蛋白质组
计算生物学
转录组
生物
蛋白质组学
基因组
鉴定(生物学)
基因
生物信息学
遗传学
基因表达
植物
作者
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
出处
期刊:Cancer Cell
[Cell Press]
日期:2022-07-14
卷期号:40 (8): 835-849.e8
被引量:117
标识
DOI:10.1016/j.ccell.2022.06.010
摘要
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.
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