Nonheritable cellular variability accelerates the evolutionary processes of cancer

Hybrid exons evolved by coupling transcription initiation and splicing at the nucleotide level

Exons within transcripts are traditionally classified as first, internal or last exons, each governed by different regulatory mechanisms. We recently described the widespread usage of 'hybrid' exons that serve as terminal or internal exons in different transcripts. Here, we employ an interpretable deep learning pipeline to dissect the sequence features governing the co-regulation of transcription initiation and splicing in hybrid exons. Using ENCODE data from human tissues, we identified 80 000 hybrid first-internal exons. These exons often possess a relaxed chromatin state, allowing transcription initiation within the gene body. Interestingly, transcription start sites of hybrid exons are typically centered at the 3' splice site, suggesting tight coupling between splicing and transcription initiation. We identified two subcategories of hybrid exons: the majority resemble internal exons, maintaining strong 3' splice sites, while a minority show enrichment in promoter elements, resembling first exons. Diving into the evolution of their sequences, we found that human hybrid exons with orthologous first exons in other species usually gained 3' splice sites or whole exons upstream, while those with orthologous internal exons often gained promoter elements. Overall, our findings unveil the intricate regulatory landscape of hybrid exons and reveal stronger connections between transcription initiation and RNA splicing than previously acknowledged.

The origin of novel traits in cancer

The traditional view of cancer emphasizes a genes-first process. Novel cancer traits arise by genetic mutations that spread to drive phenotypic change. However, recent data support a phenotypes-first process in which nonheritable cellular variability creates novel traits that later become heritably stabilized by genetic and epigenetic changes. Single-cell measurements reinforce the idea that phenotypes lead genotypes, showing how cancer evolution follows normal developmental plasticity and creates novel traits by recombining parts of different cellular developmental programs. In parallel, studies in evolutionary biology also support a phenotypes-first process driven by developmental plasticity and developmental recombination. These advances in cancer research and evolutionary biology mutually reinforce a revolution in our understanding of how cells and organisms evolve novel traits in response to environmental challenges.

Heterogeneity generating capacity in tumorigenesis and cancer therapeutics

Cells of multicellular organisms generate heterogeneity in a controlled and transient fashion during embryogenesis, which can be reactivated in pathologies such as cancer. Although genomic heterogeneity is an important part of tumorigenesis, continuous generation of phenotypic heterogeneity is central for the adaptation of cancer cells to the challenges of tumorigenesis and response to therapy. Here I discuss the capacity of generating heterogeneity, hereafter called cell hetness, in cancer cells both as the activation of hetness oncogenes and inactivation of hetness tumor suppressor genes, which increase the generation of heterogeneity, ultimately producing an increase in adaptability and cell fitness. Transcriptomic high hetness states in therapy-tolerant cell states denote its importance in cancer resistance to therapy. The definition of the concept of hetness will allow the understanding of its origins, its control during embryogenesis, its loss of control in tumorigenesis and cancer therapeutics and its active targeting.

The effects of an obesogenic diet on behavior and cognition in zebrafish (Danio rerio): Trait average, variability, repeatability, and behavioral syndromes

The obesity epidemic, largely driven by the accessibility of ultra-processed high-energy foods, is one of the most pressing public health challenges of the 21st century. Consequently, there is increasing concern about the impacts of diet-induced obesity on behavior and cognition. While research on this matter continues, to date, no study has explicitly investigated the effect of obesogenic diet on variance and covariance (correlation) in behavioral traits. Here, we examined how an obesogenic versus control diet impacts means and (co-)variances of traits associated with body condition, behavior, and cognition in a laboratory population of ~160 adult zebrafish (Danio rerio). Overall, an obesogenic diet increased variation in several zebrafish traits. Zebrafish on an obesogenic diet were significantly heavier and displayed higher body weight variability; fasting blood glucose levels were similar between control and treatment zebrafish. During behavioral assays, zebrafish on the obesogenic diet displayed more exploratory behavior and were less reactive to video stimuli with conspecifics during a personality test, but these significant differences were sex-specific. Zebrafish on an obesogenic diet also displayed repeatable responses in aversive learning tests whereas control zebrafish did not, suggesting an obesogenic diet resulted in more consistent, yet impaired, behavioral responses. Where behavioral syndromes existed (inter-class correlations between personality traits), they did not differ between obesogenic and control zebrafish groups. By integrating a multifaceted, holistic approach that incorporates components of (co-)variances, future studies will greatly benefit by quantifying neglected dimensions of obesogenic diets on behavioral changes.

Combining Evolution and Cancer Therapy: A Review of the Mathematical Approach

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Conventional cancer therapy kills tumors by applying the maximum tolerable dose of therapy. However, it leads to the development of tumoral heterogeneity and resistance, hence leading to therapy failure and progression. It is necessary to design therapies keeping in mind the evolutionary dynamics of tumors to minimize resistance and delay progression. Mathematical models are of great importance in oncology as they assist in the recreation of the tumor microenvironment, predict the outcomes of treatment strategies and elucidate fundamentals of tumor growth and resistance development. The body of literature covering models which incorporate evolutionary dynamics is vast. This paper provides an overview of existing models of “evolutionary therapy”, including ordinary differential equations, fitness, and probability functions.

