dc.contributor.author | Martínez-Jiménez, F | |
dc.contributor.author | Overington, JP | |
dc.contributor.author | Al-Lazikani, B | |
dc.contributor.author | Marti-Renom, MA | |
dc.date.accessioned | 2017-07-14T10:43:25Z | |
dc.date.issued | 2017-04-24 | |
dc.identifier.citation | Scientific reports, 2017, 7 pp. 46632 - ? | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/702 | |
dc.identifier.eissn | 2045-2322 | |
dc.identifier.doi | 10.1038/srep46632 | |
dc.description.abstract | 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. | |
dc.format | Electronic | |
dc.format.extent | 46632 - ? | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | NATURE PORTFOLIO | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Humans | |
dc.subject | Carcinoma, Non-Small-Cell Lung | |
dc.subject | Lung Neoplasms | |
dc.subject | Neoplasm Proteins | |
dc.subject | Antineoplastic Agents | |
dc.subject | Drug Delivery Systems | |
dc.subject | Point Mutation | |
dc.subject | Models, Biological | |
dc.subject | Gefitinib | |
dc.subject | Adenocarcinoma of Lung | |
dc.title | Rational design of non-resistant targeted cancer therapies. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2017-03-22 | |
rioxxterms.versionofrecord | 10.1038/srep46632 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2017-04-24 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Scientific reports | |
pubs.notes | No embargo | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Therapeutics | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Computational Biology and Chemogenomics | |
pubs.organisational-group | /ICR | |
pubs.organisational-group | /ICR/Primary Group | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Therapeutics | |
pubs.organisational-group | /ICR/Primary Group/ICR Divisions/Cancer Therapeutics/Computational Biology and Chemogenomics | |
pubs.publication-status | Published | |
pubs.volume | 7 | |
pubs.embargo.terms | No embargo | |
icr.researchteam | Computational Biology and Chemogenomics | |
dc.contributor.icrauthor | Al-Lazikani, Bissan | |