
Sandy Anderson this workshop was based on
the premise that a deeper understanding of
cancer requ i res sc ient is ts w i l l i ng to
communicate and interact extensively across
disciplinary boundaries. By inviting a truly
interdisciplinary team of scientists to attend as
well as a shared platform for both experienced
modelers, state-of-the art experimentalists and
clinician-scientists to present their work
covering every scale of cancer growth.
Each day of the workshop, consisted of 3
primary speakers split between experimental,
mathematical and imaging such that the
experimentalist presented the biological
problem, a mathematical modeler described
modeling approaches and a imaging specialist
described the type of data available for model
validation and development. Other attendees
were invited to present posters at the poster
session and every day one poster was chosen
to give a short presentation to the group.
Each day was broadly themed with
f o c u s e s o n c a n c e r d e v e l o p m e n t ,
angiogenesis, progression, invasion and
treatment. We deliberately left 30 minutes after
every presentation for discussion which really
caused the whole workshop to be a hive of
discussion with each talk leading to extended
debates between the interdisciplinary audience
and the speaker. This created a vibrant and
exciting atmosphere that really made the whole
workshop far more successful than we had
hoped.
An interesting aside is that the MBI had to
close halfway through the meeting due to a
“snow emergency” (see pic above) that
led to a very impromptu take over of the
holiday inn meeting facilities (where most of the
participants were staying) where the talks
proceeded as planned.
For further information regarding the
workshop, including participants, abstracts and
some talks that were presented can be found
at the following link:
http://mbi.osu.edu/2008/ws4description.html
SIAM Symposium: State of the art in Computational modeling of Cancer
the tumor structure at the tissue level, they fail
to describe the tumor at the cellular level.
Kasia: So, the development of single-cell-
based models was a natural way to overcome
the limitations of continuous models.
Sandy: Yes, to adequately describe
complex spatio-temporal processes that occur
in multi-cellular organisms, a class of models is
required that simultaneously takes into account
differences between individual cells as well as
their ability to communicate and interact with
one another and their environment. Single-cell-
based models form a framework that allows for
the explicit incorporation of different properties
of individual cells, but at the same time enables
all cells to act together as one collective body.
This leads ultimately to more biologically
realistic models of heterogeneous tissues and
multi-cellular organisms and allows for a better
understanding of the principles underlying the
complex biological processes occurring during
the formation, growth and maintenance of multi-
cellular bodies.
Kasia: We noticed that over the last few
years severa l b iomathemat ic ians and
biophysicists have been working on different
computational models in which cells are
represented as individual entities. These models
e m p l o y v e r y d i f f e re n t c o m p u t a t i o n a l
approaches: Monte-Carlo simulations, energy
minimization techniques, volume conservation
laws, solutions of the equations of motion for
each individual cell or for each point on
the cell membrane. They also differ in
the level of detail that defines the cell
structure and subsequently differ in the
number of individual cells that the
model can incorporate. So, the
existence of numerous mathematical
models dealing with individual cells brought us
to the idea of putting together a collection of
papers where different computational models
are described by their authors.
Sandy: Therefore, one can use the book to
survey what is new in modern mathematics,
because very different mathematical and
computational techniques are used to define the
range of models included in our book. In some
of them cells are represented as points on the
lattice, in others as small spheres or ellipsoids,
or they have deformable shapes and contain
elastic boundaries filled with fluid. If one wants
to focus on mechanical properties of cells, there
are models that capture that level of detail but
at the expense of limiting the number of
cells the model can handle. If on the other hand
one wants to model tumor growth, then millions
of cells may need to be represented and
therefore considering the cells more simply as
single points may be more appropriate.
Kasia: Moreover, the book is accompanied
by a DVD containing simulation movies that
show all discussed models in action! They are
applied to a quite diverse set of problems, such
as tumor growth, limb development, blood
clotting, vascularization, cell chemotactic
movement, development of Dictyostelium
discoideum, tissue folding or the formation of
epithelial layers.
Sandy: And on top of that, numerous
applications presented in the book are
accompanied by experimental results and
images, since continued interactions with
experimentalists working on cellular systems is
essent ia l for bu i ld ing and
understanding good predictive
mathematical models.
