  Integrative network modeling of data arising from multiple genomic platforms
provides insight into the holistic picture of the interactive system, as well
as the flow of information across many disease domains including cancer. The
basic data structure consists of a sequence of hierarchically ordered datasets
for each individual subject, which facilitates integration of diverse inputs,
such as genomic, transcriptomic, and proteomic data. A primary analytical task
in such contexts is to model the layered architecture of networks where the
vertices can be naturally partitioned into ordered layers, dictated by multiple
platforms, and exhibit both undirected and directed relationships. We propose a
multi-layered Gaussian graphical model (mlGGM) to investigate conditional
independence structures in such multi-level genomic networks in human cancers.
We implement a Bayesian node-wise selection (BANS) approach based on variable
selection techniques that coherently accounts for the multiple types of
dependencies in mlGGM; this flexible strategy exploits edge-specific prior
knowledge and selects sparse and interpretable models. Through simulated data
generated under various scenarios, we demonstrate that BANS outperforms other
existing multivariate regression-based methodologies. Our integrative genomic
network analysis for key signaling pathways across multiple cancer types
highlights commonalities and differences of p53 integrative networks and
epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated
CHK2, that may have translational utilities of finding biomarkers and
therapeutic targets.
