Ecological therapy for cancer: defining tumors using an ecosystem paradigm suggests new opportunities for novel cancer treatments

Modeling tumors as complex ecosystems

Many cancers resist therapeutic intervention. This is fundamentally related to intratumor heterogeneity: multiple cell populations, each with different phenotypic signatures, coexist within a tumor and its metastases. Like species in an ecosystem, cancer populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity or predict its consequences. Here, we propose that the generalized Lotka-Volterra model (GLV), a standard tool to describe species-rich ecological communities, provides a suitable framework to model the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties provide a new understanding of the disease. We discuss potential extensions of the model and their application to phenotypic plasticity, cancer-immune interactions, and metastatic growth. Our work outlines a set of questions and a road map for further research in cancer ecology.

An Overview of Optic Pathway Glioma With Neurofibromatosis Type 1: Pathogenesis, Risk Factors, and Therapeutic Strategies

Optic pathway gliomas (OPGs) are most predominant pilocytic astrocytomas, which are typically diagnosed within the first decade of life. The majority of affected children with OPGs also present with neurofibromatosis type 1 (NF1), the most common tumor predisposition syndrome. OPGs in individuals with NF1 primarily affect the optic pathway and lead to visual disturbance. However, it is challenging to assess risk in asymptomatic patients without valid biomarkers. On the other hand, for symptomatic patients, there is still no effective treatment to prevent or recover vision loss. Therefore, this review summarizes current knowledge regarding the pathogenesis of NF1-associated OPGs (NF1-OPGs) from preclinical studies to seek potential prognostic markers and therapeutic targets. First, the loss of the NF1 gene activates 3 distinct Ras effector pathways, including the PI3K/AKT/mTOR pathway, the MEK/ERK pathway, and the cAMP pathway, which mediate glioma tumorigenesis. Meanwhile, non-neoplastic cells from the tumor microenvironment (microglia, T cells, neurons, etc.) also contribute to gliomagenesis via various soluble factors. Subsequently, we investigated potential genetic risk factors, molecularly targeted therapies, and neuroprotective strategies for tumor prevention and vision recovery. Last, potential directions and promising preclinical models of NF1-OPGs are presented for further research. On the whole, NF1-OPGs develop as a result of the interaction between glioma cells and the tumor microenvironment. Developing effective treatments require a better understanding of tumor molecular characteristics, as well as multistage interventions targeting both neoplastic cells and non-neoplastic cells.

Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model , to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

Updating the Definition of Cancer

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

Reuniting philosophy and science to advance cancer research

Cancers rely on multiple, heterogeneous processes at different scales, pertaining to many biomedical fields. Therefore, understanding cancer is necessarily an interdisciplinary task that requires placing specialised experimental and clinical research into a broader conceptual, theoretical, and methodological framework. Without such a framework, oncology will collect piecemeal results, with scant dialogue between the different scientific communities studying cancer. We argue that one important way forward in service of a more successful dialogue is through greater integration of applied sciences (experimental and clinical) with conceptual and theoretical approaches, informed by philosophical methods. By way of illustration, we explore six central themes: (i) the role of mutations in cancer; (ii) the clonal evolution of cancer cells; (iii) the relationship between cancer and multicellularity; (iv) the tumour microenvironment; (v) the immune system; and (vi) stem cells. In each case, we examine open questions in the scientific literature through a philosophical methodology and show the benefit of such a synergy for the scientific and medical understanding of cancer.

Novel CAF-identifiers via transcriptomic and protein level analysis in HNSC patients

Cancer-associated fibroblasts (CAFs), a prominent component of the tumor microenvironment, play an important role in tumor development, invasion, and drug resistance. The expression of distinct "CAF-markers" which separates CAFs from normal fibroblasts and epithelial cells, have traditionally been used to identify them. These commonly used CAF-markers have been reported to differ greatly across different CAF subpopulations, even within a cancer type. Using an unbiased -omic approach from public data and in-house RNAseq data from patient derived novel CAF cells, TIMP-1, SPARC, COL1A2, COL3A1 and COL1A1 were identified as potential CAF-markers by differential gene expression analysis using publicly available single cell sequencing data and in-house RNAseq data to distinguish CAF populations from tumor epithelia and normal oral fibroblasts. Experimental validation using qPCR and immunofluorescence revealed CAF-specific higher expression of TIMP-1 and COL1A2 as compared to other markers in 5 novel CAF cells, derived from patients of diverse gender, habits and different locations of head and neck squamous cell carcinoma (HNSC). Upon immunohistochemical (IHC) analysis of FFPE blocks however, COL1A2 showed better differential staining between tumor epithelia and tumor stroma. Similar data science driven approach utilizing single cell sequencing and RNAseq data from stabilized CAFs can be employed to identify CAF-markers in various cancers.