Bet-hedging in innate and adaptive immune systems

Immune system evolution is shaped by the fitness costs and trade-offs associated with mounting an immune response. Costs that arise mainly as a function of the magnitude of investment, including energetic and immunopathological costs, are well-represented in studies of immune system evolution. Less well considered, however, are the costs of immune cell plasticity and specialization. Hosts in nature encounter a large diversity of microbes and parasites that require different and sometimes conflicting immune mechanisms for defense, but it takes precious time to recognize and correctly integrate signals for an effective polarized response. In this perspective, we propose that bet-hedging can be a viable alternative to plasticity in immune cell effector function, discuss conditions under which bet-hedging is likely to be an advantageous strategy for different arms of the immune system, and present cases from both innate and adaptive immune systems that suggest bet-hedging at play.

Cancer cell states and emergent properties of the dynamic tumor system

Phenotypic heterogeneity within malignant cells of a tumor is emerging as a key property of tumorigenesis. Recent work using single-cell transcriptomics has led to the identification of distinct cancer cell states across a range of cancer types, but their functional relevance and the advantage that they provide to the tumor as a system remain elusive. We present here a definition of cancer cell states in terms of coherently and differentially expressed gene modules and review the origins, dynamics, and impact of states on the tumor system as a whole. The spectrum of cell states taken on by a malignant population may depend on cellular lineage, epigenetic history, genetic mutations, or environmental cues, which has implications for the relative stability or plasticity of individual states. Finally, evidence has emerged that malignant cells in different states may cooperate or compete within a tumor niche, thereby providing an evolutionary advantage to the tumor through increased immune evasion, drug resistance, or invasiveness. Uncovering the mechanisms that govern the origin and dynamics of cancer cell states in tumorigenesis may shed light on how heterogeneity contributes to tumor fitness and highlight vulnerabilities that can be exploited for therapy.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Phenotypic Heterogeneity in Tumor Progression, and Its Possible Role in the Onset of Cancer

Heterogeneity among isogenic cells/individuals has been known for at least 150 years. Even Mendel, working on pea plants, realized that not all tall plants were identical. However, Mendel was more interested in the discontinuous variation between genetically distinct individuals. The concept of environment dictating distinct phenotypes among isogenic individuals has since been shown to impact the evolution of populations in numerous examples at different scales of life. In this review, we discuss how phenotypic heterogeneity and its evolutionary implications exist at all levels of life, from viruses to mammals. In particular, we discuss how a particular disease condition (cancer) is impacted by heterogeneity among isogenic cells, and propose a potential role that phenotypic heterogeneity might play toward the onset of the disease.

Gene expression noise can promote the fixation of beneficial mutations in fluctuating environments

Nongenetic phenotypic variation can either speed up or slow down adaptive evolution. We show that it can speed up evolution in environments where available carbon and energy sources change over time. To this end, we use an experimentally validated model of Escherichia coli growth on two alternative carbon sources, glucose and acetate. On the superior carbon source (glucose), all cells achieve high growth rates, while on the inferior carbon source (acetate) only a small fraction of the population manages to initiate growth. Consequently, populations experience a bottleneck when the environment changes from the superior to the inferior carbon source. Growth on the inferior carbon source depends on a circuit under the control of a transcription factor that is repressed in the presence of the superior carbon source. We show that noise in the expression of this transcription factor can increase the probability that cells start growing on the inferior carbon source. In doing so, it can decrease the severity of the bottleneck and increase mean population fitness whenever this fitness is low. A modest amount of noise can also enhance the fitness effects of a beneficial allele that increases the fraction of a population initiating growth on acetate. Additionally, noise can protect this allele from extinction, accelerate its spread, and increase its likelihood of going to fixation. Central to the adaptation-enhancing principle we identify is the ability of noise to mitigate population bottlenecks, particularly in environments that fluctuate periodically. Because such bottlenecks are frequent in fluctuating environments, and because periodically fluctuating environments themselves are common, this principle may apply to a broad range of environments and organisms.

Phenotypic noise and the cost of complexity

Experimental studies demonstrate the existence of phenotypic diversity despite constant genotype and environment. Theoretical models based on a single phenotypic character predict that during an adaptation event, phenotypic noise should be positively selected far from the fitness optimum because it increases the fitness of the genotype, and then be selected against when the population reaches the optimum. It is suggested that because of this fitness gain, phenotypic noise should promote adaptive evolution. However, it is unclear how the selective advantage of phenotypic noise is linked to the rate of evolution, and whether any advantage would hold for more realistic, multidimensional phenotypes. Indeed, complex organisms suffer a cost of complexity, where beneficial mutations become rarer as the number of phenotypic characters increases. Using a quantitative genetics approach, we first show that for a one-dimensional phenotype, phenotypic noise promotes adaptive evolution on plateaus of positive fitness, independently from the direct selective advantage on fitness. Second, we show that for multidimensional phenotypes, phenotypic noise evolves to a low-dimensional configuration, with elevated noise in the direction of the fitness optimum. Such a dimensionality reduction of the phenotypic noise promotes adaptive evolution and numerical simulations show that it reduces the cost of complexity.

Heritable gene expression variability and stochasticity govern clonal heterogeneity in circadian period

A ubiquitous feature of the circadian clock across life forms is its organization as a network of cellular oscillators, with individual cellular oscillators within the network often exhibiting considerable heterogeneity in their intrinsic periods. The interaction of coupling and heterogeneity in circadian clock networks is hypothesized to influence clock’s entrainability, but our knowledge of mechanisms governing period heterogeneity within circadian clock networks remains largely elusive. In this study, we aimed to explore the principles that underlie intercellular period variation in circadian clock networks (clonal period heterogeneity). To this end, we employed a laboratory selection approach and derived a panel of 25 clonal cell populations exhibiting circadian periods ranging from 22 to 28 h. We report that a single parent clone can produce progeny clones with a wide distribution of circadian periods, and this heterogeneity, in addition to being stochastically driven, has a heritable component. By quantifying the expression of 20 circadian clock and clock-associated genes across our clone panel, we found that inheritance of expression patterns in at least three clock genes might govern clonal period heterogeneity in circadian clock networks. Furthermore, we provide evidence suggesting that heritable epigenetic variation in gene expression regulation might underlie period heterogeneity.

