DOI:10.1038/nrc2277 Identifying cancer patients who

are most at risk of developing metastases would undoubtedly save lives and also avoid overtreatment of some patients. Trey Ideker and colleagues asked whether including protein-network data alongside gene-expression data would improve the prediction of metastasis formation in patients with breast cancer.

Ideker and colleagues used the gene-expression data from two pre-vious metastases studies: those carried out by Wang and colleagues and van de Vijver and colleagues. Only the 8,141 genes that were common to both data sets were considered. Data

from patients in both studies who subsequently developed metastases were used as the metastatic class, with the remaining patients acting as the non-metastatic class. The relevant protein–protein interaction data were assembled using three approaches — two-hybrid screening, computational predictions and inter-actions gathered from the literature — and consisted of a pool of 57,235 interactions from 11,203 proteins. To integrate the gene and protein data, the authors overlaid the expression values of each gene with its cor-responding protein and searched for subnetworks for which the activities

across the patients were predictive of metastasis. Each subnetwork is suggestive of a functional pathway or complex.

The authors identified 149 dis-criminative subnetworks in the van de Vijver data and 243 in the Wang data. Importantly, the subnetworks were more reproducible between the two data sets than the original metastasis gene-expression signa-tures isolated in each study, and were also more accurate. Moreover, the subnetworks can identify proteins that are not subject to changes in gene-expression profile, such as Myc and cyclin D1, but still contribute to metastasis development — almost all of the subnetworks contained at least one of these proteins, and many of the subnetworks also contained known cancer susceptibility genes, such as HRAS and TP53. And, once a network is identified, the biological relevance of the proteins in question can also be more easily established.

With other types of genome-wide data, such as transcription-factor binding and phenotypic data, beginning to come online, the capacity for predicting metastasis and other tumour characteristics should continue to improve.

Nicola McCarthy

OrIgInal research PaPer Chuang H-Y., Lee, E., Liu, Y-T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Systems Biol. 3, 140 (2007)

T u m O u r P r O f I l I n g

Networking, protein style

R e s e a R c h h i g h l i g h t s

nATurE rEVIEWs | cancer VOluME 7 | DEcEMbEr 2007

Nature Reviews Cancer | AOP, published online 8 november 2007; doi:10.1038/nrc2277

Image courtesy of Nicola McCarthy

© 2007 Nature Publishing Group