Related Experiment Video
Updated: Dec 26, 2025
Live Imaging of Drug Responses in the Tumor Microenvironment in Mouse Models of Breast Cancer
Published on: March 24, 2013
Conducting a Virtual Clinical Trial in HER2-Negative Breast Cancer Using a Quantitative Systems Pharmacology Model
Hanwen Wang1, Richard J Sové1, Mohammad Jafarnejad1
1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Abstract:
The survival rate of patients with breast cancer has been improved by immune checkpoint blockade therapies, and the efficacy of their combinations with epigenetic modulators has shown promising results in preclinical studies. In this prospective study, we propose an ordinary differential equation (ODE)-based quantitative systems pharmacology (QSP) model to conduct an in silico virtual clinical trial and analyze potential predictive biomarkers to improve the anti-tumor response in HER2-negative breast cancer. The model is comprised of four compartments: central, peripheral, tumor, and tumor-draining lymph node, and describes immune activation, suppression, T cell trafficking, and pharmacokinetics and pharmacodynamics (PK/PD) of the therapeutic agents. We implement theoretical mechanisms of action for checkpoint inhibitors and the epigenetic modulator based on preclinical studies to investigate their effects on anti-tumor response. According to model-based simulations, we confirm the synergistic effect of the epigenetic modulator and that pre-treatment tumor mutational burden, tumor-infiltrating effector T cell (Teff) density, and Teff to regulatory T cell (Treg) ratio are significantly higher in responders, which can be potential biomarkers to be considered in clinical trials. Overall, we present a readily reproducible modular model to conduct in silico virtual clinical trials on patient cohorts of interest, which is a step toward personalized medicine in cancer immunotherapy.
Insights
This study developed a quantitative systems pharmacology model for breast cancer immunotherapy. The model identified tumor mutational burden and T cell ratios as predictive biomarkers for treatment response.
Area of Science:
- Immunology
- Pharmacology
- Computational Biology
Background:
- Immune checkpoint blockade (ICB) therapies improve survival in breast cancer.
- Combining ICB with epigenetic modulators shows promise in preclinical models.
- HER2-negative breast cancer remains a focus for improved therapeutic strategies.
Purpose of the Study:
- To develop a quantitative systems pharmacology (QSP) model for simulating virtual clinical trials in HER2-negative breast cancer.
- To analyze potential predictive biomarkers for enhancing anti-tumor response to ICB and epigenetic modulators.
- To advance personalized medicine approaches in cancer immunotherapy.
Main Methods:
- An ordinary differential equation (ODE)-based QSP model with four compartments (central, peripheral, tumor, tumor-draining lymph node) was developed.
- The model incorporates immune activation, suppression, T cell trafficking, and pharmacokinetics/pharmacodynamics (PK/PD) of therapeutic agents.
- Theoretical mechanisms of action for checkpoint inhibitors and epigenetic modulators were implemented based on preclinical data.
Main Results:
- Model simulations confirmed a synergistic effect between epigenetic modulators and ICB therapies.
- Pre-treatment tumor mutational burden, effector T cell (Teff) density, and the Teff to regulatory T cell (Treg) ratio were identified as significantly higher in responders.
- These factors emerged as potential predictive biomarkers for anti-tumor response.
Conclusions:
- A reproducible modular QSP model can facilitate in silico virtual clinical trials for patient cohorts.
- The identified biomarkers (tumor mutational burden, Teff/Treg ratio) can aid in selecting patients for immunotherapy.
- This work represents a step towards personalized medicine in breast cancer immunotherapy.
