Group phenotypic composition in cancer

[Evolution of cancer resistance in the animal kingdom]

Cancer is an inevitable collateral problem inherent in the evolution of multicellular organisms, which appeared at the end of the Precambrian. Faced to this constraint, a range of diverse anticancer defenses has evolved across the animal kingdom. Today, investigating how animal organisms, especially those of large size and long lifespan, manage cancer-related issues has both fundamental and applied outcomes, as it could inspire strategies for preventing or treating human cancers. In this article, we begin by presenting the conceptual framework for understanding evolutionary theories regarding the development of anti-cancer defenses. We then present a number of examples that have been extensively studied in recent years, including naked mole rats, elephants, whales, placozoa, xenarthras (such as sloths, armadillos and anteaters) and bats. The contributions of comparative genomics to understanding evolutionary convergences are also discussed. Finally, we emphasize that natural selection has also favored anti-cancer adaptations aimed at avoiding mutagenic environments, for example by maximizing immediate reproductive efforts in the event of cancer. Exploring these adaptive solutions holds promise for identifying novel approaches to improve human health.

The paradox of cooperation among selfish cancer cells

It is traditionally assumed that during cancer development, tumor cells abort their initially cooperative behavior (i.e., cheat) in favor of evolutionary strategies designed solely to enhance their own fitness (i.e., a "selfish" life style) at the expense of that of the multicellular organism. However, the growth and progress of solid tumors can also involve cooperation among these presumed selfish cells (which, by definition, should be noncooperative) and with stromal cells. The ultimate and proximate reasons behind this paradox are not fully understood. Here, in the light of current theories on the evolution of cooperation, we discuss the possible evolutionary mechanisms that could explain the apparent cooperative behaviors among selfish malignant cells. In addition to the most classical explanations for cooperation in cancer and in general (by-product mutualism, kin selection, direct reciprocity, indirect reciprocity, network reciprocity, group selection), we propose the idea that "greenbeard" effects are relevant to explaining some cooperative behaviors in cancer. Also, we discuss the possibility that malignant cooperative cells express or co-opt cooperative traits normally expressed by healthy cells. We provide examples where considerations of these processes could help understand tumorigenesis and metastasis and argue that this framework provides novel insights into cancer biology and potential strategies for cancer prevention and treatment.

Clonal interactions in cancer: integrating quantitative models with experimental and clinical data

Tumors consist of different genotypically distinct subpopulations-or subclones-of cells. These subclones can influence neighboring clones in a process called "clonal interaction." Conventionally, research on driver mutations in cancer has focused on their cell-autonomous effects that lead to an increase in fitness of the cells containing the driver. Recently, with the advent of improved experimental and computational technologies for investigating tumor heterogeneity and clonal dynamics, new studies have shown the importance of clonal interactions in cancer initiation, progression, and metastasis. In this review we provide an overview of clonal interactions in cancer, discussing key discoveries from a diverse range of approaches to cancer biology research. We discuss common types of clonal interactions, such as cooperation and competition, its mechanisms, and the overall effect on tumorigenesis, with important implications for tumor heterogeneity, resistance to treatment, and tumor suppression. Quantitative models-in coordination with cell culture and animal model experiments-have played a vital role in investigating the nature of clonal interactions and the complex clonal dynamics they generate. We present mathematical and computational models that can be used to represent clonal interactions and provide examples of the roles they have played in identifying and quantifying the strength of clonal interactions in experimental systems. Clonal interactions have proved difficult to observe in clinical data; however, several very recent quantitative approaches enable their detection. We conclude by discussing ways in which researchers can further integrate quantitative methods with experimental and clinical data to elucidate the critical-and often surprising-roles of clonal interactions in human cancers.

Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC.

METHODS

In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses.

RESULTS

Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05).

CONCLUSIONS

A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.

