Convergent Evolution, Evolving Evolvability, and the Origins of Lethal Cancer

A method for in silico exploration of potential glioblastoma multiforme attractors using single-cell RNA sequencing

We presented a method to find potential cancer attractors using single-cell RNA sequencing (scRNA-seq) data. We tested our method in a Glioblastoma Multiforme (GBM) dataset, an aggressive brain tumor presenting high heterogeneity. Using the cancer attractor concept, we argued that the GBM's underlying dynamics could partially explain the observed heterogeneity, with the dataset covering a representative region around the attractor. Exploratory data analysis revealed promising GBM's cellular clusters within a 3-dimensional marker space. We approximated the clusters' centroid as stable states and each cluster covariance matrix as defining confidence regions. To investigate the presence of attractors inside the confidence regions, we constructed a GBM gene regulatory network, defined a model for the dynamics, and prepared a framework for parameter estimation. An exploration of hyperparameter space allowed us to sample time series intending to simulate myriad variations of the tumor microenvironment. We obtained different densities of stable states across gene expression space and parameters displaying multistability across different clusters. Although we used our methodological approach in studying GBM, we would like to highlight its generality to other types of cancer. Therefore, this report contributes to an advance in the simulation of cancer dynamics and opens avenues to investigate potential therapeutic targets.

Human and bats genome robustness under COSMIC mutational signatures

Carcinogenesis is an evolutionary process, and mutations can fix the selected phenotypes in selective microenvironments. Both normal and neoplastic cells are robust to the mutational stressors in the microenvironment to the extent that secure their fitness. To test the robustness of genes under a range of mutagens, we developed a sequential mutation simulator, Sinabro, to simulate single base substitution under a given mutational process. Then, we developed a pipeline to measure the robustness of genes and cells under those mutagenesis processes. We discovered significant human genome robustness to the APOBEC mutational signature SBS2, which is associated with viral defense mechanisms and is implicated in cancer. Robustness evaluations across over 70,000 sequences against 41 signatures showed higher resilience under signatures predominantly causing C-to-T (G-to-A) mutations. Principal component analysis indicates the GC content at the codon's wobble position significantly influences robustness, with increased resilience noted under transition mutations compared to transversions. Then, we tested our results in bats at extremes of the lifespan-to-mass relationship and found the long-lived bat is more robust to APOBEC than the short-lived one. By revealing robustness to APOBEC ranked highest in human (and bats with much more than number of APOBEC) genome, this work bolsters the key potential role of APOBECs in aging and cancer, as well as evolved countermeasures to this innate mutagenic process. It also provides the baseline of the human and bat genome robustness under mutational processes associated with aging and cancer.

Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach

Understanding the ecological and evolutionary dynamics of populations is critical for both basic and applied purposes in a variety of biological contexts. Although several modeling frameworks have been developed to simulate eco-evolutionary dynamics, many fewer address how to model structured populations. In a prior paper, we put forth the first modeling approach to simulate eco-evolutionary dynamics in structured populations under the G function modeling framework. However, this approach does not allow for accurate simulation under fluctuating environmental conditions. To address this limitation, we draw on the study of periodic differential equations to propose a modified approach that uses a different definition of fitness more suitable for fluctuating environments. We illustrate this method with a simple toy model of life history trade-offs. The generality of this approach allows it to be used in a variety of biological contexts.

Relationship between clonal evolution and drug resistance in bladder cancer: A genomic research review

Bladder cancer stands as a prevalent global malignancy, exhibiting notable sex-based variations in both incidence and prognosis. Despite substantial strides in therapeutic approaches, the formidable challenge of drug resistance persists. The genomic landscape of bladder cancer, characterized by intricate clonal heterogeneity, emerges as a pivotal determinant in fostering this resistance. Clonal evolution, encapsulating the dynamic transformations within subpopulations of tumor cells over time, is implicated in the emergence of drug-resistant traits. Within this review, we illuminate contemporary insights into the role of clonal evolution in bladder cancer, elucidating its influence as a driver in tumor initiation, disease progression, and the formidable obstacle of therapy resistance.

A gene for all seasons: The evolutionary consequences of HIF-1 in carcinogenesis, tumor growth and metastasis

Oxygen played a pivotal role in the evolution of multicellularity during the Cambrian Explosion. Not surprisingly, responses to fluctuating oxygen concentrations are integral to the evolution of cancer-a disease characterized by the breakdown of multicellularity. Poorly organized tumor vasculature results in chaotic patterns of blood flow characterized by large spatial and temporal variations in intra-tumoral oxygen concentrations. Hypoxia-inducible growth factor (HIF-1) plays a pivotal role in enabling cells to adapt, metabolize, and proliferate in low oxygen conditions. HIF-1 is often constitutively activated in cancers, underscoring its importance in cancer progression. Here, we argue that the phenotypic changes mediated by HIF-1, in addition to adapting the cancer cells to their local environment, also "pre-adapt" them for proliferation at distant, metastatic sites. HIF-1-mediated adaptations include a metabolic shift towards anaerobic respiration or glycolysis, activation of cell survival mechanisms like phenotypic plasticity and epigenetic reprogramming, and formation of tumor vasculature through angiogenesis. Hypoxia induced epigenetic reprogramming can trigger epithelial to mesenchymal transition in cancer cells-the first step in the metastatic cascade. Highly glycolytic cells facilitate local invasion by acidifying the tumor microenvironment. New blood vessels, formed due to angiogenesis, provide cancer cells a conduit to the circulatory system. Moreover, survival mechanisms acquired by cancer cells in the primary site allow them to remodel tissue at the metastatic site generating tumor promoting microenvironment. Thus, hypoxia in the primary tumor promoted adaptations conducive to all stages of the metastatic cascade from the initial escape entry into a blood vessel, intravascular survival, extravasation into distant tissues, and establishment of secondary tumors.

