Cancer Ecology and Evolution: Positive interactions and system vulnerability

Softness or Stiffness What Contributes to Cancer and Cancer Metastasis?

Beyond the genomic and proteomic analysis of bulk and single cancer cells, a new focus of cancer research is emerging that is based on the mechanical analysis of cancer cells. Therefore, several biophysical techniques have been developed and adapted. The characterization of cancer cells, like human cancer cell lines, started with their mechanical characterization at mostly a single timepoint. A universal hypothesis has been proposed that cancer cells need to be softer to migrate and invade tissues and subsequently metastasize in targeted organs. Thus, the softness of cancer cells has been suggested to serve as a universal physical marker for the malignancy of cancer types. However, it has turned out that there exists the opposite phenomenon, namely that stiffer cancer cells are more migratory and invasive and therefore lead to more metastases. These contradictory results question the universality of the role of softness of cancer cells in the malignant progression of cancers. Another problem is that the various biophysical techniques used can affect the mechanical properties of cancer cells, making it even more difficult to compare the results of different studies. Apart from the instrumentation, the culture and measurement conditions of the cancer cells can influence the mechanical measurements. The review highlights the main advances of the mechanical characterization of cancer cells, discusses the strength and weaknesses of the approaches, and questions whether the passive mechanical characterization of cancer cells is still state-of-the art. Besides the cell models, conditions and biophysical setups, the role of the microenvironment on the mechanical characteristics of cancer cells is presented and debated. Finally, combinatorial approaches to determine the malignant potential of tumors, such as the involvement of the ECM, the cells in a homogeneous or heterogeneous association, or biological multi-omics analyses, together with the dynamic-mechanical analysis of cancer cells, are highlighted as new frontiers of research.

Mathematical models of intercellular signaling in breast cancer

BACKGROUND AND OBJECTIVES

The development and regulation of healthy and cancerous breast tissue is guided by communication between cells. Diverse signals are exchanged between cancer cells and non-cancerous cells of the tumor microenvironment (TME), influencing all stages of tumor progression. Mathematical models are essential for understanding how this complex network determines cancer progression and the effectiveness of treatment.

METHODOLOGY

We reviewed the current dynamical mathematical models of intercellular signaling in breast cancer, examining models with cancer cells only, fibroblasts, endothelial cells, macrophages and the immune system as whole. We categorized the goals and complexity of these models, to highlight how they can explain many features of cancer emergence and progression.

RESULTS

We found that dynamical models of intercellular signaling can elucidate tissue-level dysregulation in cancer by explaining: i) maintenance of non-heritable intratumor phenotypic heterogeneity, ii) transitions between tumor dormancy and accelerated invasive growth, iii) stromal support of tumor vascularization and growth factor enrichment and iv) suppression of immune infiltration and cancer surveillance. These models also provide a framework to propose novel TME-targeting treatment strategies. However, most models were focused on a highly selected and small set of signaling interactions between a few cell types, and their translational applicability were severely limited by the availability of tumor-specific data for personalized model calibration.

CONCLUSIONS AND IMPLICATIONS

Mathematical models of breast cancer have many challenges and opportunities to incorporate signaling. The four key challenges are: 1) finding ways to treat signaling networks as a context-dependent language that incorporates non-linear and non-additive responses, 2) identifying the key cell phenotypes that signals control and understanding the feedbacks between signals and phenotype that determine the progression of cancer, (3) estimating parameters of specific patient tumors early in treatment, 4) linking models with novel data collection methods that have single cell and spatial resolution. As our approaches advance, it is our hope that dynamical mathematical models of inter-cellular signaling can play a central role in identifying and testing new treatment strategies as well as forecasting impacts of disease treatment.

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.

A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Chemotherapy remains a commonly used and important treatment option for metastatic breast cancer. A majority of ER+ metastatic breast cancer patients ultimately develop resistance to chemotherapy, resulting in disease progression. We hypothesized that an "evolutionary double-bind", where treatment with one drug improves the response to a different agent, would improve the effectiveness and durability of responses to chemotherapy. This approach exploits vulnerabilities in acquired resistance mechanisms. Evolutionary models can be used in refractory cancer to identify alternative treatment strategies that capitalize on acquired vulnerabilities and resistance traits for improved outcomes. To develop and test these models, ER+ breast cancer cell lineages sensitive and resistant to chemotherapy are grown in spheroids with varied initial population frequencies to measure cross-sensitivity and efficacy of chemotherapy and add-on treatments such as disulfiram combination treatment. Different treatment schedules then assessed the best strategy for reducing the selection of resistant populations. We developed and parameterized a game-theoretic mathematical model from this in vitro experimental data, and used it to predict the existence of a double-bind where selection for resistance to chemotherapy induces sensitivity to disulfiram. The model predicts a dose-dependent re-sensitization (a double-bind) to chemotherapy for monotherapy disulfiram.