How do genetically identical cells exhibit a different circadian phenotype? This study reveals that a single parent clone can produce progeny with a wide distribution of circadian periods and that this heterogeneity, in addition to being stochastically driven, has a heritable component, likely via heritable epigenetic variation in gene expression regulation.

Utilization of plant-derived Myricetin molecule coupled with ultrasound for the synthesis of gold nanoparticles against breast cancer

Phytochemical mediated synthesis of nanoparticles has gained great interest in the field of cancer therapeutics. We attempted a simple and stable synthesis of gold nanoparticles (AuNPs) with Myricetin (Myr) adopting ultrasound-assisted method. Further, we evaluated anticancer activity of the synthesized nanoparticles. The physico-chemical properties of biosynthesized Myr-AuNPs were characterized by UV-visible spectrophotometer, Fourier-transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and particle size analysis. The study reports of Myr-AuNPs showed spherical-shaped particles with a size of < 50 nm. Stability of the particles was increased in various physiological media. Furthermore, the graph theoretical network analysis of Myr-AuNPs indicated that the probable binding with the mTOR is an effective target for breast cancer cells. In silico molecular docking study of Myr-AuNPs in human mTOR kinase was found to be strong binding. The IC₅₀ value of Myr-AuNPs was calculated as 13 μg mL^-1 against MCF-7 cell line. The AO/EB and DAPI stainings confirmed the anticancer activity by Myr-AuNPs-treated cells showed a good proportion of dead cells evidenced with formation of pro-apoptotic bodies. In addition, Myr-AuNPs exhibited depolarization of mitochondrial membrane potential and production of reactive oxygen species. This study proves that Myr-AuNPs holds great promise to use against breast cancer as a potent anticancer drug. Graphical abstract A schematic representation for the biosynthesis of Myr-AuNPs.

TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.

Cell-state dynamics and therapeutic resistance in melanoma from the perspective of MITF and IFNγ pathways

Targeted therapy and immunotherapy have greatly improved the prognosis of patients with metastatic melanoma, but resistance to these therapeutic modalities limits the percentage of patients with long-lasting responses. Accumulating evidence indicates that a persisting subpopulation of melanoma cells contributes to resistance to targeted therapy or immunotherapy, even in patients who initially have a therapeutic response; however, the root mechanism of resistance remains elusive. To address this problem, we propose a new model, in which dynamic fluctuations of protein expression at the single-cell level and longitudinal reshaping of the cellular state at the cell-population level explain the whole process of therapeutic resistance development. Conceptually, we focused on two different pivotal signalling pathways (mediated by microphthalmia-associated transcription factor (MITF) and IFNγ) to construct the evolving trajectories of melanoma and described each of the cell states. Accordingly, the development of therapeutic resistance could be divided into three main phases: early survival of cell populations, reversal of senescence, and the establishment of new homeostatic states and development of irreversible resistance. On the basis of existing data, we propose future directions in both translational research and the design of therapeutic strategies that incorporate this emerging understanding of resistance.

A Quantitative Model Explains Single-Cell Dynamics of the Adaptive Response in Escherichia coli

DNA damage caused by alkylating chemicals induces an adaptive response in Escherichia coli that increases the tolerance of cells to further damage. Signaling of the response occurs through irreversible methylation of the Ada protein, which acts as a DNA repair protein and damage sensor. Methylated Ada induces its own gene expression through a positive feedback loop. However, random fluctuations in the abundance of Ada jeopardize the reliability of the induction signal. I developed a quantitative model to test how gene expression noise and feedback amplification affect the fidelity of the adaptive response. A remarkably simple model accurately reproduced experimental observations from single-cell measurements of gene expression dynamics in a microfluidic device. Stochastic simulations showed that delays in the adaptive response are a direct consequence of the very low number of Ada molecules present to signal DNA damage. For cells that have zero copies of Ada, response activation becomes a memoryless process that is dictated by an exponential waiting time distribution between basal Ada expression events. Experiments also confirmed the model prediction that the strength of the adaptive response drops with an increasing growth rate of cells.

Every which way? On predicting tumor evolution using cancer progression models

Successful prediction of the likely paths of tumor progression is valuable for diagnostic, prognostic, and treatment purposes. Cancer progression models (CPMs) use cross-sectional samples to identify restrictions in the order of accumulation of driver mutations and thus CPMs encode the paths of tumor progression. Here we analyze the performance of four CPMs to examine whether they can be used to predict the true distribution of paths of tumor progression and to estimate evolutionary unpredictability. Employing simulations we show that if fitness landscapes are single peaked (have a single fitness maximum) there is good agreement between true and predicted distributions of paths of tumor progression when sample sizes are large, but performance is poor with the currently common much smaller sample sizes. Under multi-peaked fitness landscapes (i.e., those with multiple fitness maxima), performance is poor and improves only slightly with sample size. In all cases, detection regime (when tumors are sampled) is a key determinant of performance. Estimates of evolutionary unpredictability from the best performing CPM, among the four examined, tend to overestimate the true unpredictability and the bias is affected by detection regime; CPMs could be useful for estimating upper bounds to the true evolutionary unpredictability. Analysis of twenty-two cancer data sets shows low evolutionary unpredictability for several of the data sets. But most of the predictions of paths of tumor progression are very unreliable, and unreliability increases with the number of features analyzed. Our results indicate that CPMs could be valuable tools for predicting cancer progression but that, currently, obtaining useful predictions of paths of tumor progression from CPMs is dubious, and emphasize the need for methodological work that can account for the probably multi-peaked fitness landscapes in cancer.