Cancer cells employ lipid droplets to survive toxic stress

BACKGROUND

Lipid reprogramming is a known mechanism to increase the energetic demands of proliferating cancer cells to drive and support tumorigenesis and progression. Elevated lipid droplets (LDs) are a well-known alteration of lipid reprogramming in many cancers, including prostate cancer (PCa), and are associated with high tumor aggressiveness as well as therapy resistance. The mechanism of LD accumulation and specific LD functions are still not well understood; however, it has been shown that LDs can form as a protective mechanism against lipotoxicity and lipid peroxidation in the cell.

METHODS

This study investigated the significance of LDs in PCa. This was done by staining, imaging, image quantification, and flow cytometry analysis of LDs in PCa cells. Additionally, lipidomics and metabolomics experiments were performed to assess the difference of metabolites and lipids in control and treatment surviving cancer cells. Lastly, to assess clinical significance, multiple publicly available datasets were mined for LD-related data.

RESULTS

Our study demonstrated that prostate and breast cancer cells that survive 72 h of chemotherapy treatment have elevated LDs. These LDs formed in tandem with elevated reactive oxygen species levels to sequester damaged and excess lipids created by oxidative stress, which promoted cell survival. Additionally, by inhibiting diacylglycerol O-acyltransferase 1 (DGAT1) (which catalyzes triglyceride synthesis into LDs) and treating with chemotherapy simultaneously, we were able to decrease the overall amount of LDs and increase cancer cell death compared to treating with chemotherapy alone.

CONCLUSIONS

Overall, our study proposes a potential combination therapy of DGAT1 inhibitors and chemotherapy to increase cancer cell death.

Modeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution

Mathematical models of cancer and bacterial evolution have generally stemmed from a gene-centric framework, assuming clonal evolution via acquisition of resistance-conferring mutations and selection of their corresponding subpopulations. More recently, the role of phenotypic plasticity has been recognized and models accounting for phenotypic switching between discrete cell states (e.g., epithelial and mesenchymal) have been developed. However, seldom do models incorporate both plasticity and mutationally driven resistance, particularly when the state space is continuous and resistance evolves in a continuous fashion. In this paper, we develop a framework to model plastic and mutational mechanisms of acquiring resistance in a continuous gradual fashion. We use this framework to examine ways in which cancer and bacterial populations can respond to stress and consider implications for therapeutic strategies. Although we primarily discuss our framework in the context of cancer and bacteria, it applies broadly to any system capable of evolving via plasticity and genetic evolution.

[Hypothesis of Genetic Diversity Selection in the Occurrence and Development of Lung Cancer: Molecular Evolution and Clinical Significance]

So far, the monoclonal hypothesis of tumor occurrence and development cannot be justified. The genetic diversity selection hypothesis for the occurrence and development of lung cancer links Mendelian genetics with Darwin's theory of evolution, suggesting that the genetic diversity of tumor cell populations with polyclonal origins-monoclonal selection-subclonal expansion is the result of selection pressure. Normal cells acquire mutations in oncogenic driver genes and have a selective advantage over other cells, becoming tumor initiating cells; In the interaction with the tumor microenvironment (TME), the vast majority of initiating cells are recognized and killed by the human immune system. If immune escape occurs, the incidence of malignant tumors will greatly increase, and subclonal expansion, intratumour heterogeneity, etc. will occur. This article proposed the hypothesis of genetic diversity selection and analyzed its clinical significance.
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Tracking Karyotype Changes in Treatment-Induced Drug-Resistant Evolution

Karyotype coding, which encompasses the complete chromosome sets and their topological genomic relationships within a given species, encodes system-level information that organizes and preserves genes' function, and determines the macroevolution of cancer. This new recognition emphasizes the crucial role of karyotype characterization in cancer research. To advance this cancer cytogenetic/cytogenomic concept and its platforms, this study outlines protocols for monitoring the karyotype landscape during treatment-induced rapid drug resistance in cancer. It emphasizes four key perspectives: combinational analyses of phenotype and karyotype, a focus on the entire evolutionary process through longitudinal analysis, a comparison of whole landscape dynamics by including various types of NCCAs (including genome chaos), and the use of the same process to prioritize different genomic scales. This protocol holds promise for studying numerous evolutionary aspects of cancers, and it further enhances the power of karyotype analysis in cancer research.

Inferring Markov Chains to Describe Convergent Tumor Evolution With CIMICE

The field of tumor phylogenetics focuses on studying the differences within cancer cell populations. Many efforts are done within the scientific community to build cancer progression models trying to understand the heterogeneity of such diseases. These models are highly dependent on the kind of data used for their construction, therefore, as the experimental technologies evolve, it is of major importance to exploit their peculiarities. In this work we describe a cancer progression model based on Single Cell DNA Sequencing data. When constructing the model, we focus on tailoring the formalism on the specificity of the data. We operate by defining a minimal set of assumptions needed to reconstruct a flexible DAG structured model, capable of identifying progression beyond the limitation of the infinite site assumption. Our proposal is conservative in the sense that we aim to neither discard nor infer knowledge which is not represented in the data. We provide simulations and analytical results to show the features of our model, test it on real data, show how it can be integrated with other approaches to cope with input noise. Moreover, our framework can be exploited to produce simulated data that follows our theoretical assumptions. Finally, we provide an open source R implementation of our approach, called CIMICE, that is publicly available on BioConductor.

