Adaptation to Stochastic Temporal Variations in Intratumoral Blood Flow: The Warburg Effect as a Bet Hedging Strategy

Understanding human aging and the fundamental cell signaling link in age-related diseases: the middle-aging hypovascularity hypoxia hypothesis

Aging-related hypoxia, oxidative stress, and inflammation pathophysiology are closely associated with human age-related carcinogenesis and chronic diseases. However, the connection between hypoxia and hormonal cell signaling pathways is unclear, but such human age-related comorbid diseases do coincide with the middle-aging period of declining sex hormonal signaling. This scoping review evaluates the relevant interdisciplinary evidence to assess the systems biology of function, regulation, and homeostasis in order to discern and decipher the etiology of the connection between hypoxia and hormonal signaling in human age-related comorbid diseases. The hypothesis charts the accumulating evidence to support the development of a hypoxic milieu and oxidative stress-inflammation pathophysiology in middle-aged individuals, as well as the induction of amyloidosis, autophagy, and epithelial-to-mesenchymal transition in aging-related degeneration. Taken together, this new approach and strategy can provide the clarity of concepts and patterns to determine the causes of declining vascularity hemodynamics (blood flow) and physiological oxygenation perfusion (oxygen bioavailability) in relation to oxygen homeostasis and vascularity that cause hypoxia (hypovascularity hypoxia). The middle-aging hypovascularity hypoxia hypothesis could provide the mechanistic interface connecting the endocrine, nitric oxide, and oxygen homeostasis signaling that is closely linked to the progressive conditions of degenerative hypertrophy, atrophy, fibrosis, and neoplasm. An in-depth understanding of these intrinsic biological processes of the developing middle-aged hypoxia could provide potential new strategies for time-dependent therapies in maintaining healthspan for healthy lifestyle aging, medical cost savings, and health system sustainability.

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

Simple Summary

Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed.

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.

Roadmap on plasticity and epigenetics in cancer

The role of plasticity and epigenetics in shaping cancer evolution and response to therapy has taken center stage with recent technological advances including single cell sequencing. This roadmap article is focused on state-of-the-art mathematical and experimental approaches to interrogate plasticity in cancer, and addresses the following themes and questions: is there a formal overarching framework that encompasses both non-genetic plasticity and mutation-driven somatic evolution? How do we measure and model the role of the microenvironment in influencing/controlling non-genetic plasticity? How can we experimentally study non-genetic plasticity? Which mathematical techniques are required or best suited? What are the clinical and practical applications and implications of these concepts?

Hypoxia boosts aerobic glycolysis of carcinoma：a complex process for tumor development

Hypoxia, a common feature in malignant tumors, is mainly caused by insufficient oxygen supply. Hypoxia is closely related to cancer development, affecting cancer invasion and metastasis, energy metabolism and other pathological processes, and is not conducive to cancer treatment and prognosis. Tumor cells exacerbate metabolic abnormalities to adapt to the hypoxic microenvironment, especially to enhance aerobic glycolysis. Glycolysis leads to an acidic microenvironment in cancer tissues, enhancing cancer metastasis, deterioration and drug resistance. Therefore, hypoxia is a therapeutic target that cannot be ignored in cancer treatment. The adaptation of tumor cells to hypoxia is mainly regulated by hypoxia inducible factors (HIFs), and the stability of HIFs is improved under hypoxic conditions. HIFs can promote the glycolysis of tumors by regulating glycolytic enzymes, transporters, and participates in regulating the TCA (tricarboxylic acid) cycle. In addition, HIFs indirectly affect glycolysis through its interaction with non-coding RNAs. Therefore, targeting hypoxia and HIFs are important tumor therapies.

