Modeling genetic heterogeneity of drug response and resistance in cancer

dc.contributor.authorLaajala T.
dc.contributor.authorGerke T.
dc.contributor.authorTyekucheva S.
dc.contributor.authorCostello J.
dc.contributor.organizationfi=matematiikan ja tilastotieteen laitos|en=Department of Mathematics and Statistics|
dc.contributor.organizationfi=sovellettu matematiikka|en=Applied mathematics|
dc.contributor.organization-code1.2.246.10.2458963.20.48078768388
dc.contributor.organization-code2606100
dc.converis.publication-id43898697
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/43898697
dc.date.accessioned2022-10-28T13:46:03Z
dc.date.available2022-10-28T13:46:03Z
dc.description.abstract<p>Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. This study describes recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.<br /></p>
dc.format.pagerange14
dc.format.pagerange8
dc.identifier.eissn2452-3100
dc.identifier.jour-issn2452-3100
dc.identifier.olddbid184177
dc.identifier.oldhandle10024/167271
dc.identifier.urihttps://www.utupub.fi/handle/11111/41619
dc.identifier.urnURN:NBN:fi-fe2021042823380
dc.language.isoen
dc.okm.affiliatedauthorLaajala, Daniel
dc.okm.discipline3111 Biomedicineen_GB
dc.okm.discipline3122 Cancersen_GB
dc.okm.discipline3111 Biolääketieteetfi_FI
dc.okm.discipline3122 Syöpätauditfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA2 Scientific Article
dc.publisherElsevier Ltd
dc.publisher.countryUnited Kingdomen_GB
dc.publisher.countryBritanniafi_FI
dc.publisher.country-codeGB
dc.relation.doi10.1016/j.coisb.2019.09.003
dc.relation.ispartofjournalCurrent Opinion in Systems Biology
dc.relation.volume17
dc.source.identifierhttps://www.utupub.fi/handle/10024/167271
dc.titleModeling genetic heterogeneity of drug response and resistance in cancer
dc.year.issued2019

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