Drugs that target pathogen public goods are robust against evolved drug resistance

Cell facilitation promotes growth and survival under drug pressure in breast cancer

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

Plasmid Costs Explain Plasmid Maintenance, Irrespective of the Nature of Compensatory Mutations

Conjugative plasmids often carry virulence and antibiotic-resistant genes. Therefore, understanding the behavior of these extra-chromosomal DNA elements gives insights into their spread. Bacteria frequently replicate slower after plasmids' entry, an observation inconsistent with the plasmids' ubiquity in nature. Several hypotheses explain the maintenance of plasmids among bacterial communities. However, the numerous combinations of bacterial species and strains, plasmids, and environments claim a robust elucidatory mechanism of plasmid maintenance. Previous works have shown that donor cells already adapted to the plasmid may use the plasmid as a 'weapon' to compete with non-adapted plasmid-free cells. Computer simulations corroborated this hypothesis with a wide range of parameters. Here we show that donor cells benefit from harboring conjugative plasmids even if compensatory mutations in transconjugant cells occur in the plasmid, not on chromosomes. The advantage's leading causes are as follows: mutations take time to appear, many plasmids remain costly, and re-transfer of mutated plasmids usually occurs in sites distant to the original donors, implying little competition between these cells. Research in previous decades cautioned against uncritical acceptance of the hypothesis that resistance cost helps to preserve antibiotics' effectiveness. This work gives a new twist to this conclusion by showing that costs help antibiotic-resistant bacteria to compete with plasmid-free cells even if compensatory mutations appear in plasmids.

Two-dimensional adaptive dynamics of evolutionary public goods games: finite-size effects on fixation probability and branching time

Public goods games (PGGs) describe situations in which individuals contribute to a good at a private cost, but others can free-ride by receiving a share of the public benefit at no cost. The game occurs within local neighbourhoods, which are subsets of the whole population. Free-riding and maximal production are two extremes of a continuous spectrum of traits. We study the adaptive dynamics of production and neighbourhood size. We allow the public good production and the neighbourhood size to coevolve and observe evolutionary branching. We explain how an initially monomorphic population undergoes evolutionary branching in two dimensions to become a dimorphic population characterized by extremes of the spectrum of trait values. We find that population size plays a crucial role in determining the final state of the population. Small populations may not branch or may be subject to extinction of a subpopulation after branching. In small populations, stochastic effects become important and we calculate the probability of subpopulation extinction. Our work elucidates the evolutionary origins of heterogeneity in local PGGs among individuals of two traits (production and neighbourhood size), and the effects of stochasticity in two-dimensional trait space, where novel effects emerge.

Group phenotypic composition in cancer

Although individual cancer cells are generally considered the Darwinian units of selection in malignant populations, they frequently act as members of groups where fitness of the group cannot be reduced to the average fitness of individual group members. A growing body of studies reveals limitations of reductionist approaches to explaining biological and clinical observations. For example, induction of angiogenesis, inhibition of the immune system, and niche engineering through environmental acidification and/or remodeling of extracellular matrix cannot be achieved by single tumor cells and require collective actions of groups of cells. Success or failure of such group activities depends on the phenotypic makeup of the individual group members. Conversely, these group activities affect the fitness of individual members of the group, ultimately affecting the composition of the group. This phenomenon, where phenotypic makeup of individual group members impacts the fitness of both members and groups, has been captured in the term 'group phenotypic composition' (GPC). We provide examples where considerations of GPC could help in understanding the evolution and clinical progression of cancers and argue that use of the GPC framework can facilitate new insights into cancer biology and assist with the development of new therapeutic strategies.

Searching for Goldilocks: How Evolution and Ecology Can Help Uncover More Effective Patient-Specific Chemotherapies

Deaths from cancer are mostly due to metastatic disease that becomes resistant to therapy. A mainstay treatment for many cancers is chemotherapy, for which the dosing strategy is primarily limited by patient toxicity. While this MTD approach builds upon the intuitively appealing principle that maximum therapeutic benefit is achieved by killing the largest possible number of cancer cells, there is increasing evidence that moderation might allow host-specific features to contribute to success. We believe that a "Goldilocks Window" of submaximal chemotherapy will yield improved overall outcomes. This window combines the complex interplay of cancer cell death, immune activity, emergence of chemoresistance, and metastatic dissemination. These multiple activities driven by chemotherapy have tradeoffs that depend on the specific agents used as well as their dosing levels and schedule. Here we present evidence supporting the idea that MTD may not always be the best approach and offer suggestions toward a more personalized treatment regime that integrates insights into patient-specific eco-evolutionary dynamics.