The causes of evolvability and their evolution

Evolvability is the ability of a biological system to produce phenotypic variation that is both heritable and adaptive. It has long been the subject of anecdotal observations and theoretical work. In recent years, however, the molecular causes of evolvability have been an increasing focus of experimental work. Here, we review recent experimental progress in areas as different as the evolution of drug resistance in cancer cells and the rewiring of transcriptional regulation circuits in vertebrates. This research reveals the importance of three major themes: multiple genetic and non-genetic mechanisms to generate phenotypic diversity, robustness in genetic systems, and adaptive landscape topography. We also discuss the mounting evidence that evolvability can evolve and the question of whether it evolves adaptively.

Control of nongenetic heterogeneity in growth rate and stress tolerance of Saccharomyces cerevisiae by cyclic AMP-regulated transcription factors

Genetically identical cells exhibit extensive phenotypic variation even under constant and benign conditions. This so-called nongenetic heterogeneity has important clinical implications: within tumors and microbial infections, cells show nongenetic heterogeneity in growth rate and in susceptibility to drugs or stress. The budding yeast, Saccharomyces cerevisiae, shows a similar form of nongenetic heterogeneity in which growth rate correlates positively with susceptibility to acute heat stress at the single-cell level. Using genetic and chemical perturbations, combined with high-throughput single-cell assays of yeast growth and gene expression, we show here that heterogeneity in intracellular cyclic AMP (cAMP) levels acting through the conserved Ras/cAMP/protein kinase A (PKA) pathway and its target transcription factors, Msn2 and Msn4, underlies this nongenetic heterogeneity. Lower levels of cAMP correspond to slower growth, as shown by direct comparison of cAMP concentration in subpopulations enriched for slower vs. faster growing cells. Concordantly, an endogenous reporter of this pathway’s activity correlates with growth in individual cells. The paralogs Msn2 and Msn4 differ in their roles in nongenetic heterogeneity in a way that demonstrates slow growth and stress tolerance are not inevitably linked. Heterogeneity in growth rate requires each, whereas only Msn2 is required for heterogeneity in expression of Tsl1, a subunit of trehalose synthase that contributes to acute-stress tolerance. Perturbing nongenetic heterogeneity by mutating genes in this pathway, or by culturing wild-type cells with the cell-permeable cAMP analog 8-bromo-cAMP or the PKA inhibitor H89, significantly impacts survival of acute heat stress. Perturbations that increase intracellular cAMP levels reduce the slower-growing subpopulation and increase susceptibility to acute heat stress, whereas PKA inhibition slows growth and decreases susceptibility to acute heat stress. Loss of Msn2 reduces, but does not completely eliminate, the correlation in individual cells between growth rate and acute-stress survival, suggesting a major role for the Msn2 pathway in nongenetic heterogeneity but also a residual benefit of slow growth. Our results shed light on the genetic control of nongenetic heterogeneity and suggest a possible means of defeating bet-hedging pathogens or tumor cells by making them more uniformly susceptible to treatment.

Nongenetic heterogeneity exists when a trait differs among individuals that have identical genotypes and environments. A clonal population can maximize its long-term success in an uncertain environment by diversifying its phenotypes via nongenetic heterogeneity: the currently unfavored ones may become the favored ones when conditions change. Nongenetic heterogeneity has clinical relevance. For example, populations of tumor cells or infectious microbes show cell-to-cell differences in growth and in drug or stress tolerance. This heterogeneity hampers efficient treatment and can potentiate harmful evolution of a tumor or pathogen. We show that in budding yeast, heterogeneity in intracellular cyclic AMP levels acting through the conserved Ras/cAMP/protein kinase A (PKA) pathway and its target transcription factors, Msn2 and Msn4, underlies the nongenetic heterogeneity of both single-cell growth rate and acute heat-stress tolerance. Perturbations of this pathway significantly affect population survival upon acute heat stress. These results illuminate a mechanism of nongenetic heterogeneity and suggest the potential value of antitumor or antifungal treatment strategies that target nongenetic heterogeneity to render the tumor or pathogen population more uniformly susceptible to a second drug that aims to kill.

Hybrid epithelial/mesenchymal phenotypes promote metastasis and therapy resistance across carcinomas

Cancer metastasis and therapy resistance are the major unsolved clinical challenges, and account for nearly all cancer-related deaths. Both metastasis and therapy resistance are fueled by epithelial plasticity, the reversible phenotypic transitions between epithelial and mesenchymal phenotypes, including epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET). EMT and MET have been largely considered as binary processes, where cells detach from the primary tumor as individual units with many, if not all, traits of a mesenchymal cell (EMT) and then convert back to being epithelial (MET). However, recent studies have demonstrated that cells can metastasize in ways alternative to traditional EMT paradigm; for example, they can detach as clusters, and/or occupy one or more stable hybrid epithelial/mesenchymal (E/M) phenotypes that can be the end point of a transition. Such hybrid E/M cells can integrate various epithelial and mesenchymal traits and markers, facilitating collective cell migration. Furthermore, these hybrid E/M cells may possess higher tumor-initiation and metastatic potential as compared to cells on either end of the EMT spectrum. Here, we review in silico, in vitro, in vivo and clinical evidence for the existence of one or more hybrid E/M phenotype(s) in multiple carcinomas, and discuss their implications in tumor-initiation, tumor relapse, therapy resistance, and metastasis. Together, these studies drive the emerging notion that cells in a hybrid E/M phenotype may occupy 'metastatic sweet spot' in multiple subtypes of carcinomas, and pathways linked to this (these) hybrid E/M state(s) may be relevant as prognostic biomarkers as well as a promising therapeutic targets.

Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity

It is well known that genetic mutations can drive drug resistance and lead to tumor relapse. Here, we focus on alternate mechanisms-those without mutations, such as phenotypic plasticity and stochastic cell-to-cell variability that can also evade drug attacks by giving rise to drug-tolerant persisters. The phenomenon of persistence has been well-studied in bacteria and has also recently garnered attention in cancer. We draw a parallel between bacterial persistence and resistance against androgen deprivation therapy in prostate cancer (PCa), the primary standard care for metastatic disease. We illustrate how phenotypic plasticity and consequent mutation-independent or non-genetic heterogeneity possibly driven by protein conformational dynamics can stochastically give rise to androgen independence in PCa, and suggest that dynamic phenotypic plasticity should be considered in devising therapeutic dosing strategies designed to treat and manage PCa.

Resource competition promotes tumour expansion in experimentally evolved cancer

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

Homeostasis Back and Forth: An Ecoevolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: They are a key source of tumor heterogeneity and drive its evolution to malignancy. But, the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here, we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, because they perturb the normal regulatory dynamics of these tissues, often coopting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor-centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This ecoevolutionary view of cancer requires mechanistic mathematical models to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

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

Background

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

Methods

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

Results

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

Discussion

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

Genetic Mutations and Epigenetic Modifications: Driving Cancer and Informing Precision Medicine

Cancer treatment is undergoing a significant revolution from "one-size-fits-all" cytotoxic therapies to tailored approaches that precisely target molecular alterations. Precision strategies for drug development and patient stratification, based on the molecular features of tumors, are the next logical step in a long history of approaches to cancer therapy. In this review, we discuss the history of cancer treatment from generic natural extracts and radical surgical procedures to site-specific and combinatorial treatment regimens, which have incrementally improved patient outcomes. We discuss the related contributions of genetics and epigenetics to cancer progression and the response to targeted therapies and identify challenges and opportunities for the success of precision medicine. The identification of patients who will benefit from targeted therapies is more complex than simply identifying patients whose tumors harbour the targeted aberration, and intratumoral heterogeneity makes it difficult to determine if a precision therapy is successful during treatment. This heterogeneity enables tumors to develop resistance to targeted approaches; therefore, the rational combination of therapeutic agents will limit the threat of acquired resistance to therapeutic success. By incorporating the view of malignant transformation modulated by networks of genetic and epigenetic interactions, molecular strategies will enable precision medicine for effective treatment across cancer subtypes.

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Phenotypic heterogeneity of genetically identical cells can generate nonheritable variation in a population. Is this heterogeneity favorable for microbes? In a changing environment, the answer is a definite yes. While scholars have argued that stochastically generated variation precedes genetic changes and thereby facilitate the evolution of complex traits, this idea has remained disputed, not least because of the shortage of experimental studies. We address this long-standing and controversial issue by integrating synthetic biology, laboratory experimental evolution, and genomic analyses. We explicitly tested the mechanisms whereby phenotypic heterogeneity may promote evolvability. Our work demonstrates that phenotypic heterogeneity facilitates evolutionary rescue from deteriorating environmental stress by generating individuals with exceptionally high fitness. Remarkably, elevated phenotypic heterogeneity evolves as a direct response to stress and thereby it promotes evolution of rare combinations of mutations. These results indicate that phenotypic heterogeneity might have an important role in the evolution of key innovations.

A population genetics perspective on the determinants of intra-tumor heterogeneity

Cancer results from the acquisition of somatic alterations in a microevolutionary process that typically occurs over many years, much of which is occult. Understanding the evolutionary dynamics that are operative at different stages of progression in individual tumors might inform the earlier detection, diagnosis, and treatment of cancer. Although these processes cannot be directly observed, the resultant spatiotemporal patterns of genetic variation amongst tumor cells encode their evolutionary histories. Such intra-tumor heterogeneity is pervasive not only at the genomic level, but also at the transcriptomic, phenotypic, and cellular levels. Given the implications for precision medicine, the accurate quantification of heterogeneity within and between tumors has become a major focus of current research. In this review, we provide a population genetics perspective on the determinants of intra-tumor heterogeneity and approaches to quantify genetic diversity. We summarize evidence for different modes of evolution based on recent cancer genome sequencing studies and discuss emerging evolutionary strategies to therapeutically exploit tumor heterogeneity. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Puzzles in modern biology. II. Language, cancer and the recursive processes of evolutionary innovation

Human language emerged abruptly. Diverse body forms evolved suddenly. Seed-bearing plants spread rapidly. How do complex evolutionary innovations arise so quickly? Resolving alternative claims remains difficult. The great events of the past happened a long time ago. Cancer provides a model to study evolutionary innovation. A tumor must evolve many novel traits to become an aggressive cancer. I use what we know or could study about cancer to describe the key processes of innovation. In general, evolutionary systems form a hierarchy of recursive processes. Those recursive processes determine the rates at which innovations are generated, spread and transmitted. I relate the recursive processes to abrupt evolutionary innovation.

