Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component
John W Hickey1, Eran Agmon2, Nina Horowitz3
1Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Abstract:
Cancer progression is a complex process involving interactions that unfold across molecular, cellular, and tissue scales. These multiscale interactions have been difficult to measure and to simulate. Here, we integrated CODEX multiplexed tissue imaging with multiscale modeling software to model key action points that influence the outcome of T cell therapies with cancer. The initial phenotype of therapeutic T cells influences the ability of T cells to convert tumor cells to an inflammatory, anti-proliferative phenotype. This T cell phenotype could be preserved by structural reprogramming to facilitate continual tumor phenotype conversion and killing. One takeaway is that controlling the rate of cancer phenotype conversion is critical for control of tumor growth. The results suggest new design criteria and patient selection metrics for T cell therapies, call for a rethinking of T cell therapeutic implementation, and provide a foundation for synergistically integrating multiplexed imaging data with multiscale modeling of the cancer-immune interface. A record of this paper's transparent peer review process is included in the supplemental information.
Insights
Controlling cancer phenotype conversion is key for tumor growth control in T cell therapies. Integrating tissue imaging with multiscale modeling offers new insights for cancer treatment design.
Area of Science:
- Oncology
- Immunology
- Computational Biology
Background:
- Cancer progression involves complex multiscale interactions (molecular, cellular, tissue).
- Measuring and simulating these multiscale interactions in cancer is challenging.
- T cell therapies are crucial for cancer treatment but their efficacy can be limited.
Purpose of the Study:
- To integrate CODEX multiplexed tissue imaging with multiscale modeling software.
- To model key factors influencing T cell therapy outcomes in cancer.
- To understand how initial T cell phenotype affects tumor cell conversion.
Main Methods:
- Utilized CODEX multiplexed tissue imaging for high-resolution cellular analysis.
- Employed multiscale modeling software to simulate cancer-immune interactions.
- Analyzed the influence of therapeutic T cell phenotype on tumor cell conversion.
Main Results:
- Initial therapeutic T cell phenotype impacts tumor cell conversion to an inflammatory, anti-proliferative state.
- Structural reprogramming can preserve T cell phenotype for sustained tumor conversion and killing.
- The rate of cancer phenotype conversion is critical for controlling tumor growth.
Conclusions:
- Findings suggest new design criteria and patient selection metrics for T cell therapies.
- Results advocate for a reevaluation of T cell therapeutic implementation strategies.
- Provides a framework for integrating imaging data with multiscale modeling of the cancer-immune interface.