Evo-devo perspectives on cancer

The integration of evolutionary and developmental approaches into the field of evolutionary developmental biology has opened new areas of inquiry- from understanding the evolution of development and its underlying genetic and molecular mechanisms to addressing the role of development in evolution. For the last several decades, the terms 'evolution' and 'development' have been increasingly linked to cancer, in many different frameworks and contexts. This mini-review, as part of a special issue on Evolutionary Developmental Biology, discusses the main areas in cancer research that have been addressed through the lenses of both evolutionary and developmental biology, though not always fully or explicitly integrated in an evo-devo framework. First, it briefly introduces the current views on carcinogenesis that invoke evolutionary and/or developmental perspectives. Then, it discusses the main mechanisms proposed to have specifically evolved to suppress cancer during the evolution of multicellularity. Lastly, it considers whether the evolution of multicellularity and development was shaped by the threat of cancer (a cancer-evo-devo perspective), and/or whether the evolution of developmental programs and life history traits can shape cancer resistance/risk in various lineages (an evo-devo-cancer perspective). A proper evolutionary developmental framework for cancer, both as a disease and in terms of its natural history (in the context of the evolution of multicellularity and development as well as life history traits), could bridge the currently disparate evolutionary and developmental perspectives and uncover aspects that will provide new insights for cancer prevention and treatment.

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

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

Nocturnal circulating tumor cells: The ultimate selective filter in cancer progression?

The survival duration of circulating tumor cells (CTCs) in the vasculature is a critical parameter in the establishment of the metastatic cascade. Diamantopoulou and colleagues demonstrate that the metastatic capacity of CTCs is strongly influenced by circadian rhythms, suggesting a rationale for the time-controlled interrogation and treatment of metastatic cancers.

Linking neural crest development to neuroblastoma pathology

Although rare, childhood (paediatric) cancers are a major cause of death in young children. Unlike many adult cancers, paediatric cancers, such as neuroblastoma (NB), are developmental diseases that rarely show genetic predispositions. NB is the most common extracranial solid tumour in children, accounting for ∼15% of paediatric cancer deaths. This heterogeneous cancer arises from undifferentiated neural crest-derived progenitor cells. As neural crest cells are multipotent and migratory, they are often considered the embryonic paradigm of cancer stem cells. However, very little is known about the events that trigger tumour initiation and progression. Here, we discuss recent insights into sympathoadrenal lineage specification, as well as genetic factors associated with NB. With this in mind, we consider the molecular underpinnings of NB in the context of developmental trajectories of the neural crest lineage. This allows us to compare distinct subtypes of the disease and gene-function interactions during sensitive phases of neural crest development.

From developmental to atavistic bet-hedging: How cancer cells pervert the exploitation of random single-cell phenotypic fluctuations

Stochastic gene expression plays a leading developmental role through its contribution to cell differentiation. It is also proposed to promote phenotypic diversification in malignant cells. However, it remains unclear if these two forms of cellular bet-hedging are identical or rather display distinct features. Here we argue that bet-hedging phenomena in cancer cells are more similar to those occurring in unicellular organisms than to those of normal metazoan cells. We further propose that the atavistic bet-hedging strategies in cancer originate from a hijacking of the normal developmental bet-hedging of metazoans. Finally, we discuss the constraints that may shape the atavistic bet-hedging strategies of cancer cells.

Scales of Cancer Evolution: Selfish Genome or Cooperating Cells?

Simple Summary

Cancer continuously evolves its ability to survive in time-varying microenvironment, which results, regarding the therapeutic context, in its therapeutic resistance. As it is accepted that the development of resistance is the direct consequence of intratumour heterogeneity, its evolutionary etiology is intensively studied. Models of carinogenesis are often assessed accordingly to how well they fit into the evolutionary scenario. In the paper, the relevant observations and concepts in cancer research, such as intratumour heterogeneity, cell plasticity, and Markov cell state dynamics, are reviewed and integrated into an evolutionary model. The possibility that the interaction between cancer cells can be interpreted as cooperation is proposed.

Abstract

The exploitation of the evolutionary modus operandi of cancer to steer its progression towards drug sensitive cancer cells is a challenging research topic. Integrating evolutionary principles into cancer therapy requires properly identified selection level, the relevant timescale, and the respective fitness of the principal selection unit on that timescale. Interpretation of some features of cancer progression, such as increased heterogeneity of isogenic cancer cells, is difficult from the most straightforward evolutionary view with the cancer cell as the principal selection unit. In the paper, the relation between the two levels of intratumour heterogeneity, genetic, due to genetic instability, and non-genetic, due to phenotypic plasticity, is reviewed and the evolutionary role of the latter is outlined. In analogy to the evolutionary optimization in a changing environment, the cell state dynamics in cancer clones are interpreted as the risk diversifying strategy bet hedging, optimizing the balance between the exploitation and exploration of the cell state space.