Tissue homogeneity requires inhibition of unequal gene silencing during development

Multicellular organisms can generate and maintain homogenous populations of cells that make up individual tissues. However, cellular processes that can disrupt homogeneity and how organisms overcome such disruption are unknown. We found that ∼100-fold differences in expression from a repetitive DNA transgene can occur between intestinal cells in Caenorhabditis elegans These differences are caused by gene silencing in some cells and are actively suppressed by parental and zygotic factors such as the conserved exonuclease ERI-1. If unsuppressed, silencing can spread between some cells in embryos but can be repeat specific and independent of other homologous loci within each cell. Silencing can persist through DNA replication and nuclear divisions, disrupting uniform gene expression in developed animals. Analysis at single-cell resolution suggests that differences between cells arise during early cell divisions upon unequal segregation of an initiator of silencing. Our results suggest that organisms with high repetitive DNA content, which include humans, could use similar developmental mechanisms to achieve and maintain tissue homogeneity.

A stochastic model for immunotherapy of cancer

We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are modelled as single particles (individuals) in the stochastic system. The main expansions of the model are distinguishing cancer cells by phenotype and genotype, including environment-dependent phenotypic plasticity that does not affect the genotype, taking into account the effects of therapy and introducing a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic models) may help to understand the resistance of tumours to therapeutic approaches and may have non-trivial consequences on tumour treatment protocols. This is supported through numerical simulations.

Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells

Cancer therapy has traditionally focused on eliminating fast-growing populations of cells. Yet, an increasing body of evidence suggests that small subpopulations of cancer cells can evade strong selective drug pressure by entering a 'persister' state of negligible growth. This drug-tolerant state has been hypothesized to be part of an initial strategy towards eventual acquisition of bona fide drug-resistance mechanisms. However, the diversity of drug-resistance mechanisms that can expand from a persister bottleneck is unknown. Here we compare persister-derived, erlotinib-resistant colonies that arose from a single, EGFR-addicted lung cancer cell. We find, using a combination of large-scale drug screening and whole-exome sequencing, that our erlotinib-resistant colonies acquired diverse resistance mechanisms, including the most commonly observed clinical resistance mechanisms. Thus, the drug-tolerant persister state does not limit--and may even provide a latent reservoir of cells for--the emergence of heterogeneous drug-resistance mechanisms.

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

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

Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations

There has been increasing awareness in the wider biological community of the role of clonal phenotypic heterogeneity in playing key roles in phenomena such as cellular bet-hedging and decision making, as in the case of the phage-λ lysis/lysogeny and B. Subtilis competence/vegetative pathways. Here, we report on the effect of stochasticity in growth rate, cellular memory/intermittency, and its relation to phenotypic heterogeneity. We first present a linear stochastic differential model with finite auto-correlation time, where a randomly fluctuating growth rate with a negative average is shown to result in exponential growth for sufficiently large fluctuations in growth rate. We then present a non-linear stochastic self-regulation model where the loss of coherent self-regulation and an increase in noise can induce a shift from bounded to unbounded growth. An important consequence of these models is that while the average change in phenotype may not differ for various parameter sets, the variance of the resulting distributions may considerably change. This demonstrates the necessity of understanding the influence of variance and heterogeneity within seemingly identical clonal populations, while providing a mechanism for varying functional consequences of such heterogeneity. Our results highlight the importance of a paradigm shift from a deterministic to a probabilistic view of clonality in understanding selection as an optimization problem on noise-driven processes, resulting in a wide range of biological implications, from robustness to environmental stress to the development of drug resistance.

Implications of the Hybrid Epithelial/Mesenchymal Phenotype in Metastasis

Transitions between epithelial and mesenchymal phenotypes – the epithelial to ­mesenchymal transition (EMT) and its reverse the mesenchymal to epithelial transition (MET) – are hallmarks of cancer metastasis. While transitioning between the epithelial and mesenchymal phenotypes, cells can also attain a hybrid epithelial/mesenchymal (E/M) (i.e., partial or intermediate EMT) phenotype. Cells in this phenotype have mixed epithelial (e.g., adhesion) and mesenchymal (e.g., migration) properties, thereby allowing them to move collectively as clusters. If these clusters reach the bloodstream intact, they can give rise to clusters of circulating tumor cells (CTCs), as have often been seen experimentally. Here, we review the operating principles of the core regulatory network for EMT/MET that acts as a “three-way” switch giving rise to three distinct phenotypes – E, M and hybrid E/M – and present a theoretical framework that can elucidate the role of many other players in regulating epithelial plasticity. Furthermore, we highlight recent studies on partial EMT and its association with drug resistance and tumor-initiating potential; and discuss how cell–cell communication between cells in a partial EMT phenotype can enable the formation of clusters of CTCs. These clusters can be more apoptosis-resistant and have more tumor-initiating potential than singly moving CTCs with a wholly mesenchymal (complete EMT) phenotype. Also, more such clusters can be formed under inflammatory conditions that are often generated by various therapies. Finally, we discuss the multiple advantages that the partial EMT or hybrid E/M phenotype have as compared to a complete EMT phenotype and argue that these collectively migrating cells are the primary “bad actors” of metastasis.

Conceptual models for the initiation of hepatitis B virus-associated hepatocellular carcinoma

Although chronic hepatitis B virus (HBV) infection is a known risk factor for the development of hepatocellular carcinoma (HCC), the steps involved in the progression from normal liver to HCC are poorly understood. In this review, we apply five conceptual models, previously proposed by Vineis et al. to explain carcinogenesis in general, to explore the possible steps involved in the initiation and evolution of HBV-associated HCC. Available data suggest that the most suitable and inclusive model is based on evolution of hepatocyte subpopulations. In this evolutionary model, HCC-associated changes are driven by selection and subsequent clonal expansion of phenotypically altered hepatocyte subpopulations in the microenvironment of the HBV-infected liver. This model can incorporate the wide range of mechanisms proposed to play a role in the initiation of HCC including oncogenic HBV proteins, integration of HBV DNA and chronic inflammation of the liver. The model may assist in the early prevention, detection and treatment of HCC and may guide future studies of the initiation of HBV-associated HCC.

