Overlooking evolution: a systematic analysis of cancer relapse and therapeutic resistance research

Impact of Chemotherapeutic Stress Depends on The Nature of Breast Cancer Spheroid and Induce Behavioral Plasticity to Resistant Population

Cellular or tumor dormancy, identified recently as one of the main reasons behind post-therapy recurrence, can be caused by diverse reasons. Chemotherapy has recently been recognized as one of such reasons. However, in-depth studies of chemotherapy-induced dormancy are lacking due to the absence of an in vitro human-relevant model tailor-made for such a scenario. This report utilized multicellular breast cancer spheroid to create a primary platform for establishing a chemotherapy-induced dormancy model. It is observed that extreme chemotherapeutic stress affects invasive and non-invasive spheroids differently. Non-invasive spheroids exhibit more resilience and maintain viability and migrational ability, while invasive spheroids display heightened susceptibility and improved tumorigenic capacity. Heterogenous spheroids exhibit increased tumorigenic capacity while show minimal survival ability. Further probing of chemotherapeutically dormant spheroids is needed to understand the molecular mechanism and identify dormancy-related markers to achieve therapeutic success in the future.

Adaptive therapy: a tumor therapy strategy based on Darwinian evolution theory

Cancer progression is a dynamic process of continuous evolution, in which genetic diversity and heterogeneity are generated by clonal and subclonal amplification based on random mutations. Traditional cancer treatment strategies have a great challenge, which often leads to treatment failure due to drug resistance. Integrating evolutionary dynamics into treatment regimens may be an effective way to overcome the problem of drug resistance. In particular, a potential treatment is adaptive therapy, which strategy advocates containment strategies that adjust the treatment cycles according to tumor evolution to control the growth of treatment-resistant cells. In this review, we first summarize the shortcomings of traditional tumor treatment methods in evolution and then introduce the theoretical basis and research status of adaptive therapy. By analyzing the limitations of adaptive therapy and exploring possible solutions, we can broaden people's understanding of adaptive therapy and provide new insights and strategies for tumor treatment.

Spatial simulation of autologous cell defection for cancer treatment

Cancer cells are highly cooperative in a nepotistic way and evolutionarily dynamic. Present cancer treatments often overlook these aspects, inducing the selection of resistant cancer cells and the corresponding relapse. As an alternative method of cancer elimination, autologous cell defection (ACD) was suggested by which modified cancer cells parasitically reliant on other cancer cells are implemented to the cancer cluster. Specifically, modified cancer cells should not produce costly growth factors that promote the growth of other cancer cells while receiving the benefit of exposure to such growth factors. Analytical models and rudimentary experiments up to date provide the medical feasibility of this method. In this study, I built comprehensive spatial simulation models by embracing the effects of the multiple growth factors, the Warburg effect, mutations and immunity. The simulation results based on planar spatial structures indicate that implementation of the defective modified tumours may replace the existing cancer cluster and defective cells would later collapse by themselves. Furthermore, I built a mathematical model that compares the fitness of the cells adjacent to the hypertumour-cancer interface. I also calculated whether anticancer drugs that reduce the effects of the growth factors promote or demote the utility of ACD under diverse fitness functions. The computational examination implies that anticancer drugs may impede the therapeutic effect of ACD when there is a strong concavity in the fitness function. The analysis results could work as a general guidance for effective ACD that may expand the paradigm of cancer treatment.

Treatment of evolving cancers will require dynamic decision support

Cancer research has traditionally focused on developing new agents, but an underexplored question is that of the dose and frequency of existing drugs. Based on the modus operandi established in the early days of chemotherapies, most drugs are administered according to predetermined schedules that seek to deliver the maximum tolerated dose and are only adjusted for toxicity. However, we believe that the complex, evolving nature of cancer requires a more dynamic and personalized approach. Chronicling the milestones of the field, we show that the impact of schedule choice crucially depends on processes driving treatment response and failure. As such, cancer heterogeneity and evolution dictate that a one-size-fits-all solution is unlikely-instead, each patient should be mapped to the strategy that best matches their current disease characteristics and treatment objectives (i.e. their 'tumorscape'). To achieve this level of personalization, we need mathematical modeling. In this perspective, we propose a five-step 'Adaptive Dosing Adjusted for Personalized Tumorscapes (ADAPT)' paradigm to integrate data and understanding across scales and derive dynamic and personalized schedules. We conclude with promising examples of model-guided schedule personalization and a call to action to address key outstanding challenges surrounding data collection, model development, and integration.

