Rational design of non-resistant targeted cancer therapies

Drug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive an...

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Autores: Martínez-Jiménez, Francisco, 1988-, Overington, John P., Al-Lazikani, Bissan, Martí Renom, Marc A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2017
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/35243
Acceso en línea:http://hdl.handle.net/10230/35243
http://dx.doi.org/10.1038/srep46632
Access Level:acceso abierto
Palabra clave:Gefitinib
Adenocarcinoma of lung
Drug discovery
Cancer therapy
Drug resistance
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spelling Rational design of non-resistant targeted cancer therapiesMartínez-Jiménez, Francisco, 1988-Overington, John P.Al-Lazikani, BissanMartí Renom, Marc A.GefitinibAdenocarcinoma of lungDrug discoveryCancer therapyDrug resistanceDrug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive analysis of the mutational landscape that can prospectively estimate drug-resistance mutations. Here we describe and computationally validate a framework that combines the cancer-specific likelihood with the resistance impact to enable the detection of single point mutations with the highest chance to be responsible of resistance to a particular targeted cancer therapy. Moreover, for these treatment-threatening mutations, the model proposes alternative therapies overcoming the resistance. We exemplified the applicability of the model using EGFR-gefitinib treatment for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC) and the ERK2-VTX11e treatment for melanoma and colorectal cancer. Our model correctly identified the phenotype known resistance mutations, including the classic EGFR-T790M and the ERK2-P58L/S/T mutations. Moreover, the model predicted new previously undescribed mutations as potentially responsible of drug resistance. Finally, we provided a map of the predicted sensitivity of alternative ERK2 and EGFR inhibitors, with a particular highlight of two molecules with a low predicted resistance impact.The project was supported by the Spanish MINECO to M.A.M.-R. (BFU2010-19310). We also acknowledge the support of the Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa 2013-2017 (SEV-2012-0208) and the CERCA Programme of the Generalitat de Catalunya.Nature Publishing Group201820182017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/35243http://dx.doi.org/10.1038/srep46632reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésScientific Reports. 2017 Apr 24;7:46632info:eu-repo/grantAgreement/ES/3PN/BFU2010-19310© The Author(s) 2017. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/352432026-05-29T05:05:01Z
dc.title.none.fl_str_mv Rational design of non-resistant targeted cancer therapies
title Rational design of non-resistant targeted cancer therapies
spellingShingle Rational design of non-resistant targeted cancer therapies
Martínez-Jiménez, Francisco, 1988-
Gefitinib
Adenocarcinoma of lung
Drug discovery
Cancer therapy
Drug resistance
title_short Rational design of non-resistant targeted cancer therapies
title_full Rational design of non-resistant targeted cancer therapies
title_fullStr Rational design of non-resistant targeted cancer therapies
title_full_unstemmed Rational design of non-resistant targeted cancer therapies
title_sort Rational design of non-resistant targeted cancer therapies
dc.creator.none.fl_str_mv Martínez-Jiménez, Francisco, 1988-
Overington, John P.
Al-Lazikani, Bissan
Martí Renom, Marc A.
author Martínez-Jiménez, Francisco, 1988-
author_facet Martínez-Jiménez, Francisco, 1988-
Overington, John P.
Al-Lazikani, Bissan
Martí Renom, Marc A.
author_role author
author2 Overington, John P.
Al-Lazikani, Bissan
Martí Renom, Marc A.
author2_role author
author
author
dc.subject.none.fl_str_mv Gefitinib
Adenocarcinoma of lung
Drug discovery
Cancer therapy
Drug resistance
topic Gefitinib
Adenocarcinoma of lung
Drug discovery
Cancer therapy
Drug resistance
description Drug resistance is one of the major problems in targeted cancer therapy. A major cause of resistance is changes in the amino acids that form the drug-target binding site. Despite of the numerous efforts made to individually understand and overcome these mutations, there is a lack of comprehensive analysis of the mutational landscape that can prospectively estimate drug-resistance mutations. Here we describe and computationally validate a framework that combines the cancer-specific likelihood with the resistance impact to enable the detection of single point mutations with the highest chance to be responsible of resistance to a particular targeted cancer therapy. Moreover, for these treatment-threatening mutations, the model proposes alternative therapies overcoming the resistance. We exemplified the applicability of the model using EGFR-gefitinib treatment for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC) and the ERK2-VTX11e treatment for melanoma and colorectal cancer. Our model correctly identified the phenotype known resistance mutations, including the classic EGFR-T790M and the ERK2-P58L/S/T mutations. Moreover, the model predicted new previously undescribed mutations as potentially responsible of drug resistance. Finally, we provided a map of the predicted sensitivity of alternative ERK2 and EGFR inhibitors, with a particular highlight of two molecules with a low predicted resistance impact.
publishDate 2017
dc.date.none.fl_str_mv 2017
2018
2018
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/35243
http://dx.doi.org/10.1038/srep46632
url http://hdl.handle.net/10230/35243
http://dx.doi.org/10.1038/srep46632
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Scientific Reports. 2017 Apr 24;7:46632
info:eu-repo/grantAgreement/ES/3PN/BFU2010-19310
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
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