Studies expand and update an encyclopedia of cancer cell lines
Researchers take a broad, multi-faceted look at a large collection of cancer cell lines, creating a new trove of data to explore for drug development opportunities.
By Tom Ulrich
Credit: Susanna M. Hamilton, Broad Communications
Large libraries of cancer cell lines — collections of cells that represent tumor types seen in cancer patients — can yield profound insights into tumors' unique genetic features and their sensitivities to current and potential treatments. The data produced by these libraries is invaluable for developing new therapeutic options for patients.
A multi-center research team has now greatly augmented this cancer research resource by incorporating new cell lines and adding new data spanning the molecular spectrum from sequence to expression to protein. Writing in Nature, the team — led by core institute member William Sellers, institute member on leave Levi Garraway, and Broad alumni Mahmoud Ghandi and Franklin Huang — report a major expansion of the CCLE dataset, which now includes:
"We suspect that there are ways of looking beyond pairwise correlations like expression and protein levels to identify states of cancer that only reveal themselves when you see all the data in aggregate," Sellers explained. "We hope that with all of the data available, the community will help draw those macro-level pictures, enabling improved drug discovery efforts broadly in industry and academia."
"These data, along with statistical models, allow us to see otherwise-hidden connections between genetic and epigenetic errors in cancer cells and changes in those cells' metabolic profiles," Li said. "The data reveal metabolic dependencies that, for instance, point to opportunities to expand the use of the anti-cancer drug asparaginase, and to exploit levels of a metabolite called kynurenine as a prognostic biomarker for certain kinds of immunotherapy."
The CCLE collection provides the backbone for two large-scale cancer discovery efforts. One is the DepMap project, an effort being undertaken at the Broad Institute and at the Sanger Institute to systematically identify genetic dependencies (vulnerabilities that might serve as targets for designing new therapies or repurposing existing ones) across hundreds of cancer cell lines using RNA interference, CRISPR, and drug screens.
The second is PRISM, a system that uses genetically-barcoded versions of the CCLE cell lines to identify biomarkers that could be used to predict tumors' responses to different drug compounds.
"Taken together, these datasets constitute a massive community resource for anyone in the cancer research field using cell line models," Sellers said. "One can't overestimate the data's power for discovery and for understanding cancer biology mechanisms across tumor types."
These two studies received support from the National Cancer Institute, Novartis, and other sources.
Ghandi M, Huang F, et al. Next generation characterization and functional mapping of the Cancer Cell Line Encyclopedia. Nature. Online May 8, 2019. DOI: 10.1038/s41586-019-1186-3.
Li H, et al. The landscape of cancer cell line metabolism. Nature Medicine. Online May 8, 2019. DOI: 10.1038/s41591-019-0404-8.