Abstract Cancer genomics has produced extensive information on cancer-associated genes but the number and specificity of cancer driver mutations remains a matter of debate. We constructed a bipartite network in which 7665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer-associated genes. We show that 27% of the tumors can be assigned to statistically supported modules, most of which encompass 1-2 cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene-specificity of driver mutations. Linear regression of the mutational loads in cancer-associated genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers is ~2, with a range of 1 to 5. Cancers that are associated to modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.
Lina Wadi, Liis Uusküla-Reimand, Keren Isaev, Shimin Shuai, Vincent Huang, Minggao Liang, John D Thompson, Yao Li, Luyao Ruan, Marta Paczkowska, Michał Krassowski, Irakli Dzneladze, Ken J. Kron, Alexander Murison, Parisa Mazrooei, Robert G. Bristow, Jared T. Simpson, Mathieu Lupien, Michael D. Wilson, Lincoln Stein, Paul C. Boutros, Jüri Reimand
Julia Matas, Brendan F. Kohrn, Jeanne Fredrickson, Kelly Carter, Ming Yu, Ting Wang, Xianyong Gui, Thierry Soussi, Vı́ctor Moreno, William M. Grady, aaa bbb, Rosa Ana Risques
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