Harnessing bacterial interactions to manage infections: a review on the opportunistic pathogen Pseudomonas aeruginosa as a case example

During infections, bacterial pathogens can engage in a variety of interactions with each other, ranging from the cooperative sharing of resources to deadly warfare. This is especially relevant in opportunistic infections, where different strains and species often co-infect the same patient and interact in the host. Here, we review the relevance of these social interactions during opportunistic infections using the human pathogen Pseudomonas aeruginosa as a case example. In particular, we discuss different types of pathogen-pathogen interactions, involving both cooperation and competition, and elaborate on how they impact virulence in multi-strain and multi-species infections. We then review evolutionary dynamics within pathogen populations during chronic infections. We particuarly discuss how local adaptation through niche separation, evolutionary successions and antagonistic co-evolution between pathogens can alter virulence and the damage inflicted on the host. Finally, we outline how studying bacterial social dynamics could be used to manage infections. We show that a deeper appreciation of bacterial evolution and ecology in the clinical context is important for understanding microbial infections and can inspire novel treatment strategies.

In-vivo microscopy reveals the impact of Pseudomonas aeruginosa social interactions on host colonization

Pathogenic bacteria engage in social interactions to colonize hosts, which include quorum-sensing-mediated communication and the secretion of virulence factors that can be shared as "public goods" between individuals. While in-vitro studies demonstrated that cooperative individuals can be displaced by "cheating" mutants freeriding on social acts, we know less about social interactions in infections. Here, we developed a live imaging system to track virulence factor expression and social strain interactions in the human pathogen Pseudomonas aeruginosa colonizing the gut of Caenorhabditis elegans. We found that shareable siderophores and quorum-sensing systems are expressed during infections, affect host gut colonization, and benefit non-producers. However, non-producers were unable to successfully cheat and outcompete producers. Our results indicate that the limited success of cheats is due to a combination of the down-regulation of virulence factors over the course of the infection, the fact that each virulence factor examined contributed to but was not essential for host colonization, and the potential for negative frequency-dependent selection. Our findings shed new light on bacterial social interactions in infections and reveal potential limits of therapeutic approaches that aim to capitalize on social dynamics between strains for infection control.

Natural Selection Between Two Games with Applications to Game Theoretical Models of Cancer

Evolutionary game theory has been used extensively to study single games as applied to cancer, including in the context of metabolism, development of resistance, and even games between tumor and treatment. However, the situation when several games are being played against each other at the same time has not yet been investigated. Here, we describe a mathematical framework for analyzing natural selection not just between strategies, but between games. We provide theoretical analysis of situations of natural selection between the games of Prisoner's dilemma and Hawk-Dove, and demonstrate that while the dynamics of cooperators and defectors within their respective games is as expected, the distribution of games changes over time due to natural selection. We also investigate the question of mutual invasibility of games with respect to different strategies and different initial population composition. We conclude with a discussion of how the proposed approach can be applied to other games in cancer, such as motility versus stability strategies that underlie the process of metastatic invasion.

Neighborhood size-effects shape growing population dynamics in evolutionary public goods games

An evolutionary game emerges when a subset of individuals incur costs to provide benefits to all individuals. Public goods games (PGG) cover the essence of such dilemmas in which cooperators are prone to exploitation by defectors. We model the population dynamics of a non-linear PGG and consider density-dependence on the global level, while the game occurs within local neighborhoods. At low cooperation, increases in the public good provide increasing returns. At high cooperation, increases provide diminishing returns. This mechanism leads to diverse evolutionarily stable strategies, including monomorphic and polymorphic populations, and neighborhood-size-driven state changes, resulting in hysteresis between equilibria. Stochastic or strategy-dependent variations in neighborhood sizes favor coexistence by destabilizing monomorphic states. We integrate our model with experiments of cancer cell growth and confirm that our framework describes PGG dynamics observed in cellular populations. Our findings advance the understanding of how neighborhood-size effects in PGG shape the dynamics of growing populations.

