Evolutionary biology of cancer

Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

The evolutionary theory of cancer: challenges and potential solutions

The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.

Evolution of transmissible cancers: An adaptive, plastic strategy of selfish genetic elements?

A growing number of studies have applied evolutionary and ecological principles to understanding cancer. However, few such studies have examined whether phenotypic plasticity--the ability of a single individual or genome to respond differently to different environmental circumstances--can impact the origin and spread of cancer. Here, we propose the adaptive horizontal transmission hypothesis to explain how flexible decision-making by selfish genetic elements can cause them to spread from the genome of their original host into the genomes of other hosts through the evolution of transmissible cancers. Specifically, we hypothesize that such cancers appear when the likelihood of successful vertical transmission is sufficiently low relative to the likelihood of successful horizontal transmission. We develop an evolutionary optimization model of this hypothesis, highlight empirical findings that support it, and offer suggestions for future research. Generally, phenotypically plastic selfish genetic elements might play an important role in the evolution of transmissible cancers.

Advancements in Understanding the Hide-and-Seek Strategy of Hibernating Breast Cancer Cells and Their Implications in Oncology from a Broader Perspective: A Comprehensive Overview

Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies.

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.

Pan-cancer surveys indicate cell cycle-related roles of primate-specific genes in tumors and embryonic cerebrum

BACKGROUND

Despite having been extensively studied, it remains largely unclear why humans bear a particularly high risk of cancer. The antagonistic pleiotropy hypothesis predicts that primate-specific genes (PSGs) tend to promote tumorigenesis, while the molecular atavism hypothesis predicts that PSGs involved in tumors may represent recently derived duplicates of unicellular genes. However, these predictions have not been tested.

RESULTS

By taking advantage of pan-cancer genomic data, we find the upregulation of PSGs across 13 cancer types, which is facilitated by copy-number gain and promoter hypomethylation. Meta-analyses indicate that upregulated PSGs (uPSGs) tend to promote tumorigenesis and to play cell cycle-related roles. The cell cycle-related uPSGs predominantly represent derived duplicates of unicellular genes. We prioritize 15 uPSGs and perform an in-depth analysis of one unicellular gene-derived duplicate involved in the cell cycle, DDX11. Genome-wide screening data and knockdown experiments demonstrate that DDX11 is broadly essential across cancer cell lines. Importantly, non-neutral amino acid substitution patterns and increased expression indicate that DDX11 has been under positive selection. Finally, we find that cell cycle-related uPSGs are also preferentially upregulated in the highly proliferative embryonic cerebrum.

CONCLUSIONS

Consistent with the predictions of the atavism and antagonistic pleiotropy hypotheses, primate-specific genes, especially those PSGs derived from cell cycle-related genes that emerged in unicellular ancestors, contribute to the early proliferation of the human cerebrum at the cost of hitchhiking by similarly highly proliferative cancer cells.

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

Identification of Cell Surface Molecules That Determine the Macrophage Activation Threshold Associated With an Early Stage of Malignant Transformation

The ability of immune cells to sense changes associated with malignant transformation as early as possible is likely to be important for the successful outcome of cancer immunosurveillance. In this process, the immune system faces a trade-off between elimination of cells harboring premalignant or malignant changes, and autoimmune pathologies. We hypothesized that the immune system has therefore evolved a threshold for the stage of transformation from normal to fully malignant cells that first provides a threat (danger) signal requiring a response. We co-cultured human macrophages with a unique set of genetically related human cell lines that recapitulate successive stages in breast cancer development: MCF10A (immortalized, normal); MCFNeoT (benign hyperplasia); MCFT1 (atypical hyperplasia); MCFCA1 (invasive cancer). Using cytokines-based assays, we found that macrophages were inert towards MCF10A and MCFNeoT but were strongly activated by MCFT1 and MCFCA1 to produce inflammatory cytokines, placing the threshold for recognition between two premalignant stages, the earlier stage MCFNeoT and the more advanced MCFT1. The cytokine activation threshold paralleled the threshold for enhanced phagocytosis. Using proteomic and transcriptomic approaches, we identified surface molecules, some of which are well-known tumor-associated antigens, that were absent or expressed at low levels in MCF10A and MCFNeoT but turned on or over-expressed in MCFT1 and MCFCA1. Adding antibodies specific for two of these molecules, Annexin-A1 and CEACAM1, inhibited macrophage activation, supporting their role as cancer "danger signals" recognized by macrophages.

Phenotypic plasticity through disposable genetic adaptation in ciliates

Ciliates have an extraordinary genetic system in which each cell harbors two distinct kinds of nucleus, a transcriptionally active somatic nucleus and a quiescent germline nucleus. The latter undergoes classical, heritable genetic adaptation, while adaptation of the somatic nucleus is only short-term and thus disposable. The ecological and evolutionary relevance of this nuclear dimorphism have never been well formalized, which is surprising given the long history of using ciliates such as Tetrahymena and Paramecium as model organisms. We present a novel, alternative explanation for ciliate nuclear dimorphism which, we argue, should be considered an instrument of phenotypic plasticity by somatic selection on the level of the ciliate clone, as if it were a diffuse multicellular organism. This viewpoint helps to put some enigmatic aspects of ciliate biology into perspective and presents the diversity of ciliates as a large natural experiment that we can exploit to study phenotypic plasticity and organismality.