A modelling framework for cancer ecology and evolution

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

"Patchiness" in mechanical stiffness across a tumor as an early-stage marker for malignancy

Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a "patchiness" index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the "patchiness" of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.

Modeling cancer's ecological and evolutionary dynamics

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

Ecology of Human Medical Enterprises: From Disease Ecology of Zoonoses, Cancer Ecology Through to Medical Ecology of Human Microbiomes

In nature, the interaction between pathogens and their hosts is only one of a handful of interaction relationships between species, including parasitism, predation, competition, symbiosis, commensalism, and among others. From a non-anthropocentric view, parasitism has relatively fewer essential differences from the other relationships; but from an anthropocentric view, parasitism and predation against humans and their well-beings and belongings are frequently related to heinous diseases. Specifically, treating (managing) diseases of humans, crops and forests, pets, livestock, and wildlife constitute the so-termed medical enterprises (sciences and technologies) humans endeavor in biomedicine and clinical medicine, veterinary, plant protection, and wildlife conservation. In recent years, the significance of ecological science to medicines has received rising attentions, and the emergence and pandemic of COVID-19 appear accelerating the trend. The facts that diseases are simply one of the fundamental ecological relationships in nature, and the study of the relationships between species and their environment is a core mission of ecology highlight the critical importance of ecological science. Nevertheless, current studies on the ecology of medical enterprises are highly fragmented. Here, we (i) conceptually overview the fields of disease ecology of wildlife, cancer ecology and evolution, medical ecology of human microbiome-associated diseases and infectious diseases, and integrated pest management of crops and forests, across major medical enterprises. (ii) Explore the necessity and feasibility for a unified medical ecology that spans biomedicine, clinical medicine, veterinary, crop (forest and wildlife) protection, and biodiversity conservation. (iii) Suggest that a unified medical ecology of human diseases is both necessary and feasible, but laissez-faire terminologies in other human medical enterprises may be preferred. (iv) Suggest that the evo-eco paradigm for cancer research can play a similar role of evo-devo in evolutionary developmental biology. (v) Summarized 40 key ecological principles/theories in current disease-, cancer-, and medical-ecology literatures. (vi) Identified key cross-disciplinary discovery fields for medical/disease ecology in coming decade including bioinformatics and computational ecology, single cell ecology, theoretical ecology, complexity science, and the integrated studies of ecology and evolution. Finally, deep understanding of medical ecology is of obvious importance for the safety of human beings and perhaps for all living things on the planet.

The tumour immune microenvironment in oesophageal cancer

Oesophageal cancer (OC) is an inflammation-associated malignancy linked to gastro-oesophageal reflux disease, obesity and tobacco use. Knowledge of the microenvironment of oesophageal tumours is relevant to our understanding of the development of OC and its biology, and has major implications for understanding the response to standard therapies and immunotherapies, as well as for uncovering novel targets. In this context, we discuss what is known about the TME in OC from tumour initiation to development and progression, and how this is relevant to therapy sensitivity and resistance in the two major types of OC. We provide an immunological characterisation of the OC TME and discuss its prognostic implications with specific comparison with the Immunoscore and immune-hot, -cold, altered-immunosuppressed and -altered-excluded models. Targeted therapeutics for the TME under pre-clinical and clinical investigation in OCs are also summarised. A deeper understanding of the TME will enable the development of combination approaches to concurrently target the tumour cells and TME delivering precision medicine to OC patients.

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.

Testing Multi-Task Cancer Evolution: How Do We Test Ecological Hypotheses in Cancer?

Recently several authors described a family of models, according to which different cancer types and subtypes fall within a space of selective trade-offs between archetypes that maximize the performance of different tasks: cell division, biomass and energy production, lipogenesis, immune interaction, and invasion and tissue remodeling. On this picture, inter- and intratumor heterogeneity can be explained in part as a product of these selective trade-offs in different cancers, at different stages of cancer progression. The aim of this Perspective is to critically assess this approach. I use this case study to consider more generally both the advantages of using ecological models in the context of cancer, and the challenges facing testing of such models.

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.

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.

Special Collection on Ecological and Evolutionary Approaches to Cancer Control: Cancer Finds a Conceptual Home

Despite a century of intense investigation, cancer biology and treatment remain plagued by unanswered questions. Even basic questions regarding the fundamental forces driving the formation of cancer remain controversial. Recent approaches view cancer in the context of a complex web of interactions among cancer cells of the tumor, together with their interactions with the many cells and constituents of the complex and highly dynamic tumor microenvironment. As seen in this special collection, we believe that viewing cancer as a process of evolution driven by ongoing ecological processes playing out within a dynamic environment offers many insights and potential new pathways for cancer control.