Stratification of non-small cell lung adenocarcinoma patients with EGFR actionable mutations based on drug-resistant stem cell genes

EGFR-TKIs were used in NSCLC patients with actionable EGFR mutations and prolong prognosis. However, most patients treated with EGFR-TKIs developed resistance within around one year. This suggests that residual EGFR-TKIs resistant cells may eventually lead to relapse. Predicting resistance risk in patients will facilitate individualized management. Herein, we built an EGFR-TKIs resistance prediction (R-index) model and validate in cell line, mice, and cohort. We found significantly higher R-index value in resistant cell lines, mice models and relapsed patients. Patients with an elevated R-index had significantly shorter relapse time. We also found that the glycolysis pathway and the KRAS upregulation pathway were related to EGFR-TKIs resistance. MDSC is a significant immunosuppression factor in the resistant microenvironment. Our model provides an executable method for assessing patient resistance status based on transcriptional reprogramming and may contribute to the clinical translation of patient individual management and the study of unclear resistance mechanisms.

The Multi-Faceted Consequences of NRF2 Activation throughout Carcinogenesis

The oxidative balance of a cell is maintained by the Kelch-like ECH-associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (NRF2) pathway. This cytoprotective pathway detoxifies reactive oxygen species and xenobiotics. The role of the KEAP1/NRF2 pathway as pro-tumorigenic or anti-tumorigenic throughout stages of carcinogenesis (including initiation, promotion, progression, and metastasis) is complex. This mini review focuses on key studies describing how the KEAP1/NRF2 pathway affects cancer at different phases. The data compiled suggest that the roles of KEAP1/NRF2 in cancer are highly dependent on context; specifically, the model used (carcinogen-induced vs genetic), the tumor type, and the stage of cancer. Moreover, emerging data suggests that KEAP1/NRF2 is also important for regulating the tumor microenvironment and how its effects are amplified either by epigenetics or in response to co-occurring mutations. Further elucidation of the complexity of this pathway is needed in order to develop novel pharmacological tools and drugs to improve patient outcomes.

Modeling cancer's ecological and evolutionary dynamics

In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.

Accurate Identification of Subclones in Tumor Genomes

Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Find the path of least resistance: Adaptive therapy to delay treatment failure and improve outcomes

Cytotoxic chemotherapy and targeted therapies help people with advanced cancers, but for most, treatment fails. Cancer heterogeneity is one cause of treatment failure, but also suggests an opportunity to improve outcomes; reconceptualising cancer therapy as an ecological problem offers the strategy of adaptive therapy. If an agent is active against a patient's cancer, instead of traditional continuous dosing at the maximum tolerated dose until treatment failure, the patient and their oncologist may instead choose to pause treatment as soon as the cancer responds. When tumour burden increases, the cancer is rechallenged with the same agent in hope of delivering another response, ideally before symptoms occur or quality-of-life is impacted. These 'loops' of 'pause/restart' allows an active treatment to be used strategically, to delay the development of evolutionary selection within the cancer, delaying the onset of treatment resistance, controlling the cancer for longer. Modelling predicts patients can navigate several 'loops', potentially increasing the utility of an active treatment by multiples, and early trials suggest at least doubling of progression-free survival. In this narrative review we confront how cancer heterogeneity limits treatment effectiveness, re-examine cancer as an ecological problem, review the data supporting adaptive therapy and outline the challenges and opportunities faced in clinical practice to implement this evolutionary concept. In an era where multiple novel active anti-neoplastic agents are being used with ancient inflexibile maximum tolerated dose for maximum duration approaches, adaptive dosing offers a personalised, n = 1 approach to cancer therapy selection.

Resistance Mechanisms Influencing Oncolytic Virotherapy, a Systematic Analysis

Resistance to therapy is a frequently observed phenomenon in the treatment of cancer, and as with other cancer therapeutics, therapies based on oncolytic viruses also face the challenges of resistance, such as humoral and cellular antiviral responses, and tumor-associated interferon-mediated resistance. In order to identify additional mechanisms of resistance that may contribute to therapeutic failure, we developed a systematic search strategy for studies published in PubMed. We analyzed 6143 articles on oncolytic virotherapy and found that approximately 8% of these articles use resistance terms in the abstract and/or title. Of these 439 articles, 87 were original research. Most of the findings reported pertain to resistance mediated by tumor-cell-dependent interferon signaling. Yet, mechanisms such as epigenetic modifications, hypoxia-mediated inhibition, APOBEC-mediated resistance, virus entry barriers, and spatiotemporal restriction to viral spread, although not frequently assessed, were demonstrated to play a major role in resistance. Similarly, our results suggest that the stromal compartment consisting of, but not limited to, myeloid cells, fibroblasts, and epithelial cells requires more study in relation to therapy resistance using oncolytic viruses. Thus, our findings emphasize the need to assess the stromal compartment and to identify novel mechanisms that play an important role in conferring resistance to oncolytic virotherapy.

