MBI Emphasis Year on Cancer and Its Environment 2014-2015
EXTENDED DESCRIPTION OF "Cancer and its Environment" EMPHASIS YEAR
There has been a dramatic increase in recent years in the number of mathematical models of cancer. These models range, in the spatial scale, from the molecular level of gene/protein networks and signaling molecules to the macroscopic level of cell proliferation/migration and tissue growth. The models also vary in the temporal scale, from cell-cycle time, measured in hours, to tumor metastasis, evolving over a period of years. Historically, models of cancer were focused on tumor size utilizing growth laws such as exponential, logistic and Gompertian. The underlying ordinary differential equations (ODEs) that drove these simple laws are still used today although often with the addition of other compartments. Compartmental models, based on ODEs, place different subpopulations of the tumor into different compartments e.g. proliferating, quiescent and necrotic. Diffusion is often the next logical addition and when combined with growth leads to Fisher type systems and more generally reaction diffusion systems. These partial differential equation models have been used to study almost every aspect of cancer. In the last decade or so individual based models have come to the fore as natural way to connect with experimental approaches to cancer. Cellular automata, cellular Potts, immersed boundary method, force based and agent based all describe different individual based approaches that differ in terms of the biological detail and physical characteristics of cells they are representing. Many of these individual based models are also hybrid by nature, since they integrate both continuous and discrete variables and a further subset are also considered multiscale. Bridging multiple distinct spatial and temporal scales often requires one to link (or embed) multiple models and can lead to computationally very intensive systems. In recent years there has been some old modeling approaches that are being newly applied to cancer, e.g. Evolutionary game theory and graph theory. One thing that is clear is that the diverse nature of cancer requires a diverse mathematical and computational tool set much of which still needs to be developed.
For a mathematical model of cancer to be useful to biologists, the model must first predict results that are in agreement with what is already known experimentally. But it must also go further and suggest hypotheses that are biologically testable. Of course, not all mathematical models satisfy one or both requirements. This is to be expected, especially when the biological process is highly complex, as is often the case with cancer progression. In such a situation, a model of cancer that establishes only a proof of concept can still make a worthwhile contribution.
The yearlong program will encompass many of the processes involved in cancer initiation and progression. Each workshop will concentrate on different aspects of the disease, but there will be strong connections among the various themes of the workshops. The first workshop will explore the relationship between the evolutionary and ecological aspects of tumor progression, with a specific focus on how models from ecology and population genetics can be used to understand tumor evolution and tumor treatment. A major problem with treating cancer is that no two cancers are identical and even within the same tumor there is genetic heterogeneity, so called inter- and intra-tumor heterogeneity.
Tumor growth depends on the tumor's ability to attract new blood vessels in a process called angiogenesis. As the tumor grows, some of its cells invade blood and lymphatic vessels, and migrate to other parts of the body, a process called metastasis. There are already many mathematical models of tumor angiogenesis, but the computational aspects of the process are still very challenging. Workshop 2 will focus on the computational aspects of tumor angiogenesis and metastasis.
Workshop 3 will discuss interactions between cancer and the immune system. It is well known that immune cells recognize tumor cells as foreign agents, but the tumor cells eventually learn to avoid immune surveillance. The big question is how to improve immune surveillance and how to strengthen the immune mechanisms in order to destroy the cancer cells. Answering these questions may lead to the development of cancer vaccines. The third workshop will focus on recent mathematical models that address these topics.
The fourth workshop will consider tumor heterogeneity by aiming to understand the relationship between phenotypic heterogeneity and the tumor microenvironment and how this drives tumor progression and drug resistance. This environment is composed of many cellular and non-cellular components that interact and influence each other in a complex and dynamic manner. Therefore this workshop will focus on the mathematical and experimental models that will facilitate our understanding of this complex dialogue.
Workshop 5 will concentrate on patient specific cancer treatments and the issue of drug resistance. Recently the clinical treatment of cancer has began to shift toward designing therapies that are tailored to a specific tumor in a specific patient rather than a one size fits all paradigm. Unfortunately, even these new patient specific therapies still suffer from drug resistance. This workshop will therefore explore how mathematical models can be used to tailor and optimize drug combinations and schedules for specific patients whilst minimizing drug resistance.
Cell proliferation is intimately linked to cell metabolism. Metabolism of cancer cells relies largely on aerobic glycolysis, a property referred to as the Warburg effect. Workshop 6 will address the mathematical modeling of metabolic networks, as well as the interface between metabolic networks and the oncogene-modified networks that drive uncontrolled proliferation and how understanding this interface will drive novel treatments.
There is increasing evidence that among the cancer cells there are cancer stem cells; these are cancer cells that, like stem cells, can reproduce an unlimited number of times. For cancer therapy to succeed, it is believed that these cancer stem cells must be totally destroyed. Questions about the phenotypic nature of these cells and the correct therapeutic approach to deal with them have not yet been resolved. These questions will be discussed in Workshop 7 with the goal of suggesting novel treatment strategies.
In summary, the emphasis year will concentrate on mathematical models of
- The processes of uncontrolled cell proliferation, invasion, angiogenesis, and metastasis;
- The interaction of the tumor with its environment; that is, the interaction with other cell types, the extracellular matrix, nutrients, growth promoting and inhibiting factors, and the immune system;
- Tumor evolution, heterogeneity, cancer treatment and drug resistance.
On the other hand, an equally important goal is to introduce oncologists, either at the research or clinical level, to the power of mathematical modeling and computational analysis for understanding complex systems, such as cancer development, and to encourage new ways of thinking about the problems in this community. The long-term objective of such modeling is twofold, first to better understand the mechanisms that underlie tumor initiation, progression and resistance and secondly to utilize this understanding to improve treatment strategies by optimal scheduling of chemotherapy, radiation treatment, and combination therapies, but success in this depends on awareness amongst oncologists of what mathematical modeling can do toward achieving this.
How successful these workshops are at stimulating the interdisciplinary conversation needed to make progress toward these dual goals will depend in large part on both the speakers and the participants, and we have endeavored to identify speakers who are not only leaders in the field, but also receptive to new approaches and new problems. The same philosophy will govern the choice of participants, with particular emphasis on attracting researchers in the early stages of their career.
