Cell plasticity in cancer cell populations

The use of microphysiological systems to model metastatic cancer

Cancer is one of the leading causes of death in the 21st century, with metastasis of cancer attributing to 90% of cancer-related deaths. Therefore, to improve patient outcomes there is a need for better preclinical models to increase the success of translating oncological therapies into the clinic. Current traditional staticin vitromodels lack a perfusable network which is critical to overcome the diffusional mass transfer limit to provide a mechanism for the exchange of essential nutrients and waste removal, and increase their physiological relevance. Furthermore, these models typically lack cellular heterogeneity and key components of the immune system and tumour microenvironment. This review explores rapidly developing strategies utilising perfusable microphysiological systems (MPS) for investigating cancer cell metastasis. In this review we initially outline the mechanisms of cancer metastasis, highlighting key steps and identifying the current gaps in our understanding of the metastatic cascade, exploring MPS focused on investigating the individual steps of the metastatic cascade before detailing the latest MPS which can investigate multiple components of the cascade. This review then focuses on the factors which can affect the performance of an MPS designed for cancer applications with a final discussion summarising the challenges and future directions for the use of MPS for cancer models.

CD146, a therapeutic target involved in cell plasticity

Since its identification as a marker for advanced melanoma in the 1980s, CD146 has been found to have multiple functions in both physiological and pathological processes, including embryonic development, tissue repair and regeneration, tumor progression, fibrosis disease, and inflammations. Subsequent research has revealed that CD146 is involved in various signaling pathways as a receptor or co-receptor in these processes. This correlation between CD146 and multiple diseases has sparked interest in its potential applications in diagnosis, prognosis, and targeted therapy. To better comprehend the versatile roles of CD146, we have summarized its research history and synthesized findings from numerous reports, proposing that cell plasticity serves as the underlying mechanism through which CD146 contributes to development, regeneration, and various diseases. Targeting CD146 would consequently halt cell state shifting during the onset and progression of these related diseases. Therefore, the development of therapy targeting CD146 holds significant practical value.

Microbial evolution-An under-appreciated driver of soil carbon cycling

Although substantial advances in predicting the ecological impacts of global change have been made, predictions of the evolutionary impacts have lagged behind. In soil ecosystems, microbes act as the primary energetic drivers of carbon cycling; however, microbes are also capable of evolving on timescales comparable to rates of global change. Given the importance of soil ecosystems in global carbon cycling, we assess the potential impact of microbial evolution on carbon-climate feedbacks in this system. We begin by reviewing the current state of knowledge concerning microbial evolution in response to global change and its specific effect on soil carbon dynamics. Through this integration, we synthesize a roadmap detailing how to integrate microbial evolution into ecosystem biogeochemical models. Specifically, we highlight the importance of microscale mechanistic soil carbon models, including choosing an appropriate evolutionary model (e.g., adaptive dynamics, quantitative genetics), validating model predictions with 'omics' and experimental data, scaling microbial adaptations to ecosystem level processes, and validating with ecosystem-scale measurements. The proposed steps will require significant investment of scientific resources and might require 10-20 years to be fully implemented. However, through the application of multi-scale integrated approaches, we will advance the integration of microbial evolution into predictive understanding of ecosystems, providing clarity on its role and impact within the broader context of environmental change.

Cancer cell plasticity, stem cell factors, and therapy resistance: how are they linked?

Cellular plasticity can occur naturally in an organism and is considered an adapting mechanism during the developmental stage. However, abnormal cellular plasticity is observed in different diseased conditions, including cancer. Cancer cell plasticity triggers the stimuli of epithelial-mesenchymal transition (EMT), abnormal epigenetic changes, expression of stem cell factors and implicated signaling pathways, etc., and helps in the maintenance of CSC phenotype. Conversely, CSC maintains the cancer cell plasticity, EMT, and epigenetic plasticity. EMT contributes to increased cell migration and greater diversity within tumors, while epigenetic changes, stem cell factors (OCT4, NANOG, and SOX2), and various signaling pathways allow cancer cells to maintain various phenotypes, giving rise to intra- and inter-tumoral heterogeneity. The intricate relationships between cancer cell plasticity and stem cell factors help the tumor cells adopt drug-tolerant states, evade senescence, and successfully acquire drug resistance with treatment dismissal. Inhibiting molecules/signaling pathways involved in promoting CSCs, cellular plasticity, EMT, and epigenetic plasticity might be helpful for successful cancer therapy management. This review discussed the role of cellular plasticity, EMT, and stem cell factors in tumor initiation, progression, reprogramming, and therapy resistance. Finally, we discussed how the intervention in this axis will help better manage cancers and improve patient survivability.

