Dataset Information

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

ABSTRACT: 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.

SUBMITTER: Guo T

PROVIDER: S-EPMC6889472 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

Guo Tiannan T Luna Augustin A Rajapakse Vinodh N VN Koh Ching Chiek CC Wu Zhicheng Z Liu Wei W Sun Yaoting Y Gao Huanhuan H Menden Michael P MP Xu Chao C Calzone Laurence L Martignetti Loredana L Auwerx Chiara C Buljan Marija M Banaei-Esfahani Amir A Ori Alessandro A Iskar Murat M Gillet Ludovic L Bi Ran R Zhang Jiangnan J Zhang Huanhuan H Yu Chenhuan C Zhong Qing Q Varma Sudhir S Schmitt Uwe U Qiu Peng P Zhang Qiushi Q Zhu Yi Y Wild Peter J PJ Garnett Mathew J MJ Bork Peer P Beck Martin M Liu Kexin K Saez-Rodriguez Julio J Elloumi Fathi F Reinhold William C WC Sander Chris C Pommier Yves Y Aebersold Ruedi R

iScience 20191031

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 ...[more]

PMID: 31733513

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Project description:BackgroundCancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.ResultsWe propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1) Protein expressions are generally consistent with mRNA expressions. (2) Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3) Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4) Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.ConclusionsThe analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.

Dataset Information

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

Publications

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines.

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