Cancer Community Ecology

Here we advocate Cancer Community Ecology as a valuable focus of study in Cancer Biology. We hypothesize that the heterogeneity and characteristics of cancer cells within tumors should vary systematically in space and time and that cancer cells form local ecological communities within tumors. These communities possess limited numbers of species determined by local conditions, with each species in a community possessing predictable traits that enable them to cope with their particular environment and coexist with each other. We start with a discussion of concepts and assumptions that ecologists use to study closely related species. We then discuss the competitive exclusion principle as a means for knowing when two species should not coexist, and as an opening towards understanding how they can. We present the five major categories of mechanisms of coexistence that operate in nature and suggest that the same mechanisms apply towards understanding the diversification and coexistence of cancer cell species. They are: Food-Safety Tradeoffs, Diet Choice, Habitat Selection, Variance Partitioning, and Competition-Colonization Tradeoffs. For each mechanism, we discuss how it works in nature, how it might work in cancers, and its implications for therapy.

How to Make a Rodent Giant: Genomic Basis and Tradeoffs of Gigantism in the Capybara, the World's Largest Rodent

Gigantism results when one lineage within a clade evolves extremely large body size relative to its small-bodied ancestors, a common phenomenon in animals. Theory predicts that the evolution of giants should be constrained by two tradeoffs. First, because body size is negatively correlated with population size, purifying selection is expected to be less efficient in species of large body size, leading to increased mutational load. Second, gigantism is achieved through generating a higher number of cells along with higher rates of cell proliferation, thus increasing the likelihood of cancer. To explore the genetic basis of gigantism in rodents and uncover genomic signatures of gigantism-related tradeoffs, we assembled a draft genome of the capybara (Hydrochoerus hydrochaeris), the world's largest living rodent. We found that the genome-wide ratio of nonsynonymous to synonymous mutations (ω) is elevated in the capybara relative to other rodents, likely caused by a generation-time effect and consistent with a nearly neutral model of molecular evolution. A genome-wide scan for adaptive protein evolution in the capybara highlighted several genes controlling postnatal bone growth regulation and musculoskeletal development, which are relevant to anatomical and developmental modifications for an increase in overall body size. Capybara-specific gene-family expansions included a putative novel anticancer adaptation that involves T-cell-mediated tumor suppression, offering a potential resolution to the increased cancer risk in this lineage. Our comparative genomic results uncovered the signature of an intragenomic conflict where the evolution of gigantism in the capybara involved selection on genes and pathways that are directly linked to cancer.

The influence of evolutionary history on human health and disease

Nearly all genetic variants that influence disease risk have human-specific origins; however, the systems they influence have ancient roots that often trace back to evolutionary events long before the origin of humans. Here, we review how advances in our understanding of the genetic architectures of diseases, recent human evolution and deep evolutionary history can help explain how and why humans in modern environments become ill. Human populations exhibit differences in the prevalence of many common and rare genetic diseases. These differences are largely the result of the diverse environmental, cultural, demographic and genetic histories of modern human populations. Synthesizing our growing knowledge of evolutionary history with genetic medicine, while accounting for environmental and social factors, will help to achieve the promise of personalized genomics and realize the potential hidden in an individual's DNA sequence to guide clinical decisions. In short, precision medicine is fundamentally evolutionary medicine, and integration of evolutionary perspectives into the clinic will support the realization of its full potential.

Blocking Aerobic Glycolysis by Targeting Pyruvate Dehydrogenase Kinase in Combination with EGFR TKI and Ionizing Radiation Increases Therapeutic Effect in Non-Small Cell Lung Cancer Cells

Simple Summary

Non-small cell lung cancer (NSCLC) patients harboring oncogenic mutations in the epidermal growth factor receptor (EGFR) inevitably develop resistance to targeted EGFR tyrosine kinase inhibitors (TKI) therapy. To support malignant features associated with cancer development and therapy resistance, the cancer cells adapt their metabolic rate and pathways. As an example, aerobic glycolysis, where the cells use glycolysis in the presence of oxygen, is frequently seen. Here we show that targeting aerobic glycolysis represents a promising strategy in cancer therapeutics.

Abstract

Increased glycolytic activity is a hallmark of cancer initiation and progression and is often observed in non-small cell lung cancer (NSCLC). Pyruvate dehydrogenase (PDH) complex acts as a gatekeeper between glycolysis and oxidative phosphorylation, and activation of PDH is known to inhibit glycolytic activity. As part of a standard therapeutic regimen, patients with NSCLC harboring oncogenic mutations in the epidermal growth factor receptor (EGFR) are treated with EGFR tyrosine kinase inhibitors (EGFR TKIs). Independent of good initial response, development of resistance to this therapy is inevitable. In the presented work, we propose that inhibition of glycolysis will add to the therapeutic effects and possibly prevent development of resistance against both EGFR TKIs and ionizing radiation in NSCLC. Analysis of transcriptome data from two independent NSCLC patient cohorts identified increased expression of pyruvate dehydrogenase kinase 1 (PDHK1) as well as upregulated expression of genes involved in glucose metabolism in tumors compared to normal tissue. We established in vitro models of development of resistance to EGFR TKIs to study metabolism and determine if targeting PDHK would prevent development of resistance to EGFR TKIs in NSCLC cells. The PDHK1 inhibitor dichloroacetate (DCA) in combination with EGFR TKIs and/or ionizing radiation was shown to increase the therapeutic effect in our NSCLC cell models. This mechanism was associated with redirected metabolism towards pyruvate oxidation and reduced lactate production, both in EGFR TKI sensitive and resistant NSCLC cells. Using DCA, the intracellular pool of pyruvate available for lactic fermentation becomes limited. Consequently, pyruvate is redirected to the mitochondria, and reinforces mitochondrial activity. Addition of DCA to cell culture deacidifies the extracellular microenvironment as less lactate is produced and excreted. In our study, we find that this redirection of metabolism adds to the therapeutic effect of EGFR TKI and ionizing radiation in NSCLC.

