Workshop 1: Ecology and Evolution of Cancer
Cancer is a problem of somatic cheating, where cells of the body enhance their fitness at the expense of the organism as a whole. The evolution of multicellularity represents a highly sophisticated form of cooperation and cheater suppression. Each independent evolution of multicellularity required suppressing somatic cheating (i.e., cancer) long enough for the organism to survive and reproduce. Here I provide a review of somatic cheating in cancer like phenomena across the tree of life including the 6 independent branches of complex multicellularity. I focus on forms of cheating that involve resource acquisition and monopolization, including upregulated metabolism and disregulated signaling for limiting resources. I describe model results showing that resource cheating may be central to cancer evolution and progression to malignant disease.
Gliomas are the most common and malignant primary tumors of the brain and are commonly treated with radiation therapy. Despite modest advances in chemotherapy and radiation, survival has changed very little over the last 50 years. Radiation therapy is one of the pillars of adjuvant therapy for GBM but despite treatment, recurrence inevitably occurs. Here we develop a mathematical model for the tumor response to radiation that takes into account the plasticity of the hierarchical structure of the tumor population. Based on this mathematical model we develop an optimized radiation delivery schedule.
Cancer evolution is a stochastic evolutionary process characterized by the accumulation of mutations and responsible for tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to describe the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular profiling data. We present recent approaches to modeling the evolution of cancer, including population genetics models of tumorigenesis, phylogenetic methods of intra-tumor subclonal diversity, and probabilistic graphical models of tumor progression, and we discuss methods for distinguishing driver from passenger mutations.
In the light of known associations between cancer and specifically: alcohol; dietary fat; obesity; vegetables, fruit, and single nutrients; hormones; and inflammation, I will make the case that the dominant theory of carcinogenesis does not do a very good job of underpinning widespread coherent epidemiologic findings.
The talk will begin with the dominant theory of carcinogenesis. What we know about some nutritional/lifestyle causes of cancer (with occasional asides on anomalies and policy implications) will follow. I will make the case that the empirical findings and the theory are ill-matched but, also, that there are other theories that can help us think more clearly about diet and cancer particularly. From this point, the talk will focus on insights from development and morphogenesis â€“ invoking clear evidence for the capacity of developing organisms to respond to environmentally generated signals and to modify their development accordingly. Noting that an important universal (but often ignored) characteristic of cancer is disrupted tissue microarchitecture, I will suggest that we can consider cancer as something like disordered development and will discuss the role of morphostats in its genesis; analogous to agents that shape developing organisms (morphogens), morphostats are agents that maintain adult tissues. I will then ask the crucial mechanistic question as to whether there is any evidence that cells in developing organisms (and, by implication, perhaps cancer cells) can directly read signals from their environment. Indeed, there is evidence that developing cells can sense their systemic and nutritional environment and that exogenous nutrients and endogenous hormones can directly regulate at least one key morphogenetic/morphostatic signaling system and determine cell fate.
Thinking about more appropriate theories of carcinogenesis may allow us to ask better questions in studies of nutrition and lifestyle.
Metastatic disease is the defining feature of advanced malignancy, yet the mechanisms by which it occurs and affects host physiology are poorly understood. Comprehensive genomic studies of human metastatic cancers has revealed striking heterogeneity both within primary tumors, but also between different metastases from the same patient. For these reasons, models which capture this heterogeneity will be necessary to design effective strategies to abrogate the metastatic phenotype. The zebrafish has recently emerged as a genetic model system in which to study cancer because of several key strengths: 1) small size allows for study of vast numbers of animals, 2) optical transparency facilitates in vivo imaging of even single cancer cells, and 3) amenability to unbiased genetic and small molecule screens. My laboratory has developed a zebrafish model of melanoma in which the BRAFV600E allele is expressed in the mitf+ melanocyte lineage. These animals develop stereotyped, 100% penetrant melanomas when crossed to a p53-/- mutant. Using highly sensitive fluorescent detection and automated imaging algorithms, we have defined metastatic capacity of these tumors, and demonstrate a metastasis initiating cell frequency of 1/250,000 cells. These cells show preferential metastases to locations such as skin, bone marrow, and eye. Using this as a platform, we are now defining the genomic characteristics of the metastatic clones, and designing unbiased screens to find genetic or epigenetic pathways that modify metastatic progression. The zebrafish offers a highly scalable in vivo system for interrogating the dynamics of metastasis over both time and space at a resolution unavailable in other model systems.
Niche construction is the process whereby organisms modify their own and/or each otherâ€™s niches through their metabolism, activities, and/or choices. This can result in changes in one or more natural selection pressures in the external environment of populations. Niche-constructing species may either alter the natural selection pressures of their own population, of other populations, or of both. In ecology, foundation species are species that have a strong role in structuring a community. Cancer cells act as a foundation species and also act as ecosystem engineers to construct new system niches. This may lead to increased genetic instability through evolutionary adaptation. Most constructed ecosystems eventually reach a point of homeostasis (equilibrium) that creates an environment that allows the foundation species to thrive. There is no evidence that cancer reaches this ecological endpoint. It is worth exploring if this can be exploited as a therapeutic target.