Cooperation among cancer cells: applying game theory to cancer

Cell cooperation promotes many of the hallmarks of cancer via the secretion of diffusible factors that can affect cancer cells or stromal cells in the tumour microenvironment. This cooperation cannot be explained simply as the collective action of cells for the benefit of the tumour because non-cooperative subclones can constantly invade and free-ride on the diffusible factors produced by the cooperative cells. A full understanding of cooperation among the cells of a tumour requires methods and concepts from evolutionary game theory, which has been used successfully in other areas of biology to understand similar problems but has been underutilized in cancer research. Game theory can provide insights into the stability of cooperation among cells in a tumour and into the design of potentially evolution-proof therapies that disrupt this cooperation.

Probing the evolutionary robustness of two repurposed drugs targeting iron uptake in Pseudomonas aeruginosa

LAY SUMMARY

We probed the evolutionary robustness of two antivirulence drugs, gallium and flucytosine, targeting the iron-scavenging pyoverdine in the opportunistic pathogen Pseudomonas aeruginosa. Using an experimental evolution approach in human serum, we showed that antivirulence treatments are not evolutionarily robust per se, but vary in their propensity to select for resistance.

BACKGROUND AND OBJECTIVES

Treatments that inhibit the expression or functioning of bacterial virulence factors hold great promise to be both effective and exert weaker selection for resistance than conventional antibiotics. However, the evolutionary robustness argument, based on the idea that antivirulence treatments disarm rather than kill pathogens, is controversial. Here, we probe the evolutionary robustness of two repurposed drugs, gallium and flucytosine, targeting the iron-scavenging pyoverdine of the opportunistic human pathogen Pseudomonas aeruginosa.

METHODOLOGY

We subjected replicated cultures of bacteria to two concentrations of each drug for 20 consecutive days in human serum as an ex vivo infection model. We screened evolved populations and clones for resistance phenotypes, including the restoration of growth and pyoverdine production, and the evolution of iron uptake by-passing mechanisms. We whole-genome sequenced evolved clones to identify the genetic basis of resistance.

RESULTS

We found that mutants resistant against antivirulence treatments readily arose, but their selective spreading varied between treatments. Flucytosine resistance quickly spread in all populations due to disruptive mutations in upp, a gene encoding an enzyme required for flucytosine activation. Conversely, resistance against gallium arose only sporadically, and was based on mutations in transcriptional regulators, upregulating pyocyanin production, a redox-active molecule promoting siderophore-independent iron acquisition. The spread of gallium resistance was presumably hampered because pyocyanin-mediated iron delivery benefits resistant and susceptible cells alike.

CONCLUSIONS AND IMPLICATIONS

Our work highlights that antivirulence treatments are not evolutionarily robust per se. Instead, evolutionary robustness is a relative measure, with specific treatments occupying different positions on a continuous scale.

Drug resistance in cancer: molecular evolution and compensatory proliferation

Targeted therapies have revolutionized cancer treatment. Unfortunately, their success is limited due to the development of drug resistance within the tumor, which is an evolutionary process. Understanding how drug resistance evolves is a prerequisite to a better success of targeted therapies. Resistance is usually explained as a response to evolutionary pressure imposed by treatment. Thus, evolutionary understanding can and should be used in the design and treatment of cancer. In this article, drug-resistance to targeted therapies is reviewed from an evolutionary standpoint. The concept of apoptosis-induced compensatory proliferation (AICP) is developed. It is shown that AICP helps to explain some of the phenomena that are observed experimentally in cancers. Finally, potential drug targets are suggested in light of AICP.

Clonal cooperativity in heterogenous cancers

Tumor heterogeneity is a major obstacle to the development of effective therapies and is thus an important focus of cancer research. Genetic and epigenetic alterations, as well as altered tumor microenvironments, result in tumors made up of diverse subclones with different genetic and phenotypic characteristics. Intratumor heterogeneity enables competition, but also supports clonal cooperation via cell-cell contact or secretion of factors, resulting in enhanced tumor progression. Here, we summarize recent findings related to interclonal interactions within a tumor and the therapeutic implications of such interactions, with an emphasis on how different subclones collaborate with each other to promote proliferation, metastasis and therapy-resistance. Furthermore, we propose that disruption of clonal cooperation by targeting key factors (such as Wnt and Hedgehog, amongst others) can be an alternative approach to improving clinical outcomes.

Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis

Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.

Natural Selection in Cancer Biology: From Molecular Snowflakes to Trait Hallmarks

Evolution by natural selection is the conceptual foundation for nearly every branch of biology and increasingly also for biomedicine and medical research. In cancer biology, evolution explains how populations of cells in tumors change over time. It is a fundamental question whether this evolutionary process is driven primarily by natural selection and adaptation or by other evolutionary processes such as founder effects and drift. In cancer biology, as in organismal evolutionary biology, there is controversy about this question and also about the use of adaptation through natural selection as a guiding framework for research. In this review, we discuss the differences and similarities between evolution among somatic cells versus evolution among organisms. We review what is known about the parameters and rate of evolution in neoplasms, as well as evidence for adaptation. We conclude that adaptation is a useful framework that accurately explains the defining characteristics of cancer. Further, convergent evolution through natural selection provides the only satisfying explanation both for how a group of diverse pathologies have enough in common to usefully share the descriptive label of "cancer" and for why this convergent condition becomes life-threatening.

Manipulating virulence factor availability can have complex consequences for infections

Given the rise of bacterial resistance against antibiotics, we urgently need alternative strategies to fight infections. Some propose we should disarm rather than kill bacteria, through targeted disruption of their virulence factors. It is assumed that this approach (i) induces weak selection for resistance because it should only minimally impact bacterial fitness, and (ii) is specific, only interfering with the virulence factor in question. Given that pathogenicity emerges from complex interactions between pathogens, hosts and their environment, such assumptions may be unrealistic. To address this issue in a test case, we conducted experiments with the opportunistic human pathogen Pseudomonas aeruginosa, where we manipulated the availability of a virulence factor, the iron‐scavenging pyoverdine, within the insect host Galleria mellonella. We observed that pyoverdine availability was not stringently predictive of virulence and affected bacterial fitness in nonlinear ways. We show that this complexity could partly arise because pyoverdine availability affects host responses and alters the expression of regulatorily linked virulence factors. Our results reveal that virulence factor manipulation feeds back on pathogen and host behaviour, which in turn affects virulence. Our findings highlight that realizing effective and evolutionarily robust antivirulence therapies will ultimately require deeper engagement with the intrinsic complexity of host–pathogen systems.

Spatial Proximity to Fibroblasts Impacts Molecular Features and Therapeutic Sensitivity of Breast Cancer Cells Influencing Clinical Outcomes

Using a three-dimensional coculture model, we identified significant subtype-specific changes in gene expression, metabolic, and therapeutic sensitivity profiles of breast cancer cells in contact with cancer-associated fibroblasts (CAF). CAF-induced gene expression signatures predicted clinical outcome and immune-related differences in the microenvironment. We found that fibroblasts strongly protect carcinoma cells from lapatinib, attributable to its reduced accumulation in carcinoma cells and an elevated apoptotic threshold. Fibroblasts from normal breast tissues and stromal cultures of brain metastases of breast cancer had similar effects as CAFs. Using synthetic lethality approaches, we identified molecular pathways whose inhibition sensitizes HER2⁺ breast cancer cells to lapatinib both in vitro and in vivo, including JAK2/STAT3 and hyaluronic acid. Neoadjuvant lapatinib therapy in HER2⁺ breast tumors lead to a significant increase of phospho-STAT3⁺ cancer cells and a decrease in the spatial proximity of proliferating (Ki67⁺) cells to CAFs impacting therapeutic responses. Our studies identify CAF-induced physiologically and clinically relevant changes in cancer cells and offer novel approaches for overcoming microenvironment-mediated therapeutic resistance. Cancer Res; 76(22); 6495-506. ©2016 AACR.

Beyond killing: Can we find new ways to manage infection?

The antibiotic pipeline is running dry and infectious disease remains a major threat to public health. An efficient strategy to stay ahead of rapidly adapting pathogens should include approaches that replace, complement or enhance the effect of both current and novel antimicrobial compounds. In recent years, a number of innovative approaches to manage disease without the aid of traditional antibiotics and without eliminating the pathogens directly have emerged. These include disabling pathogen virulence-factors, increasing host tissue damage control or altering the microbiota to provide colonization resistance, immune resistance or disease tolerance against pathogens. We discuss the therapeutic potential of these approaches and examine their possible consequences for pathogen evolution. To guarantee a longer half-life of these alternatives to directly killing pathogens, and to gain a full understanding of their population-level consequences, we encourage future work to incorporate evolutionary perspectives into the development of these treatments.