The role of memory in non-genetic inheritance and its impact on cancer treatment resistance

Intra-tumour heterogeneity is a leading cause of treatment failure and disease progression in cancer. While genetic mutations have long been accepted as a primary mechanism of generating this heterogeneity, the role of phenotypic plasticity is becoming increasingly apparent as a driver of intra-tumour heterogeneity. Consequently, understanding the role of this plasticity in treatment resistance and failure is a key component of improving cancer therapy. We develop a mathematical model of stochastic phenotype switching that tracks the evolution of drug-sensitive and drug-tolerant subpopulations to clarify the role of phenotype switching on population growth rates and tumour persistence. By including cytotoxic therapy in the model, we show that, depending on the strategy of the drug-tolerant subpopulation, stochastic phenotype switching can lead to either transient or permanent drug resistance. We study the role of phenotypic heterogeneity in a drug-resistant, genetically homogeneous population of non-small cell lung cancer cells to derive a rational treatment schedule that drives population extinction and avoids competitive release of the drug-tolerant sub-population. This model-informed therapeutic schedule results in increased treatment efficacy when compared against periodic therapy, and, most importantly, sustained tumour decay without the development of resistance.

Evolutionary bet-hedging in structured populations

As ecosystems evolve, species can become extinct due to fluctuations in the environment. This leads to the evolutionary adaption known as bet-hedging, where species hedge against these fluctuations to reduce their likelihood of extinction. Environmental variation can be either within or between generations. Previous work has shown that selection for bet-hedging against within-generational variation should not occur in large populations. However, this work has been limited by assumptions of well-mixed populations, whereas real populations usually have some degree of structure. Using the framework of evolutionary graph theory, we show that through adding competition structure to the population, within-generational variation can have a significant impact on the evolutionary process for any population size. This complements research using subdivided populations, which suggests that within-generational variation is important when local population sizes are small. Together, these conclusions provide evidence to support observations by some ecologists that are contrary to the widely held view that only between-generational environmental variation has an impact on natural selection. This provides theoretical justification for further empirical study into this largely unexplored area.

Group phenotypic composition in cancer

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

Comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments

Deterministic continuum models formulated as nonlocal partial differential equations for the evolutionary dynamics of populations structured by phenotypic traits have been used recently to address open questions concerning the adaptation of asexual species to periodically fluctuating environmental conditions. These models are usually defined on the basis of population-scale phenomenological assumptions and cannot capture adaptive phenomena that are driven by stochastic variability in the evolutionary paths of single individuals. In light of these considerations, in this paper we develop a stochastic individual-based model for the coevolution of two competing phenotype-structured cell populations that are exposed to time-varying nutrient levels and undergo spontaneous, heritable phenotypic changes with different probabilities. Here, the evolution of every cell is described by a set of rules that result in a discrete-time branching random walk on the space of phenotypic states, and nutrient levels are governed by a difference equation in which a sink term models nutrient consumption by the cells. We formally show that the deterministic continuum counterpart of this model comprises a system of nonlocal partial differential equations for the cell population density functions coupled with an ordinary differential equation for the nutrient concentration. We compare the individual-based model and its continuum analog, focusing on scenarios whereby the predictions of the two models differ. The results obtained clarify the conditions under which significant differences between the two models can emerge due to bottleneck effects that bring about both lower regularity of the density functions of the two populations and more pronounced demographic stochasticity. In particular, bottleneck effects emerge in the presence of lower probabilities of phenotypic variation and are more apparent when the two populations are characterized by lower fitness initial mean phenotypes and smaller initial levels of phenotypic heterogeneity. The emergence of these effects, and thus the agreement between the two modeling approaches, is also dependent on the initial proportions of the two populations. As an illustrative example, we demonstrate the implications of these results in the context of the mathematical modeling of the early stage of metastatic colonization of distant organs.

A Similar Speciation Process Relying on Cellular Stochasticity in Microbial and Cancer Cell Populations

Similarities between microbial and cancer cells were noticed in recent years and serve as a basis for an atavism theory of cancer. Cancer cells would rely on the reactivation of an ancestral "genetic program" that would have been repressed in metazoan cells. Here we argue that cancer cells resemble unicellular organisms mainly in their similar way to exploit cellular stochasticity to produce cell specialization and maximize proliferation. Indeed, the relationship between low stochasticity, specialization, and quiescence found in normal differentiated metazoan cells is lost in cancer. On the contrary, low stochasticity and specialization are associated with high proliferation among cancer cells, as it is observed for the "specialist" cells in microbial populations that fully exploit nutritional resources to maximize proliferation. Thus, we propose a model where the appearance of cancer phenotypes can be solely due to an adaptation and a speciation process based on initial increase in cellular stochasticity.

