A network biology approach to prostate cancer

Abstract

There is a need to identify genetic mediators of solid-tumor cancers, such as prostate cancer, where invasion and distant metastases determine the clinical outcome of the disease. Whole-genome expression profiling offers promise in this regard, but can be complicated by the challenge of identifying the genes affected by a condition from the hundreds to thousands of genes that exhibit changes in expression. Here, we show that reverse-engineered gene networks can be combined with expression profiles to compute the likelihood that genes and associated pathways are mediators of a disease. We apply our method to non-recurrent primary and metastatic prostate cancer data, and identify the androgen receptor gene (AR) among the top genetic mediators and the AR pathway as a highly enriched pathway for metastatic prostate cancer. These results were not obtained on the basis of expression change alone. We further demonstrate that the AR gene, in the context of the network, can be used as a marker to detect the aggressiveness of primary prostate cancers. This work shows that a network biology approach can be used advantageously to identify the genetic mediators and mediating pathways associated with a disease.

Figure 1

A schematic diagram of the MNI method as applied to identify genetic mediators for non-recurrent primary and metastatic prostate cancer. In phase 1, microarray data obtained from a variety of cancer cell lines or patient tissue samples are used by the MNI algorithm to infer a model of regulatory interactions between genes (blue-filled circles indicate genes, arrows indicate regulatory influences). In phase 2, test condition expression data are filtered using the reconstructed network to distinguish the genes affected by a condition (red-filled circles) from the hundreds to thousands of genes that exhibit changes in expression. This procedure is applied to non-recurrent primary prostate cancer and metastatic prostate cancer samples to identify genetic mediators.

Figure 2

Significantly enriched pathways among top 100 genetic mediators identified by MNI and expression change alone, respectively, in non-recurrent primary and metastatic prostate cancer groups.

Figure 3

AR gene rankings based on MNI and expression change alone. The MNI ranking of the AR gene moves up as an indication of the aggressiveness of the disease. Expression change alone is unable to capture the differential involvement of the AR gene in the recurrent and non-recurrent primary prostate cancer groups.