Deciphering cellular plasticity in pancreatic cancer for effective treatments

Cellular plasticity and therapy resistance are critical features of pancreatic cancer, a highly aggressive and fatal disease. The pancreas, a vital organ that produces digestive enzymes and hormones, is often affected by two main types of cancer: the pre-dominant ductal adenocarcinoma and the less common neuroendocrine tumors. These cancers are difficult to treat due to their complex biology characterized by cellular plasticity leading to therapy resistance. Cellular plasticity refers to the capability of cancer cells to change and adapt to different microenvironments within the body which includes acinar-ductal metaplasia, epithelial to mesenchymal/epigenetic/metabolic plasticity, as well as stemness. This plasticity allows heterogeneity of cancer cells, metastasis, and evasion of host's immune system and develops resistance to radiation, chemotherapy, and targeted therapy. To overcome this resistance, extensive research is ongoing exploring the intrinsic and extrinsic factors through cellular reprogramming, chemosensitization, targeting metabolic, key survival pathways, etc. In this review, we discussed the mechanisms of cellular plasticity involving cellular adaptation and tumor microenvironment and provided a comprehensive understanding of its role in therapy resistance and ways to overcome it.

Targeting vimentin: a multifaceted approach to combatting cancer metastasis and drug resistance

This comprehensive review explores vimentin as a pivotal therapeutic target in cancer treatment, with a primary focus on mitigating metastasis and overcoming drug resistance. Vimentin, a key player in cancer progression, is intricately involved in processes such as epithelial-to-mesenchymal transition (EMT) and resistance mechanisms to standard cancer therapies. The review delves into diverse vimentin inhibition strategies. Precision tools, including antibodies and nanobodies, selectively neutralize vimentin's pro-tumorigenic effects. DNA and RNA aptamers disrupt vimentin-associated signaling pathways through their adaptable binding properties. Innovative approaches, such as vimentin-targeted vaccines and microRNAs (miRNAs), harness the immune system and post-transcriptional regulation to combat vimentin-expressing cancer cells. By dissecting vimentin inhibition strategies across these categories, this review provides a comprehensive overview of anti-vimentin therapeutics in cancer treatment. It underscores the growing recognition of vimentin as a pivotal therapeutic target in cancer and presents a diverse array of inhibitors, including antibodies, nanobodies, DNA and RNA aptamers, vaccines, and miRNAs. These multifaceted approaches hold substantial promise for tackling metastasis and overcoming drug resistance, collectively presenting new avenues for enhanced cancer therapy.

Dynamic interplay of nuclear receptors in tumor cell plasticity and drug resistance: Shifting gears in malignant transformations and applications in cancer therapeutics

Recent advances have brought forth the complex interplay between tumor cell plasticity and its consequential impact on drug resistance and tumor recurrence, both of which are critical determinants of neoplastic progression and therapeutic efficacy. Various forms of tumor cell plasticity, instrumental in facilitating neoplastic cells to develop drug resistance, include epithelial-mesenchymal transition (EMT) alternatively termed epithelial-mesenchymal plasticity, the acquisition of cancer stem cell (CSC) attributes, and transdifferentiation into diverse cell lineages. Nuclear receptors (NRs) are a superfamily of transcription factors (TFs) that play an essential role in regulating a multitude of cellular processes, including cell proliferation, differentiation, and apoptosis. NRs have been implicated to play a critical role in modulating gene expression associated with tumor cell plasticity and drug resistance. This review aims to provide a comprehensive overview of the current understanding of how NRs regulate these key aspects of cancer biology. We discuss the diverse mechanisms through which NRs influence tumor cell plasticity, including EMT, stemness, and metastasis. Further, we explore the intricate relationship between NRs and drug resistance, highlighting the impact of NR signaling on chemotherapy, radiotherapy and targeted therapies. We also discuss the emerging therapeutic strategies targeting NRs to overcome tumor cell plasticity and drug resistance. This review also provides valuable insights into the current clinical trials that involve agonists or antagonists of NRs modulating various aspects of tumor cell plasticity, thereby delineating the potential of NRs as therapeutic targets for improved cancer treatment outcomes.

Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance

Cancer is a complex disease displaying a variety of cell states and phenotypes. This diversity, known as cancer cell plasticity, confers cancer cells the ability to change in response to their environment, leading to increased tumor diversity and drug resistance. This review explores the intricate landscape of cancer cell plasticity, offering a deep dive into the cellular, molecular, and genetic mechanisms that underlie this phenomenon. Cancer cell plasticity is intertwined with processes such as epithelial-mesenchymal transition and the acquisition of stem cell-like features. These processes are pivotal in the development and progression of tumors, contributing to the multifaceted nature of cancer and the challenges associated with its treatment. Despite significant advancements in targeted therapies, cancer cell adaptability and subsequent therapy-induced resistance remain persistent obstacles in achieving consistent, successful cancer treatment outcomes. Our review delves into the array of mechanisms cancer cells exploit to maintain plasticity, including epigenetic modifications, alterations in signaling pathways, and environmental interactions. We discuss strategies to counteract cancer cell plasticity, such as targeting specific cellular pathways and employing combination therapies. These strategies promise to enhance the efficacy of cancer treatments and mitigate therapy resistance. In conclusion, this review offers a holistic, detailed exploration of cancer cell plasticity, aiming to bolster the understanding and approach toward tackling the challenges posed by tumor heterogeneity and drug resistance. As articulated in this review, the delineation of cellular, molecular, and genetic mechanisms underlying tumor heterogeneity and drug resistance seeks to contribute substantially to the progress in cancer therapeutics and the advancement of precision medicine, ultimately enhancing the prospects for effective cancer treatment and patient outcomes.

Phenotypic Heterogeneity, Bidirectionality, Universal Cues, Plasticity, Mechanics, and the Tumor Microenvironment Drive Cancer Metastasis

Tumor diseases become a huge problem when they embark on a path that advances to malignancy, such as the process of metastasis. Cancer metastasis has been thoroughly investigated from a biological perspective in the past, whereas it has still been less explored from a physical perspective. Until now, the intraluminal pathway of cancer metastasis has received the most attention, while the interaction of cancer cells with macrophages has received little attention. Apart from the biochemical characteristics, tumor treatments also rely on the tumor microenvironment, which is recognized to be immunosuppressive and, as has recently been found, mechanically stimulates cancer cells and thus alters their functions. The review article highlights the interaction of cancer cells with other cells in the vascular metastatic route and discusses the impact of this intercellular interplay on the mechanical characteristics and subsequently on the functionality of cancer cells. For instance, macrophages can guide cancer cells on their intravascular route of cancer metastasis, whereby they can help to circumvent the adverse conditions within blood or lymphatic vessels. Macrophages induce microchannel tunneling that can possibly avoid mechanical forces during extra- and intravasation and reduce the forces within the vascular lumen due to vascular flow. The review article highlights the vascular route of cancer metastasis and discusses the key players in this traditional route. Moreover, the effects of flows during the process of metastasis are presented, and the effects of the microenvironment, such as mechanical influences, are characterized. Finally, the increased knowledge of cancer metastasis opens up new perspectives for cancer treatment.

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.

Glioblastoma research on zebrafish xenograft models: a systematic review

Glioblastoma (GBM) constitutes the most common primary brain tumor in adults. The challenges in GBM therapeutics have shed light on zebrafish used as a promising animal model for preclinical GBM xenograft studies without a standardized methodology. This systematic review aims to summarize the advances in zebrafish GBM xenografting, compare research protocols to pinpoint advantages and underlying limitations, and designate the predominant xenografting parameters. Based on the PRISMA checklist, we systematically searched PubMed, Scopus, and ZFIN using the keywords "glioblastoma," "xenotransplantation," and "zebrafish" for papers published from 2005 to 2022, available in English. 46 articles meeting the review criteria were examined for the zebrafish strain, cancer cell line, cell labeling technique, injected cell number, time and site of injection, and maintenance temperature. Our review designated that AB wild-type zebrafish, Casper transparent mutants, transgenic Tg(fli1:EGFP), or crossbreeding of these predominate among the zebrafish strains. Orthotopic transplantation is more commonly employed. A number of 50-100 cells injected at 48 h post-fertilization in high density and low infusion volume is considered as an effective xenografting approach. U87 cells are used for GBM angiogenesis studies, U251 for GBM proliferation studies, and patient-derived xenograft (PDX) to achieve clinical relevance. Gradual acclimatization to 32-33 °C can partly address the temperature differential between the zebrafish and the GBM cells. Zebrafish xenograft models constitute valuable tools for preclinical studies with clinical relevance regarding PDX. The GBM xenografting research requires modification based on the objective of each research team. Automation and further optimization of the protocol parameters could scale up the anticancer drug trials.

Plasticity of circulating tumor cells in small cell lung cancer

Small cell lung cancer (SCLC) is an aggressive neuroendocrine tumor with low five-year survival rates. Recently described molecular phenotypes of SCLC exhibit differential vulnerabilities heralding potential for stratified treatment. Whilst tumor biopsy in SCLC is challenging, circulating tumor cells in the liquid biopsy are prevalent and can be repeatedly sampled accommodating the dynamic plasticity of SCLC phenotypes. The aim of this study was to characterize the heterogeneity of rare circulating cells with confirmed tumor origin and to explore a liquid biopsy approach for future clinical trials of targeted therapies. This study applied the 3rd generation of a previously validated direct imaging platform to 14 chemo-naive SCLC patients and 10 non-cancerous normal donor (ND) samples. Phenotypic heterogeneity of circulating rare cells in SCLC was observed and a patient-level classification model was established to stratify SCLC patients from non-cancerous donors. Eight rare cell groups, with combinations of epithelial, endothelial, and mesenchymal biomarker expression patterns, were phenotypically characterized. The single-cell genomic analysis confirmed the cancer cell plasticity in every rare cell group harboring clonal genomic alterations. This study shows rare cell heterogeneity and confirms cellular plasticity in SCLC providing a valuable resource for better opportunities to discover novel therapeutic targets in SCLC.

