ArticleBMC evolutionary biology2020
Clonal evolution driven by superdriver mutations.
Article in BMC evolutionary biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- Article
- A single mutation may contribute to accelerated evolution of SARS-CoV-2 toward Omicron.Nature communications · 2025Article
- Estimation of Neutral Mutation Rates and Quantification of Somatic Variant Selection Using cancereffectsizeR.Cancer research · 2023Article
- Modes of Selection in Tumors as Reflected by Two Mathematical Models and Site Frequency Spectra.Frontiers in ecology and evolution · 2022Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundTumors are widely recognized to progress through clonal evolution by sequentially acquiring selectively advantageous genetic alterations that significantly contribute to tumorigenesis and thus are termned drivers. Some cancer drivers, such as TP53 point mutation or EGFR copy number gain, provide exceptional fitness gains, which, in time, can be sufficient to trigger the onset of cancer with little or no contribution from additional genetic alterations. These key alterations are called superdrivers.
resultsIn this study, we employ a Wright-Fisher model to study the interplay between drivers and superdrivers in tumor progression. We demonstrate that the resulting evolutionary dynamics follow global clonal expansions of superdrivers with periodic clonal expansions of drivers. We find that the waiting time to the accumulation of a set of superdrivers and drivers in the tumor cell population can be approximated by the sum of the individual waiting times.
conclusionsOur results suggest that superdriver dynamics dominate over driver dynamics in tumorigenesis. Furthermore, our model allows studying the interplay between superdriver and driver mutations both empirically and theoretically.
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