Cell facilitation promotes growth and survival under drug pressure in breast cancer

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

Nasopharyngeal carcinoma ecology theory: cancer as multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem

Nasopharyngeal carcinoma (NPC) is a particular entity of head neck cancer that is generally regarded as a genetic disease with diverse intertumor and intratumor heterogeneity. This perspective review mainly outlines the up-to-date knowledge of cancer ecology and NPC progression, and presents a number of conceptual stepping-stones. At the beginning, I explicitly advocate that the nature of NPC (cancer) is not a genetic disease but an ecological disease: a multidimensional spatiotemporal "unity of ecology and evolution" pathological ecosystem. The hallmarks of cancer is proposed to act as ecological factors of population fitness. Subsequently, NPC cells are described as invasive species and its metastasis as a multidirectional ecological dispersal. The foundational ecological principles include intraspecific relationship (e.g. communication) and interspecific relationship (e.g. competition, predation, parasitism and mutualism) are interpreted to understand NPC progression. "Mulberry-fish-ponds" model can well illustrate the dynamic reciprocity of cancer ecosystem. Tumor-host interface is the ecological transition zone of cancer, and tumor buddings should be recognized as ecological islands separated from the mainland. It should be noted that tumor-host interface has a significantly molecular and functional edge effect because of its curvature and irregularity. Selection driving factors and ecological therapy including hyperthermia for NPC patients, and future perspectives in such field as "ecological pathology", "multidimensional tumoriecology" are also discussed. I advance that "nothing in cancer evolution or ecology makes sense except in the light of the other". The cancer ecology tree is constructed to comprehensively point out the future research direction. Taken together, the establishment of NPC ecology theory and cancer ecology tree might provide a novel conceptual framework and paradigm for our understanding of cancer complex causal process and potential preventive and therapeutic applications for patients.

Integration of quantitative methods and mathematical approaches for the modeling of cancer cell proliferation dynamics

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer.

The Regulation of Microglial Cell Polarization in the Tumor Microenvironment: A New Potential Strategy for Auxiliary Treatment of Glioma-A Review

Glioma is the most common primary tumor of the central nervous system and normally should be treated by synthetic therapy, mainly with surgical operation assisted by radiotherapy and chemotherapy; however, the therapeutic effect has not been satisfactory, and the 5-year survival rates of anaplastic glioma and glioblastoma are 29.7% and 5.5%, respectively. To identify a more efficient strategy to treat glioma, in recent years, the influence of the inflammatory microenvironment on the progression of glioma has been studied. Various immunophenotypes exist in microglial cells, each of which has a different functional property. In this review, references about the phenotypic conversion of microglial cell polarity in the microenvironment were briefly summarized, and the differences in polarized state and function, their influences on glioma progression under different physiological and pathological conditions, and the interactive effects between the two were mainly discussed. Certain signaling molecules and regulatory pathways involved in the microglial cell polarization process were investigated, and the feasibility of targeted regulation of microglial cell conversion to an antitumor phenotype was analyzed to provide new clues for the efficient auxiliary treatment of neural glioma.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer

OBJECTIVES

To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction.

METHODS

In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55).

RESULTS

iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001).

CONCLUSIONS

iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection.

KEY POINTS

• Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Cancer cell foraging to explain bone-specific metastatic progression

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

Dual Effect of Immune Cells within Tumour Microenvironment: Pro- and Anti-Tumour Effects and Their Triggers

Our body is constantly exposed to pathogens or external threats, but with the immune response that our body can develop, we can fight off and defeat possible attacks or infections. Nevertheless, sometimes this threat comes from an internal factor. Situations such as the existence of a tumour also cause our immune system (IS) to be put on alert. Indeed, the link between immunology and cancer is evident these days, with IS being used as one of the important targets for treating cancer. Our IS is able to eliminate those abnormal or damaged cells found in our body, preventing the uncontrolled proliferation of tumour cells that can lead to cancer. However, in several cases, tumour cells can escape from the IS. It has been observed that immune cells, the extracellular matrix, blood vessels, fat cells and various molecules could support tumour growth and development. Thus, the developing tumour receives structural support, irrigation and energy, among other resources, making its survival and progression possible. All these components that accompany and help the tumour to survive and to grow are called the tumour microenvironment (TME). Given the importance of its presence in the tumour development process, this review will focus on one of the components of the TME: immune cells. Immune cells can support anti-tumour immune response protecting us against tumour cells; nevertheless, they can also behave as pro-tumoural cells, thus promoting tumour progression and survival. In this review, the anti-tumour and pro-tumour immunity of several immune cells will be discussed. In addition, the TME influence on this dual effect will be also analysed.

Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer

BACKGROUND

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking.

METHODS

Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression.

RESULTS

The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine-cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix-receptor interaction. Protein-protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients.

CONCLUSIONS

This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

Theoretical Evaluation of the Impact of Hyperthermia in Combination with Radiation Therapy in an Artificial Immune-Tumor-Ecosystem

Simple Summary

Radio-sensitizing effects of moderate or mild hyperthermia (heating up tumor cells up to 41–43 °C) in combination with radiotherapy (thermoradiotherapy) have been evaluated for decades. However, how this combination might modulate an anti-tumor immune response is not well known. To investigate the dynamic behavior of immune–tumor ecosystems in different scenarios, a model representing an artificial adaptive immune system in silico is used. Such a model may be far removed from the real situation in the patient, but it could serve as a laboratory to investigate fundamental principles of dynamics in such systems under well-controlled conditions and it could be used to generate and refine hypothesis supporting the design of clinical trials. Regarding the results of the presented computer simulations, the main effect is governed by the cellular radio-sensitization. In addition, the application of hyperthermia during the first radiotherapy fractions seems to be more effective.

Abstract

There is some evidence that radiotherapy (RT) can trigger anti-tumor immune responses. In addition, hyperthermia (HT) is known to be a tumor cell radio-sensitizer. How HT could enhance the anti-tumor immune response produced by RT is still an open question. The aim of this study is the evaluation of potential dynamic effects regarding the adaptive immune response induced by different combinations of RT fractions with HT. The adaptive immune system is considered as a trainable unit (perceptron) which compares danger signals released by necrotic or apoptotic cell death with the presence of tumor- and host tissue cell population-specific molecular patterns (antigens). To mimic the changes produced by HT such as cell radio-sensitization or increase of the blood perfusion after hyperthermia, simplistic biophysical models were included. To study the effectiveness of the different RT+HT treatments, the Tumor Control Probability (TCP) was calculated. In the considered scenarios, the major effect of HT is related to the enhancement of the cell radio-sensitivity while perfusion or heat-based effects on the immune system seem to contribute less. Moreover, no tumor vaccination effect has been observed. In the presented scenarios, HT boosts the RT cell killing but it does not fundamentally change the anti-tumor immune response.