The process of clonal evolution underpins the maintenance of a normal healthy colon, and the â€œunwanted evolutionâ€? of mutant cells leads to the development of colon cancer. However, despite the central importance, a quantification of the parameters that define the clonal evolutionary process in human colon (and indeed all human tissues) has remained lacking. Our current understanding is derived from studies performed in model organisms, and it is uncertain if and how these insights apply to humans. I will describe how we coupled a novel â€œlineage tracingâ€? strategy in human colon, that allows the fate of different clonal lineages to be visualised, with a reductionist mathematical analysis that allows us to infer the parameters governing clonal evolution in the human gut.
Our analysis has shown that human intestinal stem cells evolve through a process of neutral drift, and that the neutrality of this process is disrupted by mutation to the APC gene that functions as a key tumour-suppressor in the colon. In the colon, cells are organized into millions of â€œcryptsâ€? â€“ small tubular structures each housing a few thousand cells. Through our quantitative analysis of lineage-tracing data, we have been able to infer the number of functional stem cells per human crypt, and also how they behave over time. Further, our mathematical analysis reveals how often colon crypts divide, both in normal colon and in colon tumours. This parameterisation allows the age of colon tumours to be determined.
Finally, we have coupled multi-region sampling of established colorectal cancers with whole-genome sequencing and other genomic analysis to infer how colorectal cancers evolve. Our results imply that clonal evolution is not a process of stepwise clonal sweeps as the â€œtextbookâ€? model implies.
My particular excitement about this work is that it demonstrates how quantitative analysis of a static picture can resolve temporal dynamics. Application of these methods quantifies the dynamic process of clonal evolution that occurs in human tissues.
The â€œstartâ€? of human tumorigenesis is difficult to study because most human tumors are undetectable until they reach about 1 cm in size (~1 billion cells). However, it may be possible to reconstruct even the first few divisions after tumor initiation through the analysis of somatic mutations in large present day tumors. Using coalescent theory, â€œpublicâ€? mutations in the initiating cell will be present in all present day tumor cells. Assuming a simple exponential clonal expansion, â€œprivateâ€? mutations that arise during the first few cell divisions will be present in most but not all present day tumor cells. The earlier a private mutation occurs, the greater its frequency in the final tumor. By sampling multiple regions from the same large human tumor, it is possible to identify public and private mutations, and then infer the early events after human tumor initiation.
Cancer is an evolutionary disease where, although mutations are thought to be random, selection clearly is not. Selection is driven by the interactions between the different types of tumor cells, other cells in the tumor microenvironment and the physical microenvironment itself. Game Theory in general and Evolutionary Game Theory in particular are mathematical frameworks in which to investigate the role of the interactions between cells with different phenotypes and traits in the evolutionary dynamics of a tumor. In this presentation I will introduce the history of Game Theory, some canonical games and then proceed to describe how it has been used to model cancer, the advantages of the approach and some shortcomings.
Ecologists have long studied mimicry, in which a mimic emits some signal(s) imitating a model. A dupe receives the signal(s) and mistakes the mimic for the model, to the mimic's advantage. The literature contains a rich body of mathematical models for various cases. We focus on brood parasitism, e.g., a cuckoo substituting its egg into the nest of another bird, which incubates, hatches, and feeds it until fledging. We draw a parallel with cancerous cells mimicking phenotypes of wounded tissue, and duping the immune system into executing wound healing programs, thus cooperating in carcinogenesis. We sketch steps towards turning this into a usable model.
We'll review recent developments in the theory of two type birth-death branching processes. These studies were pioneered by Salvador Luria and Max Delbruck in 1943 to model genetic mutations arising in bacterial populations. More recent applications include the development of resistance to chemotherapy of cancer.
Short Talk: Quantitative experimental platforms to measure the evolutionary dynamics of drug resistance in vitroShannon Mumenthaler
Tumor growth and therapeutic response involve a complex evolutionary process driven in part by a heterogeneous microenvironment. Spatial and temporal gradients of nutrients, oxygen, and drug can create physical niches that drive cellular adaptation and force tumor cells to adopt various strategies to survive. As a result, the tumor microenvironment affects the fitness of cancer cells and influences the overall composition of the tumor. The ability to experimentally capture the heterogeneity of the microenvironment and resulting cellular behavior is imperative for achieving a full understanding of the evolutionary dynamics of a tumor. Mathematical models can have great utility in predicting clinical endpoints when tightly integrated with experimental measurements. Utilizing assays that deliver reproducible, quantitative, and dynamic measurements on physiologically relevant biological model systems can be extremely valuable for calibrating and validating mathematical models. Therefore, we have developed a high-throughput imaging platform that captures detailed information on the behavior of hundreds of thousands of cells in 2D, where we can assess the effects of systematically varying individual environmental parameters, and in 3D, where gradients of environmental factors co-exist. One application of this imaging approach has been to inform a stochastic compartment-based tumor model of pre-existing drug resistance in non-small cell lung cancer. Each compartment represents a specific tumor environmental niche and this integrative modeling framework is then used to predict rebound growth kinetics and tumor composition (i.e. % resistance). In particular, we provide insight into the magnitude by which the microenvironment influences these results and how one might utilize drugs that target the interface between the microenvironment and tumor cells (e.g. TH-302, a hypoxia activated pro-drug) to achieve a better clinical outcome. Identifying, measuring, and targeting tumor heterogeneity is important for the successful treatment of cancer.
