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Published on: August 1, 2018
Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis
Svenja Kemmer1,2, Mireia Berdiel-Acer3, Eileen Reinz3
1Institute of Physics, University of Freiburg, 79104 Freiburg, Germany.
Abstract:
Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand-drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.
Insights
This study developed a unified mathematical model to predict breast cancer cell response to targeted therapies by analyzing ERBB receptor and downstream signaling pathways. The model suggests anti-HER therapies may benefit patients beyond HER2-positive subtypes.
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.