Curcumin as an Adjuvant to Cancer Immunotherapy

The components of the immune system play a very sincere and crucial role in combating tumors. However, despite their firm efforts of elimination, tumor cells cleverly escape the surveillance process by adopting several immune evasion mechanisms. The conversion of immunogenicity of tumor microenvironment into tolerogenic is considered as a prime reason for tumor immune escape. Therapeutically, different immunotherapies have been adopted to block such immune escaping routes along with better clinical outcomes. Still, the therapies are haunted by several drawbacks. Over time, curcumin has been considered as a potential anti-cancer molecule. Its potentialities have been recorded against the standard hallmarks of cancer such as continuous proliferation, escaping apoptosis, continuous angiogenesis, insensitivity to growth inhibitors, tissue invasion, and metastasis. Hence, the diversity of curcumin functioning has already been established and exploration of its application with immunotherapies might open up a new avenue for scientists and clinicians. In this review, we briefly discuss the tumor's way of immune escaping, followed by various modern immunotherapies that have been used to encounter the escaping paths and their minute flaws. Finally, the conclusion has been drawn with the application of curcumin as a potential immune-adjuvant, which fearlessly could be used with immunotherapies for best outcomes.

Viewing Cancer Through the Lens of Corruption: Using Behavioral Ecology to Understand Cancer

All biological systems depend on signals for coordination: signals which pass information among agents that run the gamut from cells to organisms. However, their very importance makes signals vulnerable to subversion. How can a receiver know whether a signal is honest or deceptive? In other words, are signals necessarily a reliable indicator of agent quality or need? By drawing parallels to ecological phenomena ranging from begging by nestlings to social insects, we investigate the role of signal degradation in cancer. We thus think of cancer as a form of corruption, in which cells command huge resource investment through relatively cheap signals, just as relatively small bribes can leverage large profits. We discuss various mechanisms which prevent deceptive signaling in the natural world and within tissues. We show how cancers evolve ways to escape these controls and relate these back to evasion mechanisms in ecology. We next introduce two related concepts, co-option and collusion, and show how they play critical roles in ecology and cancer. Drawing on public policy, we propose new approaches to view treatment based on taxation, changing the incentive structure, and the recognition of corrupted signaling networks.

Changing our view of the Schistosoma granuloma to an ecological standpoint

Schistosomiasis, a neglected parasitic tropical disease that has plagued humans for centuries, remains a major public health burden. A primary challenge to understanding schistosomiasis is deciphering the most remarkable pathological feature of this disease, the granuloma - a highly dynamic and self-organized structure formed by both host and parasite components. Granulomas are considered a remarkable example of how parasites evolved with their hosts to establish complex and intimate associations. However, much remains unclear regarding life within the granuloma, and strategies to restrain its development are still lacking. Here we explore current information on the hepatic Schistosoma mansoni granuloma in the light of Ecology and propose that this intricate structure acts as a real ecosystem. The schistosomal granuloma is formed by cells (biotic component), protein scaffolds, fibres, and chemical compounds (abiotic components) with inputs/outputs of energy and matter, as complex as in classical ecosystems. We review the distinct cell populations ('species') within the granuloma and examine how they integrate with each other and interact with their microenvironment to form a multifaceted cell community in different space-time frames. The colonization of the hepatic tissue to form granulomas is explained from the point of view of an ecological succession whereby a community is able to modify its physical environment, creating conditions and resources for ecosystem construction. Remarkably, the granuloma represents a dynamic evolutionary system that undergoes progressive changes in the 'species' that compose its community over time. In line with ecological concepts, we examine the granuloma not only as a place where a community of cells is settled (spatial niche or habitat) but also as a site in which the functional activities of these combined populations occur in an orchestrated way in response to microenvironmental gradients such as cytokines and egg antigens. Finally, we assert how the levels of organization of cellular components in a granuloma as conventionally defined by Cell Biology can fit perfectly into a hierarchical structure of biological systems as defined by Ecology. By rethinking the granuloma as an integrating and evolving ecosystem, we draw attention to the inner workings of this structure that are central to the understanding of schistosomiasis and could guide its future treatment.

Metagenomic discovery and functional validation of L-asparaginases with anti-leukemic effect from the Caspian Sea

By screening 27,000 publicly available prokaryotic genomes, we recovered ca. 6300 type I and ca. 5200 type II putative L-asparaginase highlighting the vast potential of prokaryotes. Caspian water with similar salt composition to the human serum was targeted for in silico L-asparaginase screening. We screened ca. three million predicted genes of its assembled metagenomes that resulted in annotation of 87 putative L-asparaginase genes. The L-asparagine hydrolysis was experimentally confirmed by synthesizing and cloning three selected genes in E. coli. Catalytic parameters of the purified enzymes were determined to be among the most desirable reported values. Two recombinant enzymes represented remarkable anti-proliferative activity (IC50 <1IU/ml) against leukemia cell line Jurkat while no cytotoxic effect on human erythrocytes or human umbilical vein endothelial cells was detected. Similar salinity and ionic concentration of the Caspian water to the human serum highlights the potential of secretory L-asparaginases recovered from these metagenomes as potential treatment agents.