Is Cancer Reversible? Rethinking Carcinogenesis Models-A New Epistemological Tool

A growing number of studies shows that it is possible to induce a phenotypic transformation of cancer cells from malignant to benign. This process is currently known as "tumor reversion". However, the concept of reversibility hardly fits the current cancer models, according to which gene mutations are considered the primary cause of cancer. Indeed, if gene mutations are causative carcinogenic factors, and if gene mutations are irreversible, how long should cancer be considered as an irreversible process? In fact, there is some evidence that intrinsic plasticity of cancerous cells may be therapeutically exploited to promote a phenotypic reprogramming, both in vitro and in vivo. Not only are studies on tumor reversion highlighting a new, exciting research approach, but they are also pushing science to look for new epistemological tools capable of better modeling cancer.

Spatio-temporal modelling of phenotypic heterogeneity in tumour tissues and its impact on radiotherapy treatment

We present a mathematical model that describes how tumour heterogeneity evolves in a tissue slice that is oxygenated by a single blood vessel. Phenotype is identified with the stemness level of a cell and determines its proliferative capacity, apoptosis propensity and response to treatment. Our study is based on numerical bifurcation analysis and dynamical simulations of a system of coupled, non-local (in phenotypic "space") partial differential equations that link the phenotypic evolution of the tumour cells to local tissue oxygen levels. In our formulation, we consider a 1D geometry where oxygen is supplied by a blood vessel located on the domain boundary and consumed by the tumour cells as it diffuses through the tissue. For biologically relevant parameter values, the system exhibits multiple steady states; in particular, depending on the initial conditions, the tumour is either eliminated ("tumour-extinction") or it persists ("tumour-invasion"). We conclude by using the model to investigate tumour responses to radiotherapy, and focus on identifying radiotherapy strategies which can eliminate the tumour. Numerical simulations reveal how phenotypic heterogeneity evolves during treatment and highlight the critical role of tissue oxygen levels on the efficacy of radiation protocols that are commonly used in the clinic.

Learning cell identity in immunology, neuroscience, and cancer

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

Network inference from perturbation time course data

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

Background

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.

Methods

In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach.

Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.

Results

Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy.

The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).

Conclusions

We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01121-y.

Reclassifying tumour cell cycle activity in terms of its tissue of origin

Genomic alterations resulting in loss of control over the cell cycle is a fundamental hallmark of human malignancies. Whilst pan-cancer studies have broadly assessed tumour genomics and their impact on oncogenic pathways, analyses taking the baseline signalling levels in normal tissue into account are lacking. To this end, we aimed to reclassify the cell cycle activity of tumours in terms of their tissue of origin and determine if any common DNA mutations, chromosome arm-level changes or signalling pathways contribute to an increase in baseline corrected cell cycle activity. Combining normal tissue and pan-cancer data from over 13,000 samples we demonstrate that tumours of gynaecological origin show the highest levels of corrected cell cycle activity, partially owing to hormonal signalling and gene expression changes. We also show that normal and tumour tissues can be separated into groups (quadrants) of low/high cell cycle activity and propose the hypothesis of an upper limit on these activity levels in tumours.

Drug Repurposing by Tumor Tissue Editing

The combinatory use of drugs for systemic cancer therapy commonly aims at the direct elimination of tumor cells through induction of apoptosis. An alternative approach becomes the focus of attention if biological changes in tumor tissues following combinatory administration of regulatorily active drugs are considered as a therapeutic aim, e.g., differentiation, transdifferentiation induction, reconstitution of immunosurveillance, the use of alternative cell death mechanisms. Editing of the tumor tissue establishes new biological ‘hallmarks’ as a ‘pressure point’ to attenuate tumor growth. This may be achieved with repurposed, regulatorily active drug combinations, often simultaneously targeting different cell compartments of the tumor tissue. Moreover, tissue editing is paralleled by decisive functional changes in tumor tissues providing novel patterns of target sites for approved drugs. Thus, agents with poor activity in non-edited tissue may reveal new clinically meaningful outcomes. For tissue editing and targeting edited tissue novel requirements concerning drug selection and administration can be summarized according to available clinical and pre-clinical data. Monoactivity is no pre-requisite, but combinatory bio-regulatory activity. The regulatorily active dose may be far below the maximum tolerable dose, and besides inhibitory active drugs stimulatory drug activities may be integrated. Metronomic scheduling often seems to be of advantage. Novel preclinical approaches like functional assays testing drug combinations in tumor tissue are needed to select potential drugs for repurposing. The two-step drug repurposing procedure, namely establishing novel functional systems states in tumor tissues and consecutively providing novel target sites for approved drugs, facilitates the systematic identification of drug activities outside the scope of any original clinical drug approvals.

