PXD017199-2

PXD017199 is an original dataset announced via ProteomeXchange.

Title	DECIPHERING THE SIGNALING NETWORK LANDSCAPE OF BREAST CANCER IMPROVES DRUG SENSITIVITY PREDICTION
Description	Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. 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 unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future.
HostingRepository	PRIDE
AnnounceDate	2022-02-14
AnnouncementXML	Submission_2022-02-14_01:43:27.409.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Marco Tognetti
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	Q Exactive Plus

Revision	Datetime	Status	ChangeLog Entry
0	2020-01-20 03:17:30	ID requested
1	2021-04-19 23:14:31	announced
⏵ 2	2022-02-14 01:43:27	announced	2022-02-14: Updated project metadata.
3	2024-10-22 05:21:20	announced	2024-10-22: Updated project metadata.

Dataset with its publication pending

ProteomeXchange project tag: EPIC-XS

submitter keyword: Human, Breast Cancer, Cell lines

Paola Picotti
contact affiliation	Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
contact email	paola.picotti@bc.biol.ethz.ch
lab head
Marco Tognetti
contact affiliation	ETH Zurich
contact email	tognetti.marco@outlook.com
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2021/04/PXD017199

PRIDE project URI

• PRIDE
  1. PXD017199
     1. Label: PRIDE project
     2. Name: DECIPHERING THE SIGNALING NETWORK LANDSCAPE OF BREAST CANCER IMPROVES DRUG SENSITIVITY PREDICTION