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Updated: Sep 22, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Rapid Profiling of Tumor-Immune Interaction Using Acoustically Assembled Patient-Derived Cell Clusters
Zheng Ao1, Zhuhao Wu1, Hongwei Cai1
1Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 47405, USA.
Acoustically assembled patient-derived cell clusters (APCCs) accurately model tumor microenvironments and immune cell interactions. This novel 3D model rapidly predicts patient treatment responses for personalized cancer therapy.
Area of Science:
- Oncology
- Biotechnology
- Immunology
Background:
- Tumor microenvironment crosstalk between cancer cells, T cells, and myeloid-derived suppressor cells (MDSCs) is crucial for tumor progression and treatment response.
- Existing patient-derived models like organoids and 2D cultures fail to fully capture complex tumor-immune interactions due to missing cell types (e.g., MDSCs).
Purpose of the Study:
- To develop a novel 3D model, acoustically assembled patient-derived cell clusters (APCCs), that preserves original tumor and immune cell compositions.
- To enable rapid, scalable, and user-friendly modeling of 3D tumor-immune interactions and testing of treatment responses.
Main Methods:
- Utilizing 3D acoustic trappings within an extracellular matrix to assemble hundreds of APCCs within minutes.
- Incorporating sensitive, short-lived tumor-induced MDSCs into APCCs to model their dynamic suppression of T cell activity.
- Testing the combined therapeutic effects of a multi-kinase inhibitor (cabozantinib) and an anti-PD-1 immune checkpoint inhibitor (pembrolizumab) using APCCs.
Main Results:
- APCCs successfully preserved original tumor/immune cell compositions and modeled their interactions in a 3D microenvironment.
- The model effectively preserved tumor-induced MDSCs and demonstrated their T cell suppression dynamics.
- Successful modeling of the combinational therapeutic effect of cabozantinib and pembrolizumab.
Conclusions:
- APCCs offer a promising platform for predicting patient treatment responses in personalized cancer adjuvant therapy.
- This novel model can be used for screening novel cancer immunotherapies and combination therapies.
- APCCs provide a rapid, scalable, and user-friendly approach to studying complex tumor-immune interactions.
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