Searching for Goldilocks: How Evolution and Ecology Can Help Uncover More Effective Patient-Specific Chemotherapies

Deaths from cancer are mostly due to metastatic disease that becomes resistant to therapy. A mainstay treatment for many cancers is chemotherapy, for which the dosing strategy is primarily limited by patient toxicity. While this MTD approach builds upon the intuitively appealing principle that maximum therapeutic benefit is achieved by killing the largest possible number of cancer cells, there is increasing evidence that moderation might allow host-specific features to contribute to success. We believe that a "Goldilocks Window" of submaximal chemotherapy will yield improved overall outcomes. This window combines the complex interplay of cancer cell death, immune activity, emergence of chemoresistance, and metastatic dissemination. These multiple activities driven by chemotherapy have tradeoffs that depend on the specific agents used as well as their dosing levels and schedule. Here we present evidence supporting the idea that MTD may not always be the best approach and offer suggestions toward a more personalized treatment regime that integrates insights into patient-specific eco-evolutionary dynamics.

Heparanase and the hallmarks of cancer

Heparanase is the only mammalian enzyme that cleaves heparan sulphate, an important component of the extracellular matrix. This leads to the remodelling of the extracellular matrix, whilst liberating growth factors and cytokines bound to heparan sulphate. This in turn promotes both physiological and pathological processes such as angiogenesis, immune cell migration, inflammation, wound healing and metastasis. Furthermore, heparanase exhibits non-enzymatic actions in cell signalling and in regulating gene expression. Cancer is underpinned by key characteristic features that promote malignant growth and disease progression, collectively termed the 'hallmarks of cancer'. Essentially, all cancers examined to date have been reported to overexpress heparanase, leading to enhanced tumour growth and metastasis with concomitant poor patient survival. With its multiple roles within the tumour microenvironment, heparanase has been demonstrated to regulate each of these hallmark features, in turn highlighting the need for heparanase-targeted therapies. However, recent discoveries which demonstrated that heparanase can also regulate vital anti-tumour mechanisms have cast doubt on this approach. This review will explore the myriad ways by which heparanase functions as a key regulator of the hallmarks of cancer and will highlight its role as a major component within the tumour microenvironment. The dual role of heparanase within the tumour microenvironment, however, emphasises the need for further investigation into defining its precise mechanism of action in different cancer settings.

Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

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

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

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

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

A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma

Background

Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma.

Material/Methods

Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Lasso regression analysis was conducted to reduce the overfitting of model. Nomogram integrated the signature and clinicopathological characteristics was established for prognosis prediction of LUAD. The GSE30219, GSE41271, and GSE42127 datasets were analyzed for external validation. LUAD patients were separated into low-risk and high-risk subgroups based on the optimum cutoff threshold of calculated risk score. The predictive value of the signature was evaluated using Kaplan-Meier survival analysis, Harrell’s C-index, and receiver operating characteristic (ROC) curve analysis and calibration curve. Clinical- and immune-correlation of the signature was further performed. Gene Set Enrichment Analysis (GSEA) was performed for functional exploration.

Results

An immune-related signature containing 7 genes was identified. The signature exhibited reliable performance in the prediction of overall survival for LUAD with the C-index being 0.72. The areas under the curve (AUCs) of the model in 1-year risk prediction were 0.781, 0.797, 0.659, and 0.822 for TCGA-LUAD, GSE30219, GSE41271, and GSE42127 datasets, respectively. In all datasets, the signature proved to an independent risk factor for LUAD. Correlation analyses and GSEA further revealed the close relationship between the predictive biomarker and tumor immunity.

Conclusions

A Cox proportional hazard model consisting of 7 genes was identified for prognostic prediction of LUAD. The signature was highly correlated with immunity and deserves further exploration.

Histoecology: Applying Ecological Principles and Approaches to Describe and Predict Tumor Ecosystem Dynamics Across Space and Time

Cancer cells exist within a complex spatially structured ecosystem composed of resources and different cell types. As the selective pressures imposed by this environment determine the fate of cancer cells, an improved understanding of how this ecosystem evolves will better elucidate how tumors grow and respond to therapy. State of the art imaging methods can now provide highly resolved descriptions of the microenvironment, yielding the data required for a thorough study of its role in tumor growth and treatment resistance. The field of landscape ecology has been studying such species-environment relationship for decades, and offers many tools and perspectives that cancer researchers could greatly benefit from. Here, we discuss one such tool, species distribution modeling (SDM), that has the potential to, among other things, identify critical environmental factors that drive tumor evolution and predict response to therapy. SDMs only scratch the surface of how ecological theory and methods can be applied to cancer, and we believe further integration will take cancer research in exciting new and productive directions. Significance: Here we describe how species distribution modeling can be used to quantitatively describe the complex relationship between tumor cells and their microenvironment. Such a description facilitates a deeper understanding of cancers eco-evolutionary dynamics, which in turn sheds light on the factors that drive tumor growth and response to treatment.

