Cancer is a heterogeneous disease often requiring a complexity of alterations to drive a normal cell to a malignancy and ultimately to a metastatic state. While specific genetic perturbations have been implicated for initiation and progression, the most important underlying mechanisms often remain elusive. These genetic perturbations are most likely reflected by the altered expression of sets of genes or pathways, rather than individual genes, thus creating a need for models of deregulation of pathways to help provide an understanding of the mechanism of tumorigenesis. We introduce an integrative hierarchical analysis of tumor progression that discovers which a priori defined pathways are relevant either throughout or in particular steps of progression. Pathway interaction networks are inferred for these relevant pathways over the steps in progression. This is followed by the refinement of the relevant gene sets to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined gene sets. We apply this approach to model progression in prostate cancer and melanoma resulting in a deeper understanding of the mechanism of tumorigenesis. Our analysis supports previous findings for the deregulation of several pathways involved in cell cycle control and proliferation in both cancer types. A novel finding of our analysis is a connection between ErbB4/HER4.
Keywords: Cancer genomics; multi-task learning; pathway inference; gene networks; graphical models.
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