Abstract
High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.
Highlights
Biologic phenotypes emerge as a consequence of genes interacting through complex networks
A complex collection of genomic alterations occur during tumor cell evolution, including mutations, translocations, and copy number alterations
We demonstrate the method using the breast cancer data set of Chin et al [24]
Summary
Biologic phenotypes emerge as a consequence of genes interacting through complex networks. It is clear that current knowledge of which processes influence diverse cancer phenotypes is incomplete. This is especially true when it comes to understanding processes associated with disease outcome. The expression of genes within these altered segments has been demonstrated to be correlated with the copy number state of the region [3,9,12,13,14,15,16,17,18,19] It is unclear whether these recurrent patterns represent the most important set of CNAs or represent only a subset of key regions
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