Updating the Definition of Cancer

Most definitions of cancer broadly conform to the current NCI definition: "Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body." These definitions tend to describe what cancer "looks like" or "does" but do not describe what cancer "is" or "has become." While reflecting past insights, current definitions have not kept pace with the understanding that the cancer cell is itself transformed and evolving. We propose a revised definition of cancer: Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection. We believe this definition captures the essence of the majority of previous and current definitions. To the simplest definition of cancer as a disease of uncontrolled proliferation of cells, our definition adds in the adjective "transformed" to capture the many tumorigenic processes that cancer cells adopt to metastasize. To the concept of uncontrolled proliferation of transformed cells, our proposed definition then adds "subject to evolution by natural selection." The subject to evolution by natural selection modernizes the definition to include the genetic and epigenetic changes that accumulate within a population of cancer cells that lead to the lethal phenotype. Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection.

Convergent TP53 loss and evolvability in cancer

Cancer cell populations evolve by a stepwise process involving natural selection of the fittest variants within a tissue ecosystem context and as modified by therapy. Genomic scrutiny of patient samples reveals an extraordinary diversity of mutational profiles both between patients with similar cancers and within the cancer cell population of individual patients. Does this signify highly divergent evolutionary trajectories or are there repetitive and predictable patterns?Major evolutionary innovations or adaptations in different species are frequently repeated, or convergent, reflecting both common selective pressures and constraints on optimal solutions. We argue this is true of evolving cancer cells, especially with respect to the TP53 gene. Functional loss variants in TP53 are the most common genetic change in cancer. We discuss the likely microenvironmental selective pressures involved and the profound impact this has on cell fitness, evolvability and probability of subsequent drug resistance.

A mathematical investigation of polyaneuploid cancer cell memory and cross-resistance in state-structured cancer populations

The polyaneuploid cancer cell (PACC) state promotes cancer lethality by contributing to survival in extreme conditions and metastasis. Recent experimental evidence suggests that post-therapy PACC-derived recurrent populations display cross-resistance to classes of therapies with independent mechanisms of action. We hypothesize that this can occur through PACC memory, whereby cancer cells that have undergone a polyaneuploid transition (PAT) reenter the PACC state more quickly or have higher levels of innate resistance. In this paper, we build on our prior mathematical models of the eco-evolutionary dynamics of cells in the 2N+ and PACC states to investigate these two hypotheses. We show that although an increase in innate resistance is more effective at promoting cross-resistance, this trend can also be produced via PACC memory. We also find that resensitization of cells that acquire increased innate resistance through the PAT have a considerable impact on eco-evolutionary dynamics and extinction probabilities. This study, though theoretical in nature, can help inspire future experimentation to tease apart hypotheses surrounding how cross-resistance in structured cancer populations arises.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Cells in the polyaneuploid cancer cell (PACC) state have increased metastatic potential

Although metastasis is the leading cause of cancer deaths, it is quite rare at the cellular level. Only a rare subset of cancer cells (~ 1 in 1.5 billion) can complete the entire metastatic cascade: invasion, intravasation, survival in the circulation, extravasation, and colonization (i.e. are metastasis competent). We propose that cells engaging a Polyaneuploid Cancer Cell (PACC) phenotype are metastasis competent. Cells in the PACC state are enlarged, endocycling (i.e. non-dividing) cells with increased genomic content that form in response to stress. Single-cell tracking using time lapse microscopy reveals that PACC state cells have increased motility. Additionally, cells in the PACC state exhibit increased capacity for environment-sensing and directional migration in chemotactic environments, predicting successful invasion. Magnetic Twisting Cytometry and Atomic Force Microscopy reveal that cells in the PACC state display hyper-elastic properties like increased peripheral deformability and maintained peri-nuclear cortical integrity that predict successful intravasation and extravasation. Furthermore, four orthogonal methods reveal that cells in the PACC state have increased expression of vimentin, a hyper-elastic biomolecule known to modulate biomechanical properties and induce mesenchymal-like motility. Taken together, these data indicate that cells in the PACC state have increased metastatic potential and are worthy of further in vivo analysis.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

Long noncoding RNA SNHG15: A promising target in human cancers

As oncogenes or tumor suppressor genes, lncRNAs played an important role in tumorigenesis and the progression of human cancers. The lncRNA SNHG15 has recently been revealed to be dysregulated in malignant tumors, suggesting the aberrant expression of which contributes to clinical features and regulates various oncogenic processes. We have selected extensive literature focused on SNHG15 from electronic databases, including studies relevant to its clinical significance and the critical events in cancer-related processes such as cell proliferation, apoptosis, autophagy, metastasis, and drug resistance. This review summarized the current understanding of SNHG15 in cancer, mainly focusing on the pathological features, known biological functions, and underlying molecular mechanisms. Furthermore, SNHG15 has been well-documented to be an effective diagnostic and prognostic marker for tumors, offering novel therapeutic interventions in specific subsets of cancer cells.

