Jun 19, 2017

Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

BioRxiv : the Preprint Server for Biology
Heiko HornKasper Lage

Abstract

Combining molecular network information with cancer genome data can complement gene-based statistical tests to identify likely new cancer genes. However, it is challenging to experimentally validate network-based approaches at scale and thus to determine their real predictive value. Here, we developed a robust network-based statistic (NetSig) to predict cancer genes and designed and implemented a large-scale and quantitative experimental framework to compare the in vivo tumorigenic potential of 23 NetSig candidates to 25 known oncogenes and 79 random genes. Our analysis shows that genes with a significantly mutated network induce tumors at rates comparable to known oncogenes and at an order of magnitude higher than random genes. Informed by our network-based statistical approach and tumorigenesis experiments we made a targeted reanalysis of nine candidate genes in 242 oncogene-negative lung adenocarcinomas and identified two new driver genes (AKT2 and TFDP2). Together, our combined computational and experimental analyses strongly support that network-based approaches can complement gene-based statistical tests in cancer gene discovery. We illustrate a general and scalable computational and experimental workflow that can contrib...Continue Reading

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Mentioned in this Paper

AKT2 protein, human
In Vivo
Tumorigenicity
Adenocarcinoma of Lung (Disorder)
Biological Neural Networks
TFDP2 gene
Lung
Genome
Genes
Candidate Disease Gene

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