NetSig: network-based discovery from cancer genomes
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
NetSig : network-based discovery from cancer genomes. / Horn, Heiko; Lawrence, Michael S; Chouinard, Candace R; Shrestha, Yashaswi; Hu, Jessica Xin; Worstell, Elizabeth; Shea, Emily; Ilic, Nina; Kim, Eejung; Kamburov, Atanas; Kashani, Alireza; Hahn, William C; Campbell, Joshua D; Boehm, Jesse S; Getz, Gad; Lage, Kasper.
I: Nature Methods, Bind 15, 2018, s. 61-66.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - NetSig
T2 - network-based discovery from cancer genomes
AU - Horn, Heiko
AU - Lawrence, Michael S
AU - Chouinard, Candace R
AU - Shrestha, Yashaswi
AU - Hu, Jessica Xin
AU - Worstell, Elizabeth
AU - Shea, Emily
AU - Ilic, Nina
AU - Kim, Eejung
AU - Kamburov, Atanas
AU - Kashani, Alireza
AU - Hahn, William C
AU - Campbell, Joshua D
AU - Boehm, Jesse S
AU - Getz, Gad
AU - Lage, Kasper
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
U2 - 10.1038/nmeth.4514
DO - 10.1038/nmeth.4514
M3 - Journal article
C2 - 29200198
VL - 15
SP - 61
EP - 66
JO - Nature Methods
JF - Nature Methods
SN - 1548-7091
ER -
ID: 191302627