{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T05:28:30Z","timestamp":1766467710715,"version":"3.41.2"},"reference-count":34,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":11,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"TUBITAK","doi-asserted-by":"publisher","award":["118C039"],"award-info":[{"award-number":["118C039"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Identifying appropriate pharmacotherapy options from genomics results is a significant challenge in personalized oncology. However, computational methods for prioritizing drugs are underdeveloped. With the hypothesis that network-based approaches can improve the performance by extending the use of potential drug targets beyond direct interactions, we devised two network-based methods for personalized pharmacotherapy prioritization in cancer.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We developed novel personalized drug prioritization approaches, PANACEA: PersonAlized Network-based Anti-Cancer therapy EvaluAtion. In PANACEA, initially, the protein interaction network is extended with drugs, and a driverness score is assigned to each altered gene. For scoring drugs, either (i) the \u2018distance-based\u2019 method, incorporating the shortest distance between drugs and altered genes, and driverness scores, or (ii) the \u2018propagation\u2019 method involving the propagation of driverness scores via a random walk with restart framework is performed. We evaluated PANACEA using multiple datasets, and demonstrated that (i) the top-ranking drugs are relevant for cancer pharmacotherapy using TCGA data; (ii) drugs that cancer cell lines are sensitive to are identified using GDSC data; and (iii) PANACEA can perform adequately in the clinical setting using cases with known drug responses. We also illustrate that the proposed methods outperform iCAGES and PanDrugs, two previous personalized drug prioritization approaches.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The corresponding R package is available on GitHub. (https:\/\/github.com\/egeulgen\/PANACEA.git).<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad022","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T15:36:03Z","timestamp":1673451363000},"source":"Crossref","is-referenced-by-count":3,"title":["PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2090-3621","authenticated-orcid":false,"given":"Ege","family":"Ulgen","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University , Istanbul 34752, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5980-8002","authenticated-orcid":false,"given":"Ozan","family":"Ozisik","sequence":"additional","affiliation":[{"name":"Aix Marseille University, Inserm, MMG , Marseille 13385, France"}]},{"given":"Osman Ugur","family":"Sezerman","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Medical Informatics, School of Medicine, Acibadem Mehmet Ali Aydinlar University , Istanbul 34752, Turkey"}]}],"member":"286","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"2023012312151933500_btad022-B1","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"Barretina","year":"2012","journal-title":"Nature"},{"key":"2023012312151933500_btad022-B2","doi-asserted-by":"crossref","first-page":"D1138","DOI":"10.1093\/nar\/gkaa891","article-title":"Comparative Toxicogenomics Database (CTD): update 2021","volume":"49","author":"Davis","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023012312151933500_btad022-B3","doi-asserted-by":"crossref","first-page":"2259","DOI":"10.1002\/1878-0261.12564","article-title":"Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities","volume":"13","author":"Di","year":"2019","journal-title":"Mol. 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