An oncospace for human cancers

Human cancers comprise an heterogeneous array of diseases with different progression patterns and responses to therapy. However, they all develop within a host context that constrains their natural history. Since it occurs across the diversity of organisms, one can conjecture that there is order in the cancer multiverse. Is there a way to capture the broad range of tumor types within a space of the possible? Here we define the oncospace, a coordinate system that integrates the ecological, evolutionary and developmental components of cancer complexity. The spatial position of a tumor results from its departure from the healthy tissue along these three axes, and progression trajectories inform about the components driving malignancy across cancer subtypes. We postulate that the oncospace topology encodes new information regarding tumorigenic pathways, subtype prognosis, and therapeutic opportunities: treatment design could benefit from considering how to nudge tumors toward empty evolutionary dead ends in the oncospace.

Modeling cancer's ecological and evolutionary dynamics

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

Smart and Multi-Functional Magnetic Nanoparticles for Cancer Treatment Applications: Clinical Challenges and Future Prospects

Iron oxide nanoparticle (IONPs) have become a subject of interest in various biomedical fields due to their magnetism and biocompatibility. They can be utilized as heat mediators in magnetic hyperthermia (MHT) or as contrast media in magnetic resonance imaging (MRI), and ultrasound (US). In addition, their high drug-loading capacity enabled them to be therapeutic agent transporters for malignancy treatment. Hence, smartening them allows for an intelligent controlled drug release (CDR) and targeted drug delivery (TDD). Smart magnetic nanoparticles (SMNPs) can overcome the impediments faced by classical chemo-treatment strategies, since they can be navigated and release drug via external or internal stimuli. Recently, they have been synchronized with other modalities, e.g., MRI, MHT, US, and for dual/multimodal theranostic applications in a single platform. Herein, we provide an overview of the attributes of MNPs for cancer theranostic application, fabrication procedures, surface coatings, targeting approaches, and recent advancement of SMNPs. Even though MNPs feature numerous privileges over chemotherapy agents, obstacles remain in clinical usage. This review in particular covers the clinical predicaments faced by SMNPs and future research scopes in the field of SMNPs for cancer theranostics.

Origin and evolution of animal multicellularity in the light of phylogenomics and cancer genetics

The rise of animals represents a major but enigmatic event in the evolutionary history of life. In recent years, numerous studies have aimed at understanding the genetic basis of this transition. However, genome comparisons of diverse animal and protist lineages suggest that the appearance of gene families that were previously considered animal specific indeed preceded animals. Animals' unicellular relatives, such as choanoflagellates, ichthyosporeans, and filastereans, demonstrate complex life cycles including transient multicellularity as well as genetic toolkits for temporal cell differentiation, cell-to-cell communication, apoptosis, and cell adhesion. This has warranted further exploration of the genetic basis underlying transitions in cellular organization. An alternative model for the study of transitions in cellular organization is tumors, which exploit physiological programs that characterize both unicellularity and multicellularity. Tumor cells, for example, switch adhesion on and off, up- or downregulate specific cell differentiation states, downregulate apoptosis, and allow cell migration within tissues. Here, we use insights from both the fields of phylogenomics and tumor biology to review the evolutionary history of the regulatory systems of multicellularity and discuss their overlap. We claim that while evolutionary biology has contributed to an increased understanding of cancer, broad investigations into tissue-normal and transformed-can also contribute the framework for exploring animal evolution.

Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations

Recent evidence suggests that a polyaneuploid cancer cell (PACC) state may play a key role in the adaptation of cancer cells to stressful environments and in promoting therapeutic resistance. The PACC state allows cancer cells to pause cell division and to avoid DNA damage and programmed cell death. Transition to the PACC state may also lead to an increase in the cancer cell’s ability to generate heritable variation (evolvability). One way this can occur is through evolutionary triage. Under this framework, cells gradually gain resistance by scaling hills on a fitness landscape through a process of mutation and selection. Another way this can happen is through self-genetic modification whereby cells in the PACC state find a viable solution to the stressor and then undergo depolyploidization, passing it on to their heritably resistant progeny. Here, we develop a stochastic model to simulate both of these evolutionary frameworks. We examine the impact of treatment dosage and extent of self-genetic modification on eco-evolutionary dynamics of cancer cells with aneuploid and PACC states. We find that under low doses of therapy, evolutionary triage performs better whereas under high doses of therapy, self-genetic modification is favored. This study generates predictions for teasing apart these biological hypotheses, examines the implications of each in the context of cancer, and provides a modeling framework to compare Mendelian and non-traditional forms of inheritance.

Cancer cell foraging to explain bone-specific metastatic progression

Metastatic cancer is lethal and patients who suffer bone metastases fare especially poorly. Bone-specific metastatic progression in prostate and breast cancers is a highly observed example of organ-specific metastasis, or organotropism. Though research has delineated the sequential steps of the metastatic cascade, the determinants of bone-specific metastasis have remained elusive for decades. Applying fundamental ecological principles to cancer biology models of metastasis provides novel insights into metastatic organotropism. We use critical concepts from foraging theory and movement ecology to propose that observed bone-specific metastasis is the result of habitat selection by foraging cancer cells. Furthermore, we posit that cancer cells can only perform habitat selection if and when they employ a reversible motile foraging strategy. Only a very small percentage of cells in a primary tumor harbor this ability. Therefore, our habitat selection model emphasizes the importance of identifying the rare subset of cancer cells that might exhibit habitat selection, ergo achieve bone-specific metastatic colonization.