Do cell-autonomous and non-cell-autonomous effects drive the structure of tumor ecosystems?

By definition, a driver mutation confers a growth advantage to the cancer cell in which it occurs, while a passenger mutation does not: the former is usually considered as the engine of cancer progression, while the latter is not. Actually, the effects of a given mutation depend on the genetic background of the cell in which it appears, thus can differ in the subclones that form a tumor. In addition to cell-autonomous effects generated by the mutations, non-cell-autonomous effects shape the phenotype of a cancer cell. Here, we review the evidence that a network of biological interactions between subclones drives cancer cell adaptation and amplifies intra-tumor heterogeneity. Integrating the role of mutations in tumor ecosystems generates innovative strategies targeting the tumor ecosystem's weaknesses to improve cancer treatment.

Tumorigenesis: it takes a village

Although it is widely accepted that most cancers exhibit some degree of intratumour heterogeneity, we are far from understanding the dynamics that operate among subpopulations within tumours. There is growing evidence that cancer cells behave as communities, and increasing attention is now being directed towards the cooperative behaviour of subclones that can influence disease progression. As expected, these interactions can add a greater layer of complexity to therapeutic interventions in heterogeneous tumours, often leading to a poor prognosis. In this Review, we highlight studies that demonstrate such interactions in cancer and postulate ways to overcome them with better-designed therapeutic strategies.

Public goods in relation to competition, cooperation, and spite

Public goods and common-pool resources are fundamental features of biological and social systems, and pose core challenges in achieving sustainability; for such situations, the immediate interests of individuals and the societies in which they are embedded are in potential conflict, involving game-theoretic considerations whose resolution need not serve the collective good. Evolution has often confronted such dilemmas--e.g., in bacterial biofilms--in the challenges of cancer, in nitrogen fixation and chelation, in the production of antibiotics, and in collective action problems across animal groups; there is much to learn from the Darwinian resolution of these situations for how to address problems our societies face today. Addressing these problems involves understanding the emergence of cooperative agreements, from reciprocal altruism and insurance arrangements to the social norms and more formal institutions that maintain societies. At the core are the issues of how individuals and societies discount the future and the interests of others, and the degree that individual decisions are influenced by regard for others. Ultimately, as Garrett Hardin suggested, the solution to problems of the commons is in "mutual coercion, mutually agreed upon," and hence in how groups of individuals form and how they arrive at decisions that ultimately benefit all.

Evolutionary mechanism unifies the hallmarks of cancer

The basis for the gene mutation theory of cancer that dominates current molecular cancer research consists of: the belief that gene-level aberrations such as mutations are the main cause of cancers, the concept that stepwise gene mutation accumulation drives cancer progression, and the hallmarks of cancer. The research community swiftly embraced the hallmarks of cancer, as such synthesis has supported the notions that common cancer genes are responsible for the majority of cancers and the complexity of cancer can be dissected into simplified molecular principles. The gene/pathway classification based on individual hallmarks provides explanation for the large number of diverse gene mutations, which is in contrast to the original estimation that only a handful of gene mutations would be discovered. Further, these hallmarks have been highly influential as they also provide the rationale and research direction for continued gene-based cancer research. While the molecular knowledge of these hallmarks is drastically increasing, the clinical implication remains limited, as cancer dynamics cannot be summarized by a few isolated/fixed molecular principles. Furthermore, the highly heterogeneous genetic signature of cancers, including massive stochastic genome alterations, challenges the utility of continuously studying each individual gene mutation under the framework of these hallmarks. It is therefore necessary to re-evaluate the concept of cancer hallmarks through the lens of cancer evolution. In this analysis, the evolutionary basis for the hallmarks of cancer will be discussed and the evolutionary mechanism of cancer suggested by the genome theory will be employed to unify the diverse molecular mechanisms of cancer.

