NetSig: network-based discovery from cancer genomes

Heiko Horn; Michael S Lawrence; Candace R Chouinard; Yashaswi Shrestha; Jessica Xin Hu; Elizabeth Worstell; Emily Shea; Nina Ilic; Eejung Kim; Atanas Kamburov; Alireza Kashani; William C Hahn; Joshua D Campbell; Jesse S Boehm; Gad Getz; Kasper Lage

doi:10.1038/nmeth.4514

NetSig: network-based discovery from cancer genomes

Heiko Horn, Michael S Lawrence, Candace R Chouinard, Yashaswi Shrestha, Jessica Xin Hu, Elizabeth Worstell, Emily Shea, Nina Ilic, Eejung Kim, Atanas Kamburov, Alireza Kashani, William C Hahn, Joshua D Campbell, Jesse S Boehm, Gad Getz, Kasper Lage

38 Citations (Scopus)

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.

Original language	English
Journal	Nature Methods
Volume	15
Pages (from-to)	61-66
Number of pages	6
ISSN	1548-7091
DOIs	https://doi.org/10.1038/nmeth.4514
Publication status	Published - 2018
Externally published	Yes

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10.1038/nmeth.4514

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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.",

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year = "2018",

doi = "10.1038/nmeth.4514",

language = "English",

volume = "15",

pages = "61--66",

journal = "Nature Methods",

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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

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SN - 1548-7091

VL - 15

SP - 61

EP - 66

JO - Nature Methods

JF - Nature Methods

ER -

NetSig: network-based discovery from cancer genomes

Abstract

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