Evolutionary Dynamics of Treatment-Induced Resistance in Cancer Informs Understanding of Rapid Evolution in Natural Systems

Rapid evolution is ubiquitous in nature. We briefly review some of this quite broadly, particularly in the context of response to anthropogenic disturbances. Nowhere is this more evident, replicated and accessible to study than in cancer. Curiously cancer has been late - relative to fisheries, antibiotic resistance, pest management and evolution in human dominated landscapes - in recognizing the need for evolutionarily informed management strategies. The speed of evolution matters. Here, we employ game-theoretic modeling to compare time to progression with continuous maximum tolerable dose to that of adaptive therapy where treatment is discontinued when the population of cancer cells gets below half of its initial size and re-administered when the cancer cells recover, forming cycles with and without treatment. We show that the success of adaptive therapy relative to continuous maximum tolerable dose therapy is much higher if the population of cancer cells is defined by two cell types (sensitive vs. resistant in a polymorphic population). Additionally, the relative increase in time to progression increases with the speed of evolution. These results hold with and without a cost of resistance in cancer cells. On the other hand, treatment-induced resistance can be modeled as a quantitative trait in a monomorphic population of cancer cells. In that case, when evolution is rapid, there is no advantage to adaptive therapy. Initial responses to therapy are blunted by the cancer cells evolving too quickly. Our study emphasizes how cancer provides a unique system for studying rapid evolutionary changes within tumor ecosystems in response to human interventions; and allows us to contrast and compare this system to other human managed or dominated systems in nature.

Recent advances in, and challenges of, anti-angiogenesis agents for tumor chemotherapy based on vascular normalization

A major problem associated with cancer treatment is resistance-prone chemotherapeutic drugs. An increasing number of studies have documented that the occurrence of resistance tends to be associated with abnormal blood vessels. In 2001, Jain proposed the vascular normalization theory, which was recently applied to the drug-resistant treatment of tumors in the clinic. Through the intervention of angiogenesis inhibitors, remodeling the structure and function of abnormal vessels can maximize the efficacy of chemotherapeutic drugs. In this review, we systematically describe the occurrence and progress of tumor angiogenesis, as well as the pathological characteristics of tumor blood vessels. Moreover, druggable targets for vascular normalization and the development of related inhibitors are also outlined.

Collapse of Intra-Tumor Cooperation Induced by Engineered Defector Cells

Anti-cancer therapies promote clonal selection of resistant cells that evade treatment. Effective therapy must be stable against the evolution of resistance. A potential strategy based on concepts from evolutionary game theory is to impair intra-tumor cooperation using genetically modified cells in which genes coding for essential growth factors have been knocked out. Such engineered cells would spread by clonal selection, driving the collapse of intra-tumor cooperation and a consequent reduction in tumor growth. Here, I test this idea in vitro in four cancer types (neuroendocrine pancreatic cancer, mesothelioma, lung adenocarcinoma and multiple myeloma). A reduction, or even complete eradication, of the producer clone and the consequent reduction in cell proliferation, is achieved in some but not all cases by introducing a small fraction of non-producer cells in the population. I show that the collapse of intra-tumor cooperation depends on the cost/benefit ratio of growth factor production. When stable cooperation among producer and non-producer cells occurs, its collapse can be induced by increasing the number of growth factors available to the cells. Considerations on nonlinear dynamics in the framework of evolutionary game theory explain this as the result of perturbation of the equilibrium of a system that resembles a public goods game, in which the production of growth factors is a cooperative phenotype. Inducing collapse of intra-tumor cooperation by engineering cancer cells will require the identification of growth factors that are essential for the tumor and that have a high cost of production for the cell.

Treatment-induced evolutionary dynamics in nonmetastatic locally advanced rectal adenocarcinoma

Multi-modal treatment of non-metastatic locally advanced rectal adenocarcinoma (LARC) includes chemotherapy, radiation, and life-altering surgery. Although highly effective for local cancer control, metastatic failure remains significant and drives rectal cancer-related mortality. A consistent observation of this tri-modality treatment paradigm is that histologic response of the primary tumor to neoadjuvant treatment(s), which varies across patients, predicts overall oncologic outcome. In this chapter, we will examine this treatment response heterogeneity in the context of evolutionary dynamics. We hypothesize that improved understanding of eco-evolutionary pressures rendering small cancer cell populations vulnerable to extinction may influence treatment strategies and improve patient outcomes. Applying effective treatment(s) to cancer populations causes a "race to extinction." We explore principles of eco-evolutionary extinction in the context of these small cancer cell populations, evaluating how treatment(s) aim to eradicate the cancer populations to ultimately result in cure. In this chapter, we provide an evolutionary rationale for limiting continuous treatment(s) with the same agent or combination of agents to avoid selection of resistant cancer subpopulation phenotypes, allowing "evolutionary rescue." We draw upon evidence from nature demonstrating species extinction rarely occurring as a single event phenomenon, but rather a series of events in the slide to extinction. We posit that eradicating small cancer populations, similar to small populations in natural extinctions, will usually require a sequence of different external perturbations that produce negative, synergistic dynamics termed the "extinction vortex." By exploiting these unique extinction vulnerabilities of small cancer populations, the optimal therapeutic sequences may be informed by evolution-informed strategies for patients with LARC.