First principles of Hamiltonian medicine

We introduce the field of Hamiltonian medicine, which centres on the roles of genetic relatedness in human health and disease. Hamiltonian medicine represents the application of basic social-evolution theory, for interactions involving kinship, to core issues in medicine such as pathogens, cancer, optimal growth and mental illness. It encompasses three domains, which involve conflict and cooperation between: (i) microbes or cancer cells, within humans, (ii) genes expressed in humans, (iii) human individuals. A set of six core principles, based on these domains and their interfaces, serves to conceptually organize the field, and contextualize illustrative examples. The primary usefulness of Hamiltonian medicine is that, like Darwinian medicine more generally, it provides novel insights into what data will be productive to collect, to address important clinical and public health problems. Our synthesis of this nascent field is intended predominantly for evolutionary and behavioural biologists who aspire to address questions directly relevant to human health and disease.

The evolution of bacterial social life: From the ivory tower to the front lines of public health

Drug resistant bacteria are a huge and growing threat to public health. A solution exists in theory, but had not yet been put to a practical test. In this paper, it is put to a test, and performs successfully. As predicted by theory, using a drug that targets bacteria's shared secreted ‘public goods’ molecules instead of cell components, did not drive the bacterial evolution of drug resistance, and therefore retained its effectiveness. This result holds great promise for better drugs and vaccines against many infectious diseases, and also for better cancer therapies.

Drug-resistant bacteria are a huge and growing threat to public health. A solution exists in theory, but had not yet been put to a practical test. The accompanying paper by Ross-Gillespie et al., the theory is put to a test and performs successfully. As predicted, using a drug that targets bacteria's shared secreted ‘public goods’ molecules instead of cell components did not drive the bacterial evolution of drug resistance, and therefore retained its effectiveness. This result holds great promise for better drugs and vaccines against many infectious diseases, and also for better cancer therapies.

Gallium-mediated siderophore quenching as an evolutionarily robust antibacterial treatment

BACKGROUND AND OBJECTIVES

Conventional antibiotics select strongly for resistance and are consequently losing efficacy worldwide. Extracellular quenching of shared virulence factors could represent a more promising strategy because (i) it reduces the available routes to resistance (as extracellular action precludes any mutations blocking a drug's entry into cells or hastening its exit) and (ii) it weakens selection for resistance, as fitness benefits to emergent mutants are diluted across all cells in a cooperative collective. Here, we tested this hypothesis empirically.

METHODOLOGY

We used gallium to quench the iron-scavenging siderophores secreted and shared among pathogenic Pseudomonas aeruginosa bacteria, and quantitatively monitored its effects on growth in vitro. We assayed virulence in acute infections of caterpillar hosts (Galleria mellonella), and tracked resistance emergence over time using experimental evolution.

RESULTS

Gallium strongly inhibited bacterial growth in vitro, primarily via its siderophore quenching activity. Moreover, bacterial siderophore production peaked at intermediate gallium concentrations, indicating additional metabolic costs in this range. In vivo, gallium attenuated virulence and growth-even more so than in infections with siderophore-deficient strains. Crucially, while resistance soon evolved against conventional antibiotic treatments, gallium treatments retained their efficacy over time.

CONCLUSIONS

Extracellular quenching of bacterial public goods could offer an effective and evolutionarily robust control strategy.

Dynamics of growth factor production in monolayers of cancer cells and evolution of resistance to anticancer therapies

Tumor heterogeneity is well documented for many characters, including the production of growth factors, which improve tumor proliferation and promote resistance against apoptosis and against immune reaction. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment. While it has been suggested that therapies targeting growth factors are robust against evolved resistance, current therapies against growth factors, like antiangiogenic drugs, are not effective in the long term, as resistant mutants can evolve and lead to relapse. We use evolutionary game theory to study the dynamics of the production of growth factors by monolayers of cancer cells and to understand the effect of therapies that target growth factors. The dynamics depend on the production cost of the growth factor, on its diffusion range and on the type of benefit it confers to the cells. Stable heterogeneity is a typical outcome of the dynamics, while a pure equilibrium of nonproducer cells is possible under certain conditions. Such pure equilibrium can be the goal of new anticancer therapies. We show that current therapies, instead, can be effective only if growth factors are almost completely eliminated and if the reduction is almost immediate.

Evolutionary game theory of growth factor production: implications for tumour heterogeneity and resistance to therapies

Background:

Tumour heterogeneity is documented for many characters, including the production of growth factors, one of the hallmarks of cancer. What maintains heterogeneity remains an open question that has implications for diagnosis and treatment, as drugs that target growth factors are susceptible to the evolution of resistance.