Novel Medicinal Mushroom Blend as a Promising Supplement in Integrative Oncology: A Multi-Tiered Study using 4T1 Triple-Negative Mouse Breast Cancer Model

Although medicinal mushroom extracts have been proposed as promising anti-cancer agents, their precise impacts on metastatic breast cancer are still to be clarified. For this purpose, the present study exploited the effect of a novel medicinal mushroom blend, namely Micotherapy U-care, in a 4T1 triple-negative mouse breast cancer model. Mice were orally administered with Micotherapy U-care, consisting of a mixture of Agaricus blazei, Ophiocordyceps sinensis, Ganoderma lucidum, Grifola frondosa, and Lentinula edodes. The syngeneic tumor-bearing mice were generated by injecting 4T1 cells in both supplemented and non-supplemented mice. After sacrifice 35 days later, specific endpoints and pathological outcomes of the murine pulmonary tissue were evaluated. (i) Histopathological and ultrastructural analysis and (ii) immunohistochemical assessment of TGF-ß1, IL-6 and NOS2, COX2, SOD1 as markers of inflammation and oxidative stress were performed. The QoL was comparatively evaluated. Micotherapy U-care supplementation, starting before 4T1 injection and lasting until the end of the experiment, dramatically reduced the pulmonary metastases density, also triggering a decrease of fibrotic response, and reducing IL-6, NOS, and COX2 expression. SOD1 and TGF-ß1 results were also discussed. These findings support the valuable potential of Micotherapy U-care as adjuvant therapy in the critical management of triple-negative breast cancer.

An Open-Label Study of the Safety and Efficacy of Tag-7 Gene-Modified Tumor Cells-Based Vaccine in Patients with Locally Advanced or Metastatic Malignant Melanoma or Renal Cell Cancer

LESSONS LEARNED

This study showed that carefully selected patients with locally advanced and metastatic forms of malignant melanoma and renal cell carcinoma could potentially have long-term disease control with a tag-7 gene-modified tumor cells-based vaccine. Randomized clinical trials in patients whose tumors produce low amounts of immunosuppressive factors are needed to confirm this hypothesis in both the adjuvant and metastatic settings.

BACKGROUND

Immunotherapy may produce long-lasting effects on survival and toxicity. The magnitude of efficacy may be dependent on immune factors. We analyzed the results of a phase I/II study of a tag-7 gene-modified tumor cells-based vaccine (GMV) in patients with malignant melanoma (MM) or renal cell carcinoma (RCC) with biomarker analysis of immunosuppressive factors (ISFs) production by their tumor cells.

METHODS

From 2001 to 2014, 80 patients received GMV: 68 with MM and 12 with RCC. Treatment in the metastatic setting included 61 patients (MM, 51; RCC, 10), and treatment in the adjuvant setting (after complete cytoreduction) included 19 patients (MM, 17; RCC, 2). Twenty-six patients were stage III (33%), and 54 (67%) were stage IV. The patients' tumor samples were transferred to culture, transfected with tag-7 gene, and inactivated by radiation. The produced product was injected subcutaneously every 3 weeks until progression or 2 years of therapy. ISFs were measured in the supernatants of the tumor cell cultures and used as predictive factors.

RESULTS

No major safety issues or grade 5 adverse events (AEs) were seen. One grade 4 and two grade 3 AEs were registered. No AEs were registered in 89.4% of treatment cycles. No delayed AE was found. The 5-year overall survival (OS) in the intention-to-treat population was 25.1%. There were no differences between MM OS and RCC OS (log rank, p = .44). Median OS in the metastatic setting was 0.7 years and in the adjuvant setting was 3.1 years. Classification trees were built on the basis of ISF production (Fig. 1). The median OS was 6.6 years in the favorable prognosis (FP) group (major histocompatibility complex class I polypeptide-related sequence A [MICA] level ≤582 pg/mL, n = 15) and 4.6 months in the unfavorable (UF) group (MICA level >582 pg/mL, n = 12; p < .0001). No significant differences were found between classification trees based on ISFs (transforming growth factor β1 [TGF-β1], interleukin-10 [IL-10], and vascular endothelial growth factor [VEGF]). In patients with stage III-IV MM with FP, median OS was 2.3 years, with 31% patients alive at 10 years (Fig. 2) in the UF group (0.4 years; log rank, p = 1.94E-5). No FP patients received modern immunotherapy.

CONCLUSION

GMV showed high results in carefully selected patients with low ISF (TGF-β1, IL-10, and VEGF) production. The method should be further investigated in patients with FP.

Towards Multidrug Adaptive Therapy

A new ecologically inspired paradigm in cancer treatment known as "adaptive therapy" capitalizes on competitive interactions between drug-sensitive and drug-resistant subclones. The goal of adaptive therapy is to maintain a controllable stable tumor burden by allowing a significant population of treatment-sensitive cells to survive. These, in turn, suppress proliferation of the less-fit resistant populations. However, there remain several open challenges in designing adaptive therapies, particularly in extending these therapeutic concepts to multiple treatments. We present a cancer treatment case study (metastatic castrate-resistant prostate cancer) as a point of departure to illustrate three novel concepts to aid the design of multidrug adaptive therapies. First, frequency-dependent "cycles" of tumor evolution can trap tumor evolution in a periodic, controllable loop. Second, the availability and selection of treatments may limit the evolutionary "absorbing region" reachable by the tumor. Third, the velocity of evolution significantly influences the optimal timing of drug sequences. These three conceptual advances provide a path forward for multidrug adaptive therapy. SIGNIFICANCE: Driving tumor evolution into periodic, repeatable treatment cycles provides a path forward for multidrug adaptive therapy.