NUDT5 as a novel drug target and prognostic biomarker for ER-positive breast cancer

Breast cancer (BRCA) is the most common malignant tumor in women. The estrogen receptor-positive (ER⁺) subtype accounts for ∼70% of BRCA cases. Estrogen is a crucial hormone that directly stimulates the growth and development of mammary glands. Recent studies revealed that, as an estrogen cofactor, ATP has an important role in determining the action of estrogen by mediating phase separation. NUDT5 has been recognized as a key factor for ATP production in the nucleus of BRCA cells and, therefore, could represent a novel drug target for ER⁺ BRCA. Based on a survival analysis of patients with BRCA documented in The Cancer Genome Atlas (TGCA) database, we show that NUDT5 is also a potential prognostic biomarker for ER⁺ BRCA.

Ecological and Evolutionary Consequences of Anticancer Adaptations

Cellular cheating leading to cancers exists in all branches of multicellular life, favoring the evolution of adaptations to avoid or suppress malignant progression, and/or to alleviate its fitness consequences. Ecologists have until recently largely neglected the importance of cancer cells for animal ecology, presumably because they did not consider either the potential ecological or evolutionary consequences of anticancer adaptations. Here, we review the diverse ways in which the evolution of anticancer adaptations has significantly constrained several aspects of the evolutionary ecology of multicellular organisms at the cell, individual, population, species, and ecosystem levels and suggest some avenues for future research.

High-metastatic cancer cells derived exosomal miR92a-3p promotes epithelial-mesenchymal transition and metastasis of low-metastatic cancer cells by regulating PTEN/Akt pathway in hepatocellular carcinoma

Exosomes play an important role in intercellular communication and metastatic progression of hepatocellular carcinoma (HCC). However, cellular communication between heterogeneous HCC cells with different metastatic potentials and the resultant cancer progression are not fully understood in HCC. Here, HCC cells with high-metastatic capacity (97hm and Huhm) were constructed by continually exerting selective pressure on primary HCC cells (MHCC-97H and Huh7). Through performing exosomal miRNA sequencing in HCC cells with different metastatic potentials (MHCC-97H and 97hm), many significantly different miRNA candidates were found. Among these miRNAs, miR-92a-3p was the most abundant miRNA in the exosomes of highly metastatic HCC cells. Exosomal miR92a-3p was also found enriched in the plasma of HCC patient-derived xenograft mice (PDX) model with high-metastatic potential. Exosomal miR-92a-3p promotes epithelial-mesenchymal transition (EMT) in recipient cancer cells via targeting PTEN and regulating its downstream Akt/Snail signaling. Furthermore, through mRNA sequencing in HCC cells with different metastatic potentials and predicting potential transcription factors of miR92a-3p, upregulated transcript factors E2F1 and c-Myc were found in high-metastatic HCC cells promote the expression of cellular and exosomal miR-92a-3p in HCC by directly binding the promoter of its host gene, miR17HG. Clinical data showed that a high plasma exosomal miR92a-3p level was correlated with shortened overall survival and disease-free survival, indicating poor prognosis in HCC patients. In conclusion, hepatoma-derived exosomal miR92a-3p plays a critical role in the EMT progression and promoting metastasis by inhibiting PTEN and activating Akt/Snail signaling. Exosomal miR92a-3p is a potential predictive biomarker for HCC metastasis, and this may provoke the development of novel therapeutic and preventing strategies against metastasis of HCC.

Origins and Consequences of Chromosomal Instability: From Cellular Adaptation to Genome Chaos-Mediated System Survival

When discussing chromosomal instability, most of the literature focuses on the characterization of individual molecular mechanisms. These studies search for genomic and environmental causes and consequences of chromosomal instability in cancer, aiming to identify key triggering factors useful to control chromosomal instability and apply this knowledge in the clinic. Since cancer is a phenomenon of new system emergence from normal tissue driven by somatic evolution, such studies should be done in the context of new genome system emergence during evolution. In this perspective, both the origin and key outcome of chromosomal instability are examined using the genome theory of cancer evolution. Specifically, chromosomal instability was linked to a spectrum of genomic and non-genomic variants, from epigenetic alterations to drastic genome chaos. These highly diverse factors were then unified by the evolutionary mechanism of cancer. Following identification of the hidden link between cellular adaptation (positive and essential) and its trade-off (unavoidable and negative) of chromosomal instability, why chromosomal instability is the main player in the macro-cellular evolution of cancer is briefly discussed. Finally, new research directions are suggested, including searching for a common mechanism of evolutionary phase transition, establishing chromosomal instability as an evolutionary biomarker, validating the new two-phase evolutionary model of cancer, and applying such a model to improve clinical outcomes and to understand the genome-defined mechanism of organismal evolution.

Can Environmental Manipulation Help Suppress Cancer? Non-Linear Competition Among Tumor Cells in Periodically Changing Conditions

It has been shown that the tumor population growth dynamics in a periodically varying environment can drastically differ from the one in a fixed environment. Thus, the environment of a tumor can potentially be manipulated to suppress cancer progression. Diverse evolutionary processes play vital roles in cancer progression and accordingly, understanding the interplay between these processes is essential in optimizing the treatment strategy. Somatic evolution and genetic instability result in intra-tumor cell heterogeneity. Various models have been developed to analyze the interactions between different types of tumor cells. Here, models of density-dependent interaction between different types of tumor cells under fast periodical environmental changes are examined. It is illustrated that tumor population densities, which vary on a slow time scale, are affected by fast environmental variations. Finally, the intriguing density-dependent interactions in metastatic castration-resistant prostate cancer (mCRPC) in which the different types of tumor cells are defined with respect to the production of and dependence on testosterone are discussed.

