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Deciphering the signaling network of breast cancer improves drug sensitivity prediction

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 c...

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Hauptverfasser: Tognetti, Marco (VerfasserIn) , Gabor, Attila (VerfasserIn) , Yang, Mi (VerfasserIn) , Cappelletti, Valentina (VerfasserIn) , Windhager, Jonas (VerfasserIn) , Rueda, Oscar M. (VerfasserIn) , Charmpi, Konstantina (VerfasserIn) , Esmaeilishirazifard, Elham (VerfasserIn) , Bruna, Alejandra (VerfasserIn) , de Souza, Natalie (VerfasserIn) , Caldas, Carlos (VerfasserIn) , Beyer, Andreas (VerfasserIn) , Picotti, Paola (VerfasserIn) , Sáez Rodríguez, Julio (VerfasserIn) , Bodenmiller, Bernd (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: April 30, 2021
In: Cell systems
Year: 2021, Jahrgang: 12, Heft: 5, Pages: 401-418, e1-e12
ISSN:2405-4720
DOI:10.1016/j.cels.2021.04.002
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.cels.2021.04.002
Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S2405471221001113
Volltext
Verfasserangaben:Marco Tognetti, Attila Gabor, Mi Yang, Valentina Cappelletti, Jonas Windhager, Oscar M. Rueda, Konstantina Charmpi, Elham Esmaeilishirazifard, Alejandra Bruna, Natalie de Souza, Carlos Caldas, Andreas Beyer, Paola Picotti, Julio Saez-Rodriguez, and Bernd Bodenmiller
Beschreibung
Zusammenfassung: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.
Beschreibung:Gesehen am 22.02.2022
Beschreibung:Online Resource
ISSN:2405-4720
DOI:10.1016/j.cels.2021.04.002

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