You are here

Nat Methods DOI:10.1038/nmeth.4514

NetSig: network-based discovery from cancer genomes.

Publication TypeJournal Article
Year of Publication2018
AuthorsHorn, H, Lawrence, MS, Chouinard, CR, Shrestha, Y, Hu, JXin, Worstell, E, Shea, E, Ilic, N, Kim, E, Kamburov, A, Kashani, A, Hahn, WC, Campbell, JD, Boehm, JS, Getz, G, Lage, K
JournalNat Methods
Date Published2018 01
KeywordsCarcinogenesis, Computational Biology, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Humans, Mutation, Neoplasm Proteins, Neoplasms

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.


Alternate JournalNat. Methods
PubMed ID29200198
PubMed Central IDPMC5985961
Grant ListR01 CA130988 / CA / NCI NIH HHS / United States
R01 MH109903 / MH / NIMH NIH HHS / United States