Eco-oncology: Applying ecological principles to understand and manage cancer

Cancer is a disease of single cells that expresses itself at the population level. The striking similarities between initiation and growth of tumors and dynamics of biological populations, and between metastasis and ecological invasion and community dynamics suggest that oncology can benefit from an ecological perspective to improve our understanding of cancer biology. Tumors can be viewed as complex, adaptive, and evolving systems as they are spatially and temporally heterogeneous, continually interacting with each other and with the microenvironment and evolving to increase the fitness of the cancer cells. We argue that an eco-evolutionary perspective is essential to understand cancer biology better. Furthermore, we suggest that ecologically informed therapeutic approaches that combine standard of care treatments with strategies aimed at decreasing the evolutionary potential and fitness of neoplastic cells, such as disrupting cell-to-cell communication and cooperation, and preventing successful colonization of distant organs by migrating cancer cells, may be effective in managing cancer as a chronic condition.

Why is cancer not more common? A changing microenvironment may help to explain why, and suggests strategies for anti-cancer therapy

One of the great unsolved puzzles in cancer biology is not why cancers occur, but rather explaining why so few cancers occur compared with the theoretical number that could occur, given the number of progenitor cells in the body and the normal mutation rate. We hypothesized that a contributory explanation is that the tumour microenvironment (TME) is not fixed due to factors such as immune cell infiltration, and that this could impair the ability of neoplastic cells to retain a high enough fitness to become a cancer. The TME has implicitly been assumed to be static in most cancer evolution models, and we therefore developed a mathematical model of spatial cancer evolution assuming that the TME, and thus the optimum cancer phenotype, changes over time. Based on simulations, we show how cancer cell populations adapt to diverse changing TME conditions and fitness landscapes. Compared with static TMEs, which generate neutral dynamics, changing TMEs lead to complex adaptations with characteristic spatio-temporal heterogeneity involving variable fitness effects of driver mutations, subclonal mixing, subclonal competition and phylogeny patterns. In many cases, cancer cell populations fail to grow or undergo spontaneous regression, and even extinction. Our analyses predict that cancer evolution in a changing TME is challenging, and can help to explain why cancer is neither inevitable nor as common as expected. Should cancer driver mutations with effects dependent of the TME exist, they are likely to be selected. Anti-cancer prevention and treatment strategies based on changing the TME are feasible and potentially effective.

Cancer cell lines show high heritability for motility but not generation time

Tumour evolution depends on heritable differences between cells in traits affecting cell survival or replication. It is well established that cancer cells are genetically and phenotypically heterogeneous; however, the extent to which this phenotypic variation is heritable is far less well explored. Here, we estimate the broad-sense heritability (H ²) of two cell traits related to cancer hallmarks--cell motility and generation time--within populations of four cancer cell lines in vitro and find that motility is strongly heritable. This heritability is stable across multiple cell generations, with heritability values at the high end of those measured for a range of traits in natural populations of animals or plants. These findings confirm a central assumption of cancer evolution, provide a first quantification of the evolvability of key traits in cancer cells and indicate that there is ample raw material for experimental evolution in cancer cell lines. Generation time, a trait directly affecting cell fitness, shows substantially lower values of heritability than cell speed, consistent with its having been under directional selection removing heritable variation.

The scope of viral causation of human cancers: interpreting virus density from an evolutionary perspective

Most known oncogenic viruses of humans use DNA as their genomic material. Research over the past quarter century has revealed that their oncogenicity results largely from direct interference with barriers to oncogenesis. In contrast to viruses that have been accepted causes of particular cancers, candidate viral causes tend to have fewer viral than cellular genomes in the tumours. These low viral loads have caused researchers to conclude that the associated viruses are not primary causes of the associated cancers. Consideration of differential survival, reproduction and infiltration of cells in a tumour suggest, however, that viral loads could be low even when viruses are primary causes of cancer. Resolution of this issue has important implications for human health because medical research tends to be effective at preventing and controlling infectious diseases. Mathematical models may clarify the problem and help guide future research by assessing whether low viral loads are likely outcomes of the differential survival, reproduction, and infiltration of cells in a tumour and, more generally, the extent to which viruses contribute to cancer. This article is part of the theme issue 'Silent cancer agents: multi-disciplinary modelling of human DNA oncoviruses'.

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.

The evolution of resistance and tolerance as cancer defences

Although there is a plethora of cancer associated-factors that can ultimately culminate in death (cachexia, organ impairment, metastases, opportunistic infections, etc.), the focal element of every terminal malignancy is the failure of our natural defences to control unlimited cell proliferation. The reasons why our defences apparently lack efficiency is a complex question, potentially indicating that, under Darwinian terms, solutions other than preventing cancer progression are also important contributors. In analogy with host-parasite systems, we propose to call this latter option ‘tolerance’ to cancer. Here, we argue that the ubiquity of oncogenic processes among metazoans is at least partially attributable to both the limitations of resistance mechanisms and to the evolution of tolerance to cancer. Deciphering the ecological contexts of alternative responses to the cancer burden is not a semantic question, but rather a focal point in understanding the evolutionary ecology of host-tumour relationships, the evolution of our defences, as well as why and when certain cancers are likely to be detrimental for survival.

Effect Sizes of Somatic Mutations in Cancer

A major goal of cancer biology is determination of the relative importance of the genetic alterations that confer selective advantage to cancer cells. Tumor sequence surveys have frequently ranked the importance of substitutions to cancer growth by P value or a false-discovery conversion thereof. However, P values are thresholds for belief, not metrics of effect. Their frequent misuse as metrics of effect has often been vociferously decried, even in cases when the only attributable mistake was omission of effect sizes. Here, we propose an appropriate ranking-the cancer effect size, which is the selection intensity for somatic variants in cancer cell lineages. The selection intensity is a metric of the survival and reproductive advantage conferred by mutations in somatic tissue. Thus, they are of fundamental importance to oncology, and have immediate relevance to ongoing decision making in precision medicine tumor boards, to the selection and design of clinical trials, to the targeted development of pharmaceuticals, and to basic research prioritization. Within this commentary, we first discuss the scope of current methods that rank confidence in the overrepresentation of specific mutated genes in cancer genomes. Then we bring to bear recent advances that draw upon an understanding of the development of cancer as an evolutionary process to estimate the effect size of somatic variants leading to cancer. We demonstrate the estimation of the effect sizes of all recurrent single nucleotide variants in 22 cancer types, quantifying relative importance within and between driver genes.

