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Nat Methods. 2018 Jan;15(1):61-66. doi: 10.1038/nmeth.4514. Epub 2017 Dec 4.

NetSig: network-based discovery from cancer genomes.

Author information

1
Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
2
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
3
Department of Pathology and MGH Cancer Center, Massachusetts General Hospital, Boston, Massachusetts, USA.
4
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
5
Department of Medicine, Boston University School of Medicine, Boston, Massachusetts, USA.
6
Institute for Biological Psychiatry, Mental Health Center Sct. Hans, University of Copenhagen, Roskilde, Denmark.

Abstract

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.

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