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dc.contributor.authorTognetti, M
dc.contributor.authorGabor, A
dc.contributor.authorYang, M
dc.contributor.authorCappelletti, V
dc.contributor.authorWindhager, J
dc.contributor.authorRueda, OM
dc.contributor.authorCharmpi, K
dc.contributor.authorEsmaeilishirazifard, E
dc.contributor.authorBruna, A
dc.contributor.authorde Souza, N
dc.contributor.authorCaldas, C
dc.contributor.authorBeyer, A
dc.contributor.authorPicotti, P
dc.contributor.authorSaez-Rodriguez, J
dc.contributor.authorBodenmiller, B
dc.date.accessioned2021-08-04T09:45:39Z
dc.date.available2021-08-04T09:45:39Z
dc.identifier.citationCell systems, 2021, 12 (5), pp. 401 - 418.e12
dc.identifier.issn2405-4712
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4721
dc.identifier.eissn2405-4720
dc.identifier.eissn2405-4720en_US
dc.identifier.doi10.1016/j.cels.2021.04.002
dc.identifier.doi10.1016/j.cels.2021.04.002en_US
dc.description.abstractOne goal of precision medicine is to tailor effective treatments to patients' specific molecular markers of disease. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data-on more than 80 million single cells from 4,000 conditions-were used to fit mechanistic signaling network models that provide insight into how cancer cells process information. Our dynamic single-cell-based models accurately predicted drug sensitivity and identified genomic features associated with drug sensitivity, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. We observed similar trends in genotype-drug sensitivity associations in patient-derived xenograft mouse models. This work provides proof of principle that patient-specific single-cell measurements and modeling could inform effective precision medicine strategies.
dc.formatPrint-Electronic
dc.format.extent401 - 418.e12
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleDeciphering the signaling network of breast cancer improves drug sensitivity prediction.
dc.typeJournal Article
dcterms.dateAccepted2021-04-07
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1016/j.cels.2021.04.002
dc.relation.isPartOfCell systems
pubs.issue5
pubs.notesNot known
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/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Preclinical Modelling of Paediatric Cancer Evolution
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/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Preclinical Modelling of Paediatric Cancer Evolution
pubs.publication-statusPublished
pubs.volume12en_US
pubs.embargo.termsNot known
icr.researchteamPreclinical Modelling of Paediatric Cancer Evolution
icr.researchteamPreclinical Modelling of Paediatric Cancer Evolutionen_US
dc.contributor.icrauthorBruna Cabot, Alejandraen


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