Mammalian tumor-like organs. 2. Mammalian adipose has many tumor features and obesity is a tumor-like process

BACKGROUND

In previous publications, the author developed the theory of carcino-evo-devo, which predicts that evolutionarily novel organs should recapitulate some features of tumors in their development.

MAIN TEXT

Mammalian adipose is currently recognized as a multi-depot metabolic and endocrine organ consisting of several adipose tissues. Although lipid-storing cells and proteins are ancient, the adipose organ as a whole is evolutionarily novel to mammals. The adipose expansion has remarkable similarities with the growth of solid tumors. These similarities are the following: (1) The capability to unlimited expansion; (2) Reversible plasticity; (3) Induction of angiogenesis; (4) Chronic inflammation; (5) Remodeling and disfunction; (6) Systemic influence on the organism; (7) Hormone production; (8) Production of miRNAs that influence other tissues; (9) Immunosuppression; (10) DNA damage and resistance to apoptosis; (11) Destructive infiltration in other organs and tissues. These similarities include the majority of "hallmarks of cancer". In addition, lipomas are the most frequent soft tissue tumors, and similar drugs may be used for the treatment of obesity and cancer by preventing infiltration. This raises the possibility that obesity, at least in part, may represent an oncological problem. The existing similarities between adipose and tumors suggest the possible evolutionary origin of mammalian adipose from some ancestral benign mesenchymal hereditary tumors. Indeed, using a transgenic inducible zebrafish tumor model, we described many genes, which originated in fish and were expressed in fish tumors. Their human orthologs LEP, NOTCH1, SPRY1, PPARG, ID2, and CIDEA acquired functions connected with the adipose organ. They are also involved in tumor development in humans.

CONCLUSION

If the hypothesis of the evolutionary origin of the adipose organ from the ancestral hereditary tumor is correct, it may open new opportunities to resolve the oncological problem and the problem of the obesity epidemic. New interventions targeting LEP, NOTCH1, SPRY1, PPARG, ID2, and CIDEA gene network, in addition to what already is going on, can be designed for treatment and prevention of both obesity and tumors.

Nonlocal Reaction-Diffusion Equations in Biomedical Applications

Nonlocal reaction-diffusion equations describe various biological and biomedical applications. Their mathematical properties are essentially different in comparison with the local equations, and this difference can lead to important biological implications. This review will present the state of the art in the investigation of nonlocal reaction-diffusion models in biomedical applications. We will consider various models arising in mathematical immunology, neuroscience, cancer modelling, and we will discuss their mathematical properties, nonlinear dynamics, resulting spatiotemporal patterns and biological significance.

Dissecting Tumor Growth: The Role of Cancer Stem Cells in Drug Resistance and Recurrence

Simple Summary

Cancer is one of the most debated problems all over the world. Cancer stem cells are considered responsible of tumor initiation, metastasis, drug resistance, and recurrence. This subpopulation of cells has been found into the tumor bulk and showed the capacity to self-renew, differentiate, up to generate a new tumor. In the last decades, several studies have been set on the molecular mechanisms behind their specific characteristics as the Wnt/β-catenin signaling, Notch signaling, Hedgehog signaling, transcription factors, etc. The most powerful part of CSCs is represented by the niches as “promoter” of their self-renewal and “protector” from the common oncological treatment as chemotherapy and radiotherapy. In our review article we highlighted the primary mechanisms involved in CSC tumorigenesis for the setting of further targets to control the metastatic process.

Abstract

Emerging evidence suggests that a small subpopulation of cancer stem cells (CSCs) is responsible for initiation, progression, and metastasis cascade in tumors. CSCs share characteristics with normal stem cells, i.e., self-renewal and differentiation potential, suggesting that they can drive cancer progression. Consequently, targeting CSCs to prevent tumor growth or regrowth might offer a chance to lead the fight against cancer. CSCs create their niche, a specific area within tissue with a unique microenvironment that sustains their vital functions. Interactions between CSCs and their niches play a critical role in regulating CSCs’ self-renewal and tumorigenesis. Differences observed in the frequency of CSCs, due to the phenotypic plasticity of many cancer cells, remain a challenge in cancer therapeutics, since CSCs can modulate their transcriptional activities into a more stem-like state to protect themselves from destruction. This plasticity represents an essential step for future therapeutic approaches. Regarding self-renewal, CSCs are modulated by the same molecular pathways found in normal stem cells, such as Wnt/β-catenin signaling, Notch signaling, and Hedgehog signaling. Another key characteristic of CSCs is their resistance to standard chemotherapy and radiotherapy treatments, due to their capacity to rest in a quiescent state. This review will analyze the primary mechanisms involved in CSC tumorigenesis, with particular attention to the roles of CSCs in tumor progression in benign and malignant diseases; and will examine future perspectives on the identification of new markers to better control tumorigenesis, as well as dissecting the metastasis process.

