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Article: Methods and resources to access mutation-dependent effects on cancer drug treatment

TitleMethods and resources to access mutation-dependent effects on cancer drug treatment
Authors
Keywordsactionable mutation
bioinformatics tool
drug response prediction
precision medicine
targeted cancer therapy
Issue Date2020
Citation
Briefings in Bioinformatics, 2020, v. 21, n. 6, p. 1886-1903 How to Cite?
AbstractIn clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.
Persistent Identifierhttp://hdl.handle.net/10722/324488
ISSN
2023 Impact Factor: 6.8
2023 SCImago Journal Rankings: 2.143
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYao, Hongcheng-
dc.contributor.authorLiang, Qian-
dc.contributor.authorQian, Xinyi-
dc.contributor.authorWang, Junwen-
dc.contributor.authorSham, Pak Chung-
dc.contributor.authorLi, Mulin Jun-
dc.date.accessioned2023-02-03T07:03:24Z-
dc.date.available2023-02-03T07:03:24Z-
dc.date.issued2020-
dc.identifier.citationBriefings in Bioinformatics, 2020, v. 21, n. 6, p. 1886-1903-
dc.identifier.issn1467-5463-
dc.identifier.urihttp://hdl.handle.net/10722/324488-
dc.description.abstractIn clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.-
dc.languageeng-
dc.relation.ispartofBriefings in Bioinformatics-
dc.subjectactionable mutation-
dc.subjectbioinformatics tool-
dc.subjectdrug response prediction-
dc.subjectprecision medicine-
dc.subjecttargeted cancer therapy-
dc.titleMethods and resources to access mutation-dependent effects on cancer drug treatment-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bib/bbz109-
dc.identifier.pmid31750520-
dc.identifier.scopuseid_2-s2.0-85097210666-
dc.identifier.volume21-
dc.identifier.issue6-
dc.identifier.spage1886-
dc.identifier.epage1903-
dc.identifier.eissn1477-4054-
dc.identifier.isiWOS:000606289200003-

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