The Evolution and Ecology of Resistance in Cancer Therapy

Despite the continuous deployment of new treatment strategies and agents over many decades, most disseminated cancers remain fatal. Cancer cells, through their access to the vast information of the human genome, have a remarkable capacity to deploy adaptive strategies for even the most effective treatments. We note there are two critical steps in the clinical manifestation of treatment resistance. The first, which is widely investigated, requires molecular machinery necessary to eliminate the cytotoxic effect of the treatment. However, the emergence of a resistant phenotype is not in itself clinically significant. That is, resistant cells affect patient outcomes only when they succeed in the second step of resistance by proliferating into a sufficiently large population to allow tumor progression and treatment failure. Importantly, proliferation of the resistant phenotype is by no means certain and, in fact, depends on complex Darwinian dynamics governed by the costs and benefits of the resistance mechanisms in the context of the local environment and competing populations. Attempts to target the molecular machinery of resistance have had little clinical success largely because of the diversity within the human genome-therapeutic interruption of one mechanism simply results in its replacement by an alternative. Here we explore evolutionarily informed strategies (adaptive, double-bind, and extinction therapies) for overcoming treatment resistance that seek to understand and exploit the critical evolutionary dynamics that govern proliferation of the resistant phenotypes. In general, this approach has demonstrated that, while emergence of resistance mechanisms in cancer cells to every current therapy is inevitable, proliferation of the resistant phenotypes is not and can be delayed and even prevented with sufficient understanding of the underlying eco-evolutionary dynamics.

Integrating evolutionary dynamics into cancer therapy

Many effective drugs for metastatic and/or advanced-stage cancers have been developed over the past decade, although the evolution of resistance remains the major barrier to disease control or cure. In large, diverse populations such as the cells that compose metastatic cancers, the emergence of cells that are resistant or that can quickly develop resistance is virtually inevitable and most likely cannot be prevented. However, clinically significant resistance occurs only when the pre-existing resistant phenotypes are able to proliferate extensively, a process governed by eco-evolutionary dynamics. Attempts to disrupt the molecular mechanisms of resistance have generally been unsuccessful in clinical practice. In this Review, we focus on the Darwinian processes driving the eco-evolutionary dynamics of treatment-resistant cancer populations. We describe a variety of evolutionarily informed strategies designed to increase the probability of disease control or cure by anticipating and steering the evolutionary dynamics of acquired resistance.

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Exploiting Cancer Cells Metabolic Adaptability to Enhance Therapy Response in Cancer

Despite all the progresses developed in prevention and new treatment approaches, cancer is the second leading cause of death worldwide, being chemoresistance a pivotal barrier in cancer management. Cancer cells present several mechanisms of drug resistance/tolerance and recently, growing evidence have been supporting a role of metabolism reprograming per se as a driver of chemoresistance. In fact, cancer cells display several adaptive mechanisms that allow the emergency of chemoresistance, revealing cancer as a disease that adapts and evolve along with the treatment. Therefore, clinical protocols that take into account the adaptive potential of cancer cells should be more effective than the current traditional standard protocols on the fighting against cancer.In here, some of the recent findings on the role of metabolism reprograming in cancer chemoresistance emergence will be discussed, as the potential evolutionary strategies that could unable these adaptations, hence allowing to prevent the emergency of treatment resistance, changing cancer outcome.

Tumor Microenvironment - Selective Pressures Boosting Cancer Progression

In 2018, 9.6 million deaths from cancer were estimated, being this disease the second leading cause of death worldwide. Notwithstanding all the efforts developed in prevention, diagnosis and new treatment approaches, chemoresistance seems to be inevitable, leading to cancer progression, recurrence and affecting the outcome of the disease. As more and more evidence support that cancer is an evolutionary and ecological process, this concept is rarely applied in the clinical context. In fact, cancer cells emerge and progress within an ecological niche - the tumor microenvironment - that is shared with several other cell types and that is continuously changing. Therefore, the tumor microenvironment imposes several selective pressures on cancer cells such as acidosis, hypoxia, competition for space and resources, immune predation and anti-cancer therapies, that cancer cells must be able to adapt to or will face extinction.In here, the role of the tumor microenvironment selective pressures on cancer progression will be discussed, as well as the targeting of its features/components as strategies to fight cancer.

Somatic mutations - Evolution within the individual

With the rapid advancement of sequencing technologies over the last two decades, it is becoming feasible to detect rare variants from somatic tissue samples. Studying such somatic mutations can provide deep insights into various senescence-related diseases, including cancer, inflammation, and sporadic psychiatric disorders. While it is still a difficult task to identify true somatic mutations, relentless efforts to combine experimental and computational methods have made it possible to obtain reliable data. Furthermore, state-of-the-art machine learning approaches have drastically improved the efficiency and sensitivity of these methods. Meanwhile, we can regard somatic mutations as a counterpart of germline mutations, and it is possible to apply well-formulated mathematical frameworks developed for population genetics and molecular evolution to analyze this 'somatic evolution'. For example, retrospective cell lineage tracing is a promising technique to elucidate the mechanism of pre-diseases using single-cell RNA-sequencing (scRNA-seq) data.

