- Center for Bioinformatics and Computational Biology; National Institutes of Health - Cancer Data Science Laboratory; National Institutes of Health - Laboratory of Immune Cell Biology
- Center for Bioinformatics and Computational Biology; Harvard University - Department of Biostatistics and Computational Biology; Harvard University - Cancer Center
The phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. Here, we describe a novel data-driven approach called EnGIne, that applied to TCGA data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue-specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. The GI network is accessible online via a web portal [https://amagen.shinyapps.io/cancerapp/].
Magen, Assaf and Das, Avinash and Lee, Joo Sang and Sharmin, Mahfuza and Lugo, Alexander and Gutkind, J. Silvio and Schäffer, Alejandro A. and Ruppin, Eytan and Hannenhalli, Sridhar, Beyond Synthetic Lethality: Charting the Landscape of Clinically Relevant Genetic Interactions in Cancer (February 5, 2019). Available at SSRN: https://ssrn.com/abstract=3329251 or http://dx.doi.org/10.2139/ssrn.3329251
This version of the paper has not been formally peer reviewed.
Download This Paper