Functional analysis of circulating tumour cells: the KEY to understand the biology of the metastatic cascade

Metastasis formation is the main cause of cancer-related death in patients with solid tumours. At the beginning of this process, cancer cells escape from the primary tumour to the blood circulation where they become circulating tumour cells (CTCs). Only a small subgroup of CTCs will survive during the harsh journey in the blood and colonise distant sites. The in-depth analysis of these metastasis-competent CTCs is very challenging because of their extremely low concentration in peripheral blood. So far, only few groups managed to expand in vitro and in vivo CTCs to be used as models for large-scale descriptive and functional analyses of CTCs. These models have shown already the high variability and complexity of the metastatic cascade in patients with cancer, and open a new avenue for the development of new diagnostic and therapeutic approaches.

Invasion front dynamics of interactive populations in environments with barriers

Invading populations normally comprise different subpopulations that interact while trying to overcome existing barriers against their way to occupy new areas. However, the majority of studies so far only consider single or multiple population invasion into areas where there is no resistance against the invasion. Here, we developed a model to study how cooperative/competitive populations invade in the presence of a physical barrier that should be degraded during the invasion. For one dimensional (1D) environment, we found that a Langevin equation as [Formula: see text] describing invasion front position. We then obtained how [Formula: see text] and [Formula: see text] depend on population interactions and environmental barrier intensity. In two dimensional (2D) environment, for the average interface position movements we found a Langevin equation as [Formula: see text]. Similar to the 1D case, we calculate how [Formula: see text] and [Formula: see text] respond to population interaction and environmental barrier intensity. Finally, the study of invasion front morphology through dynamic scaling analysis showed that growth exponent, [Formula: see text], depends on both population interaction and environmental barrier intensity. Saturated interface width, [Formula: see text], versus width of the 2D environment (L) also exhibits scaling behavior. Our findings show revealed that competition among subpopulations leads to more rough invasion fronts. Considering the wide range of shreds of evidence for clonal diversity in cancer cell populations, our findings suggest that interactions between such diverse populations can potentially participate in the irregularities of tumor border.

The evolution and ecology of benign tumors

Tumors are usually classified into two main categories - benign or malignant, with much more attention being devoted to the second category given that they are usually associated with more severe health issues (i.e., metastatic cancers). Here, we argue that the mechanistic distinction between benign and malignant tumors has narrowed our understanding of neoplastic processes. This review provides the first comprehensive discussion of benign tumors in the context of their evolution and ecology as well as interactions with their hosts. We compare the genetic and epigenetic profiles, cellular activities, and the involvement of viruses in benign and malignant tumors. We also address the impact of intra-tumoral cell composition and its relationship with the tumoral microenvironment. Lastly, we explore the differences in the distribution of benign and malignant neoplasia across the tree of life and provide examples on how benign tumors can also affect individual fitness and consequently the evolutionary trajectories of populations and species. Overall, our goal is to bring attention to the non-cancerous manifestations of tumors, at different scales, and to stimulate research on the evolutionary ecology of host-tumor interactions on a broader scale. Ultimately, we suggest that a better appreciation of the differences and similarities between benign and malignant tumors is fundamental to our understanding of malignancy both at mechanistic and evolutionary levels.

Bridging Tumorigenesis and Therapy Resistance With a Non-Darwinian and Non-Lamarckian Mechanism of Adaptive Evolution

Although neo-Darwinian (and less often Lamarckian) dynamics are regularly invoked to interpret cancer's multifarious molecular profiles, they shine little light on how tumorigenesis unfolds and often fail to fully capture the frequency and breadth of resistance mechanisms. This uncertainty frames one of the most problematic gaps between science and practice in modern times. Here, we offer a theory of adaptive cancer evolution, which builds on a molecular mechanism that lies outside neo-Darwinian and Lamarckian schemes. This mechanism coherently integrates non-genetic and genetic changes, ecological and evolutionary time scales, and shifts the spotlight away from positive selection towards purifying selection, genetic drift, and the creative-disruptive power of environmental change. The surprisingly simple use-it or lose-it rationale of the proposed theory can help predict molecular dynamics during tumorigenesis. It also provides simple rules of thumb that should help improve therapeutic approaches in cancer.