Methods:

I use evolutionary game theory to model collective interactions between cancer cells, to analyse the dynamics of the production of growth factors and the effect of therapies that reduce their amount.

Results:

Five types of dynamics are possible, including the coexistence of producer and non-producer cells, depending on the production cost of the growth factor, on its diffusion range and on the degree of synergy of the benefit it confers to the cells. Perturbations of the equilibrium mimicking therapies that target growth factors are effective in reducing the amount of growth factor in the long term only if the reduction is extremely efficient and immediate.

Conclusion:

Collective interactions within the tumour can maintain heterogeneity for the production of growth factors and explain why therapies like anti-angiogenic drugs and RNA interference that reduce the amount of available growth factors are effective in the short term but often lead to relapse. Alternative strategies for evolutionarily stable treatments are discussed.

Evolutionarily stable anti-cancer therapies by autologous cell defection

Game theory suggests an anti-cancer treatment based on the use of modified cancer cells that disrupt cooperation within the tumor. Cancer cells are harvested from the patient, the genes for the production of essential growth factors are knocked out in vitro and the cells are then reinserted in the tumor, where they lead to its collapse.

Game theory suggests an anti-cancer treatment based on the use of modified cancer cells that disrupt cooperation within the tumor. Cancer cells are harvested from the patient, the genes for the production of essential growth factors are knocked out in vitro and the cells are then reinserted in the tumor, where they lead to its collapse.

Background and objectives: Current anti-cancer drugs and treatments based on gene therapy are prone to the evolution of resistance, because cancer is a process of clonal selection: resistant cell lines have a selective advantage and therefore increase in frequency, eventually conferring resistance to the whole tumor and leading to relapse. An effective treatment must be evolutionarily stable, that is, immune to the invasion of resistant mutant cells. This study shows how such a treatment can be achieved by autologous cell therapy using modified cancer cells, knocked out for genes coding for diffusible factors like growth factors.

Methodology: The evolutionary dynamics of a population of cells producing diffusible factors are analyzed using a nonlinear public goods game in a structured population in which the interaction neighborhood and the update neighborhood are decoupled. The analysis of the dynamics of the system reveals what interventions can drive the population to a stable equilibrium in which no diffusible factors are produced.

Results: A treatment based on autologous knockout cell therapy can be designed to lead to the spontaneous collapse of a tumor, without targeting directly the cancer cells, their growth factors or their receptors. Critical parameters that can make the therapy effective are identified. Concepts from evolutionary game theory and mechanism design, some of which are counterintuitive, can be adopted to optimize the treatment.

Conclusions and implications: Although it shares similarities with other approaches based on gene therapy and RNA interference, the method suggested here is evolutionarily stable under certain conditions. This method, named autologous cell defection, can be carried out using existing molecular biology and cell therapy techniques.

Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity

A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework—a dynamic knowledge repository—wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline. © 2013 Wiley Periodicals, Inc.

Evolutionary foundations for cancer biology

New applications of evolutionary biology are transforming our understanding of cancer. The articles in this special issue provide many specific examples, such as microorganisms inducing cancers, the significance of within-tumor heterogeneity, and the possibility that lower dose chemotherapy may sometimes promote longer survival. Underlying these specific advances is a large-scale transformation, as cancer research incorporates evolutionary methods into its toolkit, and asks new evolutionary questions about why we are vulnerable to cancer. Evolution explains why cancer exists at all, how neoplasms grow, why cancer is remarkably rare, and why it occurs despite powerful cancer suppression mechanisms. Cancer exists because of somatic selection; mutations in somatic cells result in some dividing faster than others, in some cases generating neoplasms. Neoplasms grow, or do not, in complex cellular ecosystems. Cancer is relatively rare because of natural selection; our genomes were derived disproportionally from individuals with effective mechanisms for suppressing cancer. Cancer occurs nonetheless for the same six evolutionary reasons that explain why we remain vulnerable to other diseases. These four principles-cancers evolve by somatic selection, neoplasms grow in complex ecosystems, natural selection has shaped powerful cancer defenses, and the limitations of those defenses have evolutionary explanations-provide a foundation for understanding, preventing, and treating cancer.