LncRNA GACAT3: A Promising Biomarker and Therapeutic Target in Human Cancers

Long non-coding RNAs (lncRNAs) are a class of functional RNA molecules that do not encode proteins and are composed of more than 200 nucleotides. LncRNAs play important roles in epigenetic and gene expression regulation. The oncogenic lncRNA GACAT3 was recently discovered to be dysregulated in many tumors. Aberrant expression of GACAT3 contributes to clinical characteristics and regulates multiple oncogenic processes. The association of GACAT3 with a variety of tumors makes it a promising biomarker for diagnosis, prognosis, and targeted therapy. In this review, we integrate the current understanding of the pathological features, biological functions, and molecular mechanisms of GACAT3 in cancer. Additionally, we provide insight into the utility of GACAT3 as an effective diagnostic and prognostic marker for specific tumors, which offers novel opportunities for targeted therapeutic intervention.

Protein-Based Nanoparticles for the Imaging and Treatment of Solid Tumors: The Case of Ferritin Nanocages, a Narrative Review

Protein nanocages have been studied extensively, due to their unique architecture, exceptional biocompatibility and highly customization capabilities. In particular, ferritin nanocages (FNs) have been employed for the delivery of a vast array of molecules, ranging from chemotherapeutics to imaging agents, among others. One of the main favorable characteristics of FNs is their intrinsic targeting efficiency toward the Transferrin Receptor 1, which is overexpressed in many tumors. Furthermore, genetic manipulation can be employed to introduce novel variants that are able to improve the loading capacity, targeting capabilities and bio-availability of this versatile drug delivery system. In this review, we discuss the main characteristics of FN and the most recent applications of this promising nanotechnology in the field of oncology with a particular emphasis on the imaging and treatment of solid tumors.

Sea Turtles in the Cancer Risk Landscape: A Global Meta-Analysis of Fibropapillomatosis Prevalence and Associated Risk Factors

Several cancer risk factors (exposure to ultraviolet-B, pollution, toxins and pathogens) have been identified for wildlife, to form a “cancer risk landscape.” However, information remains limited on how the spatiotemporal variability of these factors impacts the prevalence of cancer in wildlife. Here, we evaluated the cancer risk landscape at 49 foraging sites of the globally distributed green turtle (Chelonia mydas), a species affected by fibropapillomatosis, by integrating data from a global meta-analysis of 31 publications (1994–2019). Evaluated risk factors included ultraviolet light exposure, eutrophication, toxic phytoplanktonic blooms, sea surface temperature, and the presence of mechanical vectors (parasites and symbiotic species). Prevalence was highest in areas where nutrient concentrations facilitated the emergence of toxic phytoplankton blooms. In contrast, ultraviolet light exposure and the presence of parasitic and/or symbiotic species did not appear to impact disease prevalence. Our results indicate that, to counter outbreaks of fibropapillomatosis, management actions that reduce eutrophication in foraging areas should be implemented.

The Genomic Processes of Biological Invasions: From Invasive Species to Cancer Metastases and Back Again

The concept of invasion is useful across a broad range of contexts, spanning from the fine scale landscape of cancer tumors up to the broader landscape of ecosystems. Invasion biology provides extraordinary opportunities for studying the mechanistic basis of contemporary evolution at the molecular level. Although the field of invasion genetics was established in ecology and evolution more than 50 years ago, there is still a limited understanding of how genomic level processes translate into invasive phenotypes across different taxa in response to complex environmental conditions. This is largely because the study of most invasive species is limited by information about complex genome level processes. We lack good reference genomes for most species. Rigorous studies to examine genomic processes are generally too costly. On the contrary, cancer studies are fortified with extensive resources for studying genome level dynamics and the interactions among genetic and non-genetic mechanisms. Extensive analysis of primary tumors and metastatic samples have revealed the importance of several genomic mechanisms including higher mutation rates, specific types of mutations, aneuploidy or whole genome doubling and non-genetic effects. Metastatic sites can be directly compared to primary tumor cell counterparts. At the same time, clonal dynamics shape the genomics and evolution of metastatic cancers. Clonal diversity varies by cancer type, and the tumors’ donor and recipient tissues. Still, the cancer research community has been unable to identify any common events that provide a universal predictor of “metastatic potential” which parallels findings in evolutionary ecology. Instead, invasion in cancer studies depends strongly on context, including order of events and clonal composition. The detailed studies of the behavior of a variety of human cancers promises to inform our understanding of genome level dynamics in the diversity of invasive species and provide novel insights for management.

