By merging cancer functional genetic data with information on protein interactions, scientists can explore protein complexes at massive scale.
"Dependency" mapping reveals the genetic adaptations cancer cells make to survive. And while this approach is helping identify promising treatment strategies for several cancer types, researchers have now found that it also provides an opportunity to probe the intricacies of protein interactions.
While sequencing efforts have likely identified nearly all genes involved in cancer, studies of those genes' protein products and how they interact have been much more difficult. Because proteins work together to drive the lion's share of cellular activities, the ability to explore those interactions at scale could provide insight into all manner of biological processes.
Reporting in Cell Systems, a team led at the Broad by graduate student Joshua Pan, computational biologist Robin Meyers, Cancer Data Science group associate director Aviad Tsherniak, and institute member and Epigenomics Program co-director Cigall Kadoch of the Dana-Farber Cancer Institute, describe how by combining genome-scale dependency data from the Broad Cancer Program's Dependency Map (DepMap) project with large, existing protein interaction datasets, they created a framework for examining protein complexes (assemblies of proteins that carry out coordinated tasks, such as gene transcription).
The team's underlying hypothesis was that: When a gene is knocked out (with CRISPR) or silenced (with RNA interference), the cell can no longer make that gene's protein product. The cell's functional state or survival (it's "fitness") changes as a result.
Two proteins whose losses have similar fitness effects likely perform similar functions, and may even be members of the same protein complex.
By profiling in pooled screens how all these knock-outs affect cell fitness, and comparing those profiles across hundreds of cancer cell lines, researchers can group proteins by functions and interactions, probe the roles of known protein complex members, and highlight previously unrecognized ones.
provided by Broad Institute of MIT.