Insights From the Ecology of Information to Cancer Control

Uniquely in nature, living systems must acquire, store, and act upon information. The survival and replicative fate of each normal cell in a multicellular organism is determined solely by information obtained from its surrounding tissue. In contrast, cancer cells as single-cell eukaryotes live in a disrupted, heterogeneous environment with opportunities and hazards. Thus, cancer cells, unlike normal somatic cells, must constantly obtain information from their environment to ensure survival and proliferation. In this study, we build upon a simple mathematical modeling framework developed to predict (1) how information promotes population persistence in a highly heterogeneous environment and (2) how disruption of information resulting from habitat fragmentation increases the probability of population extinction. Because (1) tumors grow in a highly heterogeneous microenvironment and (2) many cancer therapies fragment tumors into isolated, small cancer cell populations, we identify parallels between these 2 systems and develop ideas for cancer cure based on lessons gleaned from Anthropocene extinctions. In many Anthropocene extinctions, such as that of the North American heath hen (Tympanuchus cupido cupido), a large and widespread population was initially reduced and fragmented owing to overexploitation by humans (a "first strike"). After this, the small surviving populations are vulnerable to extinction from environmental or demographic stochastic disturbances (a "second strike"). Following this analogy, after a tumor is fragmented into small populations of isolated cancer cells by an initial therapy, additional treatment can be applied with the intent of extinction (cure). Disrupting a cancer cell's ability to acquire and use information in a heterogeneous environment may be an important tactic for causing extinction following an effective initial therapy. Thus, information, from the scale of cells within tumors to that of species within ecosystems, can be used to identify vulnerabilities to extinction and opportunities for novel treatment strategies.

A Mathematical Dissection of the Adaptation of Cell Populations to Fluctuating Oxygen Levels

The disordered network of blood vessels that arises from tumour angiogenesis results in variations in the delivery of oxygen into the tumour tissue. This brings about regions of chronic hypoxia (i.e. sustained low oxygen levels) and regions with alternating periods of low and relatively higher oxygen levels, and makes it necessary for cancer cells to adapt to fluctuating environmental conditions. We use a phenotype-structured model to dissect the evolutionary dynamics of cell populations exposed to fluctuating oxygen levels. In this model, the phenotypic state of every cell is described by a continuous variable that provides a simple representation of its metabolic phenotype, ranging from fully oxidative to fully glycolytic, and cells are grouped into two competing populations that undergo heritable, spontaneous phenotypic variations at different rates. Model simulations indicate that, depending on the rate at which oxygen is consumed by the cells, dynamic nonlinear interactions between cells and oxygen can stimulate chronic hypoxia and cycling hypoxia. Moreover, the model supports the idea that under chronic-hypoxic conditions lower rates of phenotypic variation lead to a competitive advantage, whereas higher rates of phenotypic variation can confer a competitive advantage under cycling-hypoxic conditions. In the latter case, the numerical results obtained show that bet-hedging evolutionary strategies, whereby cells switch between oxidative and glycolytic phenotypes, can spontaneously emerge. We explain how these results can shed light on the evolutionary process that may underpin the emergence of phenotypic heterogeneity in vascularised tumours.