Essential gene disruptions reveal complex relationships between phenotypic robustness, pleiotropy, and fitness

The concept of robustness in biology has gained much attention recently, but a mechanistic understanding of how genetic networks regulate phenotypic variation has remained elusive. One approach to understand the genetic architecture of variability has been to analyze dispensable gene deletions in model organisms; however, the most important genes cannot be deleted. Here, we have utilized two systems in yeast whereby essential genes have been altered to reduce expression. Using high-throughput microscopy and image analysis, we have characterized a large number of morphological phenotypes, and their associated variation, for the majority of essential genes in yeast. Our results indicate that phenotypic robustness is more highly dependent upon the expression of essential genes than on the presence of dispensable genes. Morphological robustness appears to be a general property of a genotype that is closely related to pleiotropy. While the fitness profile across a range of expression levels is idiosyncratic to each gene, the global pattern indicates that there is a window in which phenotypic variation can be released before fitness effects are observable.

Cancer evolution: mathematical models and computational inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

The three-way switch operation of Rac1/RhoA GTPase-based circuit controlling amoeboid-hybrid-mesenchymal transition

Metastatic carcinoma cells exhibit at least two different phenotypes of motility and invasion - amoeboid and mesenchymal. This plasticity poses a major clinical challenge for treating metastasis, while its underlying mechanisms remain enigmatic. Transitions between these phenotypes are mediated by the Rac1/RhoA circuit that responds to external signals such as HGF/SF via c-MET pathway. Using detailed modeling of GTPase-based regulation to study the Rac1/RhoA circuit's dynamics, we found that it can operate as a three-way switch. We propose to associate the circuit's three possible states to the amoeboid, mesenchymal and amoeboid/mesenchymal hybrid phenotype. In particular, we investigated the range of existence of, and the transition between, the three states (phenotypes) in response to Grb2 and Gab1 - two downstream adaptors of c-MET. The results help to explain the regulation of metastatic cells by c-MET pathway and hence can contribute to the assessment of possible clinical interventions.

A model for the epigenetic switch linking inflammation to cell transformation: deterministic and stochastic approaches

Recently, a molecular pathway linking inflammation to cell transformation has been discovered. This molecular pathway rests on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, which leads to an epigenetic switch allowing cell transformation. A transient activation of an inflammatory signal, mediated by the oncoprotein Src, activates NF-κB, which elicits the expression of Lin28. Lin28 decreases the expression of Let-7 microRNA, which results in higher level of IL6 than achieved directly by NF-κB. In turn, IL6 can promote NF-κB activation. Finally, IL6 also elicits the synthesis of STAT3, which is a crucial activator for cell transformation. Here, we propose a computational model to account for the dynamical behavior of this positive inflammatory feedback loop. By means of a deterministic model, we show that an irreversible bistable switch between a transformed and a non-transformed state of the cell is at the core of the dynamical behavior of the positive feedback loop linking inflammation to cell transformation. The model indicates that inhibitors (tumor suppressors) or activators (oncogenes) of this positive feedback loop regulate the occurrence of the epigenetic switch by modulating the threshold of inflammatory signal (Src) needed to promote cell transformation. Both stochastic simulations and deterministic simulations of a heterogeneous cell population suggest that random fluctuations (due to molecular noise or cell-to-cell variability) are able to trigger cell transformation. Moreover, the model predicts that oncogenes/tumor suppressors respectively decrease/increase the robustness of the non-transformed state of the cell towards random fluctuations. Finally, the model accounts for the potential effect of competing endogenous RNAs, ceRNAs, on the dynamics of the epigenetic switch. Depending on their microRNA targets, the model predicts that ceRNAs could act as oncogenes or tumor suppressors by regulating the occurrence of cell transformation.

An increasing amount of evidence demonstrates a close relation between inflammation and cancer development, which reveals the importance of the tumor microenvironment for the development of cancers. Recently, a molecular pathway linking inflammation to cell transformation, which is a prerequisite to cancer development, has been discovered. This molecular pathway is based on a positive inflammatory feedback loop between NF-κB, Lin28, Let-7 microRNA and IL6, allowing the occurrence of an epigenetic switch leading to cell transformation. Here, we propose a computational model to account for the dynamics of this epigenetic switch. We show that an irreversible bistable switch is at the core of the dynamics of the system. The model further indicates that oncogenes (activators of the switch) and tumor suppressors (inhibitors of the switch) regulate the occurrence of cell transformation by modulating the threshold of inflammatory signal needed to induce the switch. Stochastic simulations of the model suggest that molecular fluctuations are able to trigger cell transformation, highlighting possible links between stochasticity and cancer development. Finally, the model predicts a crucial role of competing endogenous RNAs (ceRNAs) for the dynamics of the epigenetic switch and the occurrence of cell transformation.

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

The sources and consequences of nongenetic variability in metastatic progression are largely unknown. To address these questions, we characterized a transcriptional regulatory network for the metastasis suppressor Raf kinase inhibitory protein (RKIP). We previously showed that the transcription factor BACH1 is negatively regulated by RKIP and promotes breast cancer metastasis. Here we demonstrate that BACH1 acts in a double-negative (overall positive) feedback loop to inhibit RKIP transcription in breast cancer cells. BACH1 also negatively regulates its own transcription. Analysis of the BACH1 network reveals the existence of an inverse relationship between BACH1 and RKIP involving both monostable and bistable transitions that can potentially give rise to nongenetic variability. Single-cell analysis confirmed monostable and bistable-like behavior. Treatment with histone deacetylase inhibitors or depletion of the polycomb repressor enhancer of zeste homolog 2 altered relative RKIP and BACH1 levels in a manner consistent with a prometastatic state. Together, our results suggest that the mutually repressive relationship between metastatic regulators such as RKIP and BACH1 can play a key role in determining metastatic progression in cancer.

