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dc.contributor.authorIngles Garces, AH
dc.contributor.authorPorta, N
dc.contributor.authorGraham, TA
dc.contributor.authorBanerji, U
dc.coverage.spatialNetherlands
dc.date.accessioned2023-08-22T14:43:36Z
dc.date.available2023-08-22T14:43:36Z
dc.date.issued2023-07-01
dc.identifierARTN 102583
dc.identifierS0305-7372(23)00075-0
dc.identifier.citationCancer Treatment Reviews, 2023, 118 pp. 102583 -
dc.identifier.issn0305-7372
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5947
dc.identifier.eissn1532-1967
dc.identifier.eissn1532-1967
dc.identifier.doi10.1016/j.ctrv.2023.102583
dc.description.abstractThe evolution of drug-resistant cell subpopulations causes cancer treatment failure. Current preclinical evidence shows that it is possible to model herding of clonal evolution and collateral sensitivity where an initial treatment could favourably influence the response to a subsequent one. Novel therapy strategies exploiting this understanding are being considered, and clinical trial designs for steering cancer evolution are needed. Furthermore, preclinical evidence suggests that different subsets of drug-sensitive and resistant clones could compete between themselves for nutrients/blood supply, and clones that populate a tumour do so at the expense of other clones. Treatment paradigms based on this clinical application of exploiting cell-cell competition include intermittent dosing regimens or cycling different treatments before progression. This will require clinical trial designs different from the conventional practice of evaluating responses to individual therapy regimens. Next-generation sequencing to assess clonal dynamics longitudinally will improve current radiological assessment of clinical response/resistance and be incorporated into trials exploiting evolution. Furthermore, if understood, clonal evolution can be used to therapeutic advantage, improving patient outcomes based on a new generation of clinical trials.
dc.formatPrint-Electronic
dc.format.extent102583 -
dc.languageeng
dc.language.isoeng
dc.publisherELSEVIER SCI LTD
dc.relation.ispartofCancer Treatment Reviews
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCancer evolution
dc.subjectClinical trials
dc.subjectDrug resistance
dc.subjectHumans
dc.subjectClinical Trials as Topic
dc.subjectNeoplasms
dc.subjectClonal Evolution
dc.titleClinical trial designs for evaluating and exploiting cancer evolution.
dc.typeJournal Article
dcterms.dateAccepted2023-05-23
dc.date.updated2023-08-22T14:43:10Z
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1016/j.ctrv.2023.102583
rioxxterms.licenseref.startdate2023-07-01
rioxxterms.typeJournal Article/Review
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37331179
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Pharmacology – Adaptive Therapy
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/19/20 Starting Cohort
pubs.publication-statusPublished
pubs.publisher-urlhttp://dx.doi.org/10.1016/j.ctrv.2023.102583
pubs.volume118
icr.researchteamClin Trials & Stats Unit
icr.researchteamClinical Pharmacology
dc.contributor.icrauthorPorta, Nuria
dc.contributor.icrauthorGraham, Trevor
dc.contributor.icrauthorBanerji, Udai
icr.provenanceDeposited by Mr Arek Surman on 2023-08-22. Deposit type is initial. No. of files: 1. Files: 1-s2.0-S0305737223000750-main.pdf


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