Cooperation between Noncanonical Ras Network Mutations

• Published in: Cell reports (Cambridge) Vol. 10; no. 3; pp. 307 - 316
• Main Authors: Stites, Edward C., Trampont, Paul C., Haney, Lisa B., Walk, Scott F., Ravichandran, Kodi S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 20-01-2015; Elsevier
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2014.12.035

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell and whether there is selection for certain combinations of mutations are not well understood. We investigated this problem with a mathematical model of the Ras signaling network, including a computational random mutagenesis. Modeling and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including weakly activating, noncanonical Ras mutants. Furthermore, analyzing recently available, large, cancer genomic data sets uncovered increased co-occurrence of NF1 mutations with mutations in other Ras network genes. Overall, these data suggest that combinations of Ras pathway mutations could serve the role of cancer “driver.” More generally, this work suggests that mutations that result in network instability may promote cancer in a manner analogous to genomic instability.
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•Mathematical modeling to study combinations of Ras pathway mutations•Model identifies mutations that potentiate the effects of other mutations•Experiments detect potentiation of weak RAS F28L mutation by loss of neurofibromin•Cancer genomic data analysis finds increased co-occurrence of Ras pathway mutations
Stites et al. use a mathematical model to investigate combinations of mutations in the Ras pathway. The model predicts synergy between specific combinations of mutations, and this prediction is supported by experiments. Mining human cancer genome databases finds increased co-occurrence of Ras pathway mutations, consistent with the model analysis.