Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Area of Science:

• Biochemistry
• Cell Biology
• Systems Biology

Background:

• Understanding cell signaling and phenotype control is crucial in biology.
• Breast cancer (BC) subtypes exhibit complex signaling patterns.
• Phosphoproteomic data offers insights into cellular states.

Purpose of the Study:

• To apply a novel cell State Transition Assessment and Regulation (cSTAR) approach to analyze breast cancer cell line phosphoproteomic data.
• To identify core signaling networks, causal connections, and oncogenic drivers across different BC subtypes and normal cells.
• To develop mechanistic models for controlling cell state transitions.

Main Methods:

• Application of the cell State Transition Assessment and Regulation (cSTAR) approach to single-cell phosphoproteomic data from breast cancer and normal cell lines.
• Separation of cell states into luminal, basal, and normal categories.
• Identification and analysis of signaling nodes, network topologies, and causal connections.

Main Results:

• Identified conserved core network architecture in luminal BC cells with mTOR as a key driver.
• Discovered heterogeneous core networks in basal BC cells, segregating into four subclasses with distinct drivers.
• Characterized two subclasses within normal breast tissue cells.
• Developed mechanistic cSTAR models representing digital cell twins.

Conclusions:

• The cSTAR approach effectively delineates signaling drivers and network architectures in BC subtypes.
• Mechanistic cSTAR models provide a framework for understanding and controlling cell state transitions.
• The study suggests potential therapeutic strategies involving small molecule inhibitors to normalize phosphorylation networks in BC cells.