Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Next Generation Profiling of the Cancer Cell Line Encyclopedia

Acceleration of cancer research by use of model cancer cell lines
16 May 2019
Basic science;  Translational research

Two papers published on 8 May 2019 in the Nature by the experts from the Cancer Cell Line Encyclopedia feature the landscape of cancer cell line metabolism and expended characterisation of cancer cell lines to provide a resource for the acceleration of cancer research.

In first paper the investigators from the Cancer Cell Line Encyclopedia described the results from a study that aimed to understand the metabolic diversity of cancer. They profiled 225 metabolites in 928 cell lines from more than 20 cancer types by using liquid chromatography–mass spectrometry. 

This resource enables unbiased association analysis linking the cancer metabolome to genetic alterations, epigenetic features and gene dependencies. Additionally, by screening barcoded cell lines, the investigators demonstrated that aberrant ASNS hypermethylation sensitizes subsets of gastric and hepatic cancers to asparaginase therapy. 

The analysis also revealed distinct synthesis and secretion patterns of kynurenine, an immune-suppressive metabolite, in model cancer cell lines. 

The authors concluded that their findings and related methodology provide comprehensive resources that will help clarify the landscape of cancer metabolism. 

In second study, the investigators aimed to improve understanding of the molecular features that contribute to cancer phenotypes, including drug responses, so they have expanded the characterisations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. 

Integration of these data with functional characterisations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. 

The authors concluded that the dataset and an accompanying public data portal provide a resource for the acceleration of cancer research by using model cancer cell lines. 



  1. Li H, Ning S, Ghandi M, et al. The landscape of cancer cell line metabolism. Nature Medicine 2019;25(5):850–860.
  2. Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature; Published on 8 May 2019.




Last update: 16 May 2019

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.