dc.contributor.author | Tognetti, M | |
dc.contributor.author | Gabor, A | |
dc.contributor.author | Yang, M | |
dc.contributor.author | Cappelletti, V | |
dc.contributor.author | Windhager, J | |
dc.contributor.author | Rueda, OM | |
dc.contributor.author | Charmpi, K | |
dc.contributor.author | Esmaeilishirazifard, E | |
dc.contributor.author | Bruna, A | |
dc.contributor.author | de Souza, N | |
dc.contributor.author | Caldas, C | |
dc.contributor.author | Beyer, A | |
dc.contributor.author | Picotti, P | |
dc.contributor.author | Saez-Rodriguez, J | |
dc.contributor.author | Bodenmiller, B | |
dc.date.accessioned | 2021-08-04T09:45:39Z | |
dc.date.available | 2021-08-04T09:45:39Z | |
dc.date.issued | 2021-05-19 | |
dc.identifier.citation | Cell systems, 2021, 12 (5), pp. 401 - 418.e12 | |
dc.identifier.issn | 2405-4712 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/4721 | |
dc.identifier.eissn | 2405-4720 | |
dc.identifier.doi | 10.1016/j.cels.2021.04.002 | |
dc.description.abstract | One 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.format | Print-Electronic | |
dc.format.extent | 401 - 418.e12 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | CELL PRESS | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.title | Deciphering the signaling network of breast cancer improves drug sensitivity prediction. | |
dc.type | Journal Article | |
dcterms.dateAccepted | 2021-04-07 | |
rioxxterms.version | VoR | |
rioxxterms.versionofrecord | 10.1016/j.cels.2021.04.002 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | Cell systems | |
pubs.issue | 5 | |
pubs.notes | Not 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-status | Published | |
pubs.volume | 12 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Preclinical Modelling of Paediatric Cancer Evolution | |
icr.researchteam | Preclinical Modelling of Paediatric Cancer Evolution | |
dc.contributor.icrauthor | Bruna Cabot, Alejandra | |