Application of the Moran Model in Estimating Selection Coefficient of Mutated CSF3R Clones in the Evolution of Severe Congenital Neutropenia to Myeloid Neoplasia

Dinh, Khanh N.; Corey, Seth J.; Kimmel, Marek

doi:10.3389/fphys.2020.00806

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…In blood tumors, which evolve mostly in the bone marrow, the disease is confined to a restricted environment, although cellularity of the bone marrow tends to be increased (see the analyses in Dinh et al, 2021). Moran model with selection was shown to lead to predictions consistent with clinical findings in evolution of myeloid dysplastic syndrome from severe congenital neutropenia (Wojdyla et al, 2019;Dinh et al, 2020a). In solid tumors, the growth is more expansive, although a range of growth patterns are present.…”

Section: Discussionmentioning

confidence: 95%

Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra

Kurpas

Kimmel

2022

Front. Ecol. Evol.

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The tug-of-war model was developed in a series of papers of McFarland and co-authors to account for existence of mutually counteracting rare advantageous driver mutations and more frequent slightly deleterious passenger mutations in cancer. In its original version, it was a state-dependent branching process. Because of its formulation, the tug-of-war model is of importance for tackling the problem as to whether evolution of cancerous tumors is “Darwinian” or “non-Darwinian.” We define two Time-Continuous Markov Chain versions of the model, including identical mutation processes but adopting different drift and selection components. In Model A, drift and selection process preserves expected fitness whereas in Model B it leads to non-decreasing expected fitness. We investigate these properties using mathematical analysis and extensive simulations, which detect the effect of the so-called drift barrier in Model B but not in Model A. These effects are reflected in different structure of clone genealogies in the two models. Our work is related to the past theoretical work in the field of evolutionary genetics, concerning the interplay among mutation, drift and selection, in absence of recombination (asexual reproduction), where epistasis plays a major role. Finally, we use the statistics of mutation frequencies known as the Site Frequency Spectra (SFS), to compare the variant frequencies in DNA of sequenced HER2+ breast cancers, to those based on Model A and B simulations. The tumor-based SFS are better reproduced by Model A, pointing out a possible selection pattern of HER2+ tumor evolution. To put our models in context, we carried out an exploratory study of how publicly accessible data from breast, prostate, skin and ovarian cancers fit a range of models found in the literature.

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Section: Discussionmentioning

confidence: 95%

Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra

Kurpas

Kimmel

2022

Front. Ecol. Evol.

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“…where f 0 is the initial frequency of the mutant in the population with effective population size N e [52,53]. Our earlier results on neutral dynamics and fitness provide independent measurements of the parameters in Eq.…”

Section: Superinfection Results In More Efficient Selectionmentioning

confidence: 68%

“…To provide a theoretical framework to our findings, we compare the simulation data to the fixation probabilities one would expect in a corresponding Moran model. For small selective advantage s comp , the probability of fixation is given by where f 0 is the initial frequency of the mutant in the population with effective population size N e [52, 53]. Our earlier results on neutral dynamics and fitness provide independent measurements of the parameters in Eq.…”

Section: Resultsmentioning

confidence: 90%

Superinfection exclusion: a viral strategy with short-term benefits and long-term drawbacks

Hunter

Fusco

2021

Preprint

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Viral co-infection occurs when multiple distinct viral particles infect the same host. This can impact viral evolution through intracellular interactions, complementation, reassortment and recombination. In nature many viral species are found to have a wide range of mechanisms to prevent co-infection, which raises the question of how viral evolution is impacted by this strategic choice. Here, we address this question in a model viral system, the ubiquitous bacteriophage and its host bacteria. Using a stochastic model of phage-host interactions in agent-based simulations, we first characterise the behaviour of neutral mutants and find that co-infection decreases the strength of genetic drift. We then quantify how variations in the phage life history parameters affect viral fitness. Importantly, we find that the growth rate (dis)advantage associated with variations in life history parameters can be dramatically different from the competitive (dis)advantage measured in direct-competition simulations. Additionally, we find that co-infection facilitates the fixation of beneficial mutations and the removal of deleterious ones, suggesting that selection is more efficient in co-infecting populations. We also observe, however, that in populations which allow co-infection, a mutant that prevents it displays a substantial competitive advantage over the rest of the population, and will eventually fix even if it displays a much lower growth rate in isolation. Our findings suggest that while preventing co-infection can have a negative impact on the long-term evolution of a viral population, in the short-term it is ultimately a winning strategy, possibly explaining the prevalence of phage capable of preventing co-infection in nature.

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“…In this work, we are motivated to obtain a computable expression that leads to ballpark estimates of the proportion of cells of each type similar to work by Dinh et al . ( 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Impact of Resistance on Therapeutic Design: A Moran Model of Cancer Growth

Lacy,

Jenner

2024

Bull Math Biol

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Resistance of cancers to treatments, such as chemotherapy, largely arise due to cell mutations. These mutations allow cells to resist apoptosis and inevitably lead to recurrence and often progression to more aggressive cancer forms. Sustained-low dose therapies are being considered as an alternative over maximum tolerated dose treatments, whereby a smaller drug dosage is given over a longer period of time. However, understanding the impact that the presence of treatment-resistant clones may have on these new treatment modalities is crucial to validating them as a therapeutic avenue. In this study, a Moran process is used to capture stochastic mutations arising in cancer cells, inferring treatment resistance. The model is used to predict the probability of cancer recurrence given varying treatment modalities. The simulations predict that sustained-low dose therapies would be virtually ineffective for a cancer with a non-negligible probability of developing a sub-clone with resistance tendencies. Furthermore, calibrating the model to in vivo measurements for breast cancer treatment with Herceptin, the model suggests that standard treatment regimens are ineffective in this mouse model. Using a simple Moran model, it is possible to explore the likelihood of treatment success given a non-negligible probability of treatment resistant mutations and suggest more robust therapeutic schedules.

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Application of the Moran Model in Estimating Selection Coefficient of Mutated CSF3R Clones in the Evolution of Severe Congenital Neutropenia to Myeloid Neoplasia

Cited by 7 publications

References 21 publications

Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra

Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra

Superinfection exclusion: a viral strategy with short-term benefits and long-term drawbacks

Impact of Resistance on Therapeutic Design: A Moran Model of Cancer Growth

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