In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome

In 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 pa...

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Autores: Piñeiro-Yáñez, Elena, Jiménez-Santos, María José, Gómez López, Gonzalo, Al-Shahrour, Fátima
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/692244
Acceso en línea:http://hdl.handle.net/10486/692244
https://dx.doi.org/10.3390/cancers11091361
Access Level:acceso abierto
Palabra clave:Precision medicine
Cancer genomics
Intra-tumour heterogeneity
Iin silico prescription
Bioinformatics
Pharmacogenomics
Druggable genome
Medicina
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spelling In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcomePiñeiro-Yáñez, ElenaJiménez-Santos, María JoséGómez López, GonzaloAl-Shahrour, FátimaPrecision medicineCancer genomicsIntra-tumour heterogeneityIin silico prescriptionBioinformaticsPharmacogenomicsDruggable genomeMedicinaIn 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 utilityThis work was supported by the Instituto de Salud Carlos III (ISCIII); Marie-Curie Career Integration Grant (CIG334361); and Paradifference FoundationMDPI, Basel, SwitzerlandDepartamento de BioquímicaFacultad de Medicina20192019-09-13research articlehttp://purl.org/coar/resource_type/c_2df8fbb1VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/692244https://dx.doi.org/10.3390/cancers11091361reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6922442026-06-23T12:46:27Z
dc.title.none.fl_str_mv In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
title In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
spellingShingle In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
Piñeiro-Yáñez, Elena
Precision medicine
Cancer genomics
Intra-tumour heterogeneity
Iin silico prescription
Bioinformatics
Pharmacogenomics
Druggable genome
Medicina
title_short In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
title_full In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
title_fullStr In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
title_full_unstemmed In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
title_sort In silico drug prescription for targeting cancer patient heterogeneity and prediction of clinical outcome
dc.creator.none.fl_str_mv Piñeiro-Yáñez, Elena
Jiménez-Santos, María José
Gómez López, Gonzalo
Al-Shahrour, Fátima
author Piñeiro-Yáñez, Elena
author_facet Piñeiro-Yáñez, Elena
Jiménez-Santos, María José
Gómez López, Gonzalo
Al-Shahrour, Fátima
author_role author
author2 Jiménez-Santos, María José
Gómez López, Gonzalo
Al-Shahrour, Fátima
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Bioquímica
Facultad de Medicina
dc.subject.none.fl_str_mv Precision medicine
Cancer genomics
Intra-tumour heterogeneity
Iin silico prescription
Bioinformatics
Pharmacogenomics
Druggable genome
Medicina
topic Precision medicine
Cancer genomics
Intra-tumour heterogeneity
Iin silico prescription
Bioinformatics
Pharmacogenomics
Druggable genome
Medicina
description In 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
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-09-13
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/692244
https://dx.doi.org/10.3390/cancers11091361
url http://hdl.handle.net/10486/692244
https://dx.doi.org/10.3390/cancers11091361
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI, Basel, Switzerland
publisher.none.fl_str_mv MDPI, Basel, Switzerland
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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