Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Zhaoxiang Cai, Emanuel Gonçalves, Rebecca C Poulos, …, Roger R Reddel
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.
BibTeX
@article{cai2022sanger2020,
title = {{Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities}},
author = {Zhaoxiang Cai and Emanuel Gonçalves and Rebecca C Poulos and Syd Barthorpe and Srikanth S Manda and Natasha Lucas and Alexandra Beck and Daniel Bucio-Noble and Michael Dausmann and Caitlin Hall and Michael Hecker and Jennifer Koh and Sadia Mahboob and Iman Mali and James Morris and Laura Richardson and Akila J Seneviratne and Erin Sykes and Frances Thomas and Sara Valentini and Steven G Williams and Yangxiu Wu and Dylan Xavier and Karen L MacKenzie and Peter G Hains and Brett Tully and Phillip J Robinson and Qing Zhong and Mathew J Garnett and Roger R Reddel},
journal = {Cancer Cell},
year = {2022},
doi = {10.1016/j.ccell.2022.06.010}
}