Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer
Name
nihms-967174.pdf
Description
Accepted version
Size
813.48 KB
Format
Adobe PDF
Checksum (MD5)
6f53761f6a20fe1b8c8768f0686acb7a
Author(s)
Viswanathan, Srinivas R.
Nogueira, Marina F.
Buss, Colin G.
Krill-Burger, John M.
Wawer, Mathias J.
Malolepsza, Edyta
Berger, Ashton C.
Choi, Peter S.
Shih, Juliann
Taylor, Alison M.
Date Issued
June 2018
Journal
Nature Genetics
Publisher
Springer Science and Business Media LLC
Citation
Viswanathan, Srinivas R. et al. "Genome-scale analysis identifies paralog lethality as a vulnerability of chromosome 1p loss in cancer." Nature Genetics 50, 7 (June 2018): 937–943 © 2018 The Author(s)
Version
Author's final manuscript
Abstract
Functional redundancy shared by paralog genes may afford protection against genetic perturbations, but it can also result in genetic vulnerabilities due to mutual interdependency 1-5 . Here, we surveyed genome-scale short hairpin RNA and CRISPR screening data on hundreds of cancer cell lines and identified MAGOH and MAGOHB, core members of the splicing-dependent exon junction complex, as top-ranked paralog dependencies 6-8 . MAGOHB is the top gene dependency in cells with hemizygous MAGOH deletion, a pervasive genetic event that frequently occurs due to chromosome 1p loss. Inhibition of MAGOHB in a MAGOH-deleted context compromises viability by globally perturbing alternative splicing and RNA surveillance. Dependency on IPO13, an importin-β receptor that mediates nuclear import of the MAGOH/B-Y14 heterodimer 9 , is highly correlated with dependency on both MAGOH and MAGOHB. Both MAGOHB and IPO13 represent dependencies in murine xenografts with hemizygous MAGOH deletion. Our results identify MAGOH and MAGOHB as reciprocal paralog dependencies across cancer types and suggest a rationale for targeting the MAGOHB-IPO13 axis in cancers with chromosome 1p deletion.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41588-018-0155-3