Integrative Analysis of Breast Cancer Cells Reveals an Epithelial-Mesenchymal Transition Role in Adaptation to Acidic Microenvironment

Early ducts of breast tumors are unequivocally acidic. High rates of glycolysis combined with poor perfusion lead to a congestion of acidic metabolites in the tumor microenvironment, and pre-malignant cells must adapt to this acidosis to thrive. Adaptation to acidosis selects cancer cells that can thrive in harsh conditions and are capable of outgrowing the normal or non-adapted neighbors. This selection is usually accompanied by phenotypic change. Epithelial mesenchymal transition (EMT) is one of the most important switches correlated to malignant tumor cell phenotype and has been shown to be induced by tumor acidosis. New evidence shows that the EMT switch is not a binary system and occurs on a spectrum of transition states. During confirmation of the EMT phenotype, our results demonstrated a partial EMT phenotype in our acid-adapted cell population. Using RNA sequencing and network analysis we found 10 dysregulated network motifs in acid-adapted breast cancer cells playing a role in EMT. Our further integrative analysis of RNA sequencing and SILAC proteomics resulted in recognition of S100B and S100A6 proteins at both the RNA and protein level. Higher expression of S100B and S100A6 was validated in vitro by Immunocytochemistry. We further validated our finding both in vitro and in patients' samples by IHC analysis of Tissue Microarray (TMA). Correlation analysis of S100A6 and LAMP2b as marker of acidosis in each patient from Moffitt TMA approved the acid related role of S100A6 in breast cancer patients. Also, DCIS patients with higher expression of S100A6 showed lower survival compared to lower expression. We propose essential roles of acid adaptation in cancer cells EMT process through S100 proteins such as S100A6 that can be used as therapeutic strategy targeting both acid-adapted and malignant phenotypes.

Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments

Living species, ranging from bacteria to animals, exist in environmental conditions that exhibit spatial and temporal heterogeneity which requires them to adapt. Risk-spreading through spontaneous phenotypic variations is a known concept in ecology, which is used to explain how species may survive when faced with the evolutionary risks associated with temporally varying environments. In order to support a deeper understanding of the adaptive role of spontaneous phenotypic variations in fluctuating environments, we consider a system of non-local partial differential equations modelling the evolutionary dynamics of two competing phenotype-structured populations in the presence of periodically oscillating nutrient levels. The two populations undergo heritable, spontaneous phenotypic variations at different rates. The phenotypic state of each individual is represented by a continuous variable, and the phenotypic landscape of the populations evolves in time due to variations in the nutrient level. Exploiting the analytical tractability of our model, we study the long-time behaviour of the solutions to obtain a detailed mathematical depiction of the evolutionary dynamics. The results suggest that when nutrient levels undergo small and slow oscillations, it is evolutionarily more convenient to rarely undergo spontaneous phenotypic variations. Conversely, under relatively large and fast periodic oscillations in the nutrient levels, which bring about alternating cycles of starvation and nutrient abundance, higher rates of spontaneous phenotypic variations confer a competitive advantage. We discuss the implications of our results in the context of cancer metabolism.

In Silico implementation of evolutionary paradigm in therapy design: Towards anti-cancer therapy as Darwinian process

In here presented in silico study we suggest a way how to implement the evolutionary principles into anti-cancer therapy design. We hypothesize that instead of its ongoing supervised adaptation, the therapy may be constructed as a self-sustaining evolutionary process in a dynamic fitness landscape established implicitly by evolving cancer cells, microenvironment and the therapy itself. For these purposes, we replace a unified therapy with the 'therapy species', which is a population of heterogeneous elementary therapies, and propose a way how to turn the toxicity of the elementary therapy into its fitness in a way conforming to evolutionary causation. As a result, not only the therapies govern the evolution of different cell phenotypes, but the cells' resistances govern the evolution of the therapies as well. We illustrate the approach by the minimalistic ad hoc evolutionary model. Its results indicate that the resistant cells could bias the evolution towards more toxic elementary therapies by inhibiting the less toxic ones. As the evolutionary causation of cancer drug resistance has been intensively studied for a few decades, we refer to cancer as a special case to illustrate purely theoretical analysis.

