Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis

Area of Science:

• Oncology
• Computational Biology
• Systems Biology

Background:

• Targeted cancer therapies show variable efficacy, necessitating predictive models for personalized treatment.
• Understanding the complex signaling networks, including ERBB, MAPK, and PI3K pathways, is crucial for predicting drug response.

Purpose of the Study:

• To develop an integrative mathematical model for predicting breast cancer cell response to targeted therapies.
• To investigate the role of ERBB receptor signaling and downstream pathways in mediating drug effects across different breast cancer subtypes.
• To identify key mutations influencing pathway activation and drug response.

Main Methods:

• Combined mechanistic and regression modeling to create a multi-pathway model of ERBB, MAPK, and PI3K signaling.
• Calibrated the model using time-resolved data from luminal breast cancer cell lines (MCF7, T47D) with various ligand-drug combinations.
• Applied L1 regularization to identify the impact of cell-line-specific mutations on pathway activation.

Main Results:

• A unified mathematical model accurately described ERBB signaling and downstream pathway activation in response to drugs across luminal, triple-negative, and HER2-positive breast cancer cell lines.
• Identified cell-line-specific mutations in MAPK and PI3K pathways as significant factors influencing drug response.
• Successfully predicted and experimentally validated cell proliferation responses to drug co-treatments.

Conclusions:

• The developed mathematical model provides a framework for understanding and predicting targeted therapy response in diverse breast cancer subtypes.
• Alterations in the ERBB signaling network may extend the therapeutic relevance of anti-HER therapies to non-HER2-positive breast cancers.
• Integrative modeling offers insights into personalized cancer treatment strategies.