Treating Cancer as an Invasive Species

To cure a patient's cancer is to eradicate invasive cells from the ecosystem of the body. However, the ecologic complexity of this challenge is not well understood. Here we show how results from eradications of invasive mammalian species from islands-one of the few contexts in which invasive species have been regularly cleared-inform new research directions for treating cancer. We first summarize the epidemiologic characteristics of island invader eradications and cancer treatments by analyzing recent datasets from the Database of Invasive Island Species Eradications and The Cancer Genome Atlas, detailing the superior successes of island eradication projects. Next, we compare how genetic and environmental factors impact success in each system. These comparisons illuminate a number of promising cancer research and treatment directions, such as heterogeneity engineering as motivated by gene drives and adaptive therapy; multiscale analyses of how population heterogeneity potentiates treatment resistance; and application of ecological data mining techniques to high-throughput cancer data. We anticipate that interdisciplinary comparisons between tumor progression and invasive species would inspire development of novel paradigms to cure cancer.

Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer

BACKGROUND

Despite treatment advances, there remains a significant risk of recurrence in ovarian cancer, at which stage it is usually incurable. Consequently, there is a clear need for improved patient stratification. However, at present clinical prognosticators remain largely unchanged due to the lack of reproducible methods to identify high-risk patients.

METHODS

In high-grade serous ovarian cancer patients with advanced disease, we spatially define a tumour ecological balance of stromal resource and immune hazard using high-throughput image and spatial analysis of routine histology slides. On this basis an EcoScore is developed to classify tumours by a shift in this balance towards cancer-favouring or inhibiting conditions.

FINDINGS

The EcoScore provides prognostic value stronger than, and independent of, known risk factors. Crucially, the clinical relevance of mutational burden and genomic instability differ under different stromal resource conditions, suggesting that the selective advantage of these cancer hallmarks is dependent on the context of stromal spatial structure. Under a high resource condition defined by a high level of geographical intermixing of cancer and stromal cells, selection appears to be driven by point mutations; whereas, in low resource tumours featured with high hypoxia and low cancer-immune co-localization, selection is fuelled by aneuploidy.

INTERPRETATION

Our study offers empirical evidence that cancer fitness depends on tumour spatial constraints, and presents a biological basis for developing better assessments of tumour adaptive strategies in overcoming ecological constraints including immune surveillance and hypoxia.

Cancer Ecology and Evolution: Positive interactions and system vulnerability

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

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

Most models of cancer cell population expansion assume exponential growth kinetics at low cell densities, with deviations to account for observed slowing of growth rate only at higher densities due to limited resources such as space and nutrients. However, recent preclinical and clinical observations of tumor initiation or recurrence indicate the presence of tumor growth kinetics in which growth rates scale positively with cell numbers. These observations are analogous to the cooperative behavior of species in an ecosystem described by the ecological principle of the Allee effect. In preclinical and clinical models, however, tumor growth data are limited by the lower limit of detection (i.e., a measurable lesion) and confounding variables, such as tumor microenvironment, and immune responses may cause and mask deviations from exponential growth models. In this work, we present alternative growth models to investigate the presence of an Allee effect in cancer cells seeded at low cell densities in a controlled in vitro setting. We propose a stochastic modeling framework to disentangle expected deviations due to small population size stochastic effects from cooperative growth and use the moment approach for stochastic parameter estimation to calibrate the observed growth trajectories. We validate the framework on simulated data and apply this approach to longitudinal cell proliferation data of BT-474 luminal B breast cancer cells. We find that cell population growth kinetics are best described by a model structure that considers the Allee effect, in that the birth rate of tumor cells increases with cell number in the regime of small population size. This indicates a potentially critical role of cooperative behavior among tumor cells at low cell densities with relevance to early stage growth patterns of emerging and relapsed tumors.

Targeting the tumour stroma to improve cancer therapy

Cancers are not composed merely of cancer cells alone; instead, they are complex 'ecosystems' comprising many different cell types and noncellular factors. The tumour stroma is a critical component of the tumour microenvironment, where it has crucial roles in tumour initiation, progression, and metastasis. Most anticancer therapies target cancer cells specifically, but the tumour stroma can promote the resistance of cancer cells to such therapies, eventually resulting in fatal disease. Therefore, novel treatment strategies should combine anticancer and antistromal agents. Herein, we provide an overview of the advances in understanding the complex cancer cell-tumour stroma interactions and discuss how this knowledge can result in more effective therapeutic strategies, which might ultimately improve patient outcomes.

