A miniaturized-tumor culture platform for developing anti-tumor immunotherapies.
The preservation of the tumor immune microenvironment (TIME) ex vivo is essential for investigating tumor-immune interactions and developing effective immunotherapies. However, current culture models often fail to maintain autologous immune cells or support high-throughput testing. To overcome these limitations, we establish and validate a novel miniaturized-tumor culture (MTC) platform.
To build the MTC platform, we took tumor tissues from both mouse models and human patients. We processed these tissues into 100-500 μm fragments. Supplementation with IL-2 and IL-7 in culture maintained long-term intra-tumoral T cell survival. We then evaluated whether the platform could maintain autologous lymphoid populations and respond to immune checkpoint blockade (ICB), through co-culture assays with peripheral blood mononuclear cells (PBMCs) or splenocytes. The system accurately recapitulates drug response and resistance in both immunocompetent and immunodeficient models. Finally, we screened a high-throughput drug library to identify agents that re-sensitize tumors to anti-PD-L1 therapy. To figure out the underlying mechanisms, we used bulk RNA-sequencing, flow cytometry, and targeted CXCL13 antibody neutralization.
The MTC platform demonstrated better preservation of autologous lymphoid populations and remained responsive to ICB. Co-culture assays revealed enhanced immune cell infiltration upon ICB treatment. Furthermore, the MTC models recapitulated drug response and resistance phenotypes. Our screen identified axitinib (AXI) as the most potent agent for re-sensitizing tumors to anti-PD-L1 therapy across different cancer types. Mechanistically, AXI potentiates antigen presentation in tumor and dendritic cells. It also enhances cytotoxic T-cell function via the upregulation of CXCL13. Finally, CXCL13 blockade effectively abrogated AXI-induced T cell recruitment and tumor regression.
The MTC platform serves as a high-fidelity, high-throughput tool for modeling the TIME. It provides a valuable framework for developing novel anti-cancer strategies and elucidating their underlying mechanisms of action.
To build the MTC platform, we took tumor tissues from both mouse models and human patients. We processed these tissues into 100-500 μm fragments. Supplementation with IL-2 and IL-7 in culture maintained long-term intra-tumoral T cell survival. We then evaluated whether the platform could maintain autologous lymphoid populations and respond to immune checkpoint blockade (ICB), through co-culture assays with peripheral blood mononuclear cells (PBMCs) or splenocytes. The system accurately recapitulates drug response and resistance in both immunocompetent and immunodeficient models. Finally, we screened a high-throughput drug library to identify agents that re-sensitize tumors to anti-PD-L1 therapy. To figure out the underlying mechanisms, we used bulk RNA-sequencing, flow cytometry, and targeted CXCL13 antibody neutralization.
The MTC platform demonstrated better preservation of autologous lymphoid populations and remained responsive to ICB. Co-culture assays revealed enhanced immune cell infiltration upon ICB treatment. Furthermore, the MTC models recapitulated drug response and resistance phenotypes. Our screen identified axitinib (AXI) as the most potent agent for re-sensitizing tumors to anti-PD-L1 therapy across different cancer types. Mechanistically, AXI potentiates antigen presentation in tumor and dendritic cells. It also enhances cytotoxic T-cell function via the upregulation of CXCL13. Finally, CXCL13 blockade effectively abrogated AXI-induced T cell recruitment and tumor regression.
The MTC platform serves as a high-fidelity, high-throughput tool for modeling the TIME. It provides a valuable framework for developing novel anti-cancer strategies and elucidating their underlying mechanisms of action.
Authors
Feng Feng, Li Li, Lei Lei, Peng Peng, Wang Wang, Sun Sun, Tang Tang, Guo Guo, Chu Chu, Qiao Qiao, Miao Miao, Zhu Zhu, Lam Lam, Chan Chan, Chu Chu, Yao Yao, Hou Hou, Xu Xu, Chen Chen, Deng Deng
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