Optimal chemotherapy counteracts cancer adaptive resistance in a cell-based, spatially-extended, evolutionary model

Most aggressive cancers are incurable due to their fast evolution of drug resistance. We model cancer growth and adaptive response in a simplified cell-based (CB) setting, assuming a genetic resistance to two chemotherapeutic drugs. We show that optimal administration protocols can steer cells resistance and turned it into a weakness for the disease. Our work extends the population-based (PB) model proposed by Orlando et al. (Physical Biology, 2012), in which a homogeneous population of cancer cells evolves according to a fitness landscape. The landscape models three types of trade-offs, differing on whether the cells are more, less, or equal effective when generalizing resistance to two drugs as opposed to specializing to a single one. The CB framework allows us to include genetic heterogeneity, spatial competition, and drugs diffusion, as well as realistic administration protocols. By calibrating our model on Orlando et al.'s assumptions, we show that dynamical protocols that alternate the two drugs minimize the cancer size at the end of (or at mid-points during) treatment. These results significantly differ from those obtained with the homogeneous model---suggesting static protocols under the pro-generalizing and neutral allocation trade-offs---highlighting the important role of spatial and genetic heterogeneities. Our work is the first attempt to search for optimal treatments in a CB setting, a step forward toward realistic clinical applications.

Evolution of a structured cell population endowed with plasticity of traits under constraints on and between the traits

Confronted with the biological problem of managing plasticity in cell populations, which is in particular responsible for transient and reversible drug resistance in cancer, we propose a rationale consisting of an integro-differential and a reaction-advection-diffusion equation, the properties of which are studied theoretically and numerically. By using a constructive finite volume method, we show the existence and uniqueness of a weak solution and illustrate by numerical approximations and their simulations the capacity of the model to exhibit divergence of traits. This feature may be theoretically interpreted as describing a physiological step towards multicellularity in animal evolution and, closer to present-day clinical challenges in oncology, as a possible representation of bet hedging in cancer cell populations.

Oncogenic Viruses as Entropic Drivers of Cancer Evolution

Viral infection is an indisputable causal factor for nearly 17% of all human cancers. However, the diversity and complexity of oncogenic mechanisms raises new questions as to the mechanistic role of viruses in cancer. Classical viral oncogenes have been identified for all tumor-associated viruses. These oncogenes can have multiple oncogenic activities that may or may not be utilized in a particular tumor cell. In addition, stochastic events, like viral mutation and integration, as well as heritable host susceptibilities and immune deficiencies are also implicated in tumorigenesis. A more contemporary view of tumor biology highlights the importance of evolutionary forces that select for phenotypes better adapted to a complex and changing environment. Given the challenges of prioritizing singular mechanistic causes, it may be necessary to integrate concepts from evolutionary theory and systems biology to better understand viral cancer-driving forces. Here, we propose that viral infection provides a biological “entropy” that increases genetic variation and phenotypic plasticity, accelerating the main driving forces of cancer cell evolution. Viruses can also influence the evolutionary selection criteria by altering the tumor microenvironment and immune signaling. Utilizing concepts from cancer cell evolution, population genetics, thermodynamics, and systems biology may provide new perspectives on viral oncogenesis and identify novel therapeutic strategies for treating viruses and cancer.

Improving cancer treatments via dynamical biophysical models

Despite significant advances in oncological research, cancer nowadays remains one of the main causes of mortality and morbidity worldwide. New treatment techniques, as a rule, have limited efficacy, target only a narrow range of oncological diseases, and have limited availability to the general public due their high cost. An important goal in oncology is thus the modification of the types of antitumor therapy and their combinations, that are already introduced into clinical practice, with the goal of increasing the overall treatment efficacy. One option to achieve this goal is optimization of the schedules of drugs administration or performing other medical actions. Several factors complicate such tasks: the adverse effects of treatments on healthy cell populations, which must be kept tolerable; the emergence of drug resistance due to the intrinsic plasticity of heterogeneous cancer cell populations; the interplay between different types of therapies administered simultaneously. Mathematical modeling, in which a tumor and its microenvironment are considered as a single complex system, can address this complexity and can indicate potentially effective protocols, that would require experimental verification. In this review, we consider classical methods, current trends and future prospects in the field of mathematical modeling of tumor growth and treatment. In particular, methods of treatment optimization are discussed with several examples of specific problems related to different types of treatment.