Transcriptomic analysis associated with reversal of cisplatin sensitivity in drug resistant osteosarcoma cells after a drug holiday

Background

Resistance to chemotherapy is one of the major hurdles in current cancer therapy. With the increasing occurrence of drug resistance, a paradigm shift in treatment strategy is required. Recently “medication vacation” has emerged as a unique, yet uncomplicated strategy in which withdrawal of drug pressure for certain duration allowed tumor cells to regain sensitivity to the drug. However, little is known about the molecular alterations associated with such an outcome.

Methods

In this study, human osteosarcoma (OS) cells resistant to the extensively used drug cisplatin, were withdrawn from drug pressure, and thereafter cytotoxic response of the cells to the drug was evaluated. We further performed next-generation RNA sequencing and compared transcriptome between parental (OS), resistant (OS-R) and the drug withdrawn (OS-DW) cells. Differentially expressed transcripts were identified, and biological association network (BAN), gene ontology (GO) and pathway enrichment analysis of the differentially regulated transcripts were performed to identify key events associated with withdrawal of drug pressure.

Results

Following drug withdrawal, the sensitivity of the cells to the drug was found to be regained. Analysis of the expression profile showed that key genes like, IRAK3, IL6ST, RELA, AKT1, FKBP1A and ADIPOQ went significantly down in OS-DW cells when compared to OS-R. Also, genes involved in Wnt signaling, PI3K-Akt, Notch signaling, and ABC transporters were drastically down-regulated in OS-DW cells compared to OS-R. Although, a very small subset of genes maintained similar expression pattern between OS, OS-R and OS-DW, nonetheless majority of the transcriptomic pattern of OS-DW was distinctively different and unique in comparison to either the drug sensitive OS or drug resistant OS-R cells.

Conclusion

Our data suggests that though drug withdrawal causes reversal of sensitivity, the transcriptomic pattern does not necessarily show significant match with resistant or parental control cells. We strongly believe that exploration of the molecular basis of drug holiday might facilitate additional potential alternative treatment options for aggressive and resistant cancers.

Multidrug Cancer Therapy in Metastatic Castrate-Resistant Prostate Cancer: An Evolution-Based Strategy

PURPOSE

Integration of evolutionary dynamics into systemic therapy for metastatic cancers can prolong tumor control compared with standard maximum tolerated dose (MTD) strategies. Prior investigations have focused on monotherapy, but many clinical cancer treatments combine two or more drugs. Optimizing the evolutionary dynamics in multidrug therapy is challenging because of the complex cellular interactions and the large parameter space of potential variations in drugs, doses, and treatment schedules. However, multidrug therapy also represents an opportunity to further improve outcomes using evolution-based strategies.

EXPERIMENTAL DESIGN

We examine evolution-based strategies for two-drug therapy and identify an approach that divides the treatment drugs into primary and secondary roles. The primary drug has the greatest efficacy and/or lowest toxicity. The secondary drug is applied solely to reduce the resistant population to the primary drug.

RESULTS

Simulations from the mathematical model demonstrate that the primary-secondary approach increases time to progression (TTP) compared with conventional strategies in which drugs are administered without regard to evolutionary dynamics. We apply our model to an ongoing adaptive therapy clinical trial of evolution-based administration of abiraterone to treat metastatic castrate-resistant prostate cancer. Model simulations, parameterized with data from individual patients who progressed, demonstrate that strategic application of docetaxel during abiraterone therapy would have significantly increased their TTP.

CONCLUSIONS

Mathematical models can integrate evolutionary dynamics into multidrug cancer clinical trials. This has the potential to improve outcomes and to develop clinical trials in which these mathematical models are also used to estimate the mechanism(s) of treatment failure and explore alternative strategies to improve outcomes in future trials.

Microfluidic chip combined with magnetic-activated cell sorting technology for tumor antigen-independent sorting of circulating hepatocellular carcinoma cells

Purpose

We aimed to generate a capture platform that integrates a deterministic lateral displacement (DLD) microfluidic structure with magnetic-activated cell sorting (MACS) technology for miniaturized, efficient, tumor antigen-independent circulating tumor cell (CTC) separation.

Methods

The microfluidic structure was based on the theory of DLD and was designed to remove most red blood cells and platelets. Whole Blood CD45 MicroBeads and a MACS separator were then used to remove bead-labeled white blood cells. We established HepG2 human liver cancer cells overexpressing green fluorescent protein by lentiviral transfection to simulate CTCs in blood, and these cells were then used to determine the CTC isolation efficiency of the device. The performance and clinical value of our platform were evaluated by comparison with the Abnova CytoQuest™ CR system in the separating of blood samples from 12 hepatocellular carcinoma patients undergoing liver transplantation in a clinical follow-up experiment. The isolated cells were stained and analyzed by confocal laser scanning microscopy.

Results

Using our integrated platform at the optimal flow rates for the specimen (60 µl/min) and buffer (100 µl/min per chip), we achieved an CTC yield of 85.1% ± 3.2%. In our follow-up of metastatic patients, CTCs that underwent epithelial–mesenchymal transition were found. These CTCs were missed by the CytoQuest™ CR bulk sorting approach, whereas our platform displayed increased sensitivity to EpCAM^low CTCs.