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

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

Ecology of Fear: Spines, Armor and Noxious Chemicals Deter Predators in Cancer and in Nature

In nature, many multicellular and unicellular organisms use constitutive defenses such as armor, spines, and noxious chemicals to keep predators at bay. These defenses render the prey difficult and/or dangerous to subdue and handle, which confers a strong deterrent for predators. The distinct benefit of this mode of defense is that prey can defend in place and continue activities such as foraging even under imminent threat of predation. The same qualitative types of armor-like, spine-like, and noxious defenses have evolved independently and repeatedly in nature, and we present evidence that cancer is no exception. Cancer cells exist in environments inundated with predator-like immune cells, so the ability of cancer cells to defend in place while foraging and proliferating would clearly be advantageous. We argue that these defenses repeatedly evolve in cancers and may be among the most advanced and important adaptations of cancers. By drawing parallels between several taxa exhibiting armor-like, spine-like, and noxious defenses, we present an overview of different ways these defenses can appear and emphasize how phenotypes that appear vastly different can nevertheless have the same essential functions. This cross-taxa comparison reveals how cancer phenotypes can be interpreted as anti-predator defenses, which can facilitate therapy approaches which aim to give the predators (the immune system) the upper hand. This cross-taxa comparison is also informative for evolutionary ecology. Cancer provides an opportunity to observe how prey evolve in the context of a unique predatory threat (the immune system) and varied environments.

Cancer recurrence and lethality are enabled by enhanced survival and reversible cell cycle arrest of polyaneuploid cells

We present a unifying theory to explain cancer recurrence, therapeutic resistance, and lethality. The basis of this theory is the formation of simultaneously polyploid and aneuploid cancer cells, polyaneuploid cancer cells (PACCs), that avoid the toxic effects of systemic therapy by entering a state of cell cycle arrest. The theory is independent of which of the classically associated oncogenic mutations have already occurred. PACCs have been generally disregarded as senescent or dying cells. Our theory states that therapeutic resistance is driven by PACC formation that is enabled by accessing a polyploid program that allows an aneuploid cancer cell to double its genomic content, followed by entry into a nondividing cell state to protect DNA integrity and ensure cell survival. Upon removal of stress, e.g., chemotherapy, PACCs undergo depolyploidization and generate resistant progeny that make up the bulk of cancer cells within a tumor.

The Evolutionary Ecology of Dormancy in Nature and in Cancer

Dormancy is an inactive period of an organism’s life cycle that permits it to survive through phases of unfavorable conditions in highly variable environments. Dormancy is not binary. There is a continuum of dormancy phenotypes that represent some degree of reduced metabolic activity (hypometabolism), reduced feeding, and reduced reproduction or proliferation. Similarly, normal cells and cancer cells exhibit a range of states from quiescence to long-term dormancy that permit survival in adverse environmental conditions. In contrast to organismal dormancy, which entails a reduction in metabolism, dormancy in cells (both normal and cancer) is primarily characterized by lack of cell division. “Cancer dormancy” also describes a state characterized by growth stagnation, which could arise from cells that are not necessarily hypometabolic or non-proliferative. This inconsistent terminology leads to confusion and imprecision that impedes progress in interdisciplinary research between ecologists and cancer biologists. In this paper, we draw parallels and contrasts between dormancy in cancer and other ecosystems in nature, and discuss the potential for studies in cancer to provide novel insights into the evolutionary ecology of dormancy.

Polyaneuploid Cancer Cell Dormancy: Lessons From Evolutionary Phyla

Dormancy is a key survival strategy in many organisms across the tree of life. Organisms that utilize some type of dormancy (hibernation, aestivation, brumation, diapause, and quiescence) are able to survive in habitats that would otherwise be uninhabitable. Induction into dormant states is typically caused by environmental stress. While organisms are dormant, their physical activity is minimal, and their metabolic rates are severely depressed (hypometabolism). These metabolic reductions allow for the conservation and distribution of energy while conditions in the environment are poor. When conditions are more favorable, the organisms are then able to come out of dormancy and reengage in their environment. Polyaneuploid cancer cells (PACCs), proposed mediators of cancer metastasis and resistance, access evolutionary programs and employ dormancy as a survival mechanism in response to stress. Quiescence, the type of dormancy observed in PACCs, allows these cells the ability to survive stressful conditions (e.g., hypoxia in the microenvironment, transiting the bloodstream during metastasis, and exposure to chemotherapy) by downregulating and altering metabolic function, but then increasing metabolic activities again once stress has passed. We can gain insights regarding the mechanisms underlying PACC dormancy by looking to the evolution of dormancy in different organisms.

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.

Biology transcends the limits of computation

Cognition-sensing and responding to the environment-is the unifying principle behind the genetic code, origin of life, evolution, consciousness, artificial intelligence, and cancer. However, the conventional model of biology seems to mistake cause and effect. According to the reductionist view, the causal chain in biology is chemicals → code → cognition. Despite this prevailing view, there are no examples in the literature to show that the laws of physics and chemistry can produce codes, or that codes produce cognition. Chemicals are just the physical layer of any information system. In contrast, although examples of cognition generating codes and codes controlling chemicals are ubiquitous in biology and technology, cognition remains a mystery. Thus, the central question in biology is: What is the nature and origin of cognition? In order to elucidate this pivotal question, we must cultivate a deeper understanding of information flows. Through this lens, we see that biological cognition is volitional (i.e., deliberate, intentional, or knowing), and while technology is constrained by deductive logic, living things make choices and generate novel information using inductive logic. Information has been called "the hard problem of life' and cannot be fully explained by known physical principles (Walker et al., 2017). The present paper uses information theory (the mathematical foundation of our digital age) and Turing machines (computers) to highlight inaccuracies in prevailing reductionist models of biology, and proposes that the correct causation sequence is cognition → code → chemicals.

