A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes
Heiko Horn, Michael S. Lawrence, Jessica Xin Hu, Elizabeth Worstell, Nina Ilic, Yashaswi Shrestha, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C. Hahn, Jesse S. Boehm, Gad Getz, Kasper Lage
The paper quantifies network mutation burdens across 21 tumor types to identify putative cancer genes, finding 62 new candidates with greater sensitivity for low-frequency mutations.
bioRxiv · Aug 25, 2015 · PreprintCancer Biology