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Updated: Aug 10, 2026

Tissue Engineering of a Human 3D in vitro Tumor Test System
Published on: August 6, 2013
Modeling immunotherapies in live 3D human cancer tissue bioreactors
Yizheng Zhang1, Ivan Foth1, Ahmad Makky1
1Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Germany.
Abstract:
Background: Cancer immunotherapies have shown remarkable efficacy in advanced malignancies, yet many patients remain unresponsive. This variability, along with concerns about adverse effects and healthcare costs, highlights the need for predictive biomarkers and physiologically relevant cancer models to forecast individual treatment responses. Existing systems inadequately recapitulate the human tumor microenvironment (TME), which is essential for understanding immune-tumor interactions and treatment efficacy. Here, we developed an ex vivo 3D human tissue culture model that preserves the native TME for functional immunotherapy testing. Such a short-term culture platform also supports functional precision medicine by enabling rapid ex vivo assessment of therapeutic responses to guide clinical decisions. Methods: Fresh, intact human lymph node (LN) tissue pieces were cultured in optimized perfusion bioreactors for three days, during which CAR T cell therapies and antibody-based treatments were administered. Post-culture analyses were performed using flow cytometry, histology, and multiplexed fluorescence microscopy. Results: The bioreactor system significantly improved tissue viability compared to traditional plate cultures. Novel CAR T cells with enhanced PI3K signaling exhibited superior tissue infiltration but showed comparable cytotoxicity to conventional CAR T cells. Pembrolizumab, a PD-1 inhibitor, significantly reduced lymphoma and melanoma cell viability without affecting benign LN tissues. Conclusions: This optimized bioreactor culture system provides a robust platform for evaluating immunotherapy efficacy within a physiologically relevant TME. It offers valuable potential for advancing personalized treatment strategies, accelerating the understanding of immunotherapy mechanisms, and improving clinical outcomes.
Insights
A new 3D bioreactor model preserves the human tumor microenvironment (TME) for testing cancer immunotherapies. This approach enables rapid ex vivo assessment of treatment responses, aiding personalized medicine and improving patient outcomes.
Area of Science:
- Oncology
- Immunology
- Biotechnology
Background:
- Cancer immunotherapies show variable efficacy, necessitating predictive biomarkers and better models.
- Existing models fail to fully replicate the human tumor microenvironment (TME).
- A need exists for physiologically relevant models to predict individual immunotherapy responses.
Purpose of the Study:
- To develop an ex vivo 3D human tissue culture model preserving the native TME for immunotherapy testing.
- To establish a platform for rapid ex vivo assessment of therapeutic responses to guide clinical decisions.
- To evaluate the efficacy of CAR T cell therapies and antibody-based treatments in a preserved TME.
Main Methods:
- Human lymph node (LN) tissue pieces were cultured in perfusion bioreactors for three days.
- CAR T cell therapies and antibody-based treatments were administered ex vivo.
- Tissue viability, cell infiltration, and treatment efficacy were assessed using flow cytometry, histology, and microscopy.
Main Results:
- The bioreactor system significantly improved tissue viability compared to traditional plate cultures.
- Novel CAR T cells showed enhanced tissue infiltration but similar cytotoxicity to conventional CAR T cells.
- Pembrolizumab (PD-1 inhibitor) reduced lymphoma and melanoma cell viability without harming benign LN tissues.
Conclusions:
- The optimized bioreactor culture system is a robust platform for evaluating immunotherapy efficacy in a physiologically relevant TME.
- This model has potential for advancing personalized treatment strategies and understanding immunotherapy mechanisms.
- The system may improve clinical outcomes by enabling better prediction of treatment responses.
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