A typical assumption in analytic evolutionary game theory models of cancer is that the population is inviscid: the probability of a cell with a given phenotypic strategy interacting with another depends exclusively on the respective abundance of those strategies in the population. To overcome this limitation, we show how to use the Ohtsuki-Nowak transform to approximate spatial structure and study the effect of interaction neighborhood size. In particular, we focus on the change in neighborhood size at a static boundary -- such as a blood-vessel, organ capsule, or basement membrane. In the case of the go vs. grow game, this edge effect allows a tumor with no invasive phenotypes expressed internally to have a polyclonal boundary with both invasive and non-invasive cells. We hope that our approach serves as a useful analytic compliment to the more common simulation based methods of modeling the effects of spatial structure on cancer dynamics.
Ubiquitous proliferation scheme of stem cells let them not only to replenish their own population but also nourish the population of non-stem tumour cells in a hierarchal form and create strong epigenetic heterogeneity in tumours. Cancer stem cells are believed to have strong plastic phenotypic property tuned by microenvironment which can affect their selection dynamics. We construct a general Moran type model to include differentiation and plasticity for cancer stem cell selection. We present analytical and simulation results for fixation probability and time to fixation in such a model. We apply our model to niche succession and clonal conversion in colorectal cancer both in the presence and absence of primary plasticity between stem cells in the niche and their early progenitors. We also address the effect of microenvironment by introducing a spatial model which incorporates variations in fitness parameters as well as geometry of the the organ. Our finding shows that the fixation probability is a strong function of plasticity rate and differentiation probabilities inside stem cell niche. We compare our findings with observations of Vermeulen et al (Science 2013) on stem cell dynamics of intestinal tumour initiation.
Cancers arise through a process of somatic evolution. This evolutionary process can result in substantial clonal heterogeneity. The mechanisms responsible for the coexistence of distinct clonal lineages and the biological consequences of this coexistence remain poorly understood. Based on in vivo data from a mouse xenograft model, we investigate the influence of clonal heterogeneity on tumor properties, and mathematically model competitive expansion of individual clones. We find that tumor growth can be driven by a minor cell subpopulation. This minor population of cells enhances the proliferation of all cells within a tumor by overcoming environmental constraints. Yet, this driving cell population can be outcompeted by faster proliferating competitors. This can result in tumor collapse. We describe how that non-cell autonomous driving of tumor growth supports clonal interference, stabilizes clonal heterogeneity and enables inter-clonal interactions, which can lead to new phenotypic tumor traits. When treatment is administered, heterogeneity can be reduced, also reducing evolutionary and metastatic potential. We adjust and inform our mathematical framework to model different treatment strategies and optimize treatment processes, in particular in HER2+ breast cancer tumors.
According to one influential paradigm, malignant phenotypes characterizing the "hallmarks of cancer" arise in part via natural selection acting on genetically diverse clones within a tumor. Among these hallmarks, the angiogenic switch is one of the most difficult to explain using an evolutionary narrative. While neoangiogenesis clearly benefits tumor cells, the signal creating it is a public good and therefore susceptible to free-riders. Previous modeling studies predicted that these free-riders can invade, damage and perhaps destroy developing tumors, growing as a tumor-on-a-tumor, or hypertumor. The open question becomes, why are hypertumors apparently rare? Here we show, using more realistic extensions of the original models, that selection favoring free-riding is expected to be overwhelmed by genetic drift in most cases. Adaptive dynamics analysis of a deterministic model of the energetic costs and benefits of angiogenesis and proliferation predicts the existence of an evolutionary stable (ESS) angiogenesis commitment, but this ESS is always a repeller. The expectation, then, is runaway selection for extreme vascular hypo- or hyperplasia. However, the selection gradient is very shallow compared to that for other traits, specifically proliferation. Therefore, evolutionarily unfavorable angiogenesis phenotypes may still invade if they are coupled to even marginally more favorable proliferation strategies through a mechanism logically identical to linkage disequilibrium. A simulation of this evolutionary theater predicts that this disequilibrium mechanism dominates the evolution of the angiogenic switch. We predict, then, that angiogenesis arises as an evolutionary rider on the back of selection for proliferative potential and other malignant hallmarks.