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Pan-cancer proteomic map of 949 human cell lines

Authors :
Emanuel Gonçalves
Rebecca C. Poulos
Zhaoxiang Cai
Syd Barthorpe
Srikanth S. Manda
Natasha Lucas
Alexandra Beck
Daniel Bucio-Noble
Michael Dausmann
Caitlin Hall
Michael Hecker
Jennifer Koh
Howard Lightfoot
Sadia Mahboob
Iman Mali
James Morris
Laura Richardson
Akila J. Seneviratne
Rebecca Shepherd
Erin Sykes
Frances Thomas
Sara Valentini
Steven G. Williams
Yangxiu Wu
Dylan Xavier
Karen L. MacKenzie
Peter G. Hains
Brett Tully
Phillip J. Robinson
Qing Zhong
Mathew J. Garnett
Roger R. Reddel
Source :
Cancer Cell
Publication Year :
2021

Abstract

The proteome provides unique insights into disease biology beyond the genome and transcriptome. A lack of large proteomic datasets has restricted the identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types are analyzed by mass spectrometry. Deploying a workflow to quantify 8,498 proteins, these data capture evidence of cell-type and post-transcriptional modifications. Integrating multi-omics, drug response, and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline reveals thousands of protein biomarkers of cancer vulnerabilities that are not significant at the transcript level. The power of the proteome to predict drug response is very similar to that of the transcriptome. Further, random downsampling to only 1,500 proteins has limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger) is a comprehensive resource available at https://cellmodelpassports.sanger.ac.uk.

Details

ISSN :
18783686
Volume :
40
Issue :
8
Database :
OpenAIRE
Journal :
Cancer cell
Accession number :
edsair.doi.dedup.....2b3a20c7e98090d73382989efa0e217c