Literature DB >> 28866007

Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population.

Hisashi Ohtsuki1, Hideki Innan2.   

Abstract

A cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provide a theoretical framework of the stochastic process in a cancer cell population and obtain near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell divisions and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic is very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Allele frequency spectrum; Branching theory; Cancer; Coalescent theory; Population genetics

Mesh:

Year:  2017        PMID: 28866007     DOI: 10.1016/j.tpb.2017.08.006

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  7 in total

1.  An accurate approximation for the expected site frequency spectrum in a Galton-Watson process under an infinite sites mutation model.

Authors:  John L Spouge
Journal:  Theor Popul Biol       Date:  2019-03-12       Impact factor: 1.570

2.  Discrete coalescent trees.

Authors:  Lena Collienne; Kieran Elmes; Mareike Fischer; David Bryant; Alex Gavryushkin
Journal:  J Math Biol       Date:  2021-11-05       Impact factor: 2.259

3.  J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.

Authors:  Fabrizio Angaroni; Alex Graudenzi; Alessandro Guidi; Gianluca Ascolani; Alberto d'Onofrio; Marco Antoniotti
Journal:  BMC Bioinformatics       Date:  2022-07-08       Impact factor: 3.307

4.  CellCoal: Coalescent Simulation of Single-Cell Sequencing Samples.

Authors:  David Posada
Journal:  Mol Biol Evol       Date:  2020-05-01       Impact factor: 16.240

5.  Quantification of multicellular colonization in tumor metastasis using exome-sequencing data.

Authors:  Jo Nishino; Shuichi Watanabe; Fuyuki Miya; Takashi Kamatani; Toshitaka Sugawara; Keith A Boroevich; Tatsuhiko Tsunoda
Journal:  Int J Cancer       Date:  2020-02-15       Impact factor: 7.396

6.  Evolution under Spatially Heterogeneous Selection in Solid Tumors.

Authors:  Guanghao Li; Zuyu Yang; Dafei Wu; Sixue Liu; Xuening Li; Tao Li; Yawei Li; Liji Liang; Weilong Zou; Chung-I Wu; Hurng-Yi Wang; Xuemei Lu
Journal:  Mol Biol Evol       Date:  2022-01-07       Impact factor: 16.240

7.  A unified simulation model for understanding the diversity of cancer evolution.

Authors:  Atsushi Niida; Takanori Hasegawa; Hideki Innan; Tatsuhiro Shibata; Koshi Mimori; Satoru Miyano
Journal:  PeerJ       Date:  2020-04-08       Impact factor: 2.984

  7 in total

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