Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Cancer is a complex genomic disease driven by intricate molecular pathways.
• Understanding gene network dynamics is crucial for developing targeted cancer therapies.
• Boolean networks offer a framework to model complex biological systems.

Purpose of the Study:

• To develop a data-driven methodology for constructing Boolean networks modeling breast cancer tumors.
• To define network components and topology using gene expression data (RNA-seq).
• To investigate the dynamics of malignant subnetworks and identify critical genes.

Main Methods:

• Utilized Boolean logic formalism to describe network dynamics.
• Integrated single-cell RNA-seq and interactome data.
• Applied a binarization algorithm to The Cancer Genome Atlas (TCGA) breast cancer datasets.
• Employed canalyzing functions for Boolean network construction.

Main Results:

• Successfully modeled breast cancer tumors using data-driven Boolean networks.
• Identified patient-specific attractors and critical genes associated with breast cancer subtypes.
• Revealed dynamics of malignant subnetworks of up-regulated genes.

Conclusions:

• The proposed methodology provides a basis for detecting critical genes in malignant attractor stability.
• Inhibiting these critical genes holds potential for cancer theranostics.
• This approach advances the understanding of breast cancer genomic complexity.