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dc.contributor.authorMartínez-Jiménez, F
dc.contributor.authorOverington, JP
dc.contributor.authorAl-Lazikani, B
dc.contributor.authorMarti-Renom, MA
dc.date.accessioned2017-07-14T10:43:25Z
dc.date.issued2017-04-24
dc.identifier.citationScientific reports, 2017, 7 pp. 46632 - ?
dc.identifier.issn2045-2322
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/702
dc.identifier.eissn2045-2322
dc.identifier.doi10.1038/srep46632
dc.description.abstractDrug 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.formatElectronic
dc.format.extent46632 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectCarcinoma, Non-Small-Cell Lung
dc.subjectLung Neoplasms
dc.subjectNeoplasm Proteins
dc.subjectAntineoplastic Agents
dc.subjectDrug Delivery Systems
dc.subjectPoint Mutation
dc.subjectModels, Biological
dc.subjectGefitinib
dc.subjectAdenocarcinoma of Lung
dc.titleRational design of non-resistant targeted cancer therapies.
dc.typeJournal Article
dcterms.dateAccepted2017-03-22
rioxxterms.versionofrecord10.1038/srep46632
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2017-04-24
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfScientific reports
pubs.notesNo 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-statusPublished
pubs.volume7
pubs.embargo.termsNo embargo
icr.researchteamComputational Biology and Chemogenomicsen_US
dc.contributor.icrauthorAl-Lazikani, Bissanen


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