Cancer risk landscapes: A framework to study cancer in ecosystems

Cancer is a family of diseases that has been documented in most metazoan species and ecosystems. Human induced environmental changes are increasingly exposing wildlife to carcinogenic risk factors, and negative repercussions on ecosystems and on the conservation of endangered species are already been observed. It is therefore of key importance to understand the spatiotemporal variability of those risk factors and how they interact with the biosphere to mitigate their effects. Here we introduce the concept of cancer risk landscape that can be applied to understand how species are exposed to, interact with, and modify cancer risk factors. With this publication we aim to provide a framework in order to stimulate a discussion on how to mitigate cancer-causing risk factors.

Identifying key questions in the ecology and evolution of cancer

The application of evolutionary and ecological principles to cancer prevention and treatment, as well as recognizing cancer as a selection force in nature, has gained impetus over the last 50 years. Following the initial theoretical approaches that combined knowledge from interdisciplinary fields, it became clear that using the eco-evolutionary framework is of key importance to understand cancer. We are now at a pivotal point where accumulating evidence starts to steer the future directions of the discipline and allows us to underpin the key challenges that remain to be addressed. Here, we aim to assess current advancements in the field and to suggest future directions for research. First, we summarize cancer research areas that, so far, have assimilated ecological and evolutionary principles into their approaches and illustrate their key importance. Then, we assembled 33 experts and identified 84 key questions, organized around nine major themes, to pave the foundations for research to come. We highlight the urgent need for broadening the portfolio of research directions to stimulate novel approaches at the interface of oncology and ecological and evolutionary sciences. We conclude that progressive and efficient cross-disciplinary collaborations that draw on the expertise of the fields of ecology, evolution and cancer are essential in order to efficiently address current and future questions about cancer.

How Chaotic Is Genome Chaos?

Simple Summary

Cancer genomes can undergo major restructurings involving many chromosomal locations at key stages in tumor development. This restructuring process has been designated “genome chaos” by some authors. In order to examine how chaotic cancer genome restructuring may be, the cell and molecular processes for DNA restructuring are reviewed. Examination of the action of these processes in various cancers reveals a degree of specificity that indicates genome restructuring may be sufficiently reproducible to enable possible therapies that interrupt tumor progression to more lethal forms.

Abstract

Cancer genomes evolve in a punctuated manner during tumor evolution. Abrupt genome restructuring at key steps in this evolution has been called “genome chaos.” To answer whether widespread genome change is truly chaotic, this review (i) summarizes the limited number of cell and molecular systems that execute genome restructuring, (ii) describes the characteristic signatures of DNA changes that result from activity of those systems, and (iii) examines two cases where genome restructuring is determined to a significant degree by cell type or viral infection. The conclusion is that many restructured cancer genomes display sufficiently unchaotic signatures to identify the cellular systems responsible for major oncogenic transitions, thereby identifying possible targets for therapies to inhibit tumor progression to greater aggressiveness.

Cell-cell fusions and cell-in-cell phenomena in healthy cells and cancer: Lessons from protists and invertebrates

Herein we analyze two special routes of the multinucleated cells' formation - the fusion of mononuclear cells and the formation of cell-in-cell structures - in the healthy tissues and in tumorigenesis. There are many theories of tumorigenesis based on the phenomenon of emergence of the hybrid cancer cells. We consider the phenomena, which are rarely mentioned in those theories: namely, cellularization of syncytium or coenocytes, and the reversible or irreversible somatogamy. The latter includes the short-term and the long-term vegetative (somatic) cells' fusions in the life cycles of unicellular organisms. The somatogamy and multinuclearity have repeatedly and independently emerged in various groups of unicellular eukaryotes. These phenomena are among dominant survival and biodiversity sustaining strategies in protists and we admit that they can likely play an analogous role in cancer cells.

Paradoxes of cancer: Survival at the brink

The fundamental understanding of how Cancer initiates, persists and then progresses is evolving. High-resolution technologies, including single-cell mutation and gene expression measurements, are now attainable, providing an ever-increasing insight into the molecular details. However, this higher resolution has shown that somatic mutation theory itself cannot explain the extraordinary resistance of cancer to extinction. There is a need for a more Systems-based framework of understanding cancer complexity, which in particular explains the regulation of gene expression during cell-fate decisions. Cancer displays a series of paradoxes. Here we attempt to approach them from the view-point of adaptive exploration of gene regulatory networks at the edge of order and chaos, where cell-fate is changed by oscillations between alternative regulators of cellular senescence and reprogramming operating through self-organisation. On this background, the role of polyploidy in accessing the phylogenetically pre-programmed "oncofetal attractor" state, related to unicellularity, and the de-selection of unsuitable variants at the brink of cell survival is highlighted. The concepts of the embryological and atavistic theory of cancer, cancer cell "life-cycle", and cancer aneuploidy paradox are dissected under this lense. Finally, we challenge researchers to consider that cancer "defects" are mostly the adaptation tools of survival programs that have arisen during evolution and are intrinsic of cancer. Recognition of these features should help in the development of more successful anti-cancer treatments.

Cancer cells employ an evolutionarily conserved polyploidization program to resist therapy

Unusually large cancer cells with abnormal nuclei have been documented in the cancer literature since 1858. For more than 100 years, they have been generally disregarded as irreversibly senescent or dying cells, too morphologically misshapen and chromatin too disorganized to be functional. Cell enlargement, accompanied by whole genome doubling or more, is observed across organisms, often associated with mitigation strategies against environmental change, severe stress, or the lack of nutrients. Our comparison of the mechanisms for polyploidization in other organisms and non-transformed tissues suggest that cancer cells draw from a conserved program for their survival, utilizing whole genome doubling and pausing proliferation to survive stress. These polyaneuploid cancer cells (PACCs) are the source of therapeutic resistance, responsible for cancer recurrence and, ultimately, cancer lethality.

