Estimating cancer gene pathway proximity using network interaction

It is known that the gene level aberrations for a given cancer could vary across patients. As a result, a single therapy may not be suitable for every patient. However, these genetic aberrations may occur in similar pathways across patients. Therefore a study at pathway/subnetwork is more effective than at gene level. In this paper, we propose a method at this level to classify pathways (sub-networks) as functionally coupled and functionally independent. For this, we propose novel interaction measures. We show how these can be used to link and classify subnetworks using breast cancer as an example. Such methods will play an important role in patient stratification in order to develop personalized treatment options.