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dc.contributor.authorPiñeiro-Yáñez, Elena
dc.contributor.authorJiménez-Santos, María José
dc.contributor.authorGómez-López, Gonzalo 
dc.contributor.authorAl-Shahrour, Fatima
dc.identifier.citationCancers (Basel). 2019;11(9).es_ES
dc.description.abstractIn silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.es_ES
dc.description.sponsorshipThis work was supported by the Instituto de Salud Carlos III (ISCIII); Marie-Curie Career Integration Grant (CIG334361); and Paradifference Foundation. The unit at the CNIO is a member of the Spanish National Bioinformatics Institute (INB), ISCIII-Bioinformatics platform (PT17/0009/0011), of the Accion Estrategica en Salud 2013-2016 of the Programa Estatal de Investigacion Orientada a los Retos de la Sociedad, funded by the ISCIII and European Regional Development Fund (ERDF). CNIO Bioinformatics Unit is also funded by Project RETOS RTI2018-097596-B-I00, AEI-MCIU and cofounded by the European Regional Development Fund (ERDF-EU). M.J.J-S. is supported by the Spanish National Institute of Bioinformatics, Bioinformatics platform of the National Institute of Health Carlos III (PT17/0009/0011) and co-financed by European Structural and Investment Fund.es_ES
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI) es_ES
dc.subjectCancer genomicses_ES
dc.subjectDruggable genomees_ES
dc.subjectIn silico prescriptiones_ES
dc.subjectIntra-tumour heterogeneityes_ES
dc.subjectPrecision medicinees_ES
dc.titleIn Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcomees_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.contributor.funderInstituto de Salud Carlos III 
dc.contributor.funderUnión Europea 
dc.relation.publisherversion 10.3390/cancers11091361.es_ES
dc.repisalud.orgCNIOCNIO::Unidades técnicas::Unidad de Bioinformáticaes_ES
dc.relation.projectIDinfo:eu_repo/grantAgreement/ES/RETOS RTI2018-097596-B-I00es_ES
dc.rights.accessRightsopen accesses_ES

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Atribución-NoComercial-CompartirIgual 4.0 Internacional
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