Using a systems-based approach to overcome reductionist strategies in the development of diagnostics

Systems biology is a recent addition to the necessary but insufficient reductionist approach used in biological research. Systems biology is focused on understanding living things as a function of their various interactions at multiple levels: not simply as a sum of all their individual parts at any one level. This integrative approach yields predictive models of the normal state, the disease state and therapeutic actions. Although molecular biology has collected an enormous amount of information, including the sequencing of the entire human genome in the year 2000, few real-world applications have resulted from this molecular approach. The pharmaceutical industry's R&D expenditure has increased substantially since 2000, but the number of approved therapeutics has dropped simultaneously, due in part to over-reliance on reductionist genomic, and not systems, approaches. Instead of using reductionist genomics approaches alone, genomics should be incorporated into a multi-level systems biology approach to develop diagnostics and therapeutics.

Oncolytic virotherapy and immunogenic cancer cell death: sharpening the sword for improved cancer treatment strategies

Oncolytic viruses are novel immunotherapeutics with increasingly promising outcomes in cancer patient clinical trials. Preclinical and clinical studies have uncovered the importance of virus-induced activation of antitumor immune responses for optimal therapeutic efficacy. Recently, several classes of chemotherapeutics have been shown to cause immunogenic cancer cell death characterized by the release of immunomodulatory molecules that activate antigen-presenting cells and thus trigger the induction of more potent anticancer adaptive immune responses. In preclinical models, several oncolytic viruses induce immunogenic cell death, which is associated with increased cross-priming of tumor-associated antigens. In this review, we discuss the recent advances in immunogenic cancer cell death as induced by chemotherapeutic treatments, including the roles of relevant danger-associated molecular patterns and signaling pathways, and highlighting the significance of the endoplasmic reticulum (ER) stress response. As virtually all viruses modulate both ER stress and cell death responses, we provide perspectives on future research directions that can be explored to optimize oncolytic viruses, alone or in combination with targeted drug therapies, as potent immunogenic cancer cell death-inducing agents. We propose that such optimized virus-drug synergistic strategies will improve the therapeutic outcomes for many currently intractable cancers.

Genetic and nongenetic determinants of cell growth variation assessed by high-throughput microscopy

In microbial populations, growth initiation and proliferation rates are major components of fitness and therefore likely targets of selection. We used a high-throughput microscopy assay, which enables simultaneous analysis of tens of thousands of microcolonies, to determine the sources and extent of growth rate variation in the budding yeast (Saccharomyces cerevisiae) in different glucose environments. We find that cell growth rates are regulated by the extracellular concentration of glucose as proposed by Monod (1949), but that significant heterogeneity in growth rates is observed among genetically identical individuals within an environment. Yeast strains isolated from different geographic locations and habitats differ in their growth rate responses to different glucose concentrations. Inheritance patterns suggest that the genetic determinants of growth rates in different glucose concentrations are distinct. In addition, we identified genotypes that differ in the extent of variation in growth rate within an environment despite nearly identical mean growth rates, providing evidence that alleles controlling phenotypic variability segregate in yeast populations. We find that the time to reinitiation of growth (lag) is negatively correlated with growth rate, yet this relationship is strain-dependent. Between environments, the respirative activity of individual cells negatively correlates with glucose abundance and growth rate, but within an environment respirative activity and growth rate show a positive correlation, which we propose reflects differences in protein expression capacity. Our study quantifies the sources of genetic and nongenetic variation in cell growth rates in different glucose environments with unprecedented precision, facilitating their molecular genetic dissection.

The details in the distributions: why and how to study phenotypic variability

Phenotypic variability is present even when genetic and environmental differences between cells are reduced to the greatest possible extent. For example, genetically identical bacteria display differing levels of resistance to antibiotics, clonal yeast populations demonstrate morphological and growth-rate heterogeneity, and mouse blastomeres from the same embryo have stochastic differences in gene expression. However, the distributions of phenotypes present among isogenic organisms are often overlooked; instead, many studies focus on population aggregates such as the mean. The details of these distributions are relevant to major questions in diverse fields, including the evolution of antimicrobial-drug and chemotherapy resistance. We review emerging experimental and statistical techniques that allow rigorous analysis of phenotypic variability and thereby may lead to advances across the biological sciences.

Preventive evolutionary medicine of cancers

Evolutionary theory predicts that once an individual reaches an age of sufficiently low Darwinian fitness, (s)he will have reduced chances of keeping cancerous lesions in check. While we clearly need to better understand the emergence of precursor states and early malignancies as well as their mitigation by the microenvironment and tissue architecture, we argue that lifestyle changes and preventive therapies based in an evolutionary framework, applied to identified high-risk populations before incipient neoplasms become clinically detectable and chemoresistant lineages emerge, are currently the most reliable way to control or eliminate early tumours. Specifically, the relatively low levels of (epi)genetic heterogeneity characteristic of many if not most incipient lesions will mean a relatively limited set of possible adaptive traits and associated costs compared to more advanced cancers, and thus a more complete and predictable understanding of treatment options and outcomes. We propose a conceptual model for preventive treatments and discuss the many associated challenges.