Deciphering the signaling network of breast cancer improves drug sensitivity prediction.
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ICR Authors
Authors
Tognetti, M
Gabor, A
Yang, M
Cappelletti, V
Windhager, J
Rueda, OM
Charmpi, K
Esmaeilishirazifard, E
Bruna, A
de Souza, N
Caldas, C
Beyer, A
Picotti, P
Saez-Rodriguez, J
Bodenmiller, B
Gabor, A
Yang, M
Cappelletti, V
Windhager, J
Rueda, OM
Charmpi, K
Esmaeilishirazifard, E
Bruna, A
de Souza, N
Caldas, C
Beyer, A
Picotti, P
Saez-Rodriguez, J
Bodenmiller, B
Document Type
Journal Article
Date
2021-05-19
Date Accepted
2021-04-07
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.
Citation
Cell systems, 2021, 12 (5), pp. 401 - 418.e12
Source Title
Publisher
CELL PRESS
ISSN
2405-4712
eISSN
2405-4720
Collections
Research Team
Preclinical Modelling of Paediatric Cancer Evolution
Preclinical Modelling of Paediatric Cancer Evolution
Preclinical Modelling of Paediatric Cancer Evolution