iScience (Nov 2019)

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

  • Tiannan Guo,
  • Augustin Luna,
  • Vinodh N. Rajapakse,
  • Ching Chiek Koh,
  • Zhicheng Wu,
  • Wei Liu,
  • Yaoting Sun,
  • Huanhuan Gao,
  • Michael P. Menden,
  • Chao Xu,
  • Laurence Calzone,
  • Loredana Martignetti,
  • Chiara Auwerx,
  • Marija Buljan,
  • Amir Banaei-Esfahani,
  • Alessandro Ori,
  • Murat Iskar,
  • Ludovic Gillet,
  • Ran Bi,
  • Jiangnan Zhang,
  • Huanhuan Zhang,
  • Chenhuan Yu,
  • Qing Zhong,
  • Sudhir Varma,
  • Uwe Schmitt,
  • Peng Qiu,
  • Qiushi Zhang,
  • Yi Zhu,
  • Peter J. Wild,
  • Mathew J. Garnett,
  • Peer Bork,
  • Martin Beck,
  • Kexin Liu,
  • Julio Saez-Rodriguez,
  • Fathi Elloumi,
  • William C. Reinhold,
  • Chris Sander,
  • Yves Pommier,
  • Ruedi Aebersold

Journal volume & issue
Vol. 21
pp. 664 – 680

Abstract

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Summary: 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. : Biological Sciences; Systems Biology; Proteomics; Cancer Systems Biology Subject Areas: Biological Sciences, Systems Biology, Proteomics, Cancer Systems Biology