Integration of Network Biology and Imaging to Study Cancer Phenotypes and Responses

Abstract

Ever growing "omics" data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.

Fig. 1

Overview of the data acquisition and analytics pipeline.

Fig. 2

Survival analysis of MB mice model shows the significant improved survival of ATO treated samples.

Fig. 3

Longitudinal MRI of the ND2-SmoA1 MB mice. The upper-left corner shows the mouse experiment set.

Fig. 4

Simulation validation of DDN method of identifying differential and shared edges between two conditions.

Fig. 5

Differential network of breast cancer Cell Cycle pathway between early recurrent and non-recurrent patients.

Fig. 6

DDN result of chromosome stable and unstable groups on TCGA ovarian cancer data in Cell Cycle and Apoptosis pathways. Red edges are unstable groups. Green edges are stable groups.

Fig. 7

MRI-based tumor growth rates shows reduced growth rates in ATO treated samples.

Fig. 8

Differential analysis of RPMA data highlight the top 20 proteins modulated by ATO in vivo.

Fig. 9

“Hubs” of the network rewiring by DDN analysis. Red edges are connections in MB destroyed by ATO treatment, and green edges are connections established after ATO treatment.

Fig. 10

A focused network with proteins closely involved in MB signaling provides more insights into how ATO works to treat MB in vivo. Edges with different colors have specific meanings: blue - fully recovered by treatment (edges destroyed by tumor are regained); green - weakly created connection after treatment; red - not recovered; black - fully broken down tumor connection; gray - partially broken down tumor connection.