Cancer Ecology and Evolution: Positive interactions and system vulnerability

Parallels of cancer with ecology and evolution have provided new insights into the initiation and spread of cancer, and new approaches to therapy. This review describes those parallels while emphasizing some key contrasts. We argue that cancers are less like invasive species than like native species or even crops that have escaped control, and that ecological control and homeo-static control differ fundamentally through both their ends and their means. From our focus on the role of positive interactions in control processes, we introduce a novel mathematical modeling framework that tracks how individual cell lineages arise, and how the many layers of control break down in the emergence of cancer. The next generation of therapies must continue to look beyond cancers as being created by individual renegade cells and address not only the network of interactions those cells inhabit, but the evolutionary logic that created those interactions and their intrinsic vulnerability.

Rewiring E2F1 with classical NHEJ via APLF suppression promotes bladder cancer invasiveness

Background

Bladder cancer progression has been associated with dysfunctional repair of double-strand breaks (DSB), a deleterious type of DNA lesions that fuel genomic instability. Accurate DSB repair relies on two distinct pathways, homologous recombination (HR) and classical non-homologous end-joining (c-NHEJ). The transcription factor E2F1 supports HR-mediated DSB repair and protects genomic stability. However, invasive bladder cancers (BC) display, in contrast to non-invasive stages, genomic instability despite their high E2F1 levels. Hence, E2F1 is either inefficient in controlling DSB repair in this setting, or rewires the repair apparatus towards alternative, error-prone DSB processing pathways.

Methods

RT-PCR and immunoblotting, in combination with bioinformatics tools were applied to monitor c-NHEJ factors status in high-E2F1-expressing, invasive BC versus low-E2F1-expressing, non-invasive BC. In vivo binding of E2F1 on target gene promoters was demonstrated by ChIP assays and E2F1 CRISPR-Cas9 knockdown. MIR888-dependent inhibition of APLF by E2F1 was demonstrated using overexpression and knockdown experiments, in combination with luciferase assays. Methylation status of MIR888 promoter was monitored by methylation-specific PCR. The changes in invasion potential and the DSB repair efficiency were estimated by Boyden chamber assays and pulse field electrophoresis, correspondingly.

Results

Herein, we show that E2F1 directly transactivates the c-NHEJ core factors Artemis, DNA-PKcs, ligase IV, NHEJ1, Ku70/Ku80 and XRCC4, but indirectly inhibits APLF, a chromatin modifier regulating c-NHEJ. Inhibition is achieved by miR-888-5p, a testis-specific, X-linked miRNA which, in normal tissues, is often silenced via promoter methylation. Upon hypomethylation in invasive BC cells, MIR888 is transactivated by E2F1 and represses APLF. Consequently, E2F1/miR-888/APLF rewiring is established, generating conditions of APLF scarcity that compromise proper c-NHEJ function. Perturbation of the E2F1/miR-888/APLF axis restores c-NHEJ and ameliorates cell invasiveness. Depletion of miR-888 can establish a ‘high E2F1/APLF/DCLRE1C’ signature, which was found to be particularly favorable for BC patient survival.

Conclusion

Suppression of the ‘out-of-context’ activity of miR-888 improves DSB repair and impedes invasiveness by restoring APLF.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1286-9) contains supplementary material, which is available to authorized users.

Combination of 247 Genome-Wide Association Studies Reveals High Cancer Risk as a Result of Evolutionary Adaptation

Analysis of GLOBOCAN-2012 data shows clearly here that cancer incidence worldwide is highly related with low average annual temperatures and extreme low temperatures. This applies for all cancers together or separately for many frequent or rare cancer types (all cancers P = 9.49×10-18). Supporting fact is that Inuit people, living at extreme low temperatures, have the highest cancer rates today. Hypothesizing an evolutionary explanation, 240 cancer genome-wide association studies, and seven genome-wide association studies for cold and high-altitude adaptation were combined. A list of 1,377 cancer-associated genes was created to initially investigate whether cold selected genes are enriched with cancer-associated genes. Among Native Americans, Inuit and Eskimos, the highest association was observed for Native Americans (P = 6.7×10-5). An overall or a meta-analysis approach confirmed further this result. Similar approach for three populations living at extreme high altitude, revealed high association for Andeans-Tibetans (P = 1.3×10-11). Overall analysis or a meta-analysis was also significant. A separate analysis showed special selection for tumor suppressor genes. These results can be viewed along with those of previous functional studies that showed that reduced apoptosis potential due to specific p53 variants (the most important tumor suppressor gene) is beneficial in high-altitude and cold environments. In conclusion, this study shows that genetic variants selected for adaptation at extreme environmental conditions can increase cancer risk later on age. This is in accordance with antagonistic pleiotropy hypothesis.

