Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities
Emanuel GonçalvesiD, Rebecca C. PoulosiD, Zhaoxiang CaiiD, Syd BarthorpeiD, Srikanth Srinivas MandaiD, Natasha LucasiD, Alexandra BeckiD, Daniel Bucio-NobleiD, Michael DausmanniD, Caitlin HalliD, Michael Hecker, Jennifer M.S. KohiD, Howard W. LightfootiD, Sadia MahboobiD, Iman D. MaliiD, James Morris, Laura RichardsoniD, Akila J. Seneviratne, Rebecca ShepherdiD, Erin K. SykesiD, Frances ThomasiD, Sara Valentini, Steven G. WilliamsiD, Yangxiu WuiD, Dylan XavieriD, , Roger R. ReddeliD
Abstract
The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant 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 revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.
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