Construction and analysis of sample-specific driver modules for breast cancer

Area of Science:

• Genomics and Systems Biology
• Cancer Research
• Bioinformatics

Background:

• Understanding individual somatic mutations and methylation aberrations is crucial for precision medicine.
• Gene interaction networks link genotype/epigenotype to phenotype, but individual mutation/methylation effects on networks are unclear.
• Developing methods to identify sample-specific perturbation networks is needed.

Purpose of the Study:

• To develop a sample-specific driver module construction method using network theory.
• To identify individual perturbation networks driven by mutations or methylation aberrations.
• To understand the functional impact of genetic and epigenetic alterations at an individual level.

Main Methods:

• Utilized 2-order network theory and hub-gene theory for driver module construction.
• Integrated multi-omics data from breast cancer: genomics, transcriptomics, epigenomics, and interactomics.
• Developed a sample-specific approach to identify perturbation networks.

Main Results:

• Integrated multi-omics data to reveal synergistic methylation-mutation collaboration at the individual level.
• Identified a common driver pattern of breast cancer through driver modules, linked to cancer occurrence and development.
• Constructed driver modules reflecting survival prognosis and malignancy, including subtype-specific modules.

Conclusions:

• Explored driver modules in individual cancers, enhancing understanding of breast cancer mechanisms driven by mutations and methylation.
• Provided insights into driver networks, connecting genetic/epigenetic variations to cancer phenotypes.
• Aimed to facilitate identification of novel therapeutic combinations for gene mutations and drugs.