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Published on: August 15, 2019
Optimization of Immune Checkpoint Blockade via a Multiscale Model System
Anne M Talkington1,2, Anthony J Kearsley1
1Applied and Computational Mathematics Division National Institute of Standards and Technology Gaithersburg Maryland USA.
Abstract:
Cancer progresses when cancer cells selectively bind to inhibitory receptors on a T cell surface, downregulating tumor immune response. One standard-of-care strategy to combat this process is immune checkpoint blockade. Immune checkpoint blockade occurs when a therapeutic agent binds to, and inhibits, inhibitory receptors on a T cell surface, such that immune stimulation is favored when T cells and cancer cells interact. However, many cancers fail to respond to immune checkpoint blockade treatments. Here we explore a whole-tumor and an individual cell-focused model system to test expected outcomes of blockade perturbations in tumor-immune interactions. We first observe a transition point at which patients become more likely to reach "remission" or "stable disease" as a terminal state, and a "progressive disease" state is less likely. We propose a physical, agent-based framework for testing blockade strategies at the cellular level. This offers valuable guidance for blockade efficacy optimization in future development and design of therapeutic antibodies.
Insights
This study introduces a new agent-based model to optimize immune checkpoint blockade therapies for cancer. The model helps predict treatment outcomes and improve therapeutic antibody design for better patient responses.
Area of Science:
- Immunology
- Computational Biology
- Oncology
Background:
- Cancer immune evasion involves T cell inhibitory receptors, which dampen anti-tumor responses.
- Immune checkpoint blockade (ICB) is a standard therapy that targets these receptors to enhance anti-tumor immunity.
- Many patients do not respond to current ICB treatments, necessitating improved strategies.
Purpose of the Study:
- To develop and validate a computational framework for evaluating immune checkpoint blockade strategies.
- To investigate tumor-immune interactions at both the whole-tumor and single-cell levels.
- To identify factors influencing patient response to ICB therapies.
Main Methods:
- Development of a physical, agent-based model simulating tumor-immune dynamics.
- Analysis of a transition point predicting patient disease states (remission, stable, or progressive disease).
- Exploration of blockade perturbations within the model system.
Main Results:
- The model identified a critical transition point influencing patient outcomes.
- Simulations demonstrated the potential to predict responses to different blockade strategies.
- The framework provides insights into optimizing ICB efficacy.
Conclusions:
- The proposed agent-based model offers a valuable tool for understanding and optimizing immune checkpoint blockade.
- This approach can guide the development of more effective therapeutic antibodies.
- Computational modeling is crucial for advancing personalized cancer immunotherapy.
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