Lineage Plasticity in Cancer: The Tale of a Skin-Walker

Lineage plasticity, the switching of cells from one lineage to another, has been recognized as a cardinal property essential for embryonic development, tissue repair and homeostasis. However, such a highly regulated process goes awry when cancer cells exploit this inherent ability to their advantage, resulting in tumorigenesis, relapse, metastasis and therapy resistance. In this review, we summarize our current understanding on the role of lineage plasticity in tumor progression and therapeutic resistance in multiple cancers. Lineage plasticity can be triggered by treatment itself and is reported across various solid as well as liquid tumors. Here, we focus on the importance of lineage switching in tumor progression and therapeutic resistance of solid tumors such as the prostate, lung, hepatocellular and colorectal carcinoma and the myeloid and lymphoid lineage switch observed in leukemias. Besides this, we also discuss the role of epithelial-mesenchymal transition (EMT) in facilitating the lineage switch in biphasic cancers such as aggressive carcinosarcomas. We also discuss the mechanisms involved, current therapeutic approaches and challenges that lie ahead in taming the scourge of lineage plasticity in cancer.

Pan-Cancer Survey of Tumor Mass Dormancy and Underlying Mutational Processes

Tumor mass dormancy is the key intermediate step between immune surveillance and cancer progression, yet due to its transitory nature it has been difficult to capture and characterize. Little is understood of its prevalence across cancer types and of the mutational background that may favor such a state. While this balance is finely tuned internally by the equilibrium between cell proliferation and cell death, the main external factors contributing to tumor mass dormancy are immunological and angiogenic. To understand the genomic and cellular context in which tumor mass dormancy may develop, we comprehensively profiled signals of immune and angiogenic dormancy in 9,631 cancers from the Cancer Genome Atlas and linked them to tumor mutagenesis. We find evidence for immunological and angiogenic dormancy-like signals in 16.5% of bulk sequenced tumors, with a frequency of up to 33% in certain tissues. Mutations in the CASP8 and HRAS oncogenes were positively selected in dormant tumors, suggesting an evolutionary pressure for controlling cell growth/apoptosis signals. By surveying the mutational damage patterns left in the genome by known cancer risk factors, we found that aging-induced mutations were relatively depleted in these tumors, while patterns of smoking and defective base excision repair were linked with increased tumor mass dormancy. Furthermore, we identified a link between APOBEC mutagenesis and dormancy, which comes in conjunction with immune exhaustion and may partly depend on the expression of the angiogenesis regulator PLG as well as interferon and chemokine signals. Tumor mass dormancy also appeared to be impaired in hypoxic conditions in the majority of cancers. The microenvironment of dormant cancers was enriched in cytotoxic and regulatory T cells, as expected, but also in macrophages and showed a reduction in inflammatory Th17 signals. Finally, tumor mass dormancy was linked with improved patient survival outcomes. Our analysis sheds light onto the complex interplay between dormancy, exhaustion, APOBEC activity and hypoxia, and sets directions for future mechanistic explorations.

Cellular Darwinism: Regulatory networks, stochasticity, and selection in cancer development

There are strong parallels between the evolutionary origin of species within populations of organisms and new concepts for the origin of cancers within cell populations in the tissues of the body. The analogy is that cancers can be regarded as a new somatic species developing within the host organism. In both cases, understanding the processes involved requires a multi-scale analysis, including higher-level control of genetic and epigenetic changes. A key to developing successful therapeutic strategies will be to identify the processes that control heterogeneity in tissues. These include processes outside the currently dominant theory of evolution, i.e. the Modern Synthesis. Specifically, organisms can partially direct both genetic and epigenetic changes through the harnessing of chance. The loci and rates of mutation and of genome reorganisation are forms of targeted functional reorganisation of genomes. They are more likely to result in functional reorganisations compared to the slow accumulation of point mutations.

