Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations.
Identifieur interne : 001A69 ( PubMed/Curation ); précédent : 001A68; suivant : 001A70Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations.
Auteurs : Tommaso Lorenzi [Royaume-Uni] ; Rebecca H. Chisholm [Australie] ; Jean Clairambault [France]Source :
- Biology direct [ 1745-6150 ] ; 2016.
Abstract
A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.
DOI: 10.1186/s13062-016-0143-4
PubMed: 27550042
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations.</title><author><name sortKey="Lorenzi, Tommaso" sort="Lorenzi, Tommaso" uniqKey="Lorenzi T" first="Tommaso" last="Lorenzi">Tommaso Lorenzi</name><affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK. tl47@st-andrews.ac.uk.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS</wicri:regionArea></affiliation></author><author><name sortKey="Chisholm, Rebecca H" sort="Chisholm, Rebecca H" uniqKey="Chisholm R" first="Rebecca H" last="Chisholm">Rebecca H. Chisholm</name><affiliation wicri:level="1"><nlm:affiliation>School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052</wicri:regionArea></affiliation></author><author><name sortKey="Clairambault, Jean" sort="Clairambault, Jean" uniqKey="Clairambault J" first="Jean" last="Clairambault">Jean Clairambault</name><affiliation wicri:level="1"><nlm:affiliation>INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589</wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27550042</idno><idno type="pmid">27550042</idno><idno type="doi">10.1186/s13062-016-0143-4</idno><idno type="wicri:Area/PubMed/Corpus">001A93</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001A93</idno><idno type="wicri:Area/PubMed/Curation">001A69</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">001A69</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations.</title><author><name sortKey="Lorenzi, Tommaso" sort="Lorenzi, Tommaso" uniqKey="Lorenzi T" first="Tommaso" last="Lorenzi">Tommaso Lorenzi</name><affiliation wicri:level="1"><nlm:affiliation>School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK. tl47@st-andrews.ac.uk.</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS</wicri:regionArea></affiliation></author><author><name sortKey="Chisholm, Rebecca H" sort="Chisholm, Rebecca H" uniqKey="Chisholm R" first="Rebecca H" last="Chisholm">Rebecca H. Chisholm</name><affiliation wicri:level="1"><nlm:affiliation>School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052</wicri:regionArea></affiliation></author><author><name sortKey="Clairambault, Jean" sort="Clairambault, Jean" uniqKey="Clairambault J" first="Jean" last="Clairambault">Jean Clairambault</name><affiliation wicri:level="1"><nlm:affiliation>INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589, France.</nlm:affiliation><country xml:lang="fr">France</country><wicri:regionArea>INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589</wicri:regionArea></affiliation></author></analytic><series><title level="j">Biology direct</title><idno type="eISSN">1745-6150</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">27550042</PMID><DateCreated><Year>2016</Year><Month>08</Month><Day>23</Day></DateCreated><DateRevised><Year>2017</Year><Month>02</Month><Day>20</Day></DateRevised><Article PubModel="Electronic"><Journal><ISSN IssnType="Electronic">1745-6150</ISSN><JournalIssue CitedMedium="Internet"><Volume>11</Volume><PubDate><Year>2016</Year><Month>Aug</Month><Day>23</Day></PubDate></JournalIssue><Title>Biology direct</Title><ISOAbbreviation>Biol. Direct</ISOAbbreviation></Journal><ArticleTitle>Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations.</ArticleTitle><Pagination><MedlinePgn>43</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1186/s13062-016-0143-4</ELocationID><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the 'maximum-tolerated-dose paradigm', as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.</AbstractText><AbstractText Label="REVIEWERS" NlmCategory="METHODS">This article was reviewed by Angela Pisco, Sébastien Benzekry and Heiko Enderling.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Lorenzi</LastName><ForeName>Tommaso</ForeName><Initials>T</Initials><Identifier Source="ORCID">http://orcid.org/0000-0001-9165-1666</Identifier><AffiliationInfo><Affiliation>School of Mathematics and Statistics, University of St Andrews, North Haugh, St Andrews, KY16 9SS, UK. tl47@st-andrews.ac.uk.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Chisholm</LastName><ForeName>Rebecca H</ForeName><Initials>RH</Initials><AffiliationInfo><Affiliation>School of Biotechnology and Biomolecular Sciences, University of New South Wales, NSW, Sydney, 2052, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Clairambault</LastName><ForeName>Jean</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>INRIA Paris Research Centre, MAMBA team, 2, rue Simone Iff, CS 42112, Paris Cedex 12, 75589, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Sorbonne Universités, UPMC Univ. 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