Modeling Cancer Progression via Pathway Dependencies

Abstract

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. Certain genetic perturbations have been implicated for initiation and progression. However, to a great extent, 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 mechanisms 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 pathways to those genes most differentially expressed in particular disease stages. The final analysis infers a gene interaction network for these refined pathways. We apply this approach to model progression in prostate cancer and melanoma, resulting in a deeper understanding of the mechanisms 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 and primary prostate cancer. Cancer is a complex disease with many subtypes that differ substantially with respect to their onset, progression, and response to treatment. Better understanding of the etiology and mechanism of cancer should help improve the diagnosis, prognosis, and treatment of cancer that will kill more than half a million Americans this year alone. Our study illustrates how integration of data over multiple stages and modeling tumorigenesis at the level of regulatory pathways or sets of genes provide robust and interpretable novel hypotheses concerning root genetic causes responsible for cancer initiation, progression, and invasion. Our modeling approach is one of the first approaches that combines multiple microarray datasets in a truly integrative framework that promotes the interpretability of important factors or pathways in one or more datasets. We apply this analysis of tumor progression to both prostate cancer and melanoma to provide information that can lead to the identification of novel biomarkers and give a basis for how genetic disruptions serve to alter actions in specific cell types.