A Key Pathway to Cancer Resilience: The Role of Autophagy in Glioblastomas

There are no effective strategies for the successful treatment of glioblastomas (GBM). Current therapeutic modalities effectively target bulk tumor cells but leave behind marginal GBM cells that escape from the surgical margins and radiotherapy field, exhibiting high migratory phenotype and resistance to all available anti-glioma therapies. Drug resistance is mostly driven by tumor cell plasticity: a concept associated with reactivating transcriptional programs in response to adverse and dynamic conditions from the tumor microenvironment. Autophagy, or “self-eating”, pathway is an emerging target for cancer therapy and has been regarded as one of the key drivers of cell plasticity in response to energy demanding stress conditions. Many studies shed light on the importance of autophagy as an adaptive mechanism, protecting GBM cells from unfavorable conditions, while others recognize that autophagy can kill those cells by triggering a non-apoptotic cell death program, called ‘autophagy cell death’ (ACD). In this review, we carefully analyzed literature data and conclude that there is no clear evidence indicating the presence of ACD under pathophysiological settings in GBM disease. It seems to be exclusively induced by excessive (supra-physiological) stress signals, mostly from in vitro cell culture studies. Instead, pre-clinical and clinical data indicate that autophagy is an emblematic example of the ‘dark-side’ of a rescue pathway that contributes profoundly to a pro-tumoral adaptive response. From a standpoint of treating the real human disease, only combinatorial therapy targeting autophagy with cytotoxic drugs in the adjuvant setting for GBM patients, associated with the development of less toxic and more specific autophagy inhibitors, may inhibit adaptive response and enhance the sensibility of glioma cells to conventional therapies.

Phenotypic variation modulates the growth dynamics and response to radiotherapy of solid tumours under normoxia and hypoxia

In cancer, treatment failure and disease recurrence have been associated with small subpopulations of cancer cells with a stem-like phenotype. In this paper, we develop and investigate a phenotype-structured model of solid tumour growth in which cells are structured by a stemness level, which varies continuously between stem-like and terminally differentiated behaviours. Cell evolution is driven by proliferation and death, as well as advection and diffusion with respect to the stemness structure variable. Here, the magnitude and sign of the advective flux are allowed to vary with the oxygen level. We use the model to investigate how the environment, in particular oxygen levels, affects the tumour's population dynamics and composition, and its response to radiotherapy. We use a combination of numerical and analytical techniques to quantify how under physiological oxygen levels the cells evolve to a differentiated phenotype and under low oxygen level (i.e., hypoxia) they de-differentiate. Under normoxia, the proportion of cancer stem cells is typically negligible and the tumour may ultimately become extinct whereas under hypoxia cancer stem cells comprise a dominant proportion of the tumour volume, enhancing radio-resistance and favouring the tumour's long-term survival. We then investigate how such phenotypic heterogeneity impacts the tumour's response to treatment with radiotherapy under normoxia and hypoxia. Of particular interest is establishing how the presence of radio-resistant cancer stem cells can facilitate a tumour's regrowth following radiotherapy. We also use the model to show how radiation-induced changes in tumour oxygen levels can give rise to complex re-growth dynamics. For example, transient periods of hypoxia induced by damage to tumour blood vessels may rescue the cancer cell population from extinction and drive secondary regrowth.

Epithelial-to-mesenchymal-like transition events in melanoma

Epithelial-to-mesenchymal transition (EMT), a process through which epithelial tumor cells acquire mesenchymal phenotypic properties, contributes to both metastatic dissemination and therapy resistance in cancer. Accumulating evidence indicates that nonepithelial tumors, including melanoma, can also gain mesenchymal-like properties that increase their metastatic propensity and decrease their sensitivity to therapy. In this review, we discuss recent findings, illustrating the striking similarities-but also knowledge gaps-between the biology of mesenchymal-like state(s) in melanoma and mesenchymal state(s) from epithelial cancers. Based on this comparative analysis, we suggest hypothesis-driven experimental approaches to further deepen our understanding of the EMT-like process in melanoma and how such investigations may pave the way towards the identification of clinically relevant biomarkers for prognosis and new therapeutic strategies.

Group Behavior and Emergence of Cancer Drug Resistance

Drug resistance is a major impediment in cancer. Although it is generally thought that acquired drug resistance is due to genetic mutations, emerging evidence indicates that nongenetic mechanisms also play an important role. Resistance emerges through a complex interplay of clonal groups within a heterogeneous tumor and the surrounding microenvironment. Traits such as phenotypic plasticity, intercellular communication, and adaptive stress response, act in concert to ensure survival of intermediate reversible phenotypes, until permanent, resistant clones can emerge. Understanding the role of group behavior, and the underlying nongenetic mechanisms, can lead to more efficacious treatment designs and minimize or delay emergence of resistance.

Reconciling Non-Genetic Plasticity with Somatic Evolution in Cancer

Post-treatment progression of tumors is commonly explained by somatic Darwinian evolution (i.e., selection of cells carrying genetic mutations that create more aggressive cell traits). But cancer genome and transcriptome analyses now paint a picture far more complex, prompting us to see beyond the Darwinian scheme: non-genetic cell phenotype plasticity explained by alternative stable gene expression states ('attractors'), may also produce aggressive phenotypes that can be selected for, without mutations. Worse, treatment may even induce cell state transitions into more malignant attractors. We review recent evidence for non-genetic mechanisms of progression, explain the theoretical foundation of attractor transitions behind treatment-induced increase of aggressiveness, and provide a framework for unifying genetic and non-genetic dynamics in tumor progression.