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To maintain optimal fitness, a cell must balance the risk of inadequate energy reserve for response to a potentially fatal perturbation against the long-term cost of maintaining high concentrations of ATP to meet occasional spikes in demand. Here we apply a game theoretic approach to address the dynamics of energy production and expenditure in eukaryotic cells. Conventionally, glucose metabolism is viewed as a function of oxygen concentrations in which the more efficient oxidation of glucose to CO2 and H2O produces all or nearly all ATP except under hypoxic conditions when less efficient (2 ATP/ glucose vs. about 36ATP/glucose) anaerobic metabolism of glucose to lactic acid provides an emergency backup. We propose an alternative in which energy production is governed by the complex temporal and spatial dynamics of intracellular ATP demand. In the short term, a cell must provide energy for constant baseline needs but also maintain capacity to rapidly respond to fluxes in demand particularly due to external perturbations on the cell membrane. Similarly, longer-term dynamics require a trade-off between the cost of maintaining high metabolic capacity to meet uncommon spikes in demand versus the risk of unsuccessfully responding to threats or opportunities. Here we develop a model and computationally explore the cell's optimal mix of glycolytic and oxidative capacity. We find the Warburg effect, high glycolytic metabolism even under normoxic conditions, is represents a metabolic strategy that allow cancer cells to optimally meet energy demands posed by stochastic or fluctuating tumor environments.

Abiraterone treats metastatic castrate-resistant prostate cancer by inhibiting CYP17A, an enzyme for testosterone auto-production. With standard dosing, evolution of resistance with treatment failure (radiographic progression) occurs at a median of ~16.5 months. We hypothesize time to progression (TTP) could be increased by integrating evolutionary dynamics into therapy. We developed an evolutionary game theory model using Lotka–Volterra equations with three competing cancer “species”: androgen dependent, androgen producing, and androgen independent. Simulations with standard abiraterone dosing demonstrate strong selection for androgen-independent cells and rapid treatment failure. Adaptive therapy, using patient-specific tumor dynamics to inform on/off treatment cycles, suppresses proliferation of androgen-independent cells and lowers cumulative drug dose. In a pilot clinical trial, 10 of 11 patients maintained stable oscillations of tumor burdens; median TTP is at least 27 months with reduced cumulative drug use of 47% of standard dosing. The outcomes show significant improvement over published studies and a contemporaneous population. Evolution of resistance is a common cause of cancer treatment failure and tumor progression. Here, the authors present a method for integrating evolutionary principles based on adaptive therapy into abiraterone therapy for metastatic castrate-resistant prostate cancer and show the positive results of an interim analysis of a trial cohort.

Tumors are not static masses of cells but dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with (1) the tumor niche (edge or core), (2) cell turnover rates, (3) the nature of the tradeoff between traits, and (4) whether deaths occur in response to demographic or environmental stochasticity. Using a spatially-explicit agent-based model, we observe how two traits (proliferation rate and migration speed) evolve under different tradeoff conditions with different turnover rates. Migration rate is favored over proliferation at the tumor ’ s edge and vice-versa for the interior. Increasing cell turnover rates slightly slows tumor growth but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration, while a convex tradeoff tends to favor proliferation, often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff favors migration at low death rates, but switches to proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation, and environmental stochasticity favors migration. While all of these diverse factors contribute to the ecology, heterogeneity, and evolution of a tumor, their effects may be predictable and empirically accessible.

Based on a consensus conference of experts in the evolution and ecology of cancer, this article proposes a framework for classifying tumours that includes four evolutionary and ecological processes: neoplastic cell diversity and changes over time in that diversity, hazards to cell survival and available resources. Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research.

Fish populations subject to heavy exploitation are expected to evolve over time smaller average body sizes. We introduce Stackelberg evolutionary game theory to show how fisheries management should be adjusted to mitigate the potential negative effects of such evolutionary changes. We present the game of a fisheries manager versus a fish population, where the former adjusts the harvesting rate and the net size to maximize profit, while the latter responds by evolving the size at maturation to maximize the fitness. We analyze three strategies: i) ecologically enlightened (leading to a Nash equilibrium in game-theoretic terms); ii) evolutionarily enlightened (leading to a Stackelberg equilibrium) and iii) domestication (leading to team optimum) and the corresponding outcomes for both the fisheries manager and the fish. Domestication results in the largest size for the fish and the highest profit for the manager. With the Nash approach the manager tends to adopt a high harvesting rate and a small net size that eventually leads to smaller fish. With the Stackelberg approach the manager selects a bigger net size and scales back the harvesting rate, which lead to a bigger fish size and a higher profit. Overall, our results encourage managers to take the fish evolutionary dynamics into account. Moreover, we advocate for the use of Stackelberg evolutionary game theory as a tool for providing insights into the eco-evolutionary consequences of exploiting evolving resources.

