Literature DB >> 29750997

The role of spatial variations of abiotic factors in mediating intratumour phenotypic heterogeneity.

Tommaso Lorenzi1, Chandrasekhar Venkataraman1, Alexander Lorz2, Mark A J Chaplain3.   

Abstract

We present here a space- and phenotype-structured model of selection dynamics between cancer cells within a solid tumour. In the framework of this model, we combine formal analyses with numerical simulations to investigate in silico the role played by the spatial distribution of abiotic components of the tumour microenvironment in mediating phenotypic selection of cancer cells. Numerical simulations are performed both on the 3D geometry of an in silico multicellular tumour spheroid and on the 3D geometry of an in vivo human hepatic tumour, which was imaged using computerised tomography. The results obtained show that inhomogeneities in the spatial distribution of oxygen, currently observed in solid tumours, can promote the creation of distinct local niches and lead to the selection of different phenotypic variants within the same tumour. This process fosters the emergence of stable phenotypic heterogeneity and supports the presence of hypoxic cells resistant to cytotoxic therapy prior to treatment. Our theoretical results demonstrate the importance of integrating spatial data with ecological principles when evaluating the therapeutic response of solid tumours to cytotoxic therapy.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Finite element methods; Intratumour heterogeneity; Mathematical oncology; Partial differential equations; Phenotypic selection

Mesh:

Year:  2018        PMID: 29750997     DOI: 10.1016/j.jtbi.2018.05.002

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  6 in total

1.  Investigation of solid tumor progression with account of proliferation/migration dichotomy via Darwinian mathematical model.

Authors:  Maxim Kuznetsov; Andrey Kolobov
Journal:  J Math Biol       Date:  2019-10-01       Impact factor: 2.259

2.  A Mathematical Framework for Modelling the Metastatic Spread of Cancer.

Authors:  Linnea C Franssen; Tommaso Lorenzi; Andrew E F Burgess; Mark A J Chaplain
Journal:  Bull Math Biol       Date:  2019-03-22       Impact factor: 1.758

3.  Evolutionary dynamics of competing phenotype-structured populations in periodically fluctuating environments.

Authors:  Aleksandra Ardaševa; Robert A Gatenby; Alexander R A Anderson; Helen M Byrne; Philip K Maini; Tommaso Lorenzi
Journal:  J Math Biol       Date:  2019-10-22       Impact factor: 2.259

4.  A Mathematical Study of the Influence of Hypoxia and Acidity on the Evolutionary Dynamics of Cancer.

Authors:  Giada Fiandaca; Marcello Delitala; Tommaso Lorenzi
Journal:  Bull Math Biol       Date:  2021-06-15       Impact factor: 1.758

5.  Modelling collective cell migration: neural crest as a model paradigm.

Authors:  Rasa Giniūnaitė; Ruth E Baker; Paul M Kulesa; Philip K Maini
Journal:  J Math Biol       Date:  2019-10-05       Impact factor: 2.259

6.  Dissection of the mutation accumulation process during bacterial range expansions.

Authors:  Lars Bosshard; Stephan Peischl; Martin Ackermann; Laurent Excoffier
Journal:  BMC Genomics       Date:  2020-03-23       Impact factor: 3.969

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.