Kasia: Right, mathematical models will not
eliminate biological experiments but instead will
help motivate them by generating hypotheses
and determining the key factors and processes
that need to be tested. To build a mathematical
model of a cell, we have to make it much
simpler than in reality by taking into account
only the most important features, but we also
want to represent differences between individual
cells as well as their ability to communicate and
interact with one another and their surroundings.
Single-cell-based models are ideal for these
purposes and allow for a more realistic
representation of biological tissues and multi-
cellular organisms as they can capture the
principles underlying the complex biological
processes.
Sandy: And therefore, we would like to
address this book equally to scientists already
modeling multi-cellular processes and to
students starting their research in the field of
mathematical biology to give them a flavor of
the different techniques that they can use in
their studies. We asked our contributing authors
to include a detailed description of their
particular model and an extensive review of
suitable biological and medical applications.
And, of course, all simulation movies of the
presented models and applications are on the
DVD!
Kasia and Sandy: So, we hope that the
readers will enjoy using this book as much as
we have enjoyed working on it.
BOOK BY IMO MEMBERSThe “Single-Cell-Based Models in Biology and Medicine” book in the eyes of its editors Sandy Anderson & Kasia Rejniak
“We hope that readers will
enjoy this book as much as we have enjoyed working
on it”
14
Here we present a small selection of the images
generated using the mathematical and computational models
that we are developing within the IMO. Aesthetically
interesting and visually diverse they represent many different
aspect of cancer, including initiation, growth, angiogenesis,
invasion and treatment. The visual representation of in silico model output is an important aspect of
IMO as it drives much of the communication between the multiple disciplines that interact with us. To
paraphrase the old analogy: a picture speaks of... a thousand cubic millimeters! The spatial variation
that occurs in both cancer and its immediate microenvironment are of great interest to the IMO as we
believe that this heterogeneity is what promotes tumor development and inhibits treatment.
Understanding how variations in tissue density, oxygen distribution, drug concentration, and stroma
interact with and regulate tumor cell heterogeneity is a central question were are interested in.
Spatial variation however, is only one part of this picture the other is temporal dynamics. Many
of the images shown here have animated counterparts that dynamically represent the time evolution
of the development/growth/treatment process often from a single initiating cell. Both the spatial and
temporal dynamics of cancer are natural outputs of in silico models, which are often difficult to
obtain with the same detail or frequency form in vitro or in vivo systems. Quantification of these
dynamics and calibration with experimentally measured snapshots via imaging will be a cornerstone
for model validation.
We are currently developing both graphically rich and interactive simulation tools for several of our computational models such that our
experimental colleagues can have their own hands on in silico experience. Eventually, these tools will be available online for all to use and hopefully
facilitate further collaboration and integration.
COMPUTATIONAL IMAGES FROM IMO RESEARCH
15
CAN YOU BUILD ME A MODEL PLEASE?
BY SANDY ANDERSON One of the most common responses that
IMO receives when we try to init iate
collaborations is either that in silico modeling is
far too simple to produce anything informative
or it has some magical property that solves
complex problems without any experimental
input. These two extremes highlight a real lack
of understanding that we must address
directly, after all we are the strangers in a
strange land. The schematic on the right
represents an over simplified attempt to
explain how we can build cancer models
together. It does not encompass all possible
scenarios and we are not constrained by only
this approach.
The single most important aspect of
developing mathematical models is the
motivation i.e. what question do we want to
answer? In the context of cancer this might be
one specific to a given type or might be
relevant to all cancers. Often finding the
question (or the right question) is the biggest
hurdle to start a collaboration. Critical to
defining the question, however, is the dialogue
- we need to talk to each other, perhaps
several times. This dialogue really is where the
common language begins to develop, allowing
us to tease out the key variables and core
processes of the system and how best to
represent them theoretically. This reduces the
complexity of the model and aids in the
subsequent understanding of the results,
however, its worth pointing out that its natural
to always want to add more complexity. Thats
why the integrated approach is so important,
the biological-mathematical dialogue should
converge on a minimal model.
Minimal models make for simpler
visualization and parameterization. There are
then some technical aspects that need to be
addressed in terms of the precise modeling
approach to be used, how it will be solved and
presented - this is where the IMOs diversity
becomes important. The different backgrounds
and interests of the group ensure we take the
best approach, and visualization is something
we take great pride in (see page 12).