Conclusions

Our platform, which integrates microfluidic and MACS technology, is an attractive method for high-efficiency CTC isolation regardless of surface epitopes.

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.

Nonlinear adaptive control of competitive release and chemotherapeutic resistance

We use a three-component replicator system with healthy cells, sensitive cells, and resistant cells, with a prisoner's dilemma payoff matrix from evolutionary game theory, to model and control the nonlinear dynamical system governing the ecological mechanism of competitive release by which tumors develop chemotherapeutic resistance. The control method we describe is based on nonlinear trajectory design and energy transfer methods first introduced in the orbital mechanics literature for Hamiltonian systems. For continuous therapy, the basin boundaries of attraction associated with the chemo-sensitive population and the chemo-resistant population for increasing values of chemo-concentrations have an intertwined spiral structure with extreme sensitivity to changes in chemo-concentration level as well as sensitivity with respect to resistant mutations. For time-dependent therapies, we introduce an orbit transfer method to construct continuous families of periodic (closed) orbits by switching the chemo-dose at carefully chosen times and appropriate levels to design schedules that are superior to both maximum tolerated dose (MTD) schedules and low-dose metronomic (LDM) schedules, both of which ultimately lead to fixation of sensitive cells or resistant cells. By keeping the three subpopulations of cells in competition with each other indefinitely, we avoid fixation of the cancer cell population and regrowth of a resistant tumor. The method can be viewed as a way to dynamically shape the average population fitness landscape of a tumor to steer the chemotherapeutic response curve. We show that the method is remarkably insensitive to initial conditions and small changes in chemo-dosages, an important criterion for turning the method into an actionable strategy.

Capitalizing on competition: An evolutionary model of competitive release in metastatic castration resistant prostate cancer treatment

The development of chemotherapeutic resistance resulting in tumor relapse is largely the consequence of the mechanism of competitive release of pre-existing resistant tumor cells selected for regrowth after chemotherapeutic agents attack the previously dominant chemo-sensitive population. We introduce a prisoner's dilemma game theoretic mathematical model based on the replicator of three competing cell populations: healthy (cooperators), sensitive (defectors), and resistant (defectors) cells. The model is shown to recapitulate prostate-specific antigen measurement data from three clinical trials for metastatic castration-resistant prostate cancer patients treated with 1) prednisone, 2) mitoxantrone and prednisone and 3) docetaxel and prednisone. Continuous maximum tolerated dose schedules reduce the sensitive cell population, initially shrinking tumor burden, but subsequently "release" the resistant cells from competition to re-populate and re-grow the tumor in a resistant form. The evolutionary model allows us to quantify responses to conventional (continuous) therapeutic strategies as well as to design adaptive strategies.These novel adaptive strategies are robust to small perturbations in timing and extend simulated time to relapse from continuous therapy administration.

Short- and long-term evolution in our arms race with cancer: Why the war on cancer is winnable

Human society is engaged in an arms race against cancer, which pits one evolutionary process-human cultural evolution as we develop novel cancer therapies-against another evolutionary process-the ability of oncogenic selection operating among cancer cells to select for lineages that are resistant to our therapies. Cancer cells have a powerful ability to evolve resistance over the short term, leading to patient relapse following an initial period of apparent treatment efficacy. However, we are the beneficiaries of a fundamental asymmetry in our arms race against cancer: Whereas our cultural evolution is a long-term and continuous process, resistance evolution in cancer cells operates only over the short term and is discontinuous - all resistance adaptations are lost each time a cancer patient dies. Thus, our cultural adaptations are permanent, whereas cancer's genetic adaptations are ephemeral. Consequently, over the long term, there is good reason to expect that we will emerge as the winners in our war against cancer.

Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer

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.

Cancer and intercellular cooperation

The major transitions approach in evolutionary biology has shown that the intercellular cooperation that characterizes multicellular organisms would never have emerged without some kind of multilevel selection. Relying on this view, the Evolutionary Somatic view of cancer considers cancer as a breakdown of intercellular cooperation and as a loss of the balance between selection processes that take place at different levels of organization (particularly single cell and individual organism). This seems an elegant unifying framework for healthy organism, carcinogenesis, tumour proliferation, metastasis and other phenomena such as ageing. However, the gene-centric version of Darwinian evolution, which is often adopted in cancer research, runs into empirical problems: proto-tumoural and tumoural features in precancerous cells that would undergo 'natural selection' have proved hard to demonstrate; cells are radically context-dependent, and some stages of cancer are poorly related to genetic change. Recent perspectives propose that breakdown of intercellular cooperation could depend on 'fields' and other higher-level phenomena, and could be even mutations independent. Indeed, the field would be the context, allowing (or preventing) genetic mutations to undergo an intra-organism process analogous to natural selection. The complexities surrounding somatic evolution call for integration between multiple incomplete frameworks for interpreting intercellular cooperation and its pathologies.

