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UC San Diego researchers improve accuracy of breast cancer prognoses
Date:10/16/2007

sify and diagnose cancer and other diseases.

Our work supports the notion that cancer is a disease of pathways, said Ideker. The keys for understanding at least some of these pathways are encoded in protein networks.

The new study uses the same gene expression data used in two well-known studies: Vijver et al. in Nature and Wang et al. in the Lancet. Each study yielded a set of about 70 single-gene markers that are now used in hospitals to help predict the likelihood of breast cancer metastasis.

We saw about a 9 percent increase in metastasis prediction accuracy over the two main sets of individual gene markers, said Ideker, who explained that his team raised metastatic prediction accuracy for breast cancer to roughly 72 percent. But there is still plenty of room for improvement, he said.

The big difference between our work and the work outlined in Vijver and Wang is that we painted the existing gene expression data onto newly available maps of protein interactions, said Ideker. Some refer to these maps as wiring diagrams.

By focusing on how the proteins within cells interact, the researchers were able to look at the aggregate behavior of genes that are connected in functional networks. This approach improved their ability to predict which tumors would spread.

Using a mathematical approach for the prediction of metastasis (involving both machine learning and dimensionality reduction), the researchers calculated the average behavior of subnetworks of proteins and used this information to uncover subnetworks that predict metastasis better than individual gene markers.

The team uncovered 149 discriminative subnetworks consisting of 618 genes from the patients from the van de Vijver et al. data set and 243 discriminative subnetworks with 906 genes from the Wang et al. data set.

Each subnetwork is suggestive of a distinct functional pathway or complex, yielding many known and novel pathway hypoth
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Contact: Rex Graham
ragraham@ucsd.edu
858-822-3075
University of California - San Diego
Source:Eurekalert

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