It is important to realize that what sets In
si l ico models apart from other useful
quantitative approaches (such statistics or
i n f o r m a t i c s ) i s m e c h a n i s m . We a re
fundamentally interested in understanding why
certain outcomes occur. By integrating the
relevant biological processes within our
theoretical framework we can generate
testable predictions and hypothesize novel
mechan i sms . V i t a l t o t h i s i s mode l
parameterization (and validation) which can
only be achieved via experimental testing and
o b s e r v a t i o n . I n s i l i c o m o d e l d r i v e n
experimentation and imaging are precisely why
we believe being integrated within Moffitt will
be beneficial.
H a v i n g a c c u r a t e e x p e r i m e n t a l
measurements of the variables and processes
that are driving the system will allow for much
more accurate model predictions. It will also
highlight the model limitations and where
refinements need to be made. Having a fully
parameterized model that is sufficiently refined
will allow us to make predictions of the tumor
growth dynamics, how it will respond to
different treatments and how best to optimize a
given treatment. Ultimately, this means that we
can tailor in silico models to a specific patient
and make predictions directly relevant to that
patient.
Hopefully his will lead to in silico models
becoming an integrated part of treatment. The
schematic loop below, shows how data based
on analysis of the current patient state (e.g. via
biopsies, imaging) can parameterize models to
make predictions regarding cancer growth and
treatment that can be tested experimentally
before being applied to the patient. This should
result in tailored treatments that are optimized
for the cancer of this patient. This optimization
can continue with subsequent analysis and
repeats of the whole loop. The resulting
therapy will be by definition adaptive to the
changing needs of the patient.
This will, however, take time. We are
constrained by the experimental and clinical
data that we can obtain. Also, the resolution
and scope of this data will dictate to what
degree we can validate the in silico model
results. We are also constrained by the level of
complexity we want to incorporate into our
models, the trade-off between understanding
and sophistication is difficult to balance. There
are multiple modeling approaches at our
disposal and they cover a wide range of
resolution and scale. Choosing the
appropriate in silico approach (or combination
of approaches) should be driven by both the
question and the experimental system to which
it will be validated against. This further
emphasizes the need for integrat ion:
experiments should drive models and models
should drive experiments. This feedback
hopefully converging on novel insights into our
understanding of cancer growth and critically
the development of novel treatments.
[16]
Issue 1
LAST WORD FROM THE EDITOR Moving to Moffitt from a mathematics department in
Dundee, Scotland seemed like a monumental task. There was
always going to be a transitionary period and dealing with the
personal issues that a move of this scale presents. There is no denying it
was difficult at times and could have went smoother. One of the main
sticking points was the fact that we are mainly a computational group
and therefore needed very different computational facilities. Thankfully,
this issue has been directly addressed with the updated Moffitt cluster
and better support for our Mac centric world. Its worth stating however,
that even though its only been just over a year and a half, IMO feels like
home. Moffitt has made us feel welcome and we’ve already made
friends and collaborators. As we develop over the coming year, we
hope to integrate further with Moffitt, building new collaborations and
working in new areas. Since we have recently outgrown our space in
MRC, we’re looking forward to moving into our new space in SRB -
specially refurbished to enhance the collaborative nature of our research.
This is the first issue of the IMO newsletter, and I hope you’ve
found it interesting. I realize thats its probably unusual to produce this
type of document but I see this medium as the perfect opportunity to let
anyone interested know we are here, what we are doing and where we
are going. I’d be happy to receive any feedback, both negative and
positive, on the current issue as well as ideas for future content. This
issue would not have been possible without the numerous contributions
from IMO members and I’d personally like to thank David
Basanta, Bob Gatenby, Kasia Rejniak and Ariosto Silva for their
articles and last but not least Edward Flach for his creative flare
on the numerous schematics throughout.
I’d like to end the newsletter on a positive note - the good news is
that we’ve been fortunate enough to secure funding from two major NCI
programs, the new Physical Sciences in Oncology (PSOC) program and
the Integrative Cancer Biology program (ICBP). Both of these programs
support the application of theoretical and computational models to
cancer research. Clearly this is what IMO already does and so we are
grateful to be part of them! This should certainly keep IMO busy for the
next 5 years or so!
Enjoy the rest of 2010, and look for issue 2 next year!
Sandy Anderson (Editor).
We’re HIrINg•Several Post DOC PosItIoNs are avaIlable NOW!•MathematIcIaNs, PhysIcIsts or computer scIeNtIsts•please CoNtact US! www.moffitt.org/Imo/jobs
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