Modeling genetic heterogeneity of drug response and resistance in cancer
| dc.contributor.author | Laajala T. | |
| dc.contributor.author | Gerke T. | |
| dc.contributor.author | Tyekucheva S. | |
| dc.contributor.author | Costello J. | |
| dc.contributor.organization | fi=matematiikan ja tilastotieteen laitos|en=Department of Mathematics and Statistics| | |
| dc.contributor.organization | fi=sovellettu matematiikka|en=Applied mathematics| | |
| dc.contributor.organization-code | 1.2.246.10.2458963.20.48078768388 | |
| dc.contributor.organization-code | 2606100 | |
| dc.converis.publication-id | 43898697 | |
| dc.converis.url | https://research.utu.fi/converis/portal/Publication/43898697 | |
| dc.date.accessioned | 2022-10-28T13:46:03Z | |
| dc.date.available | 2022-10-28T13:46:03Z | |
| 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.pagerange | 14 | |
| dc.format.pagerange | 8 | |
| dc.identifier.eissn | 2452-3100 | |
| dc.identifier.jour-issn | 2452-3100 | |
| dc.identifier.olddbid | 184177 | |
| dc.identifier.oldhandle | 10024/167271 | |
| dc.identifier.uri | https://www.utupub.fi/handle/11111/41619 | |
| dc.identifier.urn | URN:NBN:fi-fe2021042823380 | |
| dc.language.iso | en | |
| dc.okm.affiliatedauthor | Laajala, Daniel | |
| dc.okm.discipline | 3111 Biomedicine | en_GB |
| dc.okm.discipline | 3122 Cancers | en_GB |
| dc.okm.discipline | 3111 Biolääketieteet | fi_FI |
| dc.okm.discipline | 3122 Syöpätaudit | fi_FI |
| dc.okm.internationalcopublication | international co-publication | |
| dc.okm.internationality | International publication | |
| dc.okm.type | A2 Scientific Article | |
| dc.publisher | Elsevier Ltd | |
| dc.publisher.country | United Kingdom | en_GB |
| dc.publisher.country | Britannia | fi_FI |
| dc.publisher.country-code | GB | |
| dc.relation.doi | 10.1016/j.coisb.2019.09.003 | |
| dc.relation.ispartofjournal | Current Opinion in Systems Biology | |
| dc.relation.volume | 17 | |
| dc.source.identifier | https://www.utupub.fi/handle/10024/167271 | |
| dc.title | Modeling genetic heterogeneity of drug response and resistance in cancer | |
| dc.year.issued | 2019 |
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