A systematic review of differential rate of use of the word “evolve” across fields

Background

Although evolution is the driving force behind many of today’s major public health and agriculture issues, both journalists and scientific researchers often do not use the term “evolve” in discussions of these topics.

Methods

In a total of 1,066 articles and 716 papers selected from 25 US newspapers and 34 scientific journals, we assess usage of the word “evolve” and its substitute words in the contexts of cancer tumor drug resistance, HIV drug resistance, mosquito insecticide resistance, and weed pesticide resistance.

Results

We find significant differences in the use of “evolve” among fields and sources. “Evolve” is used most when discussing weed pesticide resistance (25.9% in newspapers, 52.4% in journals) and least when discussing cancer tumor drug resistance (3.9% in newspapers, 9.8% in journals). On average, scientific journals use “evolve” more often (22.2%) than newspapers (7.8%). Different types of journals (general science, general clinical, cancer specific, and drug resistance specific) show significantly different “evolve” usages when discussing cancer tumor drug resistance.

Discussion

We examine potential explanations of these findings, such as the relatively recent framing of cancer in evolutionary terms, before looking at consequences of low “evolve” usage and of differential “evolve” usage across fields. Use of the word “evolve” may not reflect current understanding of the problems we examine. However, given that our ability to tackle resistance issues relies upon accurate understandings of what causes and exacerbates resistance, use of the word “evolve” when called for may help us confront these issues in the future.

How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient

When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence-and when it can be bettered.

Aggregation Effects and Population-Based Dynamics as a Source of Therapy Resistance in Cancer

OBJECTIVE

Evolution of resistance allows cancer cells to adapt and continue proliferating even when therapy is initially very effective. Most investigations of treatment resistance focus on the adaptive phenotypic properties of individual cells. We propose that the resistance of a single cell to therapy may extend beyond its own phenotypic and molecular properties and be influenced by the phenotypic properties of surrounding cells and variations in cell density. Similar variation exists in population densities of animals living in groups and can significantly affect the outcome of an external threat.

METHODS

We investigate aggregation effects in cancer therapy using Darwinian models that integrate phenotypic properties of individual cells and common population effects found in nature to simulate the dynamics of resistance and sensitivity in the diverse cellular environments within cancers.

RESULTS

We demonstrate that the density of cancer cell populations can profoundly influence response to chemotherapy independent of the properties of individual cells. Most commonly, these aggregation effects benefit the tumor allowing cells to survive even with phenotypic properties that would render them highly vulnerable to therapy in the absence of population effects.

CONCLUSION

We demonstrate aggregation effects likely play a significant role in conferring resistance to therapy on tumor cells that would otherwise be sensitive to treatment.

SIGNIFICANCE

The potential role of aggregation in outcomes from cancer therapy has not been previously investigated. Our results demonstrate these dynamics may play a key role in resistance to therapy and could be used to design evolutionarily-enlightened therapies that exploit aggregation effects to improve treatment outcomes.

Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

BACKGROUND

Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level.

SCOPE OF REVIEW

We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies.

MAJOR CONCLUSIONS

Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies.

GENERAL SIGNIFICANCE

Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Knockdown of TC-1 enhances radiosensitivity of non-small cell lung cancer via the Wnt/β-catenin pathway

Thyroid cancer 1 (TC-1, C8ofr4) is widely expressed in vertebrates and associated with many kinds of tumors. Previous studies indicated that TC-1 functions as a positive regulator in the Wnt/β-catenin signaling pathway in non-small cell lung cancer (NSCLC). However, its exact role and regulation mechanism in radiosensitivity of NSCLC are still unclear. The expression level of TC-1 was measured by qRT-PCR and western blot in NSCLC cell lines. Proliferation and apoptosis of NSCLC cells in response to TC-1 knockdown or/and radiation were determined by MTT assay and flow cytometry, respectively. The activation of the Wnt/β-catenin signaling pathway was further examined by western blot in vitro and in vivo. Compared to TC-1 siRNA or radiotherapy alone, TC-1 silencing combined with radiation inhibited cell proliferation and induced apoptosis in NSCLC cell lines by inactivating of the Wnt/β-catenin signaling pathway. Furthermore, inhibition of the Wnt/β-catenin signaling pathway by XAV939, a Wnt/β-catenin signaling inhibitor, contributed to proliferation inhibition and apoptosis induction in NSCLC A549 cells. Combinative treatment of A549 xenografts with TC-1 siRNA and radiation caused significant tumor regression and inactivation of the Wnt/β-catenin signaling pathway relative to TC-1 siRNA or radiotherapy alone. The results from in vitro and in vivo studies indicated that TC-1 silencing sensitized NSCLC cell lines to radiotherapy through the Wnt/β-catenin signaling pathway.

Summary: TC-1 silencing inhibited cell proliferation and induced apoptosis in non-small cell lung cancer (NSCLC) both in vitro and in vivo through the Wnt/β-catenin signaling pathway, thereby increasing the susceptibility of NSCLC to radiotherapy.

