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

Horn, H, Lawrence, MS, Chouinard, CR, Shrestha, Y, Hu, JX, Worstell, E, Shea, E, Ilic, N, Kim, E, Kamburov, A, Kashani, A, Hahn, WC, Campbell, JD, Boehm, JS, Getz, G & Lage, K 2018, 'NetSig: network-based discovery from cancer genomes', Nature Methods, bind 15, s. 61-66. https://doi.org/10.1038/nmeth.4514

APA

Horn, H., Lawrence, M. S., Chouinard, C. R., Shrestha, Y., Hu, J. X., Worstell, E., Shea, E., Ilic, N., Kim, E., Kamburov, A., Kashani, A., Hahn, W. C., Campbell, J. D., Boehm, J. S., Getz, G., & Lage, K. (2018). NetSig: network-based discovery from cancer genomes. Nature Methods, 15, 61-66. https://doi.org/10.1038/nmeth.4514

Vancouver

Horn H, Lawrence MS, Chouinard CR, Shrestha Y, Hu JX, Worstell E o.a. NetSig: network-based discovery from cancer genomes. Nature Methods. 2018;15:61-66. https://doi.org/10.1038/nmeth.4514

Author

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. / NetSig : network-based discovery from cancer genomes. I: Nature Methods. 2018 ; Bind 15. s. 61-66.

Bibtex

@article{e017079219f24ef89b102ef26c3840f3,
title = "NetSig: network-based discovery from cancer genomes",
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.",
author = "Heiko Horn and Lawrence, {Michael S} and Chouinard, {Candace R} and Yashaswi Shrestha and Hu, {Jessica Xin} and Elizabeth Worstell and Emily Shea and Nina Ilic and Eejung Kim and Atanas Kamburov and Alireza Kashani and Hahn, {William C} and Campbell, {Joshua D} and Boehm, {Jesse S} and Gad Getz and Kasper Lage",
year = "2018",
doi = "10.1038/nmeth.4514",
language = "English",
volume = "15",
pages = "61--66",
journal = "Nature Methods",
issn = "1548-7091",
publisher = "nature publishing group",

}

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