Multiplicity of Mesenchymal Stromal Cells: Finding the Right Route to Therapy

Over the last decade, the acceleration in the clinical use of mesenchymal stromal cells (MSCs) has been nothing short of spectacular. Perhaps most surprising is how little we know about the "MSC product." Although MSCs are being delivered to patients at an alarming rate, the regulatory requirements for MSC therapies (for example in terms of quality assurance and quality control) are nowhere near the expectations of traditional pharmaceuticals. That said, the standards that define a chemical compound or purified recombinant protein cannot be applied with the same stringency to a cell-based therapy. Biological processes are dynamic, adaptive and variable. Heterogeneity will always exist or emerge within even the most rigorously sorted clonal cell populations. With MSCs, perhaps more so than any other therapeutic cell, heterogeneity pervades at multiple levels, from the sample source to the single cell. The research and clinical communities collectively need to recognize and take steps to address this troublesome truth, to ensure that the promise of MSC-based therapies is fulfilled.

Moving the systemic evolutionary approach to cancer forward: Therapeutic implications

We have previously presented a new Systemic Evolutionary Theory of Cancer (SETOC) based on the failure of proper endosymbiosis in eukaryotic cells. Here, we propose that the progressive uncoupling of two endosymbiotic subsystems (information and energy) inside the cell, as a consequence of long-term injuries, gives rise to alterations (i) in tissue interactions and (ii) in cell organization. In the first case, we argue that the impairment of both the coherent state and the synergy between intercellular communications underpins the onset of tissue dysplasia, that usually evolves towards cancer development. In the second case, we suggest that the rupture of endosymbiosis drives a sort of cell regression towards a protist-like entity represented by the concept of "de-emergence" postulated in our systemic evolutionary approach to carcinogenesis. This conceptual association of the cancer cell with a protist-like organism could support the development of novel cancer therapeutic approaches. To this end, we propose a paradigm shift in cancer pharmacology since: i) our knowledge of cancer pathophysiology as a complex system is insufficient, despite a vast knowledge of molecular mechanisms underlying cancer; ii) current cancer pharmacology deals only with microvariables (e.g. gene or protein targets), which do not account for the integrated pathophysiology of cancer, rather than with macrovariables (e.g. pH, membrane potential, electromagnetic fields, cell communications and so on) and mesovariables (between micro and macro), such as the interaction between various cellular components including cellular organelles. This paradigm shift should allow cancer pharmacology to move forward from molecular treatments (focusing on single targets) to modular treatments that consider cancer-related processes (i.e. inflammation, coagulation, etc.) or even to a sort of ecosystemic treatment addressing the whole functioning of the "cancer ecosystem". Examples of ecosystems treatment may be natural plant derivatives that act synergistically or pulsed electromagnetic fields which can act on particular biological processes in cancer cells. In addition, we need different working theoretical models on which to base new anticancer pharmacological approaches. Finally, we examine what value our systemic evolutionary approach could add to cancer treatments, in particular in liver cancer as a paradigm for developing potential applications.

Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

The application of in-silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not utilized widely across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multi-scale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.

Eco-evolutionary causes and consequences of temporal changes in intratumoural blood flow

Temporal changes in blood flow are commonly observed in malignant tumours, but the evolutionary causes and consequences are rarely considered. We propose that stochastic temporal variations in blood flow and microenvironmental conditions arise from the eco-evolutionary dynamics of tumour angiogenesis in which cancer cells, as individual units of selection, can influence and respond only to local environmental conditions. This leads to new vessels arising from the closest available vascular structure regardless of the size or capacity of this parental vessel. These dynamics produce unstable vascular networks with unpredictable spatial and temporal variations in blood flow and microenvironmental conditions. Adaptations of evolving populations to temporally varying environments in nature include increased diversity, greater motility and invasiveness, and highly plastic phenotypes, allowing for broad metabolic adaptability and rapid shifts to high rates of proliferation and profound quiescence. These adaptive strategies, when adopted in cancer cells, promote many commonly observed phenotypic properties including those found in the stem phenotype and in epithelial-to-mesenchymal transition. Temporal variations in intratumoural blood flow, which occur through the promotion of cancer cell phenotypes that facilitate both metastatic spread and resistance to therapy, may have substantial clinical consequences.