Clonal evolution and genome stability in a 2500-year-old fungal individual

Individuals of the basidiomycete fungus Armillaria are well known for their ability to spread from woody substrate to substrate on the forest floor through the growth of rhizomorphs. Here, we made 248 collections of A. gallica in one locality in Michigan's Upper Peninsula. To identify individuals, we genotyped collections with molecular markers and somatic compatibility testing. We found several different individuals in proximity to one another, but one genetic individual stood out as exceptionally large, covering hundreds of tree root systems over approximately 75 hectares of the forest floor. Based on observed growth rates of the fungus, we estimate the minimum age of the large individual as 2500 years. With whole-genome sequencing and variant discovery, we also found that mutation had occurred within the somatic cells of the individual, reflecting its historical pattern of growth from a single point. The overall rate of mutation over the 90 mb genome, however, was extremely low. This same individual was first discovered in the late 1980s, but its full spatial extent and internal mutation dynamic was unknown at that time. The large individual of A. gallica has been remarkably resistant to genomic change as it has persisted in place.

How is the evolution of tumour resistance at organ-scale impacted by the importance of the organ for fitness?

BACKGROUND

A strong variability in cancer incidence is observed between human organs. Recently, it has been suggested that the relative contribution of organs to organism fitness (reproduction or survival) could explain at least a part of the observed variation. The objective of this study is to investigate theoretically the main factors driving the evolution of tumour resistance mechanisms of organs when their relative contribution to organism fitness is considered. We use a population-scale model where individuals can develop a tumour in a key organ (i.e. in which even a small tumour can negatively impact organism fitness), an auxiliary organ (i.e. in which only a large tumour has a relatively significant impact) or both organs because of metastasis.

RESULTS

Our simulations show that natural selection acts in two different ways to prevent cancer in a key and an auxiliary organs. In the key organ, the strategy mostly selected is the highest resistance and only a high cost of resistance mitigates this behavior. Inversely, we observe that a low resistance strategy can be selected in the auxiliary organ when the development of the tumour is slow and the effect of a large tumour on the mortality of the organism is relatively weak. Nevertheless, if the tumour can spread to a key organ, higher resistance strategies are selected in the auxiliary organ.

CONCLUSION

Finally, our study demonstrates that the relative contribution of organs to the organism fitness and the metastatic propensity of the tumour influence the evolution of tumour resistance at organ scale and should be considered by studies aiming to explain the variability in cancer incidence at organ-scale.

Evolutionary Conflict

Evolutionary conflict occurs when two parties can each affect a joint phenotype, but they gain from pushing it in opposite directions. Conflicts occur across many biological levels and domains but share many features. They are a major source of biological maladaptation. They affect biological diversity, often increasing it, at almost every level. Because opponents create selection that can be strong, persistent, and malevolent, conflict often leads to accelerated evolution and arms races. Conflicts might even drive the majority of adaptation, with pathogens leading the way as selective forces. The evolution of conflicts is complex, with outcomes determined partly by the relative evolvability of each party and partly by the kinds of power that each evolves. Power is a central issue in biology. In addition to physical strength and weapons, it includes strength from numbers and complexity; abilities to bind and block; advantageous timing; and abilities to acquire, use, and distort information.

Intermediate Levels of Antibiotics May Increase Diversity of Colony Size Phenotype in Bacteria

Antibiotics select for resistant bacteria whose existence and emergence is more likely in populations with high phenotypic and genetic diversity. Identifying the mechanisms that generate this diversity can thus have clinical consequences for drug-resistant pathogens. We show here that intermediate levels of antibiotics are associated with higher levels of phenotypic diversity in size of colony forming units (cfus), within a single bacterial population. We examine experimentally thousands of populations of bacteria subjected to different disturbance levels that are created by varying antibiotic concentrations. Based on colony sizes, we find that intermediate levels of antibiotics always result in the highest phenotypic variation of this trait. This result is supported across bacterial densities and in the presence of three different antibiotics with two different mechanisms of action. Our results suggest intermediate levels of a stressor (as opposed to very low or very high levels) could affect the phenotypic diversity of a population, at least with regards to the single trait measured here. While this study is limited to a single phenotypic trait within a single species, the results suggest examining phenotypic and genetic variation created by disturbances and stressors could be a promising way to understand and limit variation in pathogens.

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.

Cancer growth and metastasis as a metaphor of Go gaming: An Ising model approach

This work aims for modeling and simulating the metastasis of cancer, via the analogy between the cancer process and the board game Go. In the game of Go, black stones that play first could correspond to a metaphor of the birth, growth, and metastasis of cancer. Moreover, playing white stones on the second turn could correspond the inhibition of cancer invasion. Mathematical modeling and algorithmic simulation of Go may therefore benefit the efforts to deploy therapies to surpass cancer illness by providing insight into the cellular growth and expansion over a tissue area. We use the Ising Hamiltonian, that models the energy exchange in interacting particles, for modeling the cancer dynamics. Parameters in the energy function refer the biochemical elements that induce cancer birth, growth, and metastasis; as well as the biochemical immune system process of defense.

Resource competition promotes tumour expansion in experimentally evolved cancer

BACKGROUND

Tumour progression involves a series of phenotypic changes to cancer cells, each of which presents therapeutic targets. Here, using techniques adapted from microbial experimental evolution, we investigate the evolution of tumour spreading - a precursor for metastasis and tissue invasion - in environments with varied resource supply. Evolutionary theory predicts that competition for resources within a population will select for individuals to move away from a natal site (i.e. disperse), facilitating the colonisation of unexploited resources and decreasing competition between kin.

RESULTS

After approximately 100 generations in environments with low resource supply, we find that MCF7 breast cancer spheroids (small in vitro tumours) show increased spreading. Conversely, spreading slows compared to the ancestor where resource supply is high. Common garden experiments confirm that the evolutionary responses differ between selection lines; with lines evolved under low resource supply showing phenotypic plasticity in spheroid spreading rate. These differences in spreading behaviour between selection lines are heritable (stable across multiple generations), and show that the divergently evolved lines differ in their response to resource supply.