State‐structured populations are ubiquitous in biology, from the age‐structure of animal societies to the life cycles of parasitic species. Understanding how this structure contributes to eco‐evolutionary dynamics is critical not only for fundamental understanding but also for conservation and treatment purposes. Although some methods have been developed in the literature for modelling eco‐evolutionary dynamics in structured population, such methods are wholly lacking in the G function evolutionary game theoretic framework. In this paper, we integrate standard matrix population modelling into the G function framework to create a theoretical framework to probe eco‐evolutionary dynamics in structured populations. This framework encompasses age‐ and stage‐structured matrix models with basic density‐ and frequency‐dependent transition rates and probabilities. For both discrete and continuous time models, we define and characterize asymptotic properties of the system such as eco‐evolutionary equilibria (including ESSs) and the convergence stability of these equilibria. For multistate structured populations, we introduce an ergodic flow preserving folding method for analysing such models. The methods developed in this paper for state‐structured populations and their extensions to multistate‐structured populations provide a simple way to create, analyse and simulate eco‐evolutionary dynamics in structured populations. Furthermore, their generality allows these techniques to be applied to a variety of problems in ecology and evolution.

Classical evolutionary game theory allows one to analyze the population dynamics of interacting individuals playing different strategies (broadly defined) in a population. To expand the scope of this framework to allow us to examine the evolution of these individuals' strategies over time, we present the idea of a fitness-generating (G) function. Under this model, we can simultaneously consider population (ecological) and strategy (evolutionary) dynamics. In this paper, we briefly outline the differences between game theory and classical evolutionary game theory. We then introduce the G function framework, deriving the model from fundamental biological principles. We introduce the concept of a G-function species, explain the process of modeling with G functions, and define the conditions for evolutionary stable strategies (ESS). We conclude by presenting expository examples of G function model construction and simulations in the context of predator-prey dynamics and the evolution of drug resistance in cancer.

Cancer cells utilize the forces of natural selection to evolve evolvability allowing a constant supply of heritable variation that permits a cancer species to evolutionary track changing hazards and opportunities. Over time, the dynamic tumor ecosystem is exposed to extreme, catastrophic changes in the conditions of the tumor—natural (e.g., loss of blood supply) or imposed (therapeutic). While the nature of these catastrophes may be varied or unique, their common property may be to doom the current cancer phenotype unless it evolves rapidly. Poly‐aneuploid cancer cells (PACCs) may serve as efficient sources of heritable variation that allows cancer cells to evolve rapidly, speciate, evolutionarily track their environment, and most critically for patient outcome and survival, permit evolutionary rescue, therapy resistance, and metastasis. As a conditional evolutionary strategy, they permit the cancer cells to accelerate evolution under stress and slow down the generation of heritable variation when conditions are more favorable or when the cancer cells are closer to an evolutionary optimum. We hypothesize that they play a critical and outsized role in lethality by their increased capacity for invasion and motility, for enduring novel and stressful environments, and for generating heritable variation that can be dispensed to their 2N+ aneuploid progeny that make up the bulk of cancer cells within a tumor, providing population rescue in response to therapeutic stress. Targeting PACCs is essential to cancer therapy and patient cure—without the eradication of the resilient PACCs, cancer will recur in treated patients.

Convergence between species and entire clades can occur due to shared environmental conditions and shared resource use. Comparisons of biogeography between convergent clades and taxa may reveal some of these properties unique to each taxon. We sought to characterize and compare the global scale biogeography of hummingbirds (family Trochilidae), which possess unique adaptations for nectar feeding, with sunbirds (family Nectariniidae), which also feed on nectar but are more generalist in their feeding ecology. We collected the latitudinal and elevational range of all species in both clades to create species distributions along those gradients by way of empirical cumulative distribution functions. We compared those distributions to see 1) if they differed, by way of minimum difference estimation and 2) how they differed, by way of non-linear regression. Hummingbirds are shown to extend into higher elevations and latitudes compared to sunbirds, and better maintain their species number in these more extreme environments. We provide possible reasons for these patterns including dispersal limitation, land area, diversity of resources, and climatic conditions. In one particularly interesting hypothesis, we propose that hummingbirds’ unique adaptations for nectar feeding allow them to exploit resources more efficiently, gain higher intrinsic fitness, and therefore speciate and spread into more extreme climates than less efficient nectar feeding sunbirds.

Evolutionary life history theory posits that some organisms reproduce rapidly whereas others invest more resources in survival. This framework might help us to understand the diversity of phenotypes that are displayed by tumour cells, including stem cell-like phenotypes, and could have important clinical implications. Somatic evolution during cancer progression and therapy results in tumour cells that show a wide range of phenotypes, which include rapid proliferation and quiescence. Evolutionary life history theory may help us to understand the diversity of these phenotypes. Fast life history organisms reproduce rapidly, whereas those with slow life histories show less fecundity and invest more resources in survival. Life history theory also provides an evolutionary framework for phenotypic plasticity, which has potential implications for understanding 'cancer stem cells'. Life history theory suggests that different therapy dosing schedules might select for fast or slow life history cell phenotypes, with important clinical consequences.