Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

• Published in: iScience Vol. 21; pp. 664 - 680
• Main Authors: Guo, Tiannan, Luna, Augustin, Rajapakse, Vinodh N., Koh, Ching Chiek, Wu, Zhicheng, Liu, Wei, Sun, Yaoting, Gao, Huanhuan, Menden, Michael P., Xu, Chao, Calzone, Laurence, Martignetti, Loredana, Auwerx, Chiara, Buljan, Marija, Banaei-Esfahani, Amir, Ori, Alessandro, Iskar, Murat, Gillet, Ludovic, Bi, Ran, Zhang, Jiangnan, Zhang, Huanhuan, Yu, Chenhuan, Zhong, Qing, Varma, Sudhir, Schmitt, Uwe, Qiu, Peng, Zhang, Qiushi, Zhu, Yi, Wild, Peter J., Garnett, Mathew J., Bork, Peer, Beck, Martin, Liu, Kexin, Saez-Rodriguez, Julio, Elloumi, Fathi, Reinhold, William C., Sander, Chris, Pommier, Yves, Aebersold, Ruedi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 22.11.2019; Elsevier
• Subjects: Biological Sciences; Cancer Systems Biology; Proteomics; Systems Biology; Biological Sciences; Systems Biology; Proteomics; Cancer Systems Biology
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2019.10.059

Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses. [Display omitted] •High-quality NCI-60 proteotypes created using pressure cycling technology and SWATH-MS•Proteotypes improve drug response prediction in multi-omics regression analysis•∼3000 measured proteins allow investigation into protein complex stoichiometry•CellMinerCDB (discover.nci.nih.gov/cellminercdb) portal allows dataset exploration Biological Sciences; Systems Biology; Proteomics; Cancer Systems Biology