CONCLUSIONS

We observe dispersal-like behaviour and an increased sensitivity to resource availability in our selection lines, which may be a response to selection, or alternatively may be due to epigenetic changes, provoked by prolonged resource limitation, that have persisted across many cell generations. Different clinical strategies may be needed depending on whether or not tumour progression is due to natural selection. This study highlights the effectiveness of experimental evolution approaches in cancer cell populations and demonstrates how simple model systems might enable us to observe and measure key selective drivers of clinically important traits.

Optical Control of Tumor Induction in the Zebrafish

The zebrafish has become an increasingly popular and valuable cancer model over the past few decades. While most zebrafish cancer models are generated by expressing mammalian oncogenes under tissue-specific promoters, here we describe a method that allows for the precise optical control of oncogene expression in live zebrafish. We utilize this technique to transiently or constitutively activate a typical human oncogene, kRASG12V, in zebrafish embryos and investigate the developmental and tumorigenic phenotypes. We demonstrate the spatiotemporal control of oncogene expression in live zebrafish, and characterize the different tumorigenic probabilities when kRASG12V is expressed transiently or constitutively at different developmental stages. Moreover, we show that light can be used to activate oncogene expression in selected tissues and single cells without tissue-specific promoters. Our work presents a novel approach to initiate and study cancer in zebrafish, and the high spatiotemporal resolution of this method makes it a valuable tool for studying cancer initiation from single cells.

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.

Stochastic model of contact inhibition and the proliferation of melanoma in situ

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL- 147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.

Evolutionary Origins of Cancer Driver Genes and Implications for Cancer Prognosis

The cancer atavistic theory suggests that carcinogenesis is a reverse evolution process. It is thus of great interest to explore the evolutionary origins of cancer driver genes and the relevant mechanisms underlying the carcinogenesis. Moreover, the evolutionary features of cancer driver genes could be helpful in selecting cancer biomarkers from high-throughput data. In this study, through analyzing the cancer endogenous molecular networks, we revealed that the subnetwork originating from eukaryota could control the unlimited proliferation of cancer cells, and the subnetwork originating from eumetazoa could recapitulate the other hallmarks of cancer. In addition, investigations based on multiple datasets revealed that cancer driver genes were enriched in genes originating from eukaryota, opisthokonta, and eumetazoa. These results have important implications for enhancing the robustness of cancer prognosis models through selecting the gene signatures by the gene age information.

Inferring clonal structure in HTLV-1-infected individuals: towards bridging the gap between analysis and visualization

BACKGROUND

Human T cell leukemia virus type 1 (HTLV-1) causes adult T cell leukemia (ATL) in a proportion of infected individuals after a long latency period. Development of ATL is a multistep clonal process that can be investigated by monitoring the clonal expansion of HTLV-1-infected cells by isolation of provirus integration sites. The clonal composition (size, number, and combinations of clones) during the latency period in a given infected individual has not been clearly elucidated.

METHODS

We used high-throughput sequencing technology coupled with a tag system for isolating integration sites and measuring clone sizes from 60 clinical samples. We assessed the role of clonality and clone size dynamics in ATL onset by modeling data from high-throughput monitoring of HTLV-1 integration sites using single- and multiple-time-point samples.

RESULTS

From four size categories analyzed, we found that big clones (B; 513-2048 infected cells) and very big clones (VB; >2048 infected cells) had prognostic value. No sample harbored two or more VB clones or three or more B clones. We examined the role of clone size, clone combination, and the number of integration sites in the prognosis of infected individuals. We found a moderate reverse correlation between the total number of clones and the size of the largest clone. We devised a data-driven model that allows intuitive representation of clonal composition.

CONCLUSIONS

This integration site-based clonality tree model represents the complexity of clonality and provides a global view of clonality data that facilitates the analysis, interpretation, understanding, and visualization of the behavior of clones on inter- and intra-individual scales. It is fully data-driven, intuitively depicts the clonality patterns of HTLV-1-infected individuals and can assist in early risk assessment of ATL onset by reflecting the prognosis of infected individuals. This model should assist in assimilating information on clonal composition and understanding clonal expansion in HTLV-1-infected individuals.

Novel therapeutic targets on the horizon for lung cancer

Lung cancer is a leading cause of cancer-related mortality worldwide, and is classically divided into two major histological subtypes: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). Although NSCLC and SCLC are considered distinct entities with different genomic landscapes, emerging evidence highlights a convergence in therapeutically relevant targets for both histologies. In adenocarcinomas with defined alterations such as EGFR mutations and ALK translocations, targeted therapies are now first-line standard of care. By contrast, many experimental and targeted agents remain largely unsuccessful for SCLC. Intense preclinical research and clinical trials are underway to exploit unique traits of lung cancer, such as oncogene dependency, DNA damage response, angiogenesis, and cellular plasticity arising from presence of cancer stem cell lineages. In addition, the promising clinical activity observed in NSCLC in response to immune checkpoint blockade has spurred great interest in the field of immunooncology, with the scope to develop a diverse repertoire of synergistic and personalised immunotherapeutics. In this Review, we discuss novel therapeutic agents for lung cancer that are in early-stage development, and how prospective clinical trials and drug development may be shaped by a deeper understanding of this heterogeneous disease.

Does high relatedness promote cheater-free multicellularity in synthetic lifecycles?

The evolution of multicellularity is one of the key transitions in evolution and requires extreme levels of cooperation between cells. However, even when cells are genetically identical, noncooperative cheating mutants can arise that cause a breakdown in cooperation. How then, do multicellular organisms maintain cooperation between cells? A number of mechanisms that increase relatedness amongst cooperative cells have been implicated in the maintenance of cooperative multicellularity including single-cell bottlenecks and kin recognition. In this study, we explore how relatively simple biological processes such as growth and dispersal can act to increase relatedness and promote multicellular cooperation. Using experimental populations of pseudo-organisms, we found that manipulating growth and dispersal of clones of a social amoeba to create high levels of relatedness was sufficient to prevent the spread of cheating mutants. By contrast, cheaters were able to spread under low-relatedness conditions. Most surprisingly, we saw the largest increase in cheating mutants under an experimental treatment that should create intermediate levels of relatedness. This is because one of the factors raising relatedness, structured growth, also causes high vulnerability to growth rate cheaters.

