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dc.contributor.authorChisholm, Rebecca H.
dc.contributor.authorLorenzi, Tommaso
dc.contributor.authorClairambault, Jean
dc.date.accessioned2017-06-20T23:33:37Z
dc.date.available2017-06-20T23:33:37Z
dc.date.issued2016-11
dc.identifier.citationChisholm , R H , Lorenzi , T & Clairambault , J 2016 , ' Cell population heterogeneity and evolution towards drug resistance in cancer : biological and mathematical assessment, theoretical treatment optimisation ' Biochimica et Biophysica Acta - General Subjects , vol. 1860 , no. 11, Part B , pp. 2627-2645 . https://doi.org/10.1016/j.bbagen.2016.06.009en
dc.identifier.issn0304-4165
dc.identifier.otherPURE: 243564098
dc.identifier.otherPURE UUID: cad2599f-97fa-471c-b7d9-0d86c4f7c459
dc.identifier.otherRIS: urn:AA722CC0A0FD7241E79EDB9A48F64866
dc.identifier.otherScopus: 84978039247
dc.identifier.urihttp://hdl.handle.net/10023/11036
dc.description.abstractBackground. Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. Scope of review. We focus on intra-tumour heterogeneity, namely between-cell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies. Major conclusions. Plasticity in cancer cells, i.e., partial reversal to a stem-like status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies. General significance. Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
dc.format.extent19
dc.language.isoeng
dc.relation.ispartofBiochimica et Biophysica Acta - General Subjectsen
dc.rights© 2016, Elsevier BV. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.sciencedirect.com / https://dx.doi.org/10.1016/j.bbagen.2016.06.009en
dc.subjectHeterogeneityen
dc.subjectCancer cell populationsen
dc.subjectEvolutionen
dc.subjectDrug resistanceen
dc.subjectCancer therapeuticsen
dc.subjectOptimal controlen
dc.subjectQA Mathematicsen
dc.subjectQH301 Biologyen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectNDASen
dc.subject.lccQAen
dc.subject.lccQH301en
dc.subject.lccRC0254en
dc.titleCell population heterogeneity and evolution towards drug resistance in cancer : biological and mathematical assessment, theoretical treatment optimisationen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
dc.identifier.doihttps://doi.org/10.1016/j.bbagen.2016.06.009
dc.description.statusPeer revieweden
dc.date.embargoedUntil2017-06-20


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