Phylostratic Shift of Whole-Genome Duplications in Normal Mammalian Tissues towards Unicellularity Is Driven by Developmental Bivalent Genes and Reveals a Link to Cancer

Tumours were recently revealed to undergo a phylostratic and phenotypic shift to unicellularity. As well, aggressive tumours are characterized by an increased proportion of polyploid cells. In order to investigate a possible shared causation of these two features, we performed a comparative phylostratigraphic analysis of ploidy-related genes, obtained from transcriptomic data for polyploid and diploid human and mouse tissues using pairwise cross-species transcriptome comparison and principal component analysis. Our results indicate that polyploidy shifts the evolutionary age balance of the expressed genes from the late metazoan phylostrata towards the upregulation of unicellular and early metazoan phylostrata. The up-regulation of unicellular metabolic and drug-resistance pathways and the downregulation of pathways related to circadian clock were identified. This evolutionary shift was associated with the enrichment of ploidy with bivalent genes (p < 10⁻¹⁶). The protein interactome of activated bivalent genes revealed the increase of the connectivity of unicellulars and (early) multicellulars, while circadian regulators were depressed. The mutual polyploidy-c-MYC-bivalent genes-associated protein network was organized by gene-hubs engaged in both embryonic development and metastatic cancer including driver (proto)-oncogenes of viral origin. Our data suggest that, in cancer, the atavistic shift goes hand-in-hand with polyploidy and is driven by epigenetic mechanisms impinging on development-related bivalent genes.

Genome chaos: Creating new genomic information essential for cancer macroevolution

Cancer research has traditionally focused on the characterization of individual molecular mechanisms that can contribute to cancer. Due to the multiple levels of genomic and non-genomic heterogeneity, however, overwhelming molecular mechanisms have been identified, most with low clinical predictability. It is thus necessary to search for new concepts to unify these diverse mechanisms and develop better strategies to understand and treat cancer. In recent years, two-phased cancer evolution (comprised of the genome reorganization-mediated punctuated phase and gene mutation-mediated stepwise phase), initially described by tracing karyotype evolution, was confirmed by the Cancer Genome Project. In particular, genome chaos, the process of rapid and massive genome reorganization, has been commonly detected in various cancers-especially during key phase transitions, including cellular transformation, metastasis, and drug resistance-suggesting the importance of genome-level changes in cancer evolution. In this Perspective, genome chaos is used as a discussion point to illustrate new genome-mediated somatic evolutionary frameworks. By rephrasing cancer as a new system emergent from normal tissue, we present the multiple levels (or scales) of genomic and non-genomic information. Of these levels, evolutionary studies at the chromosomal level are determined to be of ultimate importance, since altered genomes change the karyotype coding and karyotype change is the key event for punctuated cellular macroevolution. Using this lens, we differentiate and analyze developmental processes and cancer evolution, as well as compare the informational relationship between genome chaos and its various subtypes in the context of macroevolution under crisis. Furthermore, the process of deterministic genome chaos is discussed to interpret apparently random events (including stressors, chromosomal variation subtypes, surviving cells with new karyotypes, and emergent stable cellular populations) as nonrandom patterns, which supports the new cancer evolutionary model that unifies genome and gene contributions during different phases of cancer evolution. Finally, the new perspective of using cancer as a model for organismal evolution is briefly addressed, emphasizing the Genome Theory as a new and necessary conceptual framework for future research and its practical implications, not only in cancer but evolutionary biology as a whole.

The Evolution and Ecology of Resistance in Cancer Therapy

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

Integrating genetic and nongenetic drivers of somatic evolution during carcinogenesis: The biplane model

The multistep transition from a normal to a malignant cellular phenotype is often termed "somatic evolution" caused by accumulating random mutations. Here, we propose an alternative model in which the initial genetic state of a cancer cell is the result of mutations that occurred throughout the lifetime of the host. However, these mutations are not carcinogenic because normal cells in multicellular organism cannot ordinarily evolve. That is, proliferation and death of normal cells are controlled by local tissue constraints typically governed by nongenomic information dynamics in the cell membrane. As a result, the cells of a multicellular organism have a fitness that is identical to the host, which is then the unit of natural selection. Somatic evolution of a cell can occur only when its fate becomes independent of host constraints. Now, survival, proliferation, and death of individual cells are dependent on Darwinian dynamics. This cellular transition from host-defined fitness to self-defined fitness may, consistent with the conventional view of carcinogenesis, result from mutations that render the cell insensitive to host controls. However, an identical state will result when surrounding tissue cannot exert control because of injury, inflammation, aging, or infection. Here, all surviving cells within the site of tissue damage default to self-defined fitness functions allowing them to evolve so that the mutations accumulated over the lifetime of the host now serve as the genetic heritage of an evolutionary unit of selection. Furthermore, tissue injury generates a new ecology cytokines and growth factors that might promote proliferation in cells with prior receptor mutations. This model integrates genetic and nongenetic dynamics into cancer development and is consistent with both clinical observations and prior experiments that divided carcinogenesis to initiation, promotion, and progression steps.

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.