Including Blood Vasculature into a Game-Theoretic Model of Cancer Dynamics

For cancer, we develop a 2-D agent-based continuous-space game-theoretical model that considers cancer cells’ proximity to a blood vessel. Based on castrate resistant metastatic prostate cancer (mCRPC), the model considers the density and frequency (eco-evolutionary) dynamics of three cancer cell types: those that require exogenous testosterone ( T + ), those producing testosterone ( T P ), and those independent of testosterone ( T - ). We model proximity to a blood vessel by imagining four zones around the vessel. Zone 0 is the blood vessel. As rings, zones 1–3 are successively farther from the blood vessel and have successively lower carrying capacities. Zone 4 represents the space too far from the blood vessel and too poor in nutrients for cancer cell proliferation. Within the other three zones that are closer to the blood vessel, the cells’ proliferation probabilities are determined by zone-specific payoff matrices. We analyzed how zone width, dispersal, interactions across zone boundaries, and blood vessel dynamics influence the eco-evolutionary dynamics of cell types within zones and across the entire cancer cell population. At equilibrium, zone 3’s composition deviates from its evolutionary stable strategy (ESS) towards that of zone 2. Zone 2 sees deviations from its ESS because of dispersal from zones 1 and 3; however, its composition begins to resemble zone 1’s more so than zone 3’s. Frequency-dependent interactions between cells across zone boundaries have little effect on zone 2’s and zone 3’s composition but have decisive effects on zone 1. The composition of zone 1 diverges dramatically from both its own ESS, but also that of zone 2. That is because T + cells (highest frequency in zone 1) benefit from interacting with T P cells (highest frequency in zone 2). Zone 1 T + cells interacting with cells in zone 2 experience a higher likelihood of encountering a T P cell than when restricted to their own zone. As expected, increasing the width of zones decreases these impacts of cross-boundary dispersal and interactions. Increasing zone widths increases the persistence likelihood of the cancer subpopulation in the face of blood vessel dynamics, where the vessel may die or become occluded resulting in the “birth” of another blood vessel elsewhere in the space. With small zone widths, the cancer cell subpopulations cannot persist. With large zone widths, blood vessel dynamics create cancer cell subpopulations that resemble the ESS of zone 3 as the larger area of zone 3 and its contribution to cells within the necrotic zone 4 mean that zones 3 and 4 provide the likeliest colonizers for the new blood vessel. In conclusion, our model provides an alternative modeling approach for considering density-dependent, frequency-dependent, and dispersal dynamics into cancer models with spatial gradients around blood vessels. Additionally, our model can consider the occurrence of circulating tumor cells (cells that disperse into the blood vessel from zone 1) and the presence of live cancer cells within the necrotic regions of a tumor.

Cellular interactions constrain tumor growth

A tumor is made up of a heterogeneous collection of cell types, all competing on a fitness landscape mediated by microenvironmental conditions that dictate their interactions. Despite the fact that much is known about cell signaling, cellular cooperation, and the functional constraints that affect cellular behavior, the specifics of how these constraints (and the range over which they act) affect the macroscopic tumor growth laws that govern total volume, mass, and carrying capacity remain poorly understood. We develop a statistical mechanics approach that focuses on the total number of possible states each cell can occupy and show how different assumptions on correlations of these states give rise to the many different macroscopic tumor growth laws used in the literature. Although it is widely understood that molecular and cellular heterogeneity within a tumor is a driver of growth, here we emphasize that focusing on the functional coupling of states at the cellular level is what determines macroscopic growth characteristics.

Intratumoral Heterogeneity in Ductal Carcinoma In Situ: Chaos and Consequence

Ductal carcinoma in situ (DCIS) is a non-invasive proliferative growth in the breast that serves as a non-obligate precursor to invasive ductal carcinoma. The widespread adoption of screening mammography has led to a steep increase in the detection of DCIS, which now comprises approximately 20% of new breast cancer diagnoses in the United States. Interestingly, the intratumoral heterogeneity (ITH) that has been observed in invasive breast cancers may have been established early in tumorigenesis, given the vast and varied ITH that has been detected in DCIS. This review will discuss the intratumoral heterogeneity of DCIS, focusing on the phenotypic and genomic heterogeneity of tumor cells, as well as the compositional heterogeneity of the tumor microenvironment. In addition, we will assess the spatial heterogeneity that is now being appreciated in these lesions, and summarize new approaches to evaluate heterogeneity of tumor and stromal cells in the context of their spatial organization. Importantly, we will discuss how a growing understanding of ITH has led to a more holistic appreciation of the complex biology of DCIS, specifically its evolution and natural history. Finally, we will consider ways in which our knowledge of DCIS ITH might be translated in the future to guide clinical care for DCIS patients.

An ecosystem framework for understanding and treating disease

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans—focusing on bacterial pathogens and cancers—but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

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

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

Thinking Cancer

Evolutionary theories are necessarily invoked for understanding cancer development at the level of species, at the level of cells and tissues, and for developing effective therapies. It is crucial to view cancer in a Darwinian light, where the differential survival of individual cells is based on heritable variations. In the process of this somatic evolution, multicellularity controls are overridden by cancer cells, which become increasingly autonomous. Ecological epigenetics also helps understand how rogue cells that have basically the same DNA as their normal cell counterpart overcome the tissue homeostasis. As we struggle to wrap our minds around the complexity of these phenomena, we apply often times anthropomorphic terms, such as subversion, hijacking, or hacking, to describe especially the most complex among them-the interaction of tumors with the immune system. In this commentary we highlight examples of the anthropomorphic thinking of cancer and try to put into context the relative meaning of terms and the mechanisms that are oftentimes invoked to justify those terms.

The complex ecosystem in non small cell lung cancer invasion

Many tumors are characterized by genetic instability, producing an assortment of genetic variants of tumor cells called subclones. These tumors and their surrounding environments form complex multi-cellular ecosystems, where subclones compete for resources and cooperate to perform multiple tasks, including cancer invasion. Our recent empirical studies revealed existence of such distinct phenotypes of cancer cells, leaders and followers, in lung cancer. These two cellular subclones exchange a complex array of extracellular signals demonstrating a symbiotic relationship at the cellular level. Here, we develop a computational model of the microenvironment of the lung cancer ecosystem to explore how the interactions between subclones can advance or inhibit invasion. We found that, due to the complexity of the ecosystem, invasion may have very different dynamics characterized by the different levels of aggressiveness. By altering the signaling environment, we could alter the ecological relationship between the cell types and the overall ecosystem development. Competition between leader and follower cell populations (defined by the limited amount of resources), positive feedback within the leader cell population (controlled by the focal adhesion kinase and fibronectin signaling), and impact of the follower cells to the leaders (represented by yet undetermined proliferation signal) all had major effects on the outcome of the collective dynamics. Specifically, our analysis revealed a class of tumors (defined by the strengths of fibronectin signaling and competition) that are particularly sensitive to manipulations of the signaling environment. These tumors can undergo irreversible changes to the tumor ecosystem that outlast the manipulations of feedbacks and have a profound impact on invasive potential. Our study predicts a complex division of labor between cancer cell subclones and suggests new treatment strategies targeting signaling within the tumor ecosystem.