Cancer: A disease at the crossroads of trade-offs

Central to evolutionary theory is the idea that living organisms face phenotypic and/or genetic trade-offs when allocating resources to competing life-history demands, such as growth, survival, and reproduction. These trade-offs are increasingly considered to be crucial to further our understanding of cancer. First, evidences suggest that neoplastic cells, as any living entities subject to natural selection, are governed by trade-offs such as between survival and proliferation. Second, selection might also have shaped trade-offs at the organismal level, especially regarding protective mechanisms against cancer. Cancer can also emerge as a consequence of additional trade-offs in organisms (e.g., eco-immunological trade-offs). Here, we review the wide range of trade-offs that occur at different scales and their relevance for understanding cancer dynamics. We also discuss how acknowledging these phenomena, in light of human evolutionary history, may suggest new guidelines for preventive and therapeutic strategies.

Multidisciplinary insight into clonal expansion of HTLV-1-infected cells in adult T-cell leukemia via modeling by deterministic finite automata coupled with high-throughput sequencing

Background

Clonal expansion of leukemic cells leads to onset of adult T-cell leukemia (ATL), an aggressive lymphoid malignancy with a very poor prognosis. Infection with human T-cell leukemia virus type-1 (HTLV-1) is the direct cause of ATL onset, and integration of HTLV-1 into the human genome is essential for clonal expansion of leukemic cells. Therefore, monitoring clonal expansion of HTLV-1–infected cells via isolation of integration sites assists in analyzing infected individuals from early infection to the final stage of ATL development. However, because of the complex nature of clonal expansion, the underlying mechanisms have yet to be clarified. Combining computational/mathematical modeling with experimental and clinical data of integration site–based clonality analysis derived from next generation sequencing technologies provides an appropriate strategy to achieve a better understanding of ATL development.

Methods

As a comprehensively interdisciplinary project, this study combined three main aspects: wet laboratory experiments, in silico analysis and empirical modeling.

Results

We analyzed clinical samples from HTLV-1–infected individuals with a broad range of proviral loads using a high-throughput methodology that enables isolation of HTLV-1 integration sites and accurate measurement of the size of infected clones. We categorized clones into four size groups, “very small”, “small”, “big”, and “very big”, based on the patterns of clonal growth and observed clone sizes. We propose an empirical formal model based on deterministic finite state automata (DFA) analysis of real clinical samples to illustrate patterns of clonal expansion.

Conclusions

Through the developed model, we have translated biological data of clonal expansion into the formal language of mathematics and represented the observed clonality data with DFA. Our data suggest that combining experimental data (absolute size of clones) with DFA can describe the clonality status of patients. This kind of modeling provides a basic understanding as well as a unique perspective for clarifying the mechanisms of clonal expansion in ATL.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0241-2) contains supplementary material, which is available to authorized users.

Cancer heterogeneity and multilayer spatial evolutionary games

Background

Evolutionary game theory (EGT) has been widely used to simulate tumour processes. In almost all studies on EGT models analysis is limited to two or three phenotypes. Our model contains four main phenotypes. Moreover, in a standard approach only heterogeneity of populations is studied, while cancer cells remain homogeneous. A multilayer approach proposed in this paper enables to study heterogeneity of single cells.

Method

In the extended model presented in this paper we consider four strategies (phenotypes) that can arise by mutations. We propose multilayer spatial evolutionary games (MSEG) played on multiple 2D lattices corresponding to the possible phenotypes. It enables simulation and investigation of heterogeneity on the player-level in addition to the population-level. Moreover, it allows to model interactions between arbitrary many phenotypes resulting from the mixture of basic traits.

Results

Different equilibrium points and scenarios (monomorphic and polymorphic populations) have been achieved depending on model parameters and the type of played game. However, there is a possibility of stable quadromorphic population in MSEG games for the same set of parameters like for the mean-field game.

Conclusion

The model assumes an existence of four possible phenotypes (strategies) in the population of cells that make up tumour. Various parameters and relations between cells lead to complex analysis of this model and give diverse results. One of them is a possibility of stable coexistence of different tumour cells within the population, representing almost arbitrary mixture of the basic phenotypes.

Reviewers

This article was reviewed by Tomasz Lipniacki, Urszula Ledzewicz and Jacek Banasiak.

The prisoner's dilemma as a cancer model

Tumor development is an evolutionary process in which a heterogeneous population of cells with different growth capabilities compete for resources in order to gain a proliferative advantage. What are the minimal ingredients needed to recreate some of the emergent features of such a developing complex ecosystem? What is a tumor doing before we can detect it? We outline a mathematical model, driven by a stochastic Moran process, in which cancer cells and healthy cells compete for dominance in the population. Each are assigned payoffs according to a Prisoner's Dilemma evolutionary game where the healthy cells are the cooperators and the cancer cells are the defectors. With point mutational dynamics, heredity, and a fitness landscape controlling birth and death rates, natural selection acts on the cell population and simulated 'cancer-like' features emerge, such as Gompertzian tumor growth driven by heterogeneity, the log-kill law which (linearly) relates therapeutic dose density to the (log) probability of cancer cell survival, and the Norton-Simon hypothesis which (linearly) relates tumor regression rates to tumor growth rates. We highlight the utility, clarity, and power that such models provide, despite (and because of) their simplicity and built-in assumptions.