Deciphering the signaling network of breast cancer improves drug sensitivity prediction
OPEN ACCESS
Author / Producer
Date
2021-05-19
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
12 (5)
Pages / Article No.
401 - 418000000000000
Publisher
Cell Press
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
cell lines; breast cancer; single-cell signaling; drug sensitivity prediction; mechanistic modeling; Cellular signaling; proteomics; EGF-MAP kinase pathway
Organisational unit
03927 - Picotti, Paola / Picotti, Paola
09735 - Bodenmiller, Bernd / Bodenmiller, Bernd
Notes
Funding
866004 - Three-dimensional dynamic views of proteomes as a novel readout for physiolgical and pathological alterations (EC)
823839 - European Proteomics Infrastructure Consortium providing Access (EC)
823839 - European Proteomics Infrastructure Consortium providing Access (EC)