The role of spatial variations of abiotic factors in mediating intratumour phenotypic heterogeneity

We present here a space- and phenotype-structured model of selection dynamics between cancer cells within a solid tumour. In the framework of this model, we combine formal analyses with numerical simulations to investigate in silico the role played by the spatial distribution of abiotic components of the tumour microenvironment in mediating phenotypic selection of cancer cells. Numerical simulations are performed both on the 3D geometry of an in silico multicellular tumour spheroid and on the 3D geometry of an in vivo human hepatic tumour, which was imaged using computerised tomography. The results obtained show that inhomogeneities in the spatial distribution of oxygen, currently observed in solid tumours, can promote the creation of distinct local niches and lead to the selection of different phenotypic variants within the same tumour. This process fosters the emergence of stable phenotypic heterogeneity and supports the presence of hypoxic cells resistant to cytotoxic therapy prior to treatment. Our theoretical results demonstrate the importance of integrating spatial data with ecological principles when evaluating the therapeutic response of solid tumours to cytotoxic therapy.

Insights into optic pathway glioma vision loss from mouse models of neurofibromatosis type 1

Neurofibromatosis type 1 (NF1) is a common cancer predisposition syndrome caused by mutations in the NF1 gene. The NF1-encoded protein (neurofibromin) is an inhibitor of the oncoprotein RAS and controls cell growth and survival. Individuals with NF1 are prone to developing low-grade tumors of the optic nerves, chiasm, tracts, and radiations, termed optic pathway gliomas (OPGs), which can cause vision loss. A paucity of surgical tumor specimens and of patient-derived xenografts for investigative studies has limited our understanding of human NF1-associated OPG (NF1-OPG). However, mice genetically engineered to harbor Nf1 gene mutations develop optic gliomas that share many features of their human counterparts. These genetically engineered mouse (GEM) strains have provided important insights into the cellular and molecular determinants that underlie mouse Nf1 optic glioma development, maintenance, and associated vision loss, with relevance by extension to human NF1-OPG disease. Herein, we review our current understanding of NF1-OPG pathobiology and describe the mechanisms responsible for tumor initiation, growth, and associated vision loss in Nf1 GEM models. We also discuss how Nf1 GEM and other preclinical models can be deployed to identify and evaluate molecularly targeted therapies for OPG, particularly as they pertain to future strategies aimed at preventing or improving tumor-associated vision loss in children with NF1.

Expression and significance of autonomic nerves and α9 nicotinic acetylcholine receptor in colorectal cancer

The present study evaluated the distribution of sympathetic and parasympathetic nerves and the expression of the α9 nicotinic acetylcholine receptor (α9nAChR) and investigated their potential association with colorectal cancer (CRC) development. The distribution of autonomic nerves and α9nAChR in CRC was detected by immunohistochemistry, which was then used to analyze their association with clinicopathological parameters and prognosis. Sympathetic fibers were primarily observed in the stroma adjacent to cancer cells, whereas parasympathetic fibers were primarily observed in the stroma away from cancer cells. Patients with samples positive for sympathetic nerve fibers had less lymph node invasion and a better prognosis compared with patients with samples negative for sympathetic nerve fibers. The expression of parasympathetic nerves in patients >60 years old was increased compared with patients ≤60 years old. The expression of parasympathetic nerves in patients with lymph node invasion was increased compared with patients without lymph node invasion. The detection of parasympathetic nerves gradually increased as CRC (T stage) advanced. Patients with parasympathetic negative samples had better prognoses compared with patients with parasympathetic positive samples. The expression of α9nAChR was principally localized in cellular membranes and the cytoplasm of CRC tissues and it was revealed to have a positive association with the number of parasympathetic nerves. Increased α9nAChR expression was observed in patients >60 years old compared with patients <60 years old. The detection rate of α9nAChR in tissues from patients with lymph node invasion was increased compared with patients without lymph node invasion. The detection of α9nAChR gradually increased as the CRC stage advanced. The prognoses for patients with α9nAChR negative tissue were improved compared with the prognoses for patients with α9nAChR positive tissue. Sympathetic nerves were primarily detected in the early phases of CRC and indicated a good prognosis. Parasympathetic nerves and α9nAChR were principally observed in the late phases of cancer and indicated a poor prognosis. The present study revealed that parasympathetic nerves may promote the progression of CRC through α9nAChR.

Combinatory use of distinct single-cell RNA-seq analytical platforms reveals the heterogeneous transcriptome response

Single-cell RNA-seq is a powerful tool for revealing heterogeneity in cancer cells. However, each of the current single-cell RNA-seq platforms has inherent advantages and disadvantages. Here, we show that combining the different single-cell RNA-seq platforms can be an effective approach to obtaining complete information about expression differences and a sufficient cellular population to understand transcriptional heterogeneity in cancers. We demonstrate that it is possible to estimate missing expression information. We further demonstrate that even in the cases where precise information for an individual gene cannot be inferred, the activity of given transcriptional modules can be analyzed. Interestingly, we found that two distinct transcriptional modules, one associated with the Aurora kinase gene and the other with the DUSP gene, are aberrantly regulated in a minor population of cells and may thus contribute to the possible emergence of dormancy or eventual drug resistance within the population.