On the origins of developmental robustness: modeling buffering mechanisms against cell-level noise.

During development, cells are subject to stochastic fluctuations in their positions (i.e. cell-level noise) that can potentially lead to morphological noise (i.e. stochastic differences between morphologies that are expected to be equal, e.g. the right and left sides of bilateral organisms). In this study, we explore new and existing hypotheses on buffering mechanisms against cell-level noise. Many of these hypotheses focus on how the boundaries between territories of gene expression remain regular and well defined, despite cell-level noise and division. We study these hypotheses and how irregular territory boundaries lead to morphological noise. To determine the consistency of the different hypotheses, we use a general computational model of development: EmbryoMaker. EmbryoMaker can implement arbitrary gene networks regulating basic cell behaviors (contraction, adhesion, etc.), signaling and tissue biomechanics. We found that buffering mechanisms based on the orientation of cell divisions cannot lead to regular boundaries but that other buffering mechanisms can (homotypic adhesion, planar contraction, non-dividing boundaries, constant signaling and majority rule hypotheses). We also explore the effects of the shape and size of the territories on morphological noise.

Modeling of senescent cell dynamics predicts a late-life decrease in cancer incidence.

Current oncogenic theories state that tumors arise from cell lineages that sequentially accumulate (epi)mutations, progressively turning healthy cells into carcinogenic ones. While those models found some empirical support, they are little predictive of intraspecies age-specific cancer incidence and of interspecies cancer prevalence. Notably, in humans and lab rodents, a deceleration (and sometimes decline) of cancer incidence rate has been found at old ages. Additionally, dominant theoretical models of oncogenesis predict that cancer risk should increase in large and/or long-lived species, which is not supported by empirical data. Here, we explore the hypothesis that cellular senescence could explain those incongruent empirical patterns. More precisely, we hypothesize that there is a trade-off between dying of cancer and of (other) ageing-related causes. This trade-off between organismal mortality components would be mediated, at the cellular scale, by the accumulation of senescent cells. In this framework, damaged cells can either undergo apoptosis or enter senescence. Apoptotic cells lead to compensatory proliferation, associated with an excess risk of cancer, whereas senescent cell accumulation leads to ageing-related mortality. To test our framework, we build a deterministic model that first describes how cells get damaged, undergo apoptosis, or enter senescence. We then translate those cellular dynamics into a compound organismal survival metric also integrating life-history traits. We address four different questions linked to our framework: can cellular senescence be adaptive, do the predictions of our model reflect epidemiological patterns observed among mammal species, what is the effect of species sizes on those answers, and what happens when senescent cells are removed? Importantly, we find that cellular senescence can optimize lifetime reproductive success. Moreover, we find that life-history traits play an important role in shaping the cellular trade-offs. Overall, we demonstrate that integrating cellular biology knowledge with eco-evolutionary principles is crucial to solve parts of the cancer puzzle.

PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets.

BACKGROUND: Plasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. Plasmid detection in large genomic datasets is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance propagation. Bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds). RESULTS: We developed PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in partially assembled genomes. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with a F1 score of 0.950. Notably, it can detect 77.4% of plasmid contigs below 1 kb with 2.8% of false positives and 99.9% of plasmid contigs over 50 kb with 2.2% of false positives. CONCLUSIONS: PlasForest outperforms other currently available tools on genomic datasets by being both sensitive and precise. The performance of PlasForest on metagenomic assemblies are currently well below those of other k-mer-based methods, and we discuss how homology-based approaches could improve plasmid detection in such datasets.

Metastasis and the evolution of dispersal.

Despite significant progress in oncology, metastasis remains the leading cause of mortality of cancer patients. Understanding the foundations of this phenomenon could help contain or even prevent it. As suggested by many ecologists and cancer biologists, metastasis could be considered through the lens of biological dispersal: the movement of cancer cells from their birth site (the primary tumour) to other habitats where they resume proliferation (metastatic sites). However, whether this model can consistently be applied to the emergence and dynamics of metastasis remains unclear. Here, we provide a broad review of various aspects of the evolution of dispersal in ecosystems. We investigate whether similar ecological and evolutionary principles can be applied to metastasis, and how these processes may shape the spatio-temporal dynamics of disseminating cancer cells. We further discuss complementary hypotheses and propose experimental approaches to test the relevance of the evolutionary ecology of dispersal in studying metastasis.

Cancer Is Not (Only) a Senescence Problem.

Age is one of the strongest predictors of cancer and risk of death from cancer. Cancer is therefore generally viewed as a senescence-related malady. However, cancer also exists at subclinical levels in humans and other animals, but its earlier effects on the body are poorly known by comparison. We argue here that cancer is a significant but ignored burden on the body and is likely to be a strong selective force from early during the lifetime of an organism. It is time to adopt this novel view of malignant pathologies to improve our understanding of the ways in which oncogenic phenomena influence the ecology and evolution of animals long before their negative impacts become evident and fatal.

Non-cell-autonomous effects yield lower clonal diversity in expanding tumors.

Recent cancer research has investigated the possibility that non-cell-autonomous (NCA) driving tumor growth can support clonal diversity (CD). Indeed, mutations can affect the phenotypes not only of their carriers ("cell-autonomous", CA effects), but also sometimes of other cells (NCA effects). However, models that have investigated this phenomenon have only considered a restricted number of clones. Here, we designed an individual-based model of tumor evolution, where clones grow and mutate to yield new clones, among which a given frequency have NCA effects on other clones' growth. Unlike previously observed for smaller assemblages, most of our simulations yield lower CD with high frequency of mutations with NCA effects. Owing to NCA effects increasing competition in the tumor, clones being already dominant are more likely to stay dominant, and emergent clones not to thrive. These results may help personalized medicine to predict intratumor heterogeneity across different cancer types for which frequency of NCA effects could be quantified.

Cancer and life-history traits: lessons from host-parasite interactions.

Despite important differences between infectious diseases and cancers, tumour development (neoplasia) can nonetheless be closely compared to infectious disease because of the similarity of their effects on the body. On this basis, we predict that many of the life-history (LH) responses observed in the context of host-parasite interactions should also be relevant in the context of cancer. Parasites are thought to affect LH traits of their hosts because of strong selective pressures like direct and indirect mortality effects favouring, for example, early maturation and reproduction. Cancer can similarly also affect LH traits by imposing direct costs and/or indirectly by triggering plastic adjustments and evolutionary responses. Here, we discuss how and why a LH focus is a potentially productive but under-exploited research direction for cancer research, by focusing our attention on similarities between infectious disease and cancer with respect to their effects on LH traits and their evolution. We raise the possibility that LH adjustments can occur in response to cancer via maternal/paternal effects and that these changes can be heritable to (adaptively) modify the LH traits of their offspring. We conclude that LH adjustments can potentially influence the transgenerational persistence of inherited oncogenic mutations in populations.

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

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

Host manipulation by cancer cells: Expectations, facts, and therapeutic implications.

Similar to parasites, cancer cells depend on their hosts for sustenance, proliferation and reproduction, exploiting the hosts for energy and resources, and thereby impairing their health and fitness. Because of this lifestyle similarity, it is predicted that cancer cells could, like numerous parasitic organisms, evolve the capacity to manipulate the phenotype of their hosts to increase their own fitness. We claim that the extent of this phenomenon and its therapeutic implications are, however, underappreciated. Here, we review and discuss what can be regarded as cases of host manipulation in the context of cancer development and progression. We elaborate on how acknowledging the applicability of these principles can offer novel therapeutic and preventive strategies. The manipulation of host phenotype by cancer cells is one more reason to adopt a Darwinian approach in cancer research.

The guardians of inherited oncogenic vulnerabilities.

Similar to seemingly maladaptive genes in general, the persistence of inherited cancer-causing mutant alleles in populations remains a challenging question for evolutionary biologists. In addition to traditional explanations such as senescence or antagonistic pleiotropy, here we put forward a new hypothesis to explain the retention of oncogenic mutations. We propose that although natural defenses evolve to prevent neoplasm formation and progression thus increasing organismal fitness, they also conceal the effects of cancer-causing mutant alleles on fitness and concomitantly protect inherited ones from purging by purifying selection. We also argue for the importance of the ecological contexts experienced by individuals and/or species. These contexts determine the locally predominant fitness-reducing risks, and hence can aid the prediction of how natural selection will influence cancer outcomes.

Cancer: an emergent property of disturbed resource-rich environments? Ecology meets personalized medicine.

For an increasing number of biologists, cancer is viewed as a dynamic system governed by evolutionary and ecological principles. Throughout most of human history, cancer was an uncommon cause of death and it is generally accepted that common components of modern culture, including increased physiological stresses and caloric intake, favor cancer development. However, the precise mechanisms for this linkage are not well understood. Here, we examine the roles of ecological and physiological disturbances and resource availability on the emergence of cancer in multicellular organisms. We argue that proliferation of 'profiteering phenotypes' is often an emergent property of disturbed, resource-rich environments at all scales of biological organization. We review the evidence for this phenomenon, explore it within the context of malignancy, and discuss how this ecological framework may offer a theoretical background for novel strategies of cancer prevention. This work provides a compelling argument that the traditional separation between medicine and evolutionary ecology remains a fundamental limitation that needs to be overcome if complex processes, such as oncogenesis, are to be completely understood.

Evolutionary perspective of cancer: myth, metaphors, and reality.

The evolutionary perspective of cancer (which origins and dynamics result from evolutionary processes) has gained significant international recognition over the past decade and generated a wave of enthusiasm among researchers. In this context, several authors proposed that insights into evolutionary and adaptation dynamics of cancers can be gained by studying the evolutionary strategies of organisms. Although this reasoning is fundamentally correct, in our opinion, it contains a potential risk of excessive adaptationism, potentially leading to the suggestion of complex adaptations that are unlikely to evolve among cancerous cells. For example, the ability of recognizing related conspecifics and adjusting accordingly behaviors as in certain free-living species appears unlikely in cancer. Indeed, despite their rapid evolutionary rate, malignant cells are under selective pressures for their altered lifestyle for only few decades. In addition, even though cancer cells can theoretically display highly sophisticated adaptive responses, it would be crucial to determine the frequency of their occurrence in patients with cancer, before therapeutic applications can be considered. Scientists who try to explain oncogenesis will need in the future to critically evaluate the metaphorical comparison of selective processes affecting cancerous cells with those affecting organisms. This approach seems essential for the applications of evolutionary biology to understand the origin of cancers, with prophylactic and therapeutic applications.