EMT, CTCs and CSCs in tumor relapse and drug-resistance

Tumor relapse and metastasis are the primary causes of poor survival rates in patients with advanced cancer despite successful resection or chemotherapeutic treatment. A primary cause of relapse and metastasis is the persistence of cancer stem cells (CSCs), which are highly resistant to chemotherapy. Although highly efficacious drugs suppressing several subpopulations of CSCs in various tissue-specific cancers are available, recurrence is still common in patients. To find more suitable therapy for relapse, the mechanisms underlying metastasis and drug-resistance associated with relapse-initiating CSCs need to be identified. Recent studies in circulating tumor cells (CTCs) of some cancer patients manifest phenotypes of both CSCs and epithelial-mesenchymal transition (EMT). These patients are unresponsive to standard chemotherapies and have low progression free survival, suggesting that EMT-positive CTCs are related to co-occur with or transform into relapse-initiating CSCs. Furthermore, EMT programming in cancer cells enables in the remodeling of extracellular matrix to break the dormancy of relapse-initiating CSCs. In this review, we extensively discuss the association of the EMT program with CTCs and CSCs to characterize a subpopulation of patients prone to relapses. Identifying the mechanisms by which EMT-transformed CTCs and CSCs initiate relapse could facilitate the development of new or enhanced personalized therapeutic regimens.

Application of Evolutionary Principles to Cancer Therapy

The dynamic cancer ecosystem, with its rich temporal and spatial diversity in environmental conditions and heritable cell phenotypes, is remarkably robust to therapeutic perturbations. Even when response to therapy is clinically complete, adaptive tumor strategies almost inevitably emerge and the tumor returns. Although evolution of resistance remains the proximate cause of death in most cancer patients, a recent analysis found that evolutionary terms were included in less than 1% of articles on the cancer treatment outcomes, and this has not changed in 30 years. Here, we review treatment methods that attempt to understand and exploit intratumoral evolution to prolong response to therapy. In general, we find that treating metastatic (i.e., noncurable) cancers using the traditional strategy aimed at killing the maximum number of tumor cells is evolutionarily unsound because, by eliminating all treatment-sensitive cells, it enables rapid proliferation of resistant populations-a well-known evolutionary phenomenon termed "competitive release." Alternative strategies, such as adaptive therapy, "ersatzdroges," and double-bind treatments, shift focus from eliminating tumor cells to evolution-based methods that suppress growth of resistant populations to maintain long-term control.

Genetic Insights in Barrett's Esophagus and Esophageal Adenocarcinoma

Beginning in the 1980s, an alarming rise in the incidence of esophageal adenocarcinoma (EA) led to screening of patients with reflux to detect Barrett's esophagus (BE) and surveillance of BE to detect early EA. This strategy, based on linear progression disease models, resulted in selective detection of BE that does not progress to EA over a lifetime (overdiagnosis) and missed BE that rapidly progresses to EA (underdiagnosis). Here we review the historical thought processes that resulted in this undesired outcome and the transformation in our understanding of genetic and evolutionary principles governing neoplastic progression that has come from application of modern genomic technologies to cancers and their precursors. This new synthesis provides improved strategies for prevention and early detection of EA by addressing the environmental and mutational processes that can determine "windows of opportunity" in time to detect rapidly progressing BE and distinguish it from slowly or nonprogressing BE.

Evolutionary strategy for systemic therapy of metastatic breast cancer: balancing response with suppression of resistance

Conventional systemic therapy for disseminated breast cancer is based on the general assumption that the greatest patient benefit is achieved by killing the maximum number of tumor cells. While this strategy often achieves a significant reduction in tumor burden, most patients with metastatic breast cancer ultimately die from their disease as therapy fails because tumor cells evolve resistance. We propose that the conventional maximum dose/maximum cell kill cancer therapy, when viewed from an evolutionary vantage, is suboptimal and likely even harmful as it accelerates evolution and growth of the resistant phenotypes that ultimately cause patient death. As an alternative, we are investigating evolutionary therapeutic strategies that shift the treatment goal from killing the maximum number of cancer cells to maximizing patient survival. Here we introduce two novel approaches for systemic therapy for metastatic breast cancer, considering the evolutionary nature of tumor progression; adaptive therapy and double-bind therapy.

Inferring mutational timing and reconstructing tumour evolutionary histories

Cancer evolution can be considered within a Darwinian framework. Both micro and macro-evolutionary theories can be applied to understand tumour progression and treatment failure. Owing to cancers' complexity and heterogeneity the rules of tumour evolution, such as the role of selection, remain incompletely understood. The timing of mutational events during tumour evolution presents diagnostic, prognostic and therapeutic opportunities. Here we review the current sampling and computational approaches for inferring mutational timing and the evidence from next generation sequencing-informed data on mutational timing across all tumour types. We discuss how this knowledge can be used to illuminate the genes and pathways that drive cancer initiation and relapse; and to support drug development and clinical trial design.