Related Experiment Video
Updated: Apr 16, 2026
A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells
Published on: August 22, 2025
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.
Insights
This study integrates network biology and in vivo imaging to reveal molecular changes in cancer. It identifies significant network rewiring in breast and ovarian cancers, and assesses tumor responses to arsenic trioxide in medulloblastoma.
Area of Science:
- Molecular biology
- Systems biology
- Bioinformatics
Background:
- Growing "omics" data offers opportunities to identify cancer biomarkers and interactions.
- Biological networks are dynamic and context-specific, requiring methods to distinguish significant alterations from background noise.
Purpose of the Study:
- To integrate network biology and imaging for studying cancer phenotypes and treatment responses at the molecular systems level.
- To develop and apply methods for detecting significant topological rewiring in molecular networks.
Main Methods:
- Differential Dependence Network (DDN) analysis to detect statistically significant topological rewiring in molecular networks.
- In vivo Magnetic Resonance Imaging (MRI) to define phenotypic sample groups for differential analysis.
- Application of DDN to breast and ovarian cancer data, and combined MRI and Reverse Phase Protein Microarray (RPMA) data for medulloblastoma.
Main Results:
- DDN analysis successfully identified significant molecular network rewiring in distinct breast cancer phenotypes.
- The study investigated the impact of genomic instability on molecular network topologies in high-grade ovarian cancer.
- Combined MRI and RPMA data analysis in a medulloblastoma model provided insights into tumor responses to arsenic trioxide.
Conclusions:
- The integration of network biology and imaging provides a powerful approach to study cancer at the molecular systems level.
- This approach can identify key molecular alterations driving cancer phenotypes and treatment responses.
- The findings contribute to understanding the complexity of therapeutic molecular biology in cancer.
