Dataset Information

Sustained Coevolution in a Stochastic Model of Cancer-Immune Interaction.

ABSTRACT: The dynamic interactions between an evolving malignancy and the adaptive immune system generate diverse evolutionary trajectories that ultimately result in tumor clearance or immune escape. Here, we create a simple mathematical model coupling T-cell recognition with an evolving cancer population that may randomly produce evasive subclones, imparting transient protection against the effector T cells. T-cell turnover declines and evasion rates together explained differences in early incidence data across almost all cancer types. Fitting the model to TRACERx evolutionary data argued in favor of substantial and sustained immune pressure exerted upon a developing tumor, suggesting that clinically observed incidence is a small proportion of all cancer initiation events. This dynamical model promises to increase our quantitative understanding of many immune escape contexts, including cancer progression and intracellular pathogenic infections. SIGNIFICANCE: The early cancer-immune interaction sculpts intratumor heterogeneity through the selection of immune-evasive clones. This study provides a mathematical framework for investigating the coevolution between an immune-evasive cancer population and the adaptive immune system.

SUBMITTER: George JT

PROVIDER: S-EPMC7179478 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Sustained Coevolution in a Stochastic Model of Cancer-Immune Interaction.

George Jason T JT Levine Herbert H

Cancer research 20191220 4

The dynamic interactions between an evolving malignancy and the adaptive immune system generate diverse evolutionary trajectories that ultimately result in tumor clearance or immune escape. Here, we create a simple mathematical model coupling T-cell recognition with an evolving cancer population that may randomly produce evasive subclones, imparting transient protection against the effector T cells. T-cell turnover declines and evasion rates together explained differences in early incidence data ...[more]

PMID: 31862779

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Project description:Components of immune systems face significant selective pressure to efficiently use organismal resources, mitigate infection, and resist parasitic manipulation. A theoretically optimal immune defense balances investment in constitutive and inducible immune components depending on the kinds of parasites encountered, but genetic and dynamic constraints can force deviation away from theoretical optima. One such potential constraint is pleiotropy, the phenomenon where a single gene affects multiple phenotypes. Although pleiotropy can prevent or dramatically slow adaptive evolution, it is prevalent in the signaling networks that compose metazoan immune systems. We hypothesized that pleiotropy is maintained in immune signaling networks despite slowed adaptive evolution because it provides some other advantage, such as forcing network evolution to compensate in ways that increase host fitness during infection. To study the effects of pleiotropy on the evolution of immune signaling networks, we used an agent-based modeling approach to evolve a population of host immune systems infected by simultaneously co-evolving parasites. Four kinds of pleiotropic restrictions on evolvability were incorporated into the networks, and their evolutionary outcomes were compared to, and competed against, non-pleiotropic networks. As the networks evolved, we tracked several metrics of immune network complexity, relative investment in inducible and constitutive defenses, and features associated with the winners and losers of competitive simulations. Our results suggest non-pleiotropic networks evolve to deploy highly constitutive immune responses regardless of parasite prevalence, but some implementations of pleiotropy favor the evolution of highly inducible immunity. These inducible pleiotropic networks are no less fit than non-pleiotropic networks and can out-compete non-pleiotropic networks in competitive simulations. These provide a theoretical explanation for the prevalence of pleiotropic genes in immune systems and highlight a mechanism that could facilitate the evolution of inducible immune responses.

Project description:BackgroundHytrosaviruses (SGHVs; Hytrosaviridae family) are double-stranded DNA (dsDNA) viruses that cause salivary gland hypertrophy (SGH) syndrome in flies. Two structurally and functionally distinct SGHVs are recognized; Glossina pallidipes SGHV (GpSGHV) and Musca domestica SGHV (MdSGHV), that infect the hematophagous tsetse fly and the filth-feeding housefly, respectively. Genome sizes and gene contents of GpSGHV (~ 190 kb; 160-174 genes) and MdSGHV (~ 124 kb; 108 genes) may reflect an evolution with the SGHV-hosts resulting in differences in pathobiology. Whereas GpSGHV can switch from asymptomatic to symptomatic infections in response to certain unknown cues, MdSGHV solely infects symptomatically. Overt SGH characterizes the symptomatic infections of SGHVs, but whereas MdSGHV induces both nuclear and cellular hypertrophy (enlarged non-replicative cells), GpSGHV induces cellular hyperplasia (enlarged replicative cells). Compared to GpSGHV's specificity to Glossina species, MdSGHV infects other sympatric muscids. The MdSGHV-induced total shutdown of oogenesis inhibits its vertical transmission, while the GpSGHV's asymptomatic and symptomatic infections promote vertical and horizontal transmission, respectively. This paper reviews the coevolution of the SGHVs and their hosts (housefly and tsetse fly) based on phylogenetic relatedness of immune gene orthologs/paralogs and compares this with other virus-insect models.ResultsWhereas MdSGHV is not vertically transmitted, GpSGHV is both vertically and horizontally transmitted, and the balance between the two transmission modes may significantly influence the pathogenesis of tsetse virus. The presence and absence of bacterial symbionts (Wigglesworthia and Sodalis) in tsetse and Wolbachia in the housefly, respectively, potentially contributes to the development of SGH symptoms. Unlike MdSGHV, GpSGHV contains not only host-derived proteins, but also appears to have evolutionarily recruited cellular genes from ancestral host(s) into its genome, which, although may be nonessential for viral replication, potentially contribute to the evasion of host's immune responses. Whereas MdSGHV has evolved strategies to counteract both the housefly's RNAi and apoptotic responses, the housefly has expanded its repertoire of immune effector, modulator and melanization genes compared to the tsetse fly.ConclusionsThe ecologies and life-histories of the housefly and tsetse fly may significantly influence coevolution of MdSGHV and GpSGHV with their hosts. Although there are still many unanswered questions regarding the pathogenesis of SGHVs, and the extent to which microbiota influence expression of overt SGH symptoms, SGHVs are attractive 'explorers' to elucidate the immune responses of their hosts, and the transmission modes of other large DNA viruses.

Dataset Information

Sustained Coevolution in a Stochastic Model of Cancer-Immune Interaction.

Publications

Sustained Coevolution in a Stochastic Model of Cancer-Immune Interaction.

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