Integration of network biology and imaging to study cancer phenotypes and responses

Ye Tian, Sean S. Wang, Zhen Zhang, Olga C. Rodriguez, Emanuel Petricoin, Ie Ming Shih, Daniel Chan, Maria Avantaggiati, Guoqiang Yu, Shaozhen Ye, Robert Clarke, Chao Wang, Bai Zhang, Yue Wang, Chris Albanese

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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.

Original languageEnglish
Article number6857391
Pages (from-to)1009-1019
Number of pages11
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
StatePublished - 1 Nov 2014
Externally